[
  {
    "slug": "standard-model-imaging-ms-brain-cord",
    "title": "Standard Model Imaging in the Brain and Spinal Cord of MS Patients: Initial Assessment and Comparison to Diffusion Tensor Imaging",
    "year": 2026,
    "datePublished": "2026-07-08",
    "authors": "Atlee Witt, Alicia E. Cronin, Bailey Busher, Isabella Stuart, Grace Sweeney, Kristin P. O'Grady, Seth A. Smith, Samantha By, Kurt Schilling",
    "authorList": [
      "Atlee Witt",
      "Alicia E. Cronin",
      "Bailey Busher",
      "Isabella Stuart",
      "Grace Sweeney",
      "Kristin P. O'Grady",
      "Seth A. Smith",
      "Samantha By",
      "Kurt Schilling"
    ],
    "kurtPosition": "last",
    "journal": "NMR in Biomedicine",
    "type": "Article",
    "doi": "https://doi.org/10.1002/nbm.70354",
    "publisher": "https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/nbm.70354",
    "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13346334/",
    "metadataSource": "https://api.crossref.org/works/10.1002/nbm.70354",
    "publicationStatus": "Published",
    "pdf": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13346334/pdf/NBM-39-e70354.pdf",
    "license": "CC BY 4.0",
    "licenseUrl": "https://creativecommons.org/licenses/by/4.0/",
    "topics": [
      "Microstructure",
      "Spinal Cord"
    ],
    "summary": "Same-session brain and spinal cord imaging compares standard diffusion tensor measurements with Standard Model Imaging in 34 people with relapsing-remitting multiple sclerosis and 36 controls. Neurite density estimates show sensitivity to pathology in both regions, while the usefulness of other measurements depends on the anatomical setting.",
    "description": "This study evaluates diffusion tensor imaging and Standard Model Imaging with free water in the brain and cervical spinal cord during the same 3T MRI session. It includes 34 people with relapsing-remitting multiple sclerosis and 36 healthy controls. Analyses compare lesions and normal-appearing white matter and assess reproducibility in controls. SMI-derived neurite density fraction provides a sensitive measurement in both anatomical regions, while comparable sensitivity from fractional anisotropy and radial diffusivity is observed in the brain. The findings support further investigation of SMI for assessment across the central nervous system.",
    "findings": [
      "Compares brain and cervical cord measurements in the same participants.",
      "SMI neurite density fraction is sensitive to pathology in both regions.",
      "Model performance and lesion characteristics differ between brain and cord."
    ],
    "image": "/assets/papers/standard-model-imaging-ms-brain-cord.webp",
    "imageAlt": "Standard model imaging parameter maps in the brain and spinal cord of a person with relapsing-remitting MS.",
    "caption": "Witt et al. (2026), Figure 4. Standard model imaging parameter maps in the brain and spinal cord of a person with relapsing-remitting MS.",
    "figureSource": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13346334/",
    "imageKind": "article-figure",
    "figureLicense": "https://creativecommons.org/licenses/by/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13346334/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13346334/"
      }
    ],
    "pdfVerification": "official-link-only",
    "url": "https://www.microstructure-connectivity-lab.com/publications/standard-model-imaging-ms-brain-cord/"
  },
  {
    "slug": "bundleparc-deep-tegmental-core",
    "title": "Automated Segmentation of Brainstem and Subcortical White Matter: Mapping the Deep Tegmental Core with BundleParc",
    "year": 2026,
    "authors": "Kurt G Schilling, Gaurav Rudravaram, Antoine Theberge, Matthew Amandola, Michael E. Kim, Kathryn L. Humphreys, Laurie Cutting, Derek Archer, Timothy J. Hohman, Angela L. Jefferson, Lori L. Beason Held, Murat Bilgel, Alzheimer’s Disease Neuroimaging Initiative, The BIOCARD Study Team, Maxime Chamberland, Maxime Descoteaux, Laurent Petit, Francois Rheault, Bennett Landman",
    "authorList": [
      "Kurt G Schilling",
      "Gaurav Rudravaram",
      "Antoine Theberge",
      "Matthew Amandola",
      "Michael E. Kim",
      "Kathryn L. Humphreys",
      "Laurie Cutting",
      "Derek Archer",
      "Timothy J. Hohman",
      "Angela L. Jefferson",
      "Lori L. Beason Held",
      "Murat Bilgel",
      "Alzheimer’s Disease Neuroimaging Initiative",
      "The BIOCARD Study Team",
      "Maxime Chamberland",
      "Maxime Descoteaux",
      "Laurent Petit",
      "Francois Rheault",
      "Bennett Landman"
    ],
    "kurtPosition": "first",
    "journal": "bioRxiv",
    "type": "Preprint",
    "doi": "https://doi.org/10.64898/2026.06.09.731210",
    "publisher": "https://doi.org/10.64898/2026.06.09.731210",
    "summary": "This preprint extends BundleParc to segment and divide 97 brainstem and subcortical white matter pathways along their trajectories. Trained from anatomically curated Human Connectome Project tractography, the method operates on native-space fiber orientation distributions. External datasets test its performance across development, aging, disease cohorts, and acquisition differences.",
    "description": "Automated tractography tools have mainly emphasized large cerebral bundles, leaving many compact brainstem and subcortical pathways underrepresented. This preprint adapts BundleParc to identify and produce ordered along-tract subdivisions for 97 pathways supporting motor, sensory, cerebellar, limbic, and homeostatic systems. Training references combine anatomy-guided tractography, inclusion and exclusion rules, outlier removal, and manual quality assurance. The method operates directly on native-space fiber orientation distributions and is evaluated in external datasets with different ages, diagnoses, resolutions, and angular sampling. Released models and reference resources are intended to support systematic studies of this anatomy, with the work currently identified as a preprint.",
    "findings": [
      "Targets 97 subcortical and brainstem pathways.",
      "Produces direct segmentation and ordered along-tract parcellation.",
      "Evaluates generalization across external cohorts and acquisition differences."
    ],
    "topics": [
      "Tractography",
      "Image Processing",
      "Lifespan"
    ],
    "source": "https://doi.org/10.64898/2026.06.09.731210",
    "publicationStatus": "Preprint — not peer reviewed",
    "pdf": "",
    "metadataSource": "https://api.crossref.org/works/10.64898/2026.06.09.731210",
    "datePublished": "2026-06-12",
    "image": "/assets/papers/bundleparc-deep-tegmental-core.webp",
    "imageAlt": "Representative brainstem and subcortical pathways used to construct the BundleParc reference labels.",
    "caption": "Kurt G Schilling et al. (2026), Figure 1. Representative brainstem and subcortical pathways used to construct the BundleParc reference labels.",
    "figureSource": "https://doi.org/10.64898/2026.06.09.731210",
    "imageKind": "article-figure",
    "figureLicense": "http://creativecommons.org/licenses/by/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13277897/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13277897/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/bundleparc-deep-tegmental-core/"
  },
  {
    "slug": "cervical-cord-lifespan-charts",
    "title": "Charting Cervical Spinal Cord Morphometry Across the Lifespan",
    "year": 2026,
    "authors": "Kurt G. Schilling, Michael E. Kim, Matthew Amandola, Chenyu Gao, Karthik Ramadass, Praitayini Kanakaraj, Sam Bogdanov, Gaurav Rudravaram, Nancy R. Newlin, Derek Archer, Timothy J. Hohman, Angela L. Jefferson, Victoria L. Morgan, Alexandra Roche, Dario J. Englot, Murat Bilgel, Lori L. Beason Held, Luigi Ferrucci, Laurie Cutting, Laura A. Barquero, Micah A. D’archangel, Tin Q. Nguyen, Kathryn L. Humphreys, Yanbin Niu, Sophia Vinci-Booher, Carissa J. Cascio, The HABS-HD Study Team, Alzheimer’s Disease Neuroimaging Initiative, The BIOCARD Study Team, Zhiyuan Li, Daniel Moyer, Simon N. Vandekar, Panpan Zhang, Samuelle St-Onge, Sandrine Bédard, Jan Valošek, Benjamin De Leener, Julien Cohen-Adad, John C. Gore, Seth Smith, Bennett A. Landman",
    "authorList": [
      "Kurt G. Schilling",
      "Michael E. Kim",
      "Matthew Amandola",
      "Chenyu Gao",
      "Karthik Ramadass",
      "Praitayini Kanakaraj",
      "Sam Bogdanov",
      "Gaurav Rudravaram",
      "Nancy R. Newlin",
      "Derek Archer",
      "Timothy J. Hohman",
      "Angela L. Jefferson",
      "Victoria L. Morgan",
      "Alexandra Roche",
      "Dario J. Englot",
      "Murat Bilgel",
      "Lori L. Beason Held",
      "Luigi Ferrucci",
      "Laurie Cutting",
      "Laura A. Barquero",
      "Micah A. D’archangel",
      "Tin Q. Nguyen",
      "Kathryn L. Humphreys",
      "Yanbin Niu",
      "Sophia Vinci-Booher",
      "Carissa J. Cascio",
      "The HABS-HD Study Team",
      "Alzheimer’s Disease Neuroimaging Initiative",
      "The BIOCARD Study Team",
      "Zhiyuan Li",
      "Daniel Moyer",
      "Simon N. Vandekar",
      "Panpan Zhang",
      "Samuelle St-Onge",
      "Sandrine Bédard",
      "Jan Valošek",
      "Benjamin De Leener",
      "Julien Cohen-Adad",
      "John C. Gore",
      "Seth Smith",
      "Bennett A. Landman"
    ],
    "kurtPosition": "first",
    "journal": "bioRxiv",
    "type": "Preprint",
    "doi": "https://doi.org/10.64898/2026.06.03.729823",
    "publisher": "https://doi.org/10.64898/2026.06.03.729823",
    "summary": "This preprint develops cervical spinal cord reference charts using 78,269 scans from 41,042 individuals across 30 datasets. Measurements from C1 to C7 capture size and shape from birth to age 100. The charts reveal regional and sex differences and support age-adjusted comparison of individual cord measurements.",
    "description": "Normative reference data are needed to interpret spinal cord size and shape across the lifespan. This preprint combines brain MRI datasets with cervical coverage and applies contrast-agnostic segmentation to 78,269 scans from 41,042 individuals aged 0–100. Measurements include cross-sectional area, diameters, and shape indices across C1–C7. The resulting models show rapid growth in childhood and adolescence, maturation in early-to-mid adulthood, and later decline, with regional and sex-related differences. Cord trajectories also show temporal relationships with brain white matter and brainstem volumes. The released framework is designed for age- and sex-specific centile scoring, with further validation needed beyond this preprint report.",
    "findings": [
      "Uses 30 datasets and 41,042 individuals spanning ages 0–100.",
      "Models size and shape at cervical levels C1–C7.",
      "Cross-sectional area typically peaks in the mid-30s before decreasing."
    ],
    "topics": [
      "Spinal Cord",
      "Lifespan"
    ],
    "source": "https://doi.org/10.64898/2026.06.03.729823",
    "publicationStatus": "Preprint — not peer reviewed",
    "pdf": "",
    "metadataSource": "https://api.crossref.org/works/10.64898/2026.06.03.729823",
    "datePublished": "2026-06-08",
    "image": "/assets/papers/cervical-cord-lifespan-charts.webp",
    "imageAlt": "Lifespan trajectories, rates of change, and population variability in cervical spinal cord morphometry.",
    "caption": "Kurt G. Schilling et al. (2026), Figure 3. Lifespan trajectories, rates of change, and population variability in cervical spinal cord morphometry.",
    "figureSource": "https://doi.org/10.64898/2026.06.03.729823",
    "imageKind": "article-figure",
    "figureLicense": "http://creativecommons.org/licenses/by/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13277863/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13277863/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/cervical-cord-lifespan-charts/"
  },
  {
    "slug": "lifespan-white-matter-asymmetry",
    "title": "Lifespan Trajectories of Asymmetry in White Matter Tracts",
    "year": 2026,
    "authors": "Sam Bogdanov, Praitayini Kanakaraj, Michael E. Kim, Jessica Samir, Chenyu Gao, Karthik Ramadass, Gaurav Rudravaram, Nancy R. Newlin, Derek Archer, Timothy J. Hohman, Angela L. Jefferson, Victoria L. Morgan, Alexandra Roche, Dario J. Englot, Susan M. Resnick, Lori L. Beason Held, Laurie E. Cutting, Laura A. Barquero, Micah A. D'Archangel, Tin Q. Nguyen, Kathryn L. Humphreys, Yanbin Niu, Sophia Vinci-Booher, Carissa J. Cascio, The HABS-HD Study Team, Alzheimer's Disease Neuroimaging Initiative, The BIOCARD Study Team, Zhiyuan Li, Simon N. Vandekar, Panpan Zhang, John C. Gore, Stephanie J. Forkel, Bennett A. Landman, Kurt G. Schilling",
    "authorList": [
      "Sam Bogdanov",
      "Praitayini Kanakaraj",
      "Michael E. Kim",
      "Jessica Samir",
      "Chenyu Gao",
      "Karthik Ramadass",
      "Gaurav Rudravaram",
      "Nancy R. Newlin",
      "Derek Archer",
      "Timothy J. Hohman",
      "Angela L. Jefferson",
      "Victoria L. Morgan",
      "Alexandra Roche",
      "Dario J. Englot",
      "Susan M. Resnick",
      "Lori L. Beason Held",
      "Laurie E. Cutting",
      "Laura A. Barquero",
      "Micah A. D'Archangel",
      "Tin Q. Nguyen",
      "Kathryn L. Humphreys",
      "Yanbin Niu",
      "Sophia Vinci-Booher",
      "Carissa J. Cascio",
      "The HABS-HD Study Team",
      "Alzheimer's Disease Neuroimaging Initiative",
      "The BIOCARD Study Team",
      "Zhiyuan Li",
      "Simon N. Vandekar",
      "Panpan Zhang",
      "John C. Gore",
      "Stephanie J. Forkel",
      "Bennett A. Landman",
      "Kurt G. Schilling"
    ],
    "kurtPosition": "last",
    "journal": "Human Brain Mapping",
    "type": "Article",
    "doi": "https://doi.org/10.1002/hbm.70519",
    "publisher": "https://onlinelibrary.wiley.com/doi/10.1002/hbm.70519",
    "summary": "White matter asymmetry changes with age and depends on which tissue or pathway feature is measured. Using 35,120 individuals from 50 studies, this work charts 30 paired pathways from birth to 100 years. It provides a reference for interpreting lateralization across development and aging.",
    "description": "The study combines data from 50 primary imaging studies to characterize white matter asymmetry in 35,120 typically developing and aging individuals. Lifespan models describe six microstructural and macrostructural features across 30 lateralized association and projection pathways. Asymmetry is present in every pathway, but its direction and magnitude can differ between measurements of the same tract. The patterns also change across life, with many pathways becoming more asymmetric in later adulthood. These charts establish a reference for studying how hemispheric differences emerge and evolve, and for evaluating departures from typical organization in future clinical and cognitive studies.",
    "findings": [
      "All 30 examined pathways showed asymmetry.",
      "Asymmetry direction and magnitude depend on the structural feature measured.",
      "Trajectories change with age, with a general increase in asymmetry during later adulthood."
    ],
    "topics": [
      "Lifespan",
      "Tractography",
      "Microstructure"
    ],
    "source": "https://onlinelibrary.wiley.com/doi/10.1002/hbm.70519",
    "publicationStatus": "Published",
    "pdf": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13241809/pdf/HBM-47-e70519.pdf",
    "metadataSource": "https://api.crossref.org/works/10.1002/hbm.70519",
    "datePublished": "2026-06-06",
    "image": "/assets/papers/lifespan-white-matter-asymmetry.webp",
    "imageAlt": "Examples of age-dependent white matter asymmetry across microstructural and macrostructural features.",
    "caption": "Sam Bogdanov et al. (2026), Figure 3. Examples of age-dependent white matter asymmetry across microstructural and macrostructural features.",
    "figureSource": "https://onlinelibrary.wiley.com/doi/10.1002/hbm.70519",
    "imageKind": "article-figure",
    "figureLicense": "https://creativecommons.org/licenses/by/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13241809/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13241809/"
      }
    ],
    "pdfVerification": "official-link-only",
    "url": "https://www.microstructure-connectivity-lab.com/publications/lifespan-white-matter-asymmetry/"
  },
  {
    "slug": "white-matter-lifespan-brain-charts",
    "title": "White matter micro- and macrostructure brain charts for the human lifespan",
    "year": 2026,
    "authors": "Michael E. Kim, Chenyu Gao, Karthik Ramadass, Nancy R. Newlin, Praitayini Kanakaraj, Sam Bogdanov, Gaurav Rudravaram, Derek Archer, Timothy J. Hohman, Angela L. Jefferson, Victoria L. Morgan, Alexandra Roche, Dario J. Englot, Susan M. Resnick, Lori L. Beason-Held, Laurie E. Cutting, Laura A. Barquero, Micah A. D’archangel, Tin Q. Nguyen, Kathryn L. Humphreys, Yanbin Niu, Sophia Vinci-Booher, Carissa J. Cascio, The HABS-HD Study Team, L. Taylor Davis, Zhiyuan Li, Simon N. Vandekar, Panpan Zhang, John C. Gore, Bennett A. Landman, Kurt G. Schilling",
    "authorList": [
      "Michael E. Kim",
      "Chenyu Gao",
      "Karthik Ramadass",
      "Nancy R. Newlin",
      "Praitayini Kanakaraj",
      "Sam Bogdanov",
      "Gaurav Rudravaram",
      "Derek Archer",
      "Timothy J. Hohman",
      "Angela L. Jefferson",
      "Victoria L. Morgan",
      "Alexandra Roche",
      "Dario J. Englot",
      "Susan M. Resnick",
      "Lori L. Beason-Held",
      "Laurie E. Cutting",
      "Laura A. Barquero",
      "Micah A. D’archangel",
      "Tin Q. Nguyen",
      "Kathryn L. Humphreys",
      "Yanbin Niu",
      "Sophia Vinci-Booher",
      "Carissa J. Cascio",
      "The HABS-HD Study Team",
      "L. Taylor Davis",
      "Zhiyuan Li",
      "Simon N. Vandekar",
      "Panpan Zhang",
      "John C. Gore",
      "Bennett A. Landman",
      "Kurt G. Schilling"
    ],
    "kurtPosition": "last",
    "journal": "Nature",
    "type": "Article",
    "doi": "https://doi.org/10.1038/s41586-026-10454-2",
    "publisher": "https://www.nature.com/articles/s41586-026-10454-2",
    "summary": "White matter growth charts built from 35,120 brain scans provide reference trajectories from birth to 100 years. The study measures tissue properties and pathway shape across 72 tracts, enabling age- and sex-adjusted comparisons. Public models, processing tools, and shareable derived data support independent use of the charts.",
    "description": "This Nature study establishes lifespan reference charts for white matter using 35,120 scans from 50 studies. It models microstructural diffusion measures and macrostructural pathway geometry across 72 tracts, describing typical values, variability, and developmental milestones from birth through age 100. The charts show that maturation and aging follow different schedules across pathways and measurements. Individual centile scores quantify departures from age- and sex-specific reference distributions, and analyses demonstrate differences in selected clinical groups. Public models support scoring new research datasets, with companion processing tools and a release of derived data permitted by the contributing studies. These cross-sectional references provide a foundation for further research and clinical validation.",
    "findings": [
      "Normative charts span birth to age 100 using 35,120 scans from 50 studies.",
      "Microstructural and macrostructural trajectories differ across 72 white matter pathways.",
      "Centile scores enable individual measurements to be compared with reference distributions.",
      "Open models, processing instructions, and permitted derived datasets accompany the study."
    ],
    "topics": [
      "Lifespan",
      "Microstructure",
      "Tractography"
    ],
    "source": "https://www.nature.com/articles/s41586-026-10454-2",
    "publicationStatus": "Published",
    "pdf": "https://www.nature.com/articles/s41586-026-10454-2.pdf",
    "metadataSource": "https://api.crossref.org/works/10.1038/s41586-026-10454-2",
    "datePublished": "2026-05-13",
    "featured": true,
    "license": "CC BY 4.0",
    "licenseUrl": "https://creativecommons.org/licenses/by/4.0/",
    "correction": "https://www.nature.com/articles/s41586-026-10693-3",
    "correctionNote": "Publisher correction, 2026-06-02: Figure 3a label corrected to White matter tracts.",
    "resources": [
      {
        "label": "Brain chart models and scoring",
        "url": "https://zenodo.org/records/18435695",
        "type": "Models",
        "description": "Version 0.2.1: lifespan models, centile curves, new-dataset alignment, and power-analysis code."
      },
      {
        "label": "Derived data",
        "url": "https://zenodo.org/records/18891848",
        "type": "Data",
        "description": "Permitted derived features and centile scores from 20 of 50 source datasets; no raw images or diagnostic information."
      },
      {
        "label": "Postprocessing pipeline",
        "url": "https://zenodo.org/records/17144461",
        "type": "Code",
        "description": "Instructions and examples for the container that measures 72 tracts and prepares brain-chart features."
      },
      {
        "label": "Processing container",
        "url": "https://hub.docker.com/r/kimm58/wm_lifespan_processing",
        "type": "Container",
        "description": "Docker image linked by the published article and verified Zenodo processing record."
      }
    ],
    "image": "/assets/papers/white-matter-lifespan-brain-charts.webp",
    "imageAlt": "Lifespan trajectories of white matter microstructure across major brain pathways.",
    "caption": "Kim et al. (2026), Figure 2. Lifespan trajectories of white matter microstructure across major brain pathways. CC BY 4.0. Age axes are logarithmic; cyan denotes males and red denotes females.",
    "figureSource": "https://www.nature.com/articles/s41586-026-10454-2",
    "figureLicense": "https://creativecommons.org/licenses/by/4.0/",
    "links": [
      {
        "url": "https://zenodo.org/records/18435695",
        "label": "Brain chart models and scoring"
      },
      {
        "url": "https://zenodo.org/records/18891848",
        "label": "Derived data"
      },
      {
        "url": "https://zenodo.org/records/17144461",
        "label": "Postprocessing pipeline"
      }
    ],
    "limitations": "The charts describe reference distributions for research. They are not, by themselves, a diagnostic test. The public data release contains permitted derived measurements from 20 of the 50 source datasets, rather than all raw MRI scans.",
    "imageKind": "article-figure",
    "pdfVerification": "content-type-and-pdf-signature",
    "url": "https://www.microstructure-connectivity-lab.com/publications/white-matter-lifespan-brain-charts/"
  },
  {
    "slug": "sex-related-tract-variability",
    "title": "Sex-related Variability of White-Matter Tracts is Robust to Tractography Methodology",
    "year": 2026,
    "authors": "Matthew Amandola, Bastien Herlin, Michael E. Kim, Simon Vandekar, Ivy Uszynski, Bennett Landman, Cyril Poupon, Kurt G. Schilling",
    "authorList": [
      "Matthew Amandola",
      "Bastien Herlin",
      "Michael E. Kim",
      "Simon Vandekar",
      "Ivy Uszynski",
      "Bennett Landman",
      "Cyril Poupon",
      "Kurt G. Schilling"
    ],
    "kurtPosition": "last",
    "journal": "Research Square",
    "type": "Preprint",
    "doi": "https://doi.org/10.21203/rs.3.rs-9439593/v1",
    "publisher": "https://www.researchsquare.com/article/rs-9439593/v1",
    "summary": "This preprint compares sex-related white matter findings from two different tractography pipelines. Microstructural results were highly consistent, while volumetric effects showed more disagreement and depended more on reconstruction sensitivity.",
    "description": "Two pipelines differing in modeling, reconstruction, and analysis were applied to the same datasets. Only one of 343 microstructural comparisons disagreed significantly, whereas volumetric effects showed substantially more disagreement. The preprint supports the reproducibility of many microstructural sex effects while highlighting greater methodological sensitivity in tract volumes.",
    "findings": [
      "Microstructural sex effects were largely consistent across the two pipelines.",
      "Volumetric findings were more sensitive to reconstruction methodology."
    ],
    "topics": [
      "Tractography",
      "Microstructure"
    ],
    "source": "https://www.researchsquare.com/article/rs-9439593/v1",
    "summarySource": "https://www.researchsquare.com/article/rs-9439593/v1",
    "publicationStatus": "Preprint",
    "pdf": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13131903/pdf/nihpp-rs9439593v1.pdf",
    "metadataSource": "https://api.crossref.org/works/10.21203/rs.3.rs-9439593/v1",
    "datePublished": "2026-04-22",
    "image": "/assets/papers/sex-related-tract-variability.webp",
    "imageAlt": "Comparison of whole-brain tractogram and bundle-specific workflows used to test the robustness of sex-related tract differences.",
    "caption": "Matthew Amandola et al. (2026), Figure 1. Comparison of whole-brain tractogram and bundle-specific workflows used to test the robustness of sex-related tract differences.",
    "figureSource": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13131903/",
    "imageKind": "article-figure",
    "figureLicense": "https://creativecommons.org/licenses/by/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13131903/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13131903/"
      }
    ],
    "pdfVerification": "official-link-only",
    "url": "https://www.microstructure-connectivity-lab.com/publications/sex-related-tract-variability/"
  },
  {
    "slug": "7t-four-way-phase-encoding",
    "title": "Advancing high-resolution 7 T diffusion MRI: Evaluating phase-encoding correction strategies for distortion correction from basic to four-way acquisitions",
    "year": 2026,
    "authors": "Kurt G. Schilling, Alexander J.S. Beckett, Matthew Amandola, Erica B. Walker, David A. Feinberg, Silvia A. Bunge, An T. Vu",
    "authorList": [
      "Kurt G. Schilling",
      "Alexander J.S. Beckett",
      "Matthew Amandola",
      "Erica B. Walker",
      "David A. Feinberg",
      "Silvia A. Bunge",
      "An T. Vu"
    ],
    "kurtPosition": "first",
    "journal": "Magnetic Resonance Imaging",
    "type": "Article",
    "doi": "https://doi.org/10.1016/j.mri.2026.110694",
    "publisher": "https://linkinghub.elsevier.com/retrieve/pii/S0730725X26000871",
    "summary": "At 0.9 mm resolution on a NexGen 7T scanner, this study compares 11 time-matched diffusion acquisition and correction strategies. Four-direction phase encoding provides the strongest geometric fidelity and repeatability in five adults scanned twice. The findings inform high-resolution protocols for both fine superficial connections and long-range pathways.",
    "description": "High-resolution diffusion MRI at 7T offers detailed tissue measurements but also amplifies geometric distortion. Five healthy adults were scanned twice with a highly sampled, four-direction phase-encoding protocol on the NexGen 7T system. The authors derived 11 time-equivalent, ten-minute acquisition strategies and compared their alignment with anatomical images and the reproducibility of diffusion tensor measurements. Full reversed-direction diffusion acquisitions outperformed correction using a single reversed non-diffusion-weighted image, and four-direction acquisition performed best among the tested strategies. The results provide practical guidance for this high-resolution setting, with the small cohort and specialized scanner defining the scope of the evidence.",
    "findings": [
      "Compared 11 time-matched strategies using repeated scans in five adults.",
      "Full reversed-encoding diffusion data outperformed a single reversed b=0 image.",
      "Four-direction encoding gave the best geometry and reproducibility among tested strategies."
    ],
    "topics": [
      "Image Processing",
      "Tractography",
      "Microstructure"
    ],
    "source": "https://linkinghub.elsevier.com/retrieve/pii/S0730725X26000871",
    "publicationStatus": "Published",
    "pdf": "",
    "metadataSource": "https://api.crossref.org/works/10.1016/j.mri.2026.110694",
    "publicationNote": "Available online 17 April 2026, as printed in supplied published PDF; September 2026 issue.",
    "datePublished": "2026-04-17",
    "image": "/assets/papers/7t-four-way-phase-encoding.webp",
    "imageAlt": "High-resolution diffusion images and tractography from the four-direction phase-encoding acquisition.",
    "caption": "Kurt G. Schilling et al. (2026), Figure 7. High-resolution diffusion images and tractography from the four-direction phase-encoding acquisition.",
    "figureSource": "https://linkinghub.elsevier.com/retrieve/pii/S0730725X26000871",
    "imageKind": "article-figure",
    "figureLicense": "http://creativecommons.org/licenses/by/4.0/",
    "url": "https://www.microstructure-connectivity-lab.com/publications/7t-four-way-phase-encoding/"
  },
  {
    "slug": "prefrontal-histology-tractography",
    "title": "Bridging Histology and Tractography: First In Vivo Visualization of Short-Range Prefrontal Connections Informed by Primate Tract-Tracing",
    "year": 2026,
    "authors": "Matthew Amandola, Michael E. Kim, François Rheault, Bennett Landman, Kurt Schilling",
    "authorList": [
      "Matthew Amandola",
      "Michael E. Kim",
      "François Rheault",
      "Bennett Landman",
      "Kurt Schilling"
    ],
    "kurtPosition": "last",
    "journal": "Human Brain Mapping",
    "type": "Article",
    "doi": "https://doi.org/10.1002/hbm.70520",
    "publisher": "https://onlinelibrary.wiley.com/doi/10.1002/hbm.70520",
    "summary": "Anatomical evidence from primate tract tracing guides reconstruction of short connections within the living human prefrontal cortex. The study maps 91 connections in 1,003 participants, finding reproducible individual patterns and substantial variation between people. It offers a framework for studying local circuitry using anatomically informed diffusion tractography.",
    "description": "Short prefrontal connections are difficult to reconstruct because their small size and complex geometry make tractography vulnerable to false positives. This study uses established nonhuman primate tract-tracing observations to guide high-resolution probabilistic tractography across five prefrontal subdivisions in 1,003 people. Reconstructions are evaluated against the histological organization, achieving precision above 80% and accuracy above 70% in that comparison. The resulting pathways show high repeatability within participants and considerable variation between individuals. These findings support anatomically informed investigations of local human circuitry while retaining the distinction between noninvasive reconstructions and direct histological observations.",
    "findings": [
      "Mapped 91 short-range connections across five prefrontal subdivisions.",
      "Reconstruction precision exceeded 80% and accuracy exceeded 70% relative to the histological reference.",
      "Connections were reproducible within individuals while varying between individuals."
    ],
    "topics": [
      "Tractography"
    ],
    "source": "https://onlinelibrary.wiley.com/doi/10.1002/hbm.70520",
    "publicationStatus": "Published",
    "pdf": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13058440/pdf/HBM-47-e70520.pdf",
    "metadataSource": "https://api.crossref.org/works/10.1002/hbm.70520",
    "datePublished": "2026-04-07",
    "image": "/assets/papers/prefrontal-histology-tractography.webp",
    "imageAlt": "Short-range dorsolateral prefrontal pathways reconstructed with guidance from primate tract-tracing evidence.",
    "caption": "Matthew Amandola et al. (2026), Figure 3. Short-range dorsolateral prefrontal pathways reconstructed with guidance from primate tract-tracing evidence.",
    "figureSource": "https://onlinelibrary.wiley.com/doi/10.1002/hbm.70520",
    "imageKind": "article-figure",
    "figureLicense": "https://creativecommons.org/licenses/by/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13058440/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13058440/"
      }
    ],
    "pdfVerification": "official-link-only",
    "url": "https://www.microstructure-connectivity-lab.com/publications/prefrontal-histology-tractography/"
  },
  {
    "slug": "preprocessing-improves-tractography",
    "title": "Did you know? State-of-the-art preprocessing diffusion MRI data can improve tractography",
    "year": 2026,
    "authors": "Kurt G. Schilling, Matthew Cieslak, Maxime Descoteaux, Bennett A. Landman, Franco Pestilli, Ariel Rokem, Stamatios N. Sotiropoulos, Jacques-Donald Tournier, Jelle Veraart",
    "authorList": [
      "Kurt G. Schilling",
      "Matthew Cieslak",
      "Maxime Descoteaux",
      "Bennett A. Landman",
      "Franco Pestilli",
      "Ariel Rokem",
      "Stamatios N. Sotiropoulos",
      "Jacques-Donald Tournier",
      "Jelle Veraart"
    ],
    "kurtPosition": "first",
    "journal": "Brain Structure and Function",
    "type": "Review",
    "doi": "https://doi.org/10.1007/s00429-026-03107-7",
    "publisher": "https://link.springer.com/10.1007/s00429-026-03107-7",
    "summary": "This review explains how modern diffusion MRI preprocessing improves the anatomical fidelity and repeatability of tractography. It brings together evidence for denoising and correction of motion, eddy currents, susceptibility distortion, and Gibbs ringing. Practical acquisition advice and integrated pipelines help researchers apply these methods consistently.",
    "description": "Tractography depends on estimates of local fiber orientation, so image noise and artifacts can propagate into reconstructed pathways. This review synthesizes evidence comparing modern preprocessing with minimally processed diffusion data. It covers denoising, motion and eddy-current correction, echo-planar distortion correction, and Gibbs-ringing removal, alongside emerging processing steps. The authors also describe publicly available integrated pipelines and practical acquisition and data-handling choices that enable effective correction. The article provides a methodological guide for improving anatomical fidelity and scan–rescan reproducibility, while emphasizing that reliable reconstruction starts with careful treatment of the underlying diffusion images.",
    "findings": [
      "Image artifacts propagate into orientation estimates and reconstructed tracts.",
      "Modern preprocessing can improve anatomical fidelity and repeatability.",
      "Integrated public pipelines support standardized application of correction methods."
    ],
    "topics": [
      "Image Processing",
      "Tractography",
      "Reviews & Consensus"
    ],
    "source": "https://link.springer.com/10.1007/s00429-026-03107-7",
    "publicationStatus": "Published",
    "pdf": "",
    "metadataSource": "https://api.crossref.org/works/10.1007/s00429-026-03107-7",
    "datePublished": "2026-03-30",
    "image": "/assets/papers/preprocessing-improves-tractography.webp",
    "imageAlt": "Effects of individual diffusion MRI preprocessing steps on fiber orientations and tractography.",
    "caption": "Kurt G. Schilling et al. (2026), Figure 1. Effects of individual diffusion MRI preprocessing steps on fiber orientations and tractography.",
    "figureSource": "https://link.springer.com/10.1007/s00429-026-03107-7",
    "imageKind": "article-figure",
    "figureLicense": "http://creativecommons.org/licenses/by/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13033464/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13033464/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/preprocessing-improves-tractography/"
  },
  {
    "slug": "white-matter-variability-lifespan",
    "title": "The average does not represent the individual: White matter variability across the brain, across the population, and across the lifespan",
    "year": 2026,
    "authors": "Kurt G Schilling, Lilit Dulyan, Eva Guzmán Chacón, Matthew Amandola, Michael Kim, Bennett A Landman, Stephanie J. Fokel",
    "authorList": [
      "Kurt G Schilling",
      "Lilit Dulyan",
      "Eva Guzmán Chacón",
      "Matthew Amandola",
      "Michael Kim",
      "Bennett A Landman",
      "Stephanie J. Fokel"
    ],
    "kurtPosition": "first",
    "journal": "Research Square",
    "type": "Preprint",
    "doi": "https://doi.org/10.21203/rs.3.rs-8654078/v1",
    "publisher": "https://www.researchsquare.com/article/rs-8654078/v1",
    "summary": "This preprint examines individual differences across 64 white matter pathways using more than 2,800 diffusion scans spanning ages 0–100. Variability follows spatial and lifespan patterns rather than behaving as random noise. Associations with behavior motivate treating anatomical diversity as an informative feature of brain organization.",
    "description": "Group averages can obscure the structured ways in which individual brains differ. This preprint measures spatial, microstructural, and macrostructural variability across 64 white matter pathways in more than 2,800 diffusion MRI scans spanning birth to age 100. Spatial variability follows a gradient from deep to superficial white matter, while between-person variability generally decreases during early development and increases with aging. Different tissue and pathway features show distinct trajectories and hemispheric patterns. Associations with behavior are strongest during development. The work proposes a framework for studying anatomical diversity and its relevance to normative modeling; its findings remain presented as preprint results.",
    "findings": [
      "Variability follows a deep-to-superficial spatial gradient.",
      "Between-person variability decreases during early development and increases with aging.",
      "Behavioral associations are strongest during development."
    ],
    "topics": [
      "Lifespan",
      "Microstructure",
      "Tractography"
    ],
    "source": "https://www.researchsquare.com/article/rs-8654078/v1",
    "publicationStatus": "Preprint — not peer reviewed",
    "pdf": "https://pure.mpg.de/pubman/item/item_3691120_1/component/file_3691121/Schilling_etal_2026_preprint.pdf",
    "metadataSource": "https://api.crossref.org/works/10.21203/rs.3.rs-8654078/v1",
    "datePublished": "2026-01-29",
    "authorNote": "Research Square and supplied PDF spell the final author surname Fokel; likely a source typo for Forkel, preserved pending author correction.",
    "image": "/assets/papers/white-matter-variability-lifespan.webp",
    "imageAlt": "Spatial variability in white matter pathways across the brain and across lifespan cohorts.",
    "caption": "Kurt G Schilling et al. (2026), Figure 2. Spatial variability in white matter pathways across the brain and across lifespan cohorts.",
    "figureSource": "https://www.researchsquare.com/article/rs-8654078/v1",
    "imageKind": "article-figure",
    "figureLicense": "https://creativecommons.org/licenses/by/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12869595/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12869595/"
      }
    ],
    "pdfVerification": "official-link-only",
    "url": "https://www.microstructure-connectivity-lab.com/publications/white-matter-variability-lifespan/"
  },
  {
    "slug": "tractography-outside-brain",
    "title": "Diffusion tractography outside the brain: the road less travelled",
    "year": 2026,
    "authors": "Kurt G. Schilling, Irvin Teh, Julien Cohen-Adad, Richard Dortch, Ibrahim Ibrahim, Nian Wang, Bruce Damon, Rory L. Cochran, Alexander Leemans",
    "authorList": [
      "Kurt G. Schilling",
      "Irvin Teh",
      "Julien Cohen-Adad",
      "Richard Dortch",
      "Ibrahim Ibrahim",
      "Nian Wang",
      "Bruce Damon",
      "Rory L. Cochran",
      "Alexander Leemans"
    ],
    "kurtPosition": "first",
    "journal": "Brain Structure and Function",
    "type": "Review",
    "doi": "https://doi.org/10.1007/s00429-025-03062-9",
    "publisher": "https://link.springer.com/10.1007/s00429-025-03062-9",
    "summary": "Tractography can probe organized tissue far beyond cerebral white matter. This review surveys applications in the spinal cord, heart, peripheral nerves, brachial plexus, kidney, skeletal muscle, and prostate. It explains the acquisition and modeling adaptations each region requires, together with the anatomical opportunities and unresolved interpretation challenges.",
    "description": "The organized structure of many tissues creates diffusion patterns that can be investigated with tractography. This review surveys applications outside the brain, including the spinal cord, heart, peripheral nerves, brachial plexus, kidney, skeletal muscle, and prostate. Each application introduces specific acquisition, modeling, and processing requirements, shaped by anatomy and physiology. Motion, susceptibility artifacts, lower anisotropy, and uncertainty about streamline interpretation remain important obstacles. By comparing these settings, the authors describe how diffusion tractography can provide noninvasive information about tissue organization beyond its conventional cerebral applications and identify methodological work needed to strengthen its biomedical use.",
    "findings": [
      "Reviews tractography across seven anatomical regions outside the brain.",
      "Acquisition and modeling must be adapted to tissue-specific anatomy and physiology.",
      "Motion, artifacts, and streamline validity remain recurring challenges."
    ],
    "topics": [
      "Tractography",
      "Spinal Cord",
      "Reviews & Consensus"
    ],
    "source": "https://link.springer.com/10.1007/s00429-025-03062-9",
    "publicationStatus": "Published",
    "pdf": "",
    "metadataSource": "https://api.crossref.org/works/10.1007/s00429-025-03062-9",
    "datePublished": "2026-01-05",
    "image": "/assets/papers/tractography-outside-brain.webp",
    "imageAlt": "Ex vivo mouse heart tractography shows the changing orientation of cardiomyocytes through the ventricular wall.",
    "caption": "Kurt G. Schilling et al. (2026), Figure 1. Ex vivo mouse heart tractography shows the changing orientation of cardiomyocytes through the ventricular wall.",
    "figureSource": "https://link.springer.com/10.1007/s00429-025-03062-9",
    "imageKind": "article-figure",
    "figureLicense": "http://creativecommons.org/licenses/by/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12769612/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12769612/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/tractography-outside-brain/"
  },
  {
    "slug": "atlas-versus-subject-tractography",
    "title": "Atlas-based templates vs. subject-specific tractography: resolving the debate",
    "year": 2025,
    "authors": "Kurt G. Schilling, Fan Zhang, J-Donald Tournier, Francesco Vergani, Stamatios N. Sotiropoulos, Ariel Rokem, Lauren J. O’Donnell",
    "authorList": [
      "Kurt G. Schilling",
      "Fan Zhang",
      "J-Donald Tournier",
      "Francesco Vergani",
      "Stamatios N. Sotiropoulos",
      "Ariel Rokem",
      "Lauren J. O’Donnell"
    ],
    "kurtPosition": "first",
    "journal": "Brain Structure and Function",
    "type": "Commentary",
    "doi": "https://doi.org/10.1007/s00429-025-02974-w",
    "publisher": "https://link.springer.com/10.1007/s00429-025-02974-w",
    "summary": "Atlas templates and subject-specific tractography answer overlapping but different anatomical questions. This debate article explains the strengths of standardized population references and individual pathway reconstruction. It outlines why methodological choices should follow the research question, especially when individual variation and pathway geometry are central to an analysis.",
    "description": "The article examines a debate from the 2024 International Society for Tractography meeting about what individual tractography adds beyond atlas templates. Atlas-based methods provide standardized anatomical labels and simplify comparisons across participants. Subject-specific tractography uses each person’s diffusion data to reconstruct pathways and can characterize anatomical variation that a population reference may not capture. The authors introduce volumetric and streamline-based atlases, explain common reconstruction strategies, and discuss the strengths and weaknesses of both approaches. Their comparison helps readers decide how the choice of method affects anatomical interpretation, individual-level measurements, and the design of tractography studies.",
    "findings": [
      "Atlases provide standardized population references and anatomical labels.",
      "Subject-specific reconstructions can capture individual pathway organization.",
      "The appropriate approach depends on the anatomical question and required measurements."
    ],
    "topics": [
      "Tractography",
      "Reviews & Consensus"
    ],
    "source": "https://link.springer.com/10.1007/s00429-025-02974-w",
    "publicationStatus": "Published",
    "pdf": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12380923/pdf/429_2025_Article_2974.pdf",
    "metadataSource": "https://api.crossref.org/works/10.1007/s00429-025-02974-w",
    "datePublished": "2025-08-26",
    "image": "/assets/papers/atlas-versus-subject-tractography.webp",
    "imageAlt": "Comparison of atlas propagation and subject-specific tractography workflows.",
    "caption": "Kurt G. Schilling et al. (2025), Figure 1. Comparison of atlas propagation and subject-specific tractography workflows.",
    "figureSource": "https://link.springer.com/10.1007/s00429-025-02974-w",
    "imageKind": "article-figure",
    "figureLicense": "https://creativecommons.org/licenses/by/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12380923/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12380923/"
      }
    ],
    "pdfVerification": "official-link-only",
    "url": "https://www.microstructure-connectivity-lab.com/publications/atlas-versus-subject-tractography/"
  },
  {
    "slug": "leveling-up-spinal-cord",
    "title": "Leveling up: along-level diffusion tensor imaging in the spinal cord of multiple sclerosis patients",
    "year": 2025,
    "authors": "Atlee A. Witt, Anna J. E. Combes, Grace Sweeney, Logan E. Prock, Delaney Houston, Seth Stubblefield, Colin D. McKnight, Kristin P. O’Grady, Seth A. Smith, Kurt G. Schilling",
    "authorList": [
      "Atlee A. Witt",
      "Anna J. E. Combes",
      "Grace Sweeney",
      "Logan E. Prock",
      "Delaney Houston",
      "Seth Stubblefield",
      "Colin D. McKnight",
      "Kristin P. O’Grady",
      "Seth A. Smith",
      "Kurt G. Schilling"
    ],
    "kurtPosition": "last",
    "journal": "Frontiers in Neuroimaging",
    "type": "Article",
    "doi": "https://doi.org/10.3389/fnimg.2025.1599966",
    "publisher": "https://www.frontiersin.org/articles/10.3389/fnimg.2025.1599966/full",
    "summary": "Analyzing spinal cord measurements by cervical level reveals localized changes in relapsing-remitting multiple sclerosis that whole-cord averages can miss. The study combines diffusion and structural measurements within white and gray matter regions. Gray matter atrophy relates to disability, while the measured microstructural changes do not show significant disability associations.",
    "description": "The spinal cord has a natural anatomical coordinate system in its cervical levels, providing a way to localize disease-related changes. This study compares people with relapsing-remitting multiple sclerosis and healthy controls using diffusion tensor and macrostructural measurements along the cord. Analyses of white matter tracts and gray matter subdivisions detect spatially restricted differences that are less apparent when measurements are averaged across the whole cord. Gray matter atrophy is associated with clinical disability, whereas microstructural measurements do not show significant correlations with disability in this cohort. The findings support level-specific analysis for characterizing heterogeneous spinal cord pathology.",
    "findings": [
      "Level-specific analyses are more sensitive to localized group differences than whole-cord averaging.",
      "White and gray matter changes vary along levels and across axial regions.",
      "Gray matter atrophy, but not measured microstructural changes, correlates with disability."
    ],
    "topics": [
      "Spinal Cord",
      "Microstructure"
    ],
    "source": "https://www.frontiersin.org/articles/10.3389/fnimg.2025.1599966/full",
    "publicationStatus": "Published",
    "metadataSource": "https://api.crossref.org/works/10.3389/fnimg.2025.1599966",
    "datePublished": "2025-08-11",
    "image": "/assets/papers/leveling-up-spinal-cord.webp",
    "imageAlt": "Whole-cord morphometry, lesion load, and diffusion measurements in healthy controls and people with relapsing-remitting MS.",
    "caption": "Atlee A. Witt et al. (2025), Figure 2. Whole-cord morphometry, lesion load, and diffusion measurements in healthy controls and people with relapsing-remitting MS.",
    "featured": true,
    "order": 3,
    "figureSource": "https://www.frontiersin.org/articles/10.3389/fnimg.2025.1599966/full",
    "imageKind": "article-figure",
    "figureLicense": "http://creativecommons.org/licenses/by/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12375631/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12375631/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/leveling-up-spinal-cord/"
  },
  {
    "slug": "neurite-soma-brain-cord",
    "title": "Characterization of neurite and soma organization in the brain and spinal cord with diffusion MRI",
    "year": 2025,
    "authors": "Kurt G. Schilling, Marco Palombo, Atlee A. Witt, Kristin P. O’Grady, Marco Pizzolato, Bennett A. Landman, Seth A. Smith",
    "authorList": [
      "Kurt G. Schilling",
      "Marco Palombo",
      "Atlee A. Witt",
      "Kristin P. O’Grady",
      "Marco Pizzolato",
      "Bennett A. Landman",
      "Seth A. Smith"
    ],
    "kurtPosition": "first",
    "journal": "Imaging Neuroscience",
    "type": "Article",
    "doi": "https://doi.org/10.1162/imag.a.111",
    "publisher": "https://direct.mit.edu/imag/article/doi/10.1162/IMAG.a.111/132048/Characterization-of-neurite-and-soma-organization",
    "summary": "A shared 3T diffusion MRI protocol characterizes brain and spinal cord microstructure using DTI, Standard Model Imaging, and SANDI. The study evaluates image quality, tissue contrast, and repeatability across the neuroaxis. It establishes the feasibility of combined measurements while identifying greater reproducibility and partial volume challenges in the cord.",
    "description": "Brain and spinal cord microstructure are often studied separately despite their close anatomical and functional relationships. This work implements a clinically feasible 3T diffusion protocol that supports diffusion tensor imaging, Standard Model Imaging, and Soma and Neurite Density Imaging across both structures. The authors evaluate image quality, regional contrasts, and reproducibility, including the first assessment of SMI and SANDI in the spinal cord reported by the study. Most model-derived metrics show repeatability comparable to DTI, although spinal cord contrasts and reproducibility are generally weaker than in the brain. The protocol offers a harmonized basis for studies across the central nervous system.",
    "findings": [
      "Evaluates DTI, SMI, and SANDI in both brain and spinal cord.",
      "Most SMI and SANDI metrics have reproducibility comparable to DTI.",
      "Cord measurements face greater partial volume and preprocessing challenges."
    ],
    "topics": [
      "Microstructure",
      "Spinal Cord"
    ],
    "source": "https://direct.mit.edu/imag/article/doi/10.1162/IMAG.a.111/132048/Characterization-of-neurite-and-soma-organization",
    "publicationStatus": "Published",
    "metadataSource": "https://api.crossref.org/works/10.1162/imag.a.111",
    "datePublished": "2025-07-31",
    "dateNote": "Available Online date printed in supplied published PDF; Crossref currently records 2025-08-19.",
    "image": "/assets/papers/neurite-soma-brain-cord.webp",
    "imageAlt": "SANDI neurite and soma parameter maps in the brain and cervical spinal cord.",
    "caption": "Kurt G. Schilling et al. (2025), Figure 6. SANDI neurite and soma parameter maps in the brain and cervical spinal cord.",
    "featured": false,
    "order": 4,
    "figureSource": "https://direct.mit.edu/imag/article/doi/10.1162/IMAG.a.111/132048/Characterization-of-neurite-and-soma-organization",
    "imageKind": "article-figure",
    "figureLicense": "https://creativecommons.org/licenses/by/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12365691/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12365691/"
      }
    ],
    "pdf": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12365691/pdf/imag-3-111.pdf",
    "pdfVerification": "official-link-only",
    "url": "https://www.microstructure-connectivity-lab.com/publications/neurite-soma-brain-cord/"
  },
  {
    "slug": "dti-alps-geometry",
    "title": "White Matter Geometry Confounds Diffusion Tensor Imaging Along Perivascular Space (DTI-ALPS) Measures",
    "year": 2025,
    "authors": "Kurt G. Schilling, Allen Newton, Chantal Tax, Markus Nilsson, Maxime Chamberland, Adam Anderson, Bennett Landman, Maxime Descoteaux",
    "authorList": [
      "Kurt G. Schilling",
      "Allen Newton",
      "Chantal Tax",
      "Markus Nilsson",
      "Maxime Chamberland",
      "Adam Anderson",
      "Bennett Landman",
      "Maxime Descoteaux"
    ],
    "kurtPosition": "first",
    "journal": "Human Brain Mapping",
    "type": "Article",
    "doi": "https://doi.org/10.1002/hbm.70282",
    "publisher": "https://onlinelibrary.wiley.com/doi/10.1002/hbm.70282",
    "summary": "The study tests whether DTI-ALPS measurements specifically reflect diffusion along perivascular spaces. Analyses of high-resolution diffusion and vascular imaging show strong effects of fiber crossings, dispersion, and undulation. These anatomical influences complicate interpreting an ALPS index as a direct measurement of glymphatic function across individuals or groups.",
    "description": "DTI-ALPS has been proposed as a noninvasive marker related to perivascular transport, but its interpretation depends on assumptions about tissue geometry. This study evaluates those assumptions using high-resolution multishell diffusion data and vascular imaging. Radial diffusion asymmetry is widespread and persists at high diffusion weighting, while crossings, dispersion, and axonal undulations can alter ALPS-related measurements independently of perivascular diffusion. Medullary vein orientations also vary in regions commonly used for ALPS analysis. The results show why white matter geometry must be considered when interpreting these indices and motivate methods that separate vascular contributions from underlying axonal structure.",
    "findings": [
      "Crossing fibers can increase ALPS indices.",
      "Dispersion and undulations generate radial asymmetry independent of perivascular diffusion.",
      "Vascular orientations vary within regions commonly used for ALPS measurements."
    ],
    "topics": [
      "Microstructure",
      "Image Processing"
    ],
    "source": "https://onlinelibrary.wiley.com/doi/10.1002/hbm.70282",
    "publicationStatus": "Published",
    "metadataSource": "https://api.crossref.org/works/10.1002/hbm.70282",
    "datePublished": "2025-07-07",
    "image": "/assets/papers/dti-alps-geometry.webp",
    "imageAlt": "Crossing fibers contribute to radial diffusion asymmetry, a geometrical confound for DTI-ALPS.",
    "caption": "Kurt G. Schilling et al. (2025), Figure 3. Crossing fibers contribute to radial diffusion asymmetry, a geometrical confound for DTI-ALPS.",
    "featured": true,
    "order": 5,
    "figureSource": "https://onlinelibrary.wiley.com/doi/10.1002/hbm.70282",
    "imageKind": "article-figure",
    "figureLicense": "https://creativecommons.org/licenses/by/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12231058/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12231058/"
      }
    ],
    "pdf": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12231058/pdf/HBM-46-e70282.pdf",
    "pdfVerification": "official-link-only",
    "url": "https://www.microstructure-connectivity-lab.com/publications/dti-alps-geometry/"
  },
  {
    "slug": "short-association-insights",
    "title": "Short association fiber tractography: key insights and surprising facts",
    "year": 2025,
    "authors": "Kurt Schilling, Fan Zhang, Claudio Román, Lauren J. O’Donnell, Pamela Guevara",
    "authorList": [
      "Kurt Schilling",
      "Fan Zhang",
      "Claudio Román",
      "Lauren J. O’Donnell",
      "Pamela Guevara"
    ],
    "kurtPosition": "first",
    "journal": "Brain Structure and Function",
    "type": "Commentary",
    "doi": "https://doi.org/10.1007/s00429-025-02966-w",
    "publisher": "https://link.springer.com/10.1007/s00429-025-02966-w",
    "summary": "Short association fibers are numerous but challenging to reconstruct because of their small size, variable anatomy, and partial volume effects. This communication summarizes key anatomical and methodological insights. It describes how better images, adapted processing, and suitable tracking parameters are opening the superficial white matter to investigation.",
    "description": "This concise communication focuses on short association fibers, the local connections that link nearby cortical areas through superficial white matter. These fibers substantially outnumber long-range connections but are difficult to study with conventional tractography because of their scale, variability, and proximity to tissue boundaries. The authors review advances in image acquisition and processing that make their reconstruction increasingly feasible. They explain why parameters and pipelines designed for long tracts may require adaptation and discuss the importance of reproducible pathway definitions. The article provides practical context for researchers extending connectome studies into this anatomically complex region.",
    "findings": [
      "Short association fibers substantially outnumber long fibers.",
      "Small size, anatomical variability, and partial volume effects complicate reconstruction.",
      "Adapted pipelines and tracking parameters are important for reproducible studies."
    ],
    "topics": [
      "Tractography",
      "Reviews & Consensus"
    ],
    "source": "https://link.springer.com/10.1007/s00429-025-02966-w",
    "publicationStatus": "Published",
    "metadataSource": "https://api.crossref.org/works/10.1007/s00429-025-02966-w",
    "datePublished": "2025-06-14",
    "image": "/assets/papers/short-association-insights.webp",
    "imageAlt": "Short association fibers reconstructed in superficial white matter using high-resolution diffusion MRI.",
    "caption": "Kurt Schilling et al. (2025), Figure 1. Short association fibers reconstructed in superficial white matter using high-resolution diffusion MRI.",
    "featured": false,
    "order": 6,
    "figureSource": "https://link.springer.com/10.1007/s00429-025-02966-w",
    "imageKind": "article-figure",
    "url": "https://www.microstructure-connectivity-lab.com/publications/short-association-insights/"
  },
  {
    "slug": "short-association-development",
    "title": "Microstructural Characterization of Short Association Fibers Related to Long-Range White Matter Tracts in Normative Development",
    "year": 2025,
    "authors": "Chloe Cho, Maxime Chamberland, Francois Rheault, Daniel Moyer, Bennett A. Landman, Kurt G. Schilling",
    "authorList": [
      "Chloe Cho",
      "Maxime Chamberland",
      "Francois Rheault",
      "Daniel Moyer",
      "Bennett A. Landman",
      "Kurt G. Schilling"
    ],
    "kurtPosition": "last",
    "journal": "Human Brain Mapping",
    "type": "Article",
    "doi": "https://doi.org/10.1002/hbm.70255",
    "publisher": "https://onlinelibrary.wiley.com/doi/10.1002/hbm.70255",
    "summary": "This study compares the development of short association fibers and long-range pathways in 616 participants aged 5.6–21.9 years. Diffusion tensor and NODDI measurements reveal shared age-related patterns alongside distinct features of superficial white matter. The results provide a reference for future studies of atypical development.",
    "description": "Short association fibers connect nearby cortical regions, yet their developmental trajectories have received less attention than those of long-range pathways. The study combines diffusion tractography, diffusion tensor imaging, and NODDI in 616 participants aged 5.6–21.9 years. Both pathway classes show broadly similar associations between age and diffusion measurements, but fractional anisotropy, axial diffusivity, and orientation dispersion distinguish superficial from deep white matter. Sex-related differences also appear in selected measures. The work provides normative descriptions of these pathways during childhood and young adulthood and motivates studies of how their development is coordinated across anatomical scales.",
    "findings": [
      "Short and long pathways share several age-associated microstructural trends.",
      "FA, axial diffusivity, and orientation dispersion distinguish the two pathway classes.",
      "Selected measurements also differed between males and females."
    ],
    "topics": [
      "Microstructure",
      "Lifespan",
      "Tractography"
    ],
    "source": "https://onlinelibrary.wiley.com/doi/10.1002/hbm.70255",
    "publicationStatus": "Published",
    "metadataSource": "https://api.crossref.org/works/10.1002/hbm.70255",
    "datePublished": "2025-06-09",
    "image": "/assets/papers/short-association-development.webp",
    "imageAlt": "Short association fibers mapped relative to long-range white matter pathways.",
    "caption": "Chloe Cho et al. (2025), Figure 3. Short association fibers mapped relative to long-range white matter pathways.",
    "featured": true,
    "order": 7,
    "figureSource": "https://onlinelibrary.wiley.com/doi/10.1002/hbm.70255",
    "imageKind": "article-figure",
    "figureLicense": "https://creativecommons.org/licenses/by/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12148645/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12148645/"
      }
    ],
    "pdf": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12148645/pdf/HBM-46-e70255.pdf",
    "pdfVerification": "official-link-only",
    "url": "https://www.microstructure-connectivity-lab.com/publications/short-association-development/"
  },
  {
    "slug": "animal-models-human-tractography",
    "title": "Animal models are useful in studying human neuroanatomy with tractography",
    "year": 2025,
    "authors": "Alard Roebroeck, Suzanne Haber, Elena Borra, Simona Schiavi, Stephanie J. Forkel, Kathleen Rockland, Tim B. Dyrby, Kurt Schilling",
    "authorList": [
      "Alard Roebroeck",
      "Suzanne Haber",
      "Elena Borra",
      "Simona Schiavi",
      "Stephanie J. Forkel",
      "Kathleen Rockland",
      "Tim B. Dyrby",
      "Kurt Schilling"
    ],
    "kurtPosition": "last",
    "journal": "Brain Structure and Function",
    "type": "Commentary",
    "doi": "https://doi.org/10.1007/s00429-025-02945-1",
    "publisher": "https://link.springer.com/10.1007/s00429-025-02945-1",
    "summary": "This short communication examines how animal models inform human tractography, drawing on a debate at the 2024 Tract-Anat Retreat. It highlights histological validation, high-resolution imaging, disease research, and comparative anatomy. The discussion also addresses species differences and the care required when translating findings to humans.",
    "description": "Animal models can connect diffusion tractography to experimental evidence that is difficult or impossible to obtain in living humans. This communication synthesizes a retreat discussion about their contribution to human neuroanatomical knowledge. The authors describe four broad applications: comparisons with histology, acquisition of detailed imaging data, investigation of disease mechanisms, and comparative neuroanatomy. They also consider limitations arising from differences in species anatomy, biology, scanner hardware, and acquisition protocols. The resulting argument supports carefully selected animal models as useful tools for improving tractography methods and interpretation, while emphasizing that biological and technical differences must inform cross-species comparisons.",
    "findings": [
      "Animal experiments support tractography validation against histological evidence.",
      "High-resolution data and controlled disease studies complement human investigations.",
      "Species and acquisition differences shape the limits of translation."
    ],
    "topics": [
      "Tractography",
      "Reviews & Consensus"
    ],
    "source": "https://link.springer.com/10.1007/s00429-025-02945-1",
    "publicationStatus": "Published",
    "pdf": "",
    "metadataSource": "https://api.crossref.org/works/10.1007/s00429-025-02945-1",
    "datePublished": "2025-05-31",
    "image": "/assets/papers/animal-models-human-tractography.webp",
    "imageAlt": "Title and author block of the commentary Animal models are useful in studying human neuroanatomy with tractography.",
    "caption": "Roebroeck et al. (2025), article title page. Article preview; this commentary contains no figures.",
    "figureSource": "https://link.springer.com/10.1007/s00429-025-02945-1",
    "imageKind": "article-preview",
    "url": "https://www.microstructure-connectivity-lab.com/publications/animal-models-human-tractography/"
  },
  {
    "slug": "white-matter-vascular-geometry",
    "title": "The relationship of white matter tract orientation to vascular geometry in the human brain",
    "year": 2025,
    "authors": "Kurt G. Schilling, Allen Newton, Chantal M. W. Tax, Maxime Chamberland, Samuel W. Remedios, Yurui Gao, Muwei Li, Catie Chang, Francois Rheault, Farshid Sepherband, Adam Anderson, John C. Gore, Bennett Landman",
    "authorList": [
      "Kurt G. Schilling",
      "Allen Newton",
      "Chantal M. W. Tax",
      "Maxime Chamberland",
      "Samuel W. Remedios",
      "Yurui Gao",
      "Muwei Li",
      "Catie Chang",
      "Francois Rheault",
      "Farshid Sepherband",
      "Adam Anderson",
      "John C. Gore",
      "Bennett Landman"
    ],
    "kurtPosition": "first",
    "journal": "Scientific Reports",
    "type": "Article",
    "doi": "https://doi.org/10.1038/s41598-025-99724-z",
    "publisher": "https://www.nature.com/articles/s41598-025-99724-z",
    "summary": "Diffusion and susceptibility-weighted imaging reveal how white matter pathways relate to the orientation of nearby blood vessels. Vessels often align with some local fiber populations but do not consistently follow the dominant tract direction or an entire pathway. The findings help interpret MRI contrasts influenced by both vascular and axonal geometry.",
    "description": "White matter axons and blood vessels both have directional organization that can influence MRI measurements. This study combines diffusion MRI and susceptibility-weighted imaging from the same healthy young adults to compare their orientations across regions and along pathways. Vascular geometry is related to local fiber organization, but vessels do not necessarily align with the dominant diffusion direction. Crossing fibers can explain alignment with alternative fiber populations within a voxel. Moreover, parallel organization along portions of a tract does not imply that vessels follow its full course. These observations refine the anatomical basis for interpreting orientation-sensitive structural and functional MRI contrasts.",
    "findings": [
      "Vessels do not consistently align with the dominant white matter orientation.",
      "Crossing fiber populations can explain local vascular–axonal alignment.",
      "Vascular organization does not follow all tracts continuously along their lengths."
    ],
    "topics": [
      "Microstructure",
      "Tractography"
    ],
    "source": "https://www.nature.com/articles/s41598-025-99724-z",
    "publicationStatus": "Published",
    "pdf": "",
    "metadataSource": "https://api.crossref.org/works/10.1038/s41598-025-99724-z",
    "datePublished": "2025-05-26",
    "image": "/assets/papers/white-matter-vascular-geometry.webp",
    "imageAlt": "Examples of alignment and misalignment between white matter tract orientations and vasculature.",
    "caption": "Kurt G. Schilling et al. (2025), Figure 8. Examples of alignment and misalignment between white matter tract orientations and vasculature.",
    "figureSource": "https://www.nature.com/articles/s41598-025-99724-z",
    "imageKind": "article-figure",
    "figureLicense": "http://creativecommons.org/licenses/by/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12106635/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12106635/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/white-matter-vascular-geometry/"
  },
  {
    "slug": "preclinical-dmri-part-2",
    "title": "Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI : Part 2—Ex vivo imaging: Added value and acquisition",
    "year": 2025,
    "authors": "Kurt G. Schilling, Francesco Grussu, Andrada Ianus, Brian Hansen, Amy F. D. Howard, Rachel L. C. Barrett, Manisha Aggarwal, Stijn Michielse, Fatima Nasrallah, Warda Syeda, Nian Wang, Jelle Veraart, Alard Roebroeck, Andrew F. Bagdasarian, Cornelius Eichner, Farshid Sepehrband, Jan Zimmermann, Lucas Soustelle, Christien Bowman, Benjamin C. Tendler, Andreea Hertanu, Ben Jeurissen, Marleen Verhoye, Lucio Frydman, Yohan van de Looij, David Hike, Jeff F. Dunn, Karla Miller, Bennett A. Landman, Noam Shemesh, Adam Anderson, Emilie McKinnon, Shawna Farquharson, Flavio Dell'Acqua, Carlo Pierpaoli, Ivana Drobnjak, Alexander Leemans, Kevin D. Harkins, Maxime Descoteaux, Duan Xu, Hao Huang, Mathieu D. Santin, Samuel C. Grant, Andre Obenaus, Gene S. Kim, Dan Wu, Denis Le Bihan, Stephen J. Blackband, Luisa Ciobanu, Els Fieremans, Ruiliang Bai, Trygve B. Leergaard, Jiangyang Zhang, Tim B. Dyrby, G. Allan Johnson, Julien Cohen-Adad, Matthew D. Budde, Ileana O. Jelescu",
    "authorList": [
      "Kurt G. Schilling",
      "Francesco Grussu",
      "Andrada Ianus",
      "Brian Hansen",
      "Amy F. D. Howard",
      "Rachel L. C. Barrett",
      "Manisha Aggarwal",
      "Stijn Michielse",
      "Fatima Nasrallah",
      "Warda Syeda",
      "Nian Wang",
      "Jelle Veraart",
      "Alard Roebroeck",
      "Andrew F. Bagdasarian",
      "Cornelius Eichner",
      "Farshid Sepehrband",
      "Jan Zimmermann",
      "Lucas Soustelle",
      "Christien Bowman",
      "Benjamin C. Tendler",
      "Andreea Hertanu",
      "Ben Jeurissen",
      "Marleen Verhoye",
      "Lucio Frydman",
      "Yohan van de Looij",
      "David Hike",
      "Jeff F. Dunn",
      "Karla Miller",
      "Bennett A. Landman",
      "Noam Shemesh",
      "Adam Anderson",
      "Emilie McKinnon",
      "Shawna Farquharson",
      "Flavio Dell'Acqua",
      "Carlo Pierpaoli",
      "Ivana Drobnjak",
      "Alexander Leemans",
      "Kevin D. Harkins",
      "Maxime Descoteaux",
      "Duan Xu",
      "Hao Huang",
      "Mathieu D. Santin",
      "Samuel C. Grant",
      "Andre Obenaus",
      "Gene S. Kim",
      "Dan Wu",
      "Denis Le Bihan",
      "Stephen J. Blackband",
      "Luisa Ciobanu",
      "Els Fieremans",
      "Ruiliang Bai",
      "Trygve B. Leergaard",
      "Jiangyang Zhang",
      "Tim B. Dyrby",
      "G. Allan Johnson",
      "Julien Cohen-Adad",
      "Matthew D. Budde",
      "Ileana O. Jelescu"
    ],
    "kurtPosition": "first",
    "journal": "Magnetic Resonance in Medicine",
    "type": "Review",
    "doi": "https://doi.org/10.1002/mrm.30435",
    "publisher": "https://onlinelibrary.wiley.com/doi/10.1002/mrm.30435",
    "summary": "The second ISMRM preclinical diffusion MRI paper examines the value of ex vivo imaging and how to acquire reliable data. It covers specimen selection, fixation, preparation, and scanning. The recommendations explain both the opportunities for detailed tissue characterization and the experimental differences that complicate comparison with living tissue.",
    "description": "Ex vivo diffusion MRI can achieve high signal-to-noise ratio, fine spatial resolution, and diffusion contrasts that are difficult to obtain in living subjects. It also enables direct comparisons with histology. This review explains those opportunities and the experimental considerations required to use them effectively. Topics include specimen and model selection, tissue fixation, sample preparation, and acquisition protocols. Because ex vivo conditions change tissue properties and measurement requirements, the authors emphasize that in vivo practices cannot simply be transferred unchanged. The recommendations aim to improve reproducibility while identifying remaining gaps in evidence and priorities for future method development.",
    "findings": [
      "Ex vivo imaging enables detailed spatial sampling and advanced diffusion contrasts.",
      "Fixation and sample preparation materially affect acquisition and interpretation.",
      "Direct comparison with histology is a major opportunity for validation."
    ],
    "topics": [
      "Reviews & Consensus",
      "Microstructure"
    ],
    "source": "https://onlinelibrary.wiley.com/doi/10.1002/mrm.30435",
    "publicationStatus": "Published",
    "metadataSource": "https://api.crossref.org/works/10.1002/mrm.30435",
    "datePublished": "2025-03-04",
    "publicationNote": "Community recommendations / best practices; the series does not claim a formal consensus.",
    "image": "/assets/papers/preclinical-dmri-part-2.webp",
    "imageAlt": "High-resolution ex vivo human imaging supports detailed tissue maps and tractography.",
    "caption": "Kurt G. Schilling et al. (2025), Figure 5. High-resolution ex vivo human imaging supports detailed tissue maps and tractography.",
    "featured": false,
    "order": 1,
    "figureSource": "https://onlinelibrary.wiley.com/doi/10.1002/mrm.30435",
    "imageKind": "article-figure",
    "figureLicense": "http://creativecommons.org/licenses/by/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11971501/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11971501/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/preclinical-dmri-part-2/"
  },
  {
    "slug": "preclinical-dmri-part-1",
    "title": "Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI : Part 1: In vivo small-animal imaging",
    "year": 2025,
    "authors": "Ileana O. Jelescu, Francesco Grussu, Andrada Ianus, Brian Hansen, Rachel L. C. Barrett, Manisha Aggarwal, Stijn Michielse, Fatima Nasrallah, Warda Syeda, Nian Wang, Jelle Veraart, Alard Roebroeck, Andrew F. Bagdasarian, Cornelius Eichner, Farshid Sepehrband, Jan Zimmermann, Lucas Soustelle, Christien Bowman, Benjamin C. Tendler, Andreea Hertanu, Ben Jeurissen, Marleen Verhoye, Lucio Frydman, Yohan van de Looij, David Hike, Jeff F. Dunn, Karla Miller, Bennett A. Landman, Noam Shemesh, Adam Anderson, Emilie McKinnon, Shawna Farquharson, Flavio Dell'Acqua, Carlo Pierpaoli, Ivana Drobnjak, Alexander Leemans, Kevin D. Harkins, Maxime Descoteaux, Duan Xu, Hao Huang, Mathieu D. Santin, Samuel C. Grant, Andre Obenaus, Gene S. Kim, Dan Wu, Denis Le Bihan, Stephen J. Blackband, Luisa Ciobanu, Els Fieremans, Ruiliang Bai, Trygve B. Leergaard, Jiangyang Zhang, Tim B. Dyrby, G. Allan Johnson, Julien Cohen-Adad, Matthew D. Budde, Kurt G. Schilling",
    "authorList": [
      "Ileana O. Jelescu",
      "Francesco Grussu",
      "Andrada Ianus",
      "Brian Hansen",
      "Rachel L. C. Barrett",
      "Manisha Aggarwal",
      "Stijn Michielse",
      "Fatima Nasrallah",
      "Warda Syeda",
      "Nian Wang",
      "Jelle Veraart",
      "Alard Roebroeck",
      "Andrew F. Bagdasarian",
      "Cornelius Eichner",
      "Farshid Sepehrband",
      "Jan Zimmermann",
      "Lucas Soustelle",
      "Christien Bowman",
      "Benjamin C. Tendler",
      "Andreea Hertanu",
      "Ben Jeurissen",
      "Marleen Verhoye",
      "Lucio Frydman",
      "Yohan van de Looij",
      "David Hike",
      "Jeff F. Dunn",
      "Karla Miller",
      "Bennett A. Landman",
      "Noam Shemesh",
      "Adam Anderson",
      "Emilie McKinnon",
      "Shawna Farquharson",
      "Flavio Dell'Acqua",
      "Carlo Pierpaoli",
      "Ivana Drobnjak",
      "Alexander Leemans",
      "Kevin D. Harkins",
      "Maxime Descoteaux",
      "Duan Xu",
      "Hao Huang",
      "Mathieu D. Santin",
      "Samuel C. Grant",
      "Andre Obenaus",
      "Gene S. Kim",
      "Dan Wu",
      "Denis Le Bihan",
      "Stephen J. Blackband",
      "Luisa Ciobanu",
      "Els Fieremans",
      "Ruiliang Bai",
      "Trygve B. Leergaard",
      "Jiangyang Zhang",
      "Tim B. Dyrby",
      "G. Allan Johnson",
      "Julien Cohen-Adad",
      "Matthew D. Budde",
      "Kurt G. Schilling"
    ],
    "kurtPosition": "last",
    "journal": "Magnetic Resonance in Medicine",
    "type": "Review",
    "doi": "https://doi.org/10.1002/mrm.30429",
    "publisher": "https://onlinelibrary.wiley.com/doi/10.1002/mrm.30429",
    "summary": "The first paper in the ISMRM preclinical diffusion MRI series presents community recommendations for imaging living small animals. It covers model selection, preparation, acquisition, processing, and interpretation, with an emphasis on reproducibility. The authors also identify open methodological questions and resources for sharing data and software.",
    "description": "Preclinical diffusion MRI supports method development, biological validation, disease studies, and comparative anatomy. This review describes the experimental choices that shape in vivo small-animal studies, from species and model selection to monitoring, scanner hardware, pulse sequences, preprocessing, and tractography. It brings together recommendations from the preclinical diffusion community and points readers to publicly available datasets and software. The authors explicitly present a snapshot of best practices rather than a formal consensus on every topic. Their central aim is to improve rigor and reproducibility while showing where evidence remains insufficient for firm guidance.",
    "findings": [
      "Covers the full workflow from animal preparation to interpretation.",
      "Explains how model, hardware, and acquisition choices affect the scientific question.",
      "Identifies open questions and public resources for reproducible research."
    ],
    "topics": [
      "Reviews & Consensus",
      "Microstructure",
      "Tractography"
    ],
    "source": "https://onlinelibrary.wiley.com/doi/10.1002/mrm.30429",
    "publicationStatus": "Published",
    "metadataSource": "https://api.crossref.org/works/10.1002/mrm.30429",
    "datePublished": "2025-02-26",
    "publicationNote": "Community recommendations / best practices; the series does not claim a formal consensus.",
    "image": "/assets/papers/preclinical-dmri-part-1.webp",
    "imageAlt": "Diffusion MRI across small-animal models illustrates differences in brain size, geometry, and tissue organization.",
    "caption": "Ileana O. Jelescu et al. (2025), Figure 2. Diffusion MRI across small-animal models illustrates differences in brain size, geometry, and tissue organization.",
    "featured": false,
    "order": 0,
    "figureSource": "https://onlinelibrary.wiley.com/doi/10.1002/mrm.30429",
    "imageKind": "article-figure",
    "figureLicense": "https://creativecommons.org/licenses/by/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11971505/",
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      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11971505/"
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    ],
    "pdf": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11971505/pdf/MRM-93-2507.pdf",
    "pdfVerification": "official-link-only",
    "url": "https://www.microstructure-connectivity-lab.com/publications/preclinical-dmri-part-1/"
  },
  {
    "slug": "preclinical-dmri-part-3",
    "title": "Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI : Part 3—Ex vivo imaging: Data processing, comparisons with microscopy, and tractography",
    "year": 2025,
    "authors": "Kurt G. Schilling, Amy F. D. Howard, Francesco Grussu, Andrada Ianus, Brian Hansen, Rachel L. C. Barrett, Manisha Aggarwal, Stijn Michielse, Fatima Nasrallah, Warda Syeda, Nian Wang, Jelle Veraart, Alard Roebroeck, Andrew F. Bagdasarian, Cornelius Eichner, Farshid Sepehrband, Jan Zimmermann, Lucas Soustelle, Christien Bowman, Benjamin C. Tendler, Andreea Hertanu, Ben Jeurissen, Marleen Verhoye, Lucio Frydman, Yohan van de Looij, David Hike, Jeff F. Dunn, Karla Miller, Bennett A. Landman, Noam Shemesh, Adam Anderson, Emilie McKinnon, Shawna Farquharson, Flavio Dell'Acqua, Carlo Pierpaoli, Ivana Drobnjak, Alexander Leemans, Kevin D. Harkins, Maxime Descoteaux, Duan Xu, Hao Huang, Mathieu D. Santin, Samuel C. Grant, Andre Obenaus, Gene S. Kim, Dan Wu, Denis Le Bihan, Stephen J. Blackband, Luisa Ciobanu, Els Fieremans, Ruiliang Bai, Trygve B. Leergaard, Jiangyang Zhang, Tim B. Dyrby, G. Allan Johnson, Julien Cohen-Adad, Matthew D. Budde, Ileana O. Jelescu",
    "authorList": [
      "Kurt G. Schilling",
      "Amy F. D. Howard",
      "Francesco Grussu",
      "Andrada Ianus",
      "Brian Hansen",
      "Rachel L. C. Barrett",
      "Manisha Aggarwal",
      "Stijn Michielse",
      "Fatima Nasrallah",
      "Warda Syeda",
      "Nian Wang",
      "Jelle Veraart",
      "Alard Roebroeck",
      "Andrew F. Bagdasarian",
      "Cornelius Eichner",
      "Farshid Sepehrband",
      "Jan Zimmermann",
      "Lucas Soustelle",
      "Christien Bowman",
      "Benjamin C. Tendler",
      "Andreea Hertanu",
      "Ben Jeurissen",
      "Marleen Verhoye",
      "Lucio Frydman",
      "Yohan van de Looij",
      "David Hike",
      "Jeff F. Dunn",
      "Karla Miller",
      "Bennett A. Landman",
      "Noam Shemesh",
      "Adam Anderson",
      "Emilie McKinnon",
      "Shawna Farquharson",
      "Flavio Dell'Acqua",
      "Carlo Pierpaoli",
      "Ivana Drobnjak",
      "Alexander Leemans",
      "Kevin D. Harkins",
      "Maxime Descoteaux",
      "Duan Xu",
      "Hao Huang",
      "Mathieu D. Santin",
      "Samuel C. Grant",
      "Andre Obenaus",
      "Gene S. Kim",
      "Dan Wu",
      "Denis Le Bihan",
      "Stephen J. Blackband",
      "Luisa Ciobanu",
      "Els Fieremans",
      "Ruiliang Bai",
      "Trygve B. Leergaard",
      "Jiangyang Zhang",
      "Tim B. Dyrby",
      "G. Allan Johnson",
      "Julien Cohen-Adad",
      "Matthew D. Budde",
      "Ileana O. Jelescu"
    ],
    "kurtPosition": "first",
    "journal": "Magnetic Resonance in Medicine",
    "type": "Review",
    "doi": "https://doi.org/10.1002/mrm.30424",
    "publisher": "https://onlinelibrary.wiley.com/doi/10.1002/mrm.30424",
    "summary": "The third ISMRM preclinical diffusion MRI paper addresses ex vivo image processing, comparisons with microscopy, and tractography. It explains how fixed-tissue experiments require choices that differ from in vivo workflows. Community recommendations and unresolved questions guide the design and interpretation of rigorous, reproducible validation studies.",
    "description": "Ex vivo diffusion MRI offers detailed images and opportunities for direct comparison with microscopy, but those advantages introduce distinct processing and interpretation challenges. The final paper in this three-part series covers preprocessing, diffusion modeling, microscopy comparisons, and tractography using ex vivo specimens. It discusses how experimental conditions and tissue preparation influence the resulting measurements and what is needed to connect MRI features to histological evidence. The authors present practical recommendations while identifying areas where firm guidance remains unavailable. The paper supports reproducible validation work and complements the acquisition-focused recommendations in Part 2 of the series.",
    "findings": [
      "Addresses preprocessing and modeling choices specific to ex vivo tissue.",
      "Discusses comparisons with microscopy and tractography validation.",
      "Highlights gaps where methodological evidence does not yet support firm guidance."
    ],
    "topics": [
      "Reviews & Consensus",
      "Image Processing",
      "Tractography"
    ],
    "source": "https://onlinelibrary.wiley.com/doi/10.1002/mrm.30424",
    "publicationStatus": "Published",
    "metadataSource": "https://api.crossref.org/works/10.1002/mrm.30424",
    "datePublished": "2025-02-26",
    "publicationNote": "Community recommendations / best practices; the series does not claim a formal consensus.",
    "image": "/assets/papers/preclinical-dmri-part-3.webp",
    "imageAlt": "Examples of artifacts addressed by preprocessing ex vivo diffusion MRI data.",
    "caption": "Kurt G. Schilling et al. (2025), Figure 1. Examples of artifacts addressed by preprocessing ex vivo diffusion MRI data.",
    "featured": false,
    "order": 2,
    "figureSource": "https://onlinelibrary.wiley.com/doi/10.1002/mrm.30424",
    "imageKind": "article-figure",
    "figureLicense": "http://creativecommons.org/licenses/by/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11971500/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11971500/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/preclinical-dmri-part-3/"
  },
  {
    "slug": "head-motion-diffusion-lifespan",
    "title": "Head Motion in Diffusion Magnetic Resonance Imaging: Quantification, Mitigation, and Structural Associations in Large, Cross-Sectional Datasets Across the Lifespan",
    "year": 2025,
    "authors": "Kurt G. Schilling, Karthik Ramadass, Viljami Sairanen, Michael E. Kim, Francois Rheault, Nancy Newlin, Tin Nguyen, Laura Barquero, Micah D'archangel, Chenyu Gao, Ema Topolnjak, Nazirah Mohd Khairi, Derek Archer, Lori L. Beason-Held, Susan M. Resnick, Timothy Hohman, Laurie Cutting, Julie Schneider, Lisa L. Barnes, David A. Bennett, Konstantinos Arfanakis, Sophia Vinci-Booher, Marilyn Albert, The BIOCARD Study Team, The Alzheimer's Disease Neuroimaging Initiative (ADNI), Aging Brain: Vasculature, Ischemia, and Behavior (ABVIB), Daniel Moyer, Bennett A. Landman",
    "authorList": [
      "Kurt G. Schilling",
      "Karthik Ramadass",
      "Viljami Sairanen",
      "Michael E. Kim",
      "Francois Rheault",
      "Nancy Newlin",
      "Tin Nguyen",
      "Laura Barquero",
      "Micah D'archangel",
      "Chenyu Gao",
      "Ema Topolnjak",
      "Nazirah Mohd Khairi",
      "Derek Archer",
      "Lori L. Beason-Held",
      "Susan M. Resnick",
      "Timothy Hohman",
      "Laurie Cutting",
      "Julie Schneider",
      "Lisa L. Barnes",
      "David A. Bennett",
      "Konstantinos Arfanakis",
      "Sophia Vinci-Booher",
      "Marilyn Albert",
      "The BIOCARD Study Team",
      "The Alzheimer's Disease Neuroimaging Initiative (ADNI)",
      "Aging Brain: Vasculature, Ischemia, and Behavior (ABVIB)",
      "Daniel Moyer",
      "Bennett A. Landman"
    ],
    "kurtPosition": "first",
    "journal": "Human Brain Mapping",
    "type": "Article",
    "doi": "https://doi.org/10.1002/hbm.70143",
    "publisher": "https://onlinelibrary.wiley.com/doi/10.1002/hbm.70143",
    "summary": "Across 13 cohorts spanning infancy to old age, this study characterizes head motion and its relationship to diffusion MRI measurements. Scan–rescan comparisons examine the effectiveness of modern preprocessing, while population analyses investigate structural associations with motion. The findings help distinguish acquisition artifacts from differences associated with the people who move.",
    "description": "Head motion can distort diffusion MRI and bias tissue measurements, but its patterns vary across individuals and populations. This study analyzes 13 cohorts spanning ages 0.1–100 years to describe motion magnitude, direction, and associations with participant characteristics. It also uses repeated scans with different motion levels to evaluate modern preprocessing and examines whether high- and low-motion groups differ in structural connectivity. The results indicate that contemporary processing can substantially mitigate detectable motion-related measurement bias in the tested comparisons. At the same time, associations between motion propensity and anatomy warrant care when interpreting between-person differences or selecting data for analysis.",
    "findings": [
      "Describes motion patterns across 13 cohorts spanning infancy through late life.",
      "Repeated scans test whether preprocessing mitigates differences associated with motion.",
      "Motion propensity can relate to structural connectivity, complicating population comparisons."
    ],
    "topics": [
      "Image Processing",
      "Lifespan",
      "Microstructure"
    ],
    "source": "https://onlinelibrary.wiley.com/doi/10.1002/hbm.70143",
    "publicationStatus": "Published",
    "pdf": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11814480/pdf/HBM-46-e70143.pdf",
    "metadataSource": "https://api.crossref.org/works/10.1002/hbm.70143",
    "datePublished": "2025-02-11",
    "image": "/assets/papers/head-motion-diffusion-lifespan.webp",
    "imageAlt": "Head motion varies across age, tending to decrease toward young adulthood and increase with aging.",
    "caption": "Kurt G. Schilling et al. (2025), Figure 5. Head motion varies across age, tending to decrease toward young adulthood and increase with aging.",
    "figureSource": "https://onlinelibrary.wiley.com/doi/10.1002/hbm.70143",
    "imageKind": "article-figure",
    "figureLicense": "https://creativecommons.org/licenses/by/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11814480/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11814480/"
      }
    ],
    "pdfVerification": "official-link-only",
    "url": "https://www.microstructure-connectivity-lab.com/publications/head-motion-diffusion-lifespan/"
  },
  {
    "slug": "spinal-cord-super-resolution",
    "title": "Measuring Impact of Super-Resolution on Spinal Cord MRI Scans: Lesion Detection Sensitivity, Variability, and Clinical Impact",
    "year": 2025,
    "authors": "Greyson A. Wintergerst, Samuel W. Remedios, Allen T. Newton, Seth A. Smith, Bennett A. Landman, Kurt G. Schilling",
    "authorList": [
      "Greyson A. Wintergerst",
      "Samuel W. Remedios",
      "Allen T. Newton",
      "Seth A. Smith",
      "Bennett A. Landman",
      "Kurt G. Schilling"
    ],
    "kurtPosition": "last",
    "journal": "2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI)",
    "type": "Conference paper",
    "doi": "https://doi.org/10.1109/isbi60581.2025.10981088",
    "publisher": "https://ieeexplore.ieee.org/document/10981088/",
    "summary": "This study tests whether super-resolution and interpolation improve automated lesion detection in spinal cord MRI from 53 people with multiple sclerosis. Higher isotropic resolution increased lesion-segmentation sensitivity, but lesion measures did not show a significant association with disability scores.",
    "description": "Anisotropic spinal cord acquisitions limit the visualization of small lesions. This conference study evaluates artificially increasing image resolution before automated lesion analysis, comparing lesion load and volume with clinical measures. Resolution changes improved detection sensitivity in the studied cohort, while the relationship between lesion burden and EDSS disability remained nonsignificant.",
    "findings": [
      "Changing image resolution increased sensitivity for automated lesion segmentation.",
      "Lesion load and volume were not significantly associated with EDSS in this cohort."
    ],
    "topics": [
      "Spinal Cord",
      "Image Processing"
    ],
    "source": "https://pubmed.ncbi.nlm.nih.gov/42077389/",
    "summarySource": "https://pubmed.ncbi.nlm.nih.gov/42077389/",
    "publicationStatus": "Published",
    "pdf": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13130011/pdf/nihms-2167797.pdf",
    "metadataSource": "https://api.crossref.org/works/10.1109/isbi60581.2025.10981088",
    "image": "/assets/papers/spinal-cord-super-resolution.webp",
    "imageAlt": "Spinal cord image resolution influences the visibility and segmentation of MS lesions.",
    "caption": "Greyson A. Wintergerst et al. (2025), Figure 2. Spinal cord image resolution influences the visibility and segmentation of MS lesions.",
    "figureSource": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13130011/",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13130011/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13130011/"
      }
    ],
    "pdfVerification": "official-link-only",
    "url": "https://www.microstructure-connectivity-lab.com/publications/spinal-cord-super-resolution/"
  },
  {
    "slug": "tractography-validation-foundations",
    "title": "Tractography validation Part 1: Foundations, numerical simulations, and phantom models",
    "year": 2025,
    "authors": "Tim B. Dyrby, Els Fieremans, Francois Rheault, Adam W. Anderson, Marco Palombo, Silvio Sarubbo, Peter Neher, Kurt G. Schilling",
    "authorList": [
      "Tim B. Dyrby",
      "Els Fieremans",
      "Francois Rheault",
      "Adam W. Anderson",
      "Marco Palombo",
      "Silvio Sarubbo",
      "Peter Neher",
      "Kurt G. Schilling"
    ],
    "kurtPosition": "last",
    "journal": "Handbook of Diffusion MR Tractography",
    "type": "Book chapter",
    "doi": "https://doi.org/10.1016/b978-0-12-818894-1.00017-3",
    "publisher": "https://linkinghub.elsevier.com/retrieve/pii/B9780128188941000173",
    "summary": "This handbook chapter introduces the foundations of tractography validation and the roles of numerical simulations and physical phantoms. It explains how controlled reference systems can test reconstruction methods at different anatomical scales. The discussion helps researchers choose validation strategies and understand the limits of their conclusions.",
    "description": "Validation asks how faithfully diffusion tractography represents anatomical connections, but different experiments test different aspects of that relationship. This chapter in the Handbook of Diffusion MR Tractography sets out the anatomical scales and methodological assumptions relevant to validation. It then discusses numerical simulations and physical phantom models as controlled systems for studying diffusion measurements and reconstructed pathways. These approaches make it possible to vary known properties and assess reconstruction behavior, while their simplifications limit direct translation to biological tissue. The chapter provides a foundation for selecting appropriate reference models and interpreting what a successful validation experiment demonstrates.",
    "findings": [
      "Validation must be matched to the anatomical scale and question of interest.",
      "Numerical simulations and physical phantoms offer controlled reference conditions.",
      "Simplified reference systems do not reproduce every property of biological tissue."
    ],
    "topics": [
      "Tractography",
      "Reviews & Consensus"
    ],
    "source": "https://linkinghub.elsevier.com/retrieve/pii/B9780128188941000173",
    "publicationStatus": "Published",
    "pdf": "",
    "metadataSource": "https://api.crossref.org/works/10.1016/b978-0-12-818894-1.00017-3",
    "image": "/assets/papers/tractography-validation-foundations.webp",
    "imageAlt": "The tractography validation process spans acquisition, local orientation estimation, tracking, and anatomical interpretation.",
    "caption": "Tim B. Dyrby et al. (2025), Figure 2. The tractography validation process spans acquisition, local orientation estimation, tracking, and anatomical interpretation.",
    "figureSource": "https://linkinghub.elsevier.com/retrieve/pii/B9780128188941000173",
    "imageKind": "article-figure",
    "url": "https://www.microstructure-connectivity-lab.com/publications/tractography-validation-foundations/"
  },
  {
    "slug": "tractography-validation-anatomical",
    "title": "Tractography validation Part 2: The use of anatomical model systems and measures for validation",
    "year": 2025,
    "authors": "Tim B. Dyrby, Silvio Sarubbo, Francois Rheault, Els Fieremans, Adam W. Anderson, Marco Palombo, Peter Neher, Kathleen S. Rockland, Kurt G. Schilling",
    "authorList": [
      "Tim B. Dyrby",
      "Silvio Sarubbo",
      "Francois Rheault",
      "Els Fieremans",
      "Adam W. Anderson",
      "Marco Palombo",
      "Peter Neher",
      "Kathleen S. Rockland",
      "Kurt G. Schilling"
    ],
    "kurtPosition": "last",
    "journal": "Handbook of Diffusion MR Tractography",
    "type": "Book chapter",
    "doi": "https://doi.org/10.1016/b978-0-12-818894-1.00020-3",
    "publisher": "https://linkinghub.elsevier.com/retrieve/pii/B9780128188941000203",
    "summary": "The second tractography-validation chapter reviews anatomical reference systems, empirical validation when ground truth is unavailable, and ways to quantify agreement. It connects validation in real tissue with the practical interpretation of reconstructed pathways.",
    "description": "This chapter covers validation using anatomical model systems and measurements from real tissue, complementing the simulations and phantoms discussed in Part 1. It explains empirical strategies for situations without a definitive ground truth and reviews measures used to quantify validation across anatomical scales.",
    "findings": [
      "Anatomical model systems connect tractography validation with real tissue.",
      "Validation measures and empirical tests must match the anatomical question."
    ],
    "topics": [
      "Tractography",
      "Reviews & Consensus"
    ],
    "source": "https://linkinghub.elsevier.com/retrieve/pii/B9780128188941000203",
    "summarySource": "https://orbit.dtu.dk/en/publications/tractography-validation-part-2-the-use-of-anatomical-model-system/",
    "publicationStatus": "Published",
    "pdf": "",
    "metadataSource": "https://api.crossref.org/works/10.1016/b978-0-12-818894-1.00020-3",
    "image": "/assets/papers/tractography-validation-anatomical.webp",
    "imageAlt": "Microdissection and ex vivo diffusion tractography compared for association pathways in the same monkey brain.",
    "caption": "Dyrby et al. (2025), Figure 4; modified from Sarubbo et al. (2019), Figure 2. Microdissection and ex vivo diffusion tractography compared for association pathways in the same monkey brain.",
    "figureSource": "https://linkinghub.elsevier.com/retrieve/pii/B9780128188941000203",
    "imageKind": "article-figure",
    "url": "https://www.microstructure-connectivity-lab.com/publications/tractography-validation-anatomical/"
  },
  {
    "slug": "tractography-validation-lessons",
    "title": "Tractography validation part 3: Lessons learned through validation studies",
    "year": 2025,
    "authors": "Kurt G. Schilling, Francois Rheault, Tim B. Dyrby",
    "authorList": [
      "Kurt G. Schilling",
      "Francois Rheault",
      "Tim B. Dyrby"
    ],
    "kurtPosition": "first",
    "journal": "Handbook of Diffusion MR Tractography",
    "type": "Book chapter",
    "doi": "https://doi.org/10.1016/b978-0-12-818894-1.00004-5",
    "publisher": "https://linkinghub.elsevier.com/retrieve/pii/B9780128188941000045",
    "summary": "This handbook chapter synthesizes what validation studies have taught us about tractography. Evidence from simulations, phantoms, histology, and anatomical comparisons reveals both reliable capabilities and persistent limitations. The chapter connects complex tissue anatomy and reconstruction assumptions to the practical interpretation and continued development of diffusion MRI tractography.",
    "description": "Building on chapters that introduce validation strategies, this contribution focuses on the lessons those experiments have produced. It draws on simulations, physical phantoms, anatomical models, and empirical studies to explain how tractography succeeds and where it oversimplifies structural connectivity. Topics include complexity in axonal geometry, differences between anatomical definitions, and the implications of modeling and reconstruction choices. By linking observations from microscopy and other reference methods to diffusion MRI behavior, the chapter helps readers interpret reconstructed pathways with appropriate anatomical context. It also shows how validation can guide the development of more informative tractography methods.",
    "findings": [
      "Validation reveals where anatomical complexity exceeds reconstruction assumptions.",
      "Different reference methods test complementary aspects of tractography.",
      "Lessons from validation inform both method development and interpretation."
    ],
    "topics": [
      "Tractography",
      "Reviews & Consensus"
    ],
    "source": "https://linkinghub.elsevier.com/retrieve/pii/B9780128188941000045",
    "publicationStatus": "Published",
    "pdf": "",
    "metadataSource": "https://api.crossref.org/works/10.1016/b978-0-12-818894-1.00004-5",
    "image": "/assets/papers/tractography-validation-lessons.webp",
    "imageAlt": "Differences in anatomical definitions and dissection protocols alter reconstructed pathway size, shape, and connections.",
    "caption": "Kurt G. Schilling et al. (2025), Figure 3. Differences in anatomical definitions and dissection protocols alter reconstructed pathway size, shape, and connections.",
    "figureSource": "https://linkinghub.elsevier.com/retrieve/pii/B9780128188941000045",
    "imageKind": "article-figure",
    "url": "https://www.microstructure-connectivity-lab.com/publications/tractography-validation-lessons/"
  },
  {
    "slug": "spinal-cord-preprocessing-triggering",
    "title": "Influence of preprocessing, distortion correction and cardiac triggering on the quality of diffusion MR images of spinal cord",
    "year": 2024,
    "authors": "Kurt G. Schilling, Anna J. E. Combes, Karthik Ramadass, Francois Rheault, Grace Sweeney, Logan Prock, Subramaniam Sriram, Julien Cohen-Adad, John C. Gore, Bennett A. Landman, Seth A. Smith, Kristin P. O’Grady",
    "authorList": [
      "Kurt G. Schilling",
      "Anna J. E. Combes",
      "Karthik Ramadass",
      "Francois Rheault",
      "Grace Sweeney",
      "Logan Prock",
      "Subramaniam Sriram",
      "Julien Cohen-Adad",
      "John C. Gore",
      "Bennett A. Landman",
      "Seth A. Smith",
      "Kristin P. O’Grady"
    ],
    "kurtPosition": "first",
    "journal": "Magnetic Resonance Imaging",
    "type": "Article",
    "doi": "https://doi.org/10.1016/j.mri.2024.01.008",
    "publisher": "https://doi.org/10.1016/j.mri.2024.01.008",
    "summary": "Tests of spinal cord diffusion preprocessing show that better overall image alignment does not necessarily improve geometry or tissue contrast within the cord. Separate experiments found that omitting cardiac triggering shortened cervical acquisitions while preserving similar image quality and diffusion measurements under the conditions studied.",
    "description": "The study evaluated four distortion-processing strategies across seven cervical and lumbar spinal cord datasets, then separately tested the effect of cardiac triggering. Distortion correction increased similarity to structural images, but this improvement was largely driven by high-contrast cerebrospinal fluid and did not consistently improve intra-cord geometry or white-to-gray matter contrast. The authors recommend at least bulk-motion correction and call for approaches adapted to cord anatomy. In the cervical experiments, non-triggered scans showed comparable artifacts, tensor measures, and reproducibility with shorter acquisitions. These results depend on the protocols and quality measures tested and should not be read as evidence that triggering is unnecessary for every spinal region or acquisition.",
    "findings": [
      "Four distortion-processing strategies were evaluated across seven cervical and lumbar cord datasets.",
      "Improved overall structural similarity did not consistently translate into improved intra-cord geometry or contrast.",
      "Removing cardiac triggering enabled shorter cervical scans with similar measured image quality and tensor reproducibility."
    ],
    "topics": [
      "Spinal Cord",
      "Image Processing"
    ],
    "source": "https://doi.org/10.1016/j.mri.2024.01.008",
    "summarySource": "https://doi.org/10.1016/j.mri.2024.01.008",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/spinal-cord-preprocessing-triggering.webp",
    "imageAlt": "Examples of spinal cord image geometry and contrast after alternative distortion-correction approaches.",
    "caption": "Kurt G. Schilling et al. (2024), Figure 1. Examples of spinal cord image geometry and contrast after alternative distortion-correction approaches.",
    "figureSource": "https://doi.org/10.1016/j.mri.2024.01.008",
    "imageKind": "article-figure",
    "figureLicense": "http://creativecommons.org/licenses/by/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11218893/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11218893/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/spinal-cord-preprocessing-triggering/"
  },
  {
    "slug": "spinal-cord-denoising",
    "title": "Denoising of diffusion MRI in the cervical spinal cord – effects of denoising strategy and acquisition on intra-cord contrast, signal modeling, and feature conspicuity",
    "year": 2023,
    "authors": "Kurt G. Schilling, Shreyas Fadnavis, Joshua Batson, Mereze Visagie, Anna J. E. Combes, Samantha By, Colin D. McKnight, Francesca Bagnato, Eleftherios Garyfallidis, Bennett A. Landman, Seth A. Smith, Kristin P. O’Grady",
    "authorList": [
      "Kurt G. Schilling",
      "Shreyas Fadnavis",
      "Joshua Batson",
      "Mereze Visagie",
      "Anna J. E. Combes",
      "Samantha By",
      "Colin D. McKnight",
      "Francesca Bagnato",
      "Eleftherios Garyfallidis",
      "Bennett A. Landman",
      "Seth A. Smith",
      "Kristin P. O’Grady"
    ],
    "kurtPosition": "first",
    "journal": "NeuroImage",
    "type": "Article",
    "doi": "https://doi.org/10.1016/j.neuroimage.2022.119826",
    "publisher": "https://doi.org/10.1016/j.neuroimage.2022.119826",
    "summary": "Phantom and human experiments compare three denoising methods for spinal cord diffusion MRI. MPPCA and Patch2Self improved image quality, tissue contrast, and precision even with the small direction counts common in clinical scans, while all tested methods improved visibility of multiple sclerosis lesions in diffusion-weighted images.",
    "description": "The study evaluated Non-Local Means, Marchenko–Pastur PCA, and Patch2Self across five experiments: a phantom test, multi-vendor acquisitions, bootstrapped parameter uncertainty, multiple sclerosis lesion conspicuity, and advanced multi-compartment modeling. All methods increased signal-to-noise ratio and lesion visibility in individual diffusion-weighted images. MPPCA and Patch2Self were particularly effective at improving intra-cord contrast and parameter precision, including acquisitions with only 16–32 diffusion-weighted images. These results support denoising as part of practical spinal cord preprocessing. Performance depended on method and acquisition, and improved image appearance or precision does not by itself establish greater biological specificity or diagnostic accuracy for the resulting model parameters.",
    "findings": [
      "Five complementary experiments evaluated image quality, bias, precision, lesion conspicuity, and model fitting.",
      "MPPCA and Patch2Self improved intra-cord contrast and diffusion parameter precision with 16–32 images.",
      "All three methods improved the conspicuity of multiple sclerosis lesions in individual diffusion-weighted images."
    ],
    "topics": [
      "Spinal Cord",
      "Image Processing",
      "Microstructure"
    ],
    "source": "https://doi.org/10.1016/j.neuroimage.2022.119826",
    "summarySource": "https://doi.org/10.1016/j.neuroimage.2022.119826",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/spinal-cord-denoising.webp",
    "imageAlt": "Spinal cord diffusion images before and after denoising across vendors and acquisition settings.",
    "caption": "Kurt G. Schilling et al. (2023), Figure 3. Spinal cord diffusion images before and after denoising across vendors and acquisition settings.",
    "figureSource": "https://doi.org/10.1016/j.neuroimage.2022.119826",
    "imageKind": "article-figure",
    "figureLicense": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9843739/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9843739/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/spinal-cord-denoising/"
  },
  {
    "slug": "synbold-disco",
    "title": "Distortion correction of functional MRI without reverse phase encoding scans or field maps",
    "year": 2023,
    "authors": "Tian Yu, Leon Y. Cai, Salvatore Torrisi, An Thanh Vu, Victoria L. Morgan, Sarah E. Goodale, Karthik Ramadass, Steven L. Meisler, Jinglei Lv, Aaron E.L. Warren, Dario J. Englot, Laurie Cutting, Catie Chang, John C. Gore, Bennett A. Landman, Kurt G. Schilling",
    "authorList": [
      "Tian Yu",
      "Leon Y. Cai",
      "Salvatore Torrisi",
      "An Thanh Vu",
      "Victoria L. Morgan",
      "Sarah E. Goodale",
      "Karthik Ramadass",
      "Steven L. Meisler",
      "Jinglei Lv",
      "Aaron E.L. Warren",
      "Dario J. Englot",
      "Laurie Cutting",
      "Catie Chang",
      "John C. Gore",
      "Bennett A. Landman",
      "Kurt G. Schilling"
    ],
    "kurtPosition": "last",
    "journal": "Magnetic Resonance Imaging",
    "type": "Article",
    "doi": "https://doi.org/10.1016/j.mri.2023.06.016",
    "publisher": "https://linkinghub.elsevier.com/retrieve/pii/S0730725X23001121",
    "summary": "SynBOLD-DisCo corrects susceptibility distortion in functional MRI using the acquired functional data and a structural image. Synthesized undistorted BOLD contrast provides a correction target when reverse phase-encoding scans or field maps are unavailable.",
    "description": "This study synthesizes an undistorted image with BOLD-like contrast and uses it as an anatomical target for fMRI distortion correction. In the evaluated data, corrected images align more closely with anatomy and approach the geometric performance of correction using reverse phase-encoded acquisitions. Public code and trained models support integration into preprocessing workflows.",
    "findings": [
      "Synthetic BOLD contrast enabled correction without additional calibration scans.",
      "Corrected functional images showed improved geometric agreement with structural anatomy."
    ],
    "topics": [
      "Image Processing",
      "Functional Connectivity"
    ],
    "source": "https://pubmed.ncbi.nlm.nih.gov/37400042/",
    "summarySource": "https://pubmed.ncbi.nlm.nih.gov/37400042/",
    "publicationStatus": "Published",
    "pdf": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10528451/pdf/nihms-1915010.pdf",
    "metadataSource": "https://api.crossref.org/works/10.1016/j.mri.2023.06.016",
    "links": [
      {
        "label": "SynBOLD-DisCo code",
        "url": "https://github.com/MASILab/SynBOLD-DisCo"
      },
      {
        "label": "Earlier conference paper",
        "url": "https://doi.org/10.1117/12.2653647"
      },
      {
        "label": "Earlier conference paper",
        "url": "/publications/synbold-disco-conference/"
      },
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10528451/"
      }
    ],
    "image": "/assets/papers/synbold-disco.webp",
    "imageAlt": "SynBOLD-DisCo synthesizes an undistorted BOLD reference to correct functional MRI geometry.",
    "caption": "Tian Yu et al. (2023), Figure 3. SynBOLD-DisCo synthesizes an undistorted BOLD reference to correct functional MRI geometry.",
    "figureSource": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10528451/",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10528451/",
    "pdfVerification": "official-link-only",
    "url": "https://www.microstructure-connectivity-lab.com/publications/synbold-disco/"
  },
  {
    "slug": "superficial-white-matter-aging",
    "title": "Short superficial white matter and aging: A longitudinal multi-site study of 1293 subjects and 2711 sessions",
    "year": 2023,
    "authors": "Kurt G. Schilling, Derek Archer, Fang-Cheng Yeh, Francois Rheault, Leon Y. Cai, Andrea Shafer, Susan M. Resnick, Timothy Hohman, Angela Jefferson, Adam W. Anderson, Hakmook Kang, Bennett A. Landman",
    "authorList": [
      "Kurt G. Schilling",
      "Derek Archer",
      "Fang-Cheng Yeh",
      "Francois Rheault",
      "Leon Y. Cai",
      "Andrea Shafer",
      "Susan M. Resnick",
      "Timothy Hohman",
      "Angela Jefferson",
      "Adam W. Anderson",
      "Hakmook Kang",
      "Bennett A. Landman"
    ],
    "kurtPosition": "first",
    "journal": "Aging Brain",
    "type": "Article",
    "doi": "https://doi.org/10.1016/j.nbas.2023.100067",
    "publisher": "https://doi.org/10.1016/j.nbas.2023.100067",
    "summary": "Short superficial white matter pathways show widespread age-related diffusion changes and regionally varying reductions in length and volume. Data from 1,293 participants distinguish changes in fibers connecting neighboring gyri from those running within a gyrus, extending aging research beyond long-range bundles.",
    "description": "Using 2,711 sessions from 1,293 participants across three longitudinal and cross-sectional datasets, the study characterized superficial white matter systems immediately beneath the cortex. Diffusivities increased and fractional anisotropy decreased with age, with prominent associations in frontal, temporal, and temporoparietal regions. Tract length and volume also declined, but their patterns differed across locations and between inter-gyral and intra-gyral systems. These macrostructural changes were slower than those observed in long-range pathways. The results establish an aging reference for connections often omitted from tract studies. Their interpretation remains subject to the difficulty of reconstructing short, curved superficial fibers and to combining data with different acquisition characteristics.",
    "findings": [
      "The study included 1,293 participants and 2,711 imaging sessions.",
      "Superficial white matter diffusivities rose and fractional anisotropy fell with age.",
      "Length and volume changes varied between inter-gyral and intra-gyral systems and were slower than in long-range pathways."
    ],
    "topics": [
      "Lifespan",
      "Tractography",
      "Microstructure"
    ],
    "source": "https://doi.org/10.1016/j.nbas.2023.100067",
    "summarySource": "https://doi.org/10.1016/j.nbas.2023.100067",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/superficial-white-matter-aging.webp",
    "imageAlt": "The short superficial white matter pathways used to characterize aging across longitudinal cohorts.",
    "caption": "Kurt G. Schilling et al. (2023), Figure 2. The short superficial white matter pathways used to characterize aging across longitudinal cohorts.",
    "figureSource": "https://doi.org/10.1016/j.nbas.2023.100067",
    "imageKind": "article-figure",
    "figureLicense": "http://creativecommons.org/licenses/by/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9937516/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9937516/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/superficial-white-matter-aging/"
  },
  {
    "slug": "superficial-white-matter-lifespan",
    "title": "Superficial white matter across development, young adulthood, and aging: volume, thickness, and relationship with cortical features",
    "year": 2023,
    "authors": "Kurt G. Schilling, Derek Archer, Francois Rheault, Ilwoo Lyu, Yuankai Huo, Leon Y. Cai, Silvia A. Bunge, Kevin S. Weiner, John C. Gore, Adam W. Anderson, Bennett A. Landman",
    "authorList": [
      "Kurt G. Schilling",
      "Derek Archer",
      "Francois Rheault",
      "Ilwoo Lyu",
      "Yuankai Huo",
      "Leon Y. Cai",
      "Silvia A. Bunge",
      "Kevin S. Weiner",
      "John C. Gore",
      "Adam W. Anderson",
      "Bennett A. Landman"
    ],
    "kurtPosition": "first",
    "journal": "Brain Structure and Function",
    "type": "Article",
    "doi": "https://doi.org/10.1007/s00429-023-02642-x",
    "publisher": "https://doi.org/10.1007/s00429-023-02642-x",
    "summary": "Imaging from 2,421 people aged 5–100 maps how the thickness and volume of superficial white matter vary across the brain and lifespan. These features follow region-specific trajectories and relate to cortical thickness and curvature, adding an often-missing component to descriptions of brain maturation and aging.",
    "description": "The authors combined large diffusion MRI datasets with superficial white matter tractography to measure regional volume and thickness in 2,421 participants aged 5–100. Superficial white matter thickness varied consistently across cortical locations, and both thickness and volume showed nonlinear cross-sectional age patterns. Absolute volume peaked around adolescence, while the fraction of total brain volume occupied by superficial white matter continued to rise with age. Thickness also related to cortical thickness and curvature. These observations provide a lifespan reference, but they do not measure within-person development. Coarse diffusion resolution, tractography limitations, and the chosen thickness definition may influence the estimated boundaries and magnitudes.",
    "findings": [
      "The cross-sectional analysis included 2,421 participants spanning ages 5–100.",
      "Superficial white matter volume and thickness followed distinct, regionally varying lifespan trajectories.",
      "Thickness was associated with cortical thickness and curvature, while the relative brain-volume fraction increased with age."
    ],
    "topics": [
      "Lifespan",
      "Tractography"
    ],
    "source": "https://doi.org/10.1007/s00429-023-02642-x",
    "summarySource": "https://doi.org/10.1007/s00429-023-02642-x",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/superficial-white-matter-lifespan.webp",
    "imageAlt": "Superficial white matter segmentation and thickness maps across representative ages.",
    "caption": "Kurt G. Schilling et al. (2023), Figure 2. Superficial white matter segmentation and thickness maps across representative ages.",
    "figureSource": "https://doi.org/10.1007/s00429-023-02642-x",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10320929/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10320929/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/superficial-white-matter-lifespan/"
  },
  {
    "slug": "synbold-disco-conference",
    "title": "SynBOLD-DisCo: synthetic BOLD images for distortion correction of fMRI without additional calibration scans",
    "year": 2023,
    "authors": "Tian Yu, Leon Y. Cai, Victoria L. Morgan, Sarah E. Goodale, Dario J. Englot, Catherine E. Chang, Bennett A. Landman, Kurt G. Schilling",
    "authorList": [
      "Tian Yu",
      "Leon Y. Cai",
      "Victoria L. Morgan",
      "Sarah E. Goodale",
      "Dario J. Englot",
      "Catherine E. Chang",
      "Bennett A. Landman",
      "Kurt G. Schilling"
    ],
    "kurtPosition": "last",
    "journal": "Medical Imaging 2023: Image Processing",
    "type": "Conference paper",
    "doi": "https://doi.org/10.1117/12.2653647",
    "publisher": "https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12464/2653647/SynBOLD-DisCo--synthetic-BOLD-images-for-distortion-correction-of/10.1117/12.2653647.full",
    "summary": "This conference paper introduces SynBOLD-DisCo, using a 3D U-Net to synthesize undistorted BOLD images for fMRI susceptibility correction. It is the proceedings contribution preceding the expanded 2023 journal study.",
    "description": "A synthetic BOLD-contrast target allows FSL topup to correct single-phase-encoding fMRI when field maps or reverse-encoding images are absent. Evaluation shows improved alignment with structural anatomy and performance close to correction using reverse phase-encoded data. This separately published SPIE contribution precedes the expanded journal article.",
    "findings": [
      "A 3D U-Net supplied an undistorted target for topup correction.",
      "The proceedings paper and subsequent journal article are distinct publications."
    ],
    "topics": [
      "Image Processing",
      "Functional Connectivity"
    ],
    "source": "https://pubmed.ncbi.nlm.nih.gov/37465092/",
    "summarySource": "https://pubmed.ncbi.nlm.nih.gov/37465092/",
    "publicationStatus": "Published",
    "pdf": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10353777/pdf/nihms-1858278.pdf",
    "metadataSource": "https://api.crossref.org/works/10.1117/12.2653647",
    "links": [
      {
        "label": "Expanded journal article",
        "url": "https://doi.org/10.1016/j.mri.2023.06.016"
      },
      {
        "label": "SynBOLD-DisCo code",
        "url": "https://github.com/MASILab/SynBOLD-DisCo"
      },
      {
        "label": "Expanded journal article",
        "url": "/publications/synbold-disco/"
      },
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10353777/"
      }
    ],
    "image": "/assets/papers/synbold-disco-conference.webp",
    "imageAlt": "Example functional MRI geometry before and after SynBOLD-DisCo correction, compared with a structural reference.",
    "caption": "Tian Yu et al. (2023), Figure 4. Example functional MRI geometry before and after SynBOLD-DisCo correction, compared with a structural reference.",
    "figureSource": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10353777/",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10353777/",
    "pdfVerification": "official-link-only",
    "url": "https://www.microstructure-connectivity-lab.com/publications/synbold-disco-conference/"
  },
  {
    "slug": "white-matter-lifespan-trajectories",
    "title": "White matter tract microstructure, macrostructure, and associated cortical gray matter morphology across the lifespan",
    "year": 2023,
    "authors": "Kurt G. Schilling, Jordan A. Chad, Maxime Chamberland, Victor Nozais, Francois Rheault, Derek Archer, Muwei Li, Yurui Gao, Leon Cai, Flavio Del’Acqua, Allen Newton, Daniel Moyer, John C. Gore, Catherine Lebel, Bennett A. Landman",
    "authorList": [
      "Kurt G. Schilling",
      "Jordan A. Chad",
      "Maxime Chamberland",
      "Victor Nozais",
      "Francois Rheault",
      "Derek Archer",
      "Muwei Li",
      "Yurui Gao",
      "Leon Cai",
      "Flavio Del’Acqua",
      "Allen Newton",
      "Daniel Moyer",
      "John C. Gore",
      "Catherine Lebel",
      "Bennett A. Landman"
    ],
    "kurtPosition": "first",
    "journal": "Imaging Neuroscience",
    "type": "Article",
    "doi": "https://doi.org/10.1162/imag_a_00050",
    "publisher": "https://doi.org/10.1162/imag_a_00050",
    "summary": "Across 2,789 imaging sessions spanning ages 0–100, this study links tract microstructure, tract geometry, and the morphology of connected cortex. Different features and pathways mature and decline on different schedules, and developmental age associations are related to patterns observed during aging.",
    "description": "Four cross-sectional datasets spanning the first century of life were analyzed with diffusion modeling and tractography to characterize 63 white matter pathways. Across 2,789 imaging sessions, the study measured tissue-related diffusion properties, bundle shape, and morphology of associated cortical gray matter. Each feature followed its own lifespan trajectory, with pathway-specific differences in the timing and rate of maturation and later decline. Relationships between features suggested coordinated changes across connected white and gray matter, and developmental age associations were related to aging associations. The findings provide normative descriptions, but cross-sectional comparisons and acquisition differences across cohorts limit inference about individual developmental trajectories or biological causation.",
    "findings": [
      "The analysis used 2,789 imaging sessions spanning ages 0–100 and characterized 63 pathways.",
      "Microstructural, macrostructural, and cortical features showed distinct pathway-specific lifespan trajectories.",
      "Age associations during development were strongly related to those observed during aging."
    ],
    "topics": [
      "Lifespan",
      "Tractography",
      "Microstructure"
    ],
    "source": "https://doi.org/10.1162/imag_a_00050",
    "summarySource": "https://doi.org/10.1162/imag_a_00050",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/white-matter-lifespan-trajectories.webp",
    "imageAlt": "Tract-specific lifespan trajectories of fractional anisotropy and intracellular volume fraction.",
    "caption": "Kurt G. Schilling et al. (2023), Figure 2. Tract-specific lifespan trajectories of fractional anisotropy and intracellular volume fraction.",
    "figureSource": "https://doi.org/10.1162/imag_a_00050",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12007540/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12007540/"
      }
    ],
    "correction": "https://doi.org/10.1162/imag_x_00158",
    "url": "https://www.microstructure-connectivity-lab.com/publications/white-matter-lifespan-trajectories/"
  },
  {
    "slug": "whole-brain-task-bold",
    "title": "Whole-brain, gray, and white matter time-locked functional signal changes with simple tasks and model-free analysis",
    "year": 2023,
    "authors": "Kurt G. Schilling, Muwei Li, Francois Rheault, Yurui Gao, Leon Cai, Yu Zhao, Lyuan Xu, Zhaohua Ding, Adam W. Anderson, Bennett A. Landman, John C. Gore",
    "authorList": [
      "Kurt G. Schilling",
      "Muwei Li",
      "Francois Rheault",
      "Yurui Gao",
      "Leon Cai",
      "Yu Zhao",
      "Lyuan Xu",
      "Zhaohua Ding",
      "Adam W. Anderson",
      "Bennett A. Landman",
      "John C. Gore"
    ],
    "kurtPosition": "first",
    "journal": "Proceedings of the National Academy of Sciences",
    "type": "Article",
    "doi": "https://doi.org/10.1073/pnas.2219666120",
    "publisher": "https://doi.org/10.1073/pnas.2219666120",
    "summary": "Simple tasks and model-free analysis reveal widespread time-locked BOLD changes in both gray and white matter. The shape of the response varies across regions and stimuli, indicating that conventional analyses can miss distributed signals when they impose a narrow response model or discard white matter.",
    "description": "The authors examined task-locked functional MRI signals across gray and white matter using simple stimuli and analyses that did not require one fixed hemodynamic response shape. A majority of both tissue types showed significant task-related BOLD changes for every stimulus examined, with regional differences in response timing and form. The same region could also respond differently to different stimuli. These observations challenge the routine exclusion of white matter signals and the assumption that task effects are confined to small cortical areas. They establish widespread hemodynamic responses, not direct evidence that every detected region has the same neural role or that BOLD alone identifies the underlying causal mechanism.",
    "findings": [
      "A majority of gray and white matter showed statistically significant time-locked changes for all tasks investigated.",
      "Different regions displayed different BOLD responses to the same task.",
      "Individual regions could display distinct response shapes for different stimuli."
    ],
    "topics": [
      "Functional Connectivity"
    ],
    "source": "https://doi.org/10.1073/pnas.2219666120",
    "summarySource": "https://doi.org/10.1073/pnas.2219666120",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/whole-brain-task-bold.webp",
    "imageAlt": "Task-locked BOLD signal changes mapped across gray matter regions and white matter pathways.",
    "caption": "Kurt G. Schilling et al. (2023), Figure 5. Task-locked BOLD signal changes mapped across gray matter regions and white matter pathways.",
    "figureSource": "https://doi.org/10.1073/pnas.2219666120",
    "imageKind": "article-figure",
    "figureLicense": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10589709/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10589709/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/whole-brain-task-bold/"
  },
  {
    "slug": "aging-white-matter-shape",
    "title": "Aging and white matter microstructure and macrostructure: a longitudinal multi-site diffusion MRI study of 1218 participants",
    "year": 2022,
    "authors": "Kurt G. Schilling, Derek Archer, Fang-Cheng Yeh, Francois Rheault, Leon Y. Cai, Colin Hansen, Qi Yang, Karthik Ramdass, Andrea T. Shafer, Susan M. Resnick, Kimberly R. Pechman, Katherine A. Gifford, Timothy J. Hohman, Angela Jefferson, Adam W. Anderson, Hakmook Kang, Bennett A. Landman",
    "authorList": [
      "Kurt G. Schilling",
      "Derek Archer",
      "Fang-Cheng Yeh",
      "Francois Rheault",
      "Leon Y. Cai",
      "Colin Hansen",
      "Qi Yang",
      "Karthik Ramdass",
      "Andrea T. Shafer",
      "Susan M. Resnick",
      "Kimberly R. Pechman",
      "Katherine A. Gifford",
      "Timothy J. Hohman",
      "Angela Jefferson",
      "Adam W. Anderson",
      "Hakmook Kang",
      "Bennett A. Landman"
    ],
    "kurtPosition": "first",
    "journal": "Brain Structure and Function",
    "type": "Article",
    "doi": "https://doi.org/10.1007/s00429-022-02503-z",
    "publisher": "https://doi.org/10.1007/s00429-022-02503-z",
    "summary": "Across 1,218 older adults, diffusion measurements and tract shape both changed with age. Microstructural trends were relatively widespread, while volume, length, and surface-area changes differed more across pathways, suggesting that bundle geometry adds complementary information about aging.",
    "description": "The study analyzed 2,459 imaging sessions from 1,218 participants aged 50–97 across one cross-sectional and two longitudinal cohorts. Linear mixed-effects models tested age associations for four microstructural and eleven macrostructural features across 120 white matter pathways. Diffusivities generally increased and anisotropy decreased with age, while most length, area, and volume measures declined. Unlike the relatively consistent microstructural trends, macrostructural changes varied across pathways, with greater decline in several projection, thalamic, and commissural tracts. These findings support combining shape and diffusion measurements in aging research. Interpretation remains dependent on the single segmentation algorithm and heterogeneous acquisitions used in the analysis.",
    "findings": [
      "The analysis covered 120 pathways in 1,218 participants and 2,459 sessions.",
      "Diffusivities increased and anisotropy decreased with age across broadly similar pathway patterns.",
      "Macrostructural age associations were more heterogeneous across pathways than the microstructural associations."
    ],
    "topics": [
      "Lifespan",
      "Tractography",
      "Microstructure"
    ],
    "source": "https://doi.org/10.1007/s00429-022-02503-z",
    "summarySource": "https://doi.org/10.1007/s00429-022-02503-z",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/aging-white-matter-shape.webp",
    "imageAlt": "White matter pathways colored by age associations with microstructural and shape measurements.",
    "caption": "Kurt G. Schilling et al. (2022), Figure 3. White matter pathways colored by age associations with microstructural and shape measurements.",
    "figureSource": "https://doi.org/10.1007/s00429-022-02503-z",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9648053/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9648053/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/aging-white-matter-shape/"
  },
  {
    "slug": "white-matter-bold-response",
    "title": "Anomalous and heterogeneous characteristics of the BOLD hemodynamic response function in white matter",
    "year": 2022,
    "authors": "Kurt G Schilling, Muwei Li, Francois Rheault, Zhaohua Ding, Adam W Anderson, Hakmook Kang, Bennett A Landman, John C Gore",
    "authorList": [
      "Kurt G Schilling",
      "Muwei Li",
      "Francois Rheault",
      "Zhaohua Ding",
      "Adam W Anderson",
      "Hakmook Kang",
      "Bennett A Landman",
      "John C Gore"
    ],
    "kurtPosition": "first",
    "journal": "Cerebral Cortex Communications",
    "type": "Article",
    "doi": "https://doi.org/10.1093/texcom/tgac035",
    "publisher": "https://academic.oup.com/cercorcomms/article/doi/10.1093/texcom/tgac035/6671419",
    "summary": "White matter has a different BOLD hemodynamic response from gray matter, and the response varies within and between pathways. These differences also relate to diffusion MRI measures of tissue microstructure.",
    "description": "Using resting-state fMRI, this study characterizes the shape and spatial variation of the white matter hemodynamic response. Features including the initial signal dip relate to tissue microstructure and differ from those in gray matter. The findings show why a single gray-matter response model may be inadequate for analyzing white matter BOLD signals.",
    "findings": [
      "Response shapes differed between white and gray matter.",
      "White matter responses varied along pathways and were associated with microstructural features."
    ],
    "topics": [
      "Functional Connectivity",
      "Microstructure"
    ],
    "source": "https://pubmed.ncbi.nlm.nih.gov/36196360/",
    "summarySource": "https://pubmed.ncbi.nlm.nih.gov/36196360/",
    "publicationStatus": "Published",
    "pdf": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9519945/pdf/tgac035.pdf",
    "metadataSource": "https://api.crossref.org/works/10.1093/texcom/tgac035",
    "image": "/assets/papers/white-matter-bold-response.webp",
    "imageAlt": "Hemodynamic response functions vary across and along white matter pathways.",
    "caption": "Kurt G Schilling et al. (2022), Figure 6. Hemodynamic response functions vary across and along white matter pathways.",
    "figureSource": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9519945/",
    "imageKind": "article-figure",
    "figureLicense": "https://creativecommons.org/licenses/by/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9519945/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9519945/"
      }
    ],
    "pdfVerification": "official-link-only",
    "url": "https://www.microstructure-connectivity-lab.com/publications/white-matter-bold-response/"
  },
  {
    "slug": "spherical-mean-sampling",
    "title": "Minimal number of sampling directions for robust measures of the spherical mean diffusion weighted signal: Effects of sampling directions, b-value, signal-to-noise ratio, hardware, and fitting strategy",
    "year": 2022,
    "authors": "Kurt G. Schilling, Marco Palombo, Kristin P. O'Grady, Anna J.E. Combes, Adam W. Anderson, Bennett A. Landman, Seth A. Smith",
    "authorList": [
      "Kurt G. Schilling",
      "Marco Palombo",
      "Kristin P. O'Grady",
      "Anna J.E. Combes",
      "Adam W. Anderson",
      "Bennett A. Landman",
      "Seth A. Smith"
    ],
    "kurtPosition": "first",
    "journal": "Magnetic Resonance Imaging",
    "type": "Article",
    "doi": "https://doi.org/10.1016/j.mri.2022.07.015",
    "publisher": "https://linkinghub.elsevier.com/retrieve/pii/S0730725X22001333",
    "summary": "Simulations and brain and spinal cord measurements establish how many diffusion directions are needed to estimate the spherical mean signal reliably. A lookup table links sampling requirements to diffusion weighting, signal-to-noise ratio, and fitting choices.",
    "description": "Spherical-mean diffusion models can support efficient microstructural imaging, but their accuracy depends on directional sampling. This work varies the number of directions, b-value, noise level, hardware, and fitting strategy in simulations, then checks those predictions with brain and spinal cord data. The resulting guidance helps balance acquisition time against measurement precision.",
    "findings": [
      "Directional sampling requirements depend on b-value, noise, and fitting strategy.",
      "Empirical brain and spinal cord measurements agreed with the simulations."
    ],
    "topics": [
      "Microstructure",
      "Image Processing",
      "Spinal Cord"
    ],
    "source": "https://pubmed.ncbi.nlm.nih.gov/35931321/",
    "summarySource": "https://pubmed.ncbi.nlm.nih.gov/35931321/",
    "publicationStatus": "Published",
    "pdf": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9904413/pdf/nihms-1864537.pdf",
    "metadataSource": "https://api.crossref.org/works/10.1016/j.mri.2022.07.015",
    "image": "/assets/papers/spherical-mean-sampling.webp",
    "imageAlt": "Brain spherical-mean diffusion signals estimated from full and reduced acquisition schemes.",
    "caption": "Kurt G. Schilling et al. (2022), Figure 7. Brain spherical-mean diffusion signals estimated from full and reduced acquisition schemes.",
    "figureSource": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9904413/",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9904413/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9904413/"
      }
    ],
    "pdfVerification": "official-link-only",
    "url": "https://www.microstructure-connectivity-lab.com/publications/spherical-mean-sampling/"
  },
  {
    "slug": "tractography-bottlenecks",
    "title": "Prevalence of white matter pathways coming into a single white matter voxel orientation: The bottleneck issue in tractography",
    "year": 2022,
    "authors": "Kurt G. Schilling, Chantal M. W. Tax, Francois Rheault, Bennett A. Landman, Adam W. Anderson, Maxime Descoteaux, Laurent Petit",
    "authorList": [
      "Kurt G. Schilling",
      "Chantal M. W. Tax",
      "Francois Rheault",
      "Bennett A. Landman",
      "Adam W. Anderson",
      "Maxime Descoteaux",
      "Laurent Petit"
    ],
    "kurtPosition": "first",
    "journal": "Human Brain Mapping",
    "type": "Article",
    "doi": "https://doi.org/10.1002/hbm.25697",
    "publisher": "https://doi.org/10.1002/hbm.25697",
    "summary": "Distinct white matter bundles frequently share the same local orientation before diverging, creating ambiguities that better crossing-fiber detection cannot resolve. This study maps such bottlenecks across the brain and finds them throughout major pathway classes, including regions often described as containing a single fiber orientation.",
    "description": "The authors segmented known white matter pathways with diffusion tractography and assigned each bundle to the voxels and individual orientations, or fixels, it traversed. This identified locations where multiple bundles run in parallel and later diverge. Bottlenecks occurred in more than 50–70% of white matter fixels across the evaluated settings and involved projection, association, and commissural pathways. Even apparently single-orientation regions could therefore contain multiple anatomical bundles. The work explains why accurate local orientation estimates do not uniquely determine long-range connections. Because prevalence was estimated using tractography-derived bundles, its exact magnitude depends on the segmentation and modeling choices rather than constituting a direct histological census.",
    "findings": [
      "Bottlenecks were estimated in more than 50–70% of white matter fixels in the evaluated analyses.",
      "Projection, association, and commissural pathways all contributed to shared local orientations.",
      "A single local fiber orientation did not imply that only one anatomical pathway passed through it."
    ],
    "topics": [
      "Tractography",
      "Microstructure"
    ],
    "source": "https://doi.org/10.1002/hbm.25697",
    "summarySource": "https://doi.org/10.1002/hbm.25697",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/tractography-bottlenecks.webp",
    "imageAlt": "Distinct pathways converge within a small occipital white matter region, illustrating the tractography bottleneck problem.",
    "caption": "Kurt G. Schilling et al. (2022), Figure 7. Distinct pathways converge within a small occipital white matter region, illustrating the tractography bottleneck problem.",
    "figureSource": "https://doi.org/10.1002/hbm.25697",
    "imageKind": "article-figure",
    "figureLicense": "http://creativecommons.org/licenses/by/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8837578/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8837578/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/tractography-bottlenecks/"
  },
  {
    "slug": "bundle-segmentation-acquisition-confounds",
    "title": "Fiber tractography bundle segmentation depends on scanner effects, vendor effects, acquisition resolution, diffusion sampling scheme, diffusion sensitization, and bundle segmentation workflow",
    "year": 2021,
    "authors": "Kurt G. Schilling, Chantal M. W. Tax, Francois Rheault, Colin Hansen, Qi Yang, Fang-Cheng Yeh, Leon Cai, Adam W. Anderson, Bennett A. Landman",
    "authorList": [
      "Kurt G. Schilling",
      "Chantal M. W. Tax",
      "Francois Rheault",
      "Colin Hansen",
      "Qi Yang",
      "Fang-Cheng Yeh",
      "Leon Cai",
      "Adam W. Anderson",
      "Bennett A. Landman"
    ],
    "kurtPosition": "first",
    "journal": "NeuroImage",
    "type": "Article",
    "doi": "https://doi.org/10.1016/j.neuroimage.2021.118451",
    "publisher": "https://doi.org/10.1016/j.neuroimage.2021.118451",
    "summary": "Four bundle-segmentation workflows reveal how scanner, vendor, and acquisition changes affect white matter measurements. Spatial resolution was a major acquisition confound, while differences between segmentation workflows exceeded every other tested source of variation, particularly near the cortex.",
    "description": "Using two multi-subject benchmark datasets, the study compared four bundle-segmentation workflows across scan repeats, scanners, vendors, spatial resolutions, diffusion direction schemes, and b-values. It assessed spatial overlap, pathway shape, and microstructural measurements within bundles. Acquisition resolution produced the largest protocol-related differences, followed by vendor and scanner effects; direction schemes and b-values were closer to scan-rescan variability. Deep white matter locations were relatively consistent, while disagreement increased near the cortical interface. Crucially, changing the segmentation workflow produced larger differences than any other studied confound. These effects varied by bundle and method, showing why harmonizing image measurements alone cannot remove all variability in tract-specific analyses.",
    "findings": [
      "Spatial resolution caused the lowest reproducibility among the acquisition factors tested.",
      "Differences between segmentation workflows exceeded the scanner and protocol confounds examined.",
      "Tractography introduced additional variability into bundle microstructure measurements beyond image-level variability."
    ],
    "topics": [
      "Tractography",
      "Image Processing"
    ],
    "source": "https://doi.org/10.1016/j.neuroimage.2021.118451",
    "summarySource": "https://doi.org/10.1016/j.neuroimage.2021.118451",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/bundle-segmentation-acquisition-confounds.webp",
    "imageAlt": "Arcuate fasciculus reconstructions vary across scanners, acquisitions, diffusion weighting, and segmentation workflows.",
    "caption": "Kurt G. Schilling et al. (2021), Figure 2. Arcuate fasciculus reconstructions vary across scanners, acquisitions, diffusion weighting, and segmentation workflows.",
    "figureSource": "https://doi.org/10.1016/j.neuroimage.2021.118451",
    "imageKind": "article-figure",
    "figureLicense": "http://creativecommons.org/licenses/by/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9933001/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9933001/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/bundle-segmentation-acquisition-confounds/"
  },
  {
    "slug": "memento-signal-generalizability",
    "title": "On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: Chronicles of the MEMENTO challenge",
    "year": 2021,
    "authors": "Alberto De Luca, Andrada Ianus, Alexander Leemans, Marco Palombo, Noam Shemesh, Hui Zhang, Daniel C. Alexander, Markus Nilsson, Martijn Froeling, Geert-Jan Biessels, Mauro Zucchelli, Matteo Frigo, Enes Albay, Sara Sedlar, Abib Alimi, Samuel Deslauriers-Gauthier, Rachid Deriche, Rutger Fick, Maryam Afzali, Tomasz Pieciak, Fabian Bogusz, Santiago Aja-Fernández, Evren Özarslan, Derek K. Jones, Haoze Chen, Mingwu Jin, Zhijie Zhang, Fengxiang Wang, Vishwesh Nath, Prasanna Parvathaneni, Jan Morez, Jan Sijbers, Ben Jeurissen, Shreyas Fadnavis, Stefan Endres, Ariel Rokem, Eleftherios Garyfallidis, Irina Sanchez, Vesna Prchkovska, Paulo Rodrigues, Bennet A. Landman, Kurt G. Schilling",
    "authorList": [
      "Alberto De Luca",
      "Andrada Ianus",
      "Alexander Leemans",
      "Marco Palombo",
      "Noam Shemesh",
      "Hui Zhang",
      "Daniel C. Alexander",
      "Markus Nilsson",
      "Martijn Froeling",
      "Geert-Jan Biessels",
      "Mauro Zucchelli",
      "Matteo Frigo",
      "Enes Albay",
      "Sara Sedlar",
      "Abib Alimi",
      "Samuel Deslauriers-Gauthier",
      "Rachid Deriche",
      "Rutger Fick",
      "Maryam Afzali",
      "Tomasz Pieciak",
      "Fabian Bogusz",
      "Santiago Aja-Fernández",
      "Evren Özarslan",
      "Derek K. Jones",
      "Haoze Chen",
      "Mingwu Jin",
      "Zhijie Zhang",
      "Fengxiang Wang",
      "Vishwesh Nath",
      "Prasanna Parvathaneni",
      "Jan Morez",
      "Jan Sijbers",
      "Ben Jeurissen",
      "Shreyas Fadnavis",
      "Stefan Endres",
      "Ariel Rokem",
      "Eleftherios Garyfallidis",
      "Irina Sanchez",
      "Vesna Prchkovska",
      "Paulo Rodrigues",
      "Bennet A. Landman",
      "Kurt G. Schilling"
    ],
    "kurtPosition": "last",
    "journal": "NeuroImage",
    "type": "Article",
    "doi": "https://doi.org/10.1016/j.neuroimage.2021.118367",
    "publisher": "https://linkinghub.elsevier.com/retrieve/pii/S1053811921006431",
    "summary": "The MEMENTO challenge tests how diffusion MRI signal models generalize to unseen measurements and different encoding schemes. Results show that fitting choices and hyperparameters matter alongside model selection, with double and oscillating encoding proving especially challenging.",
    "description": "Eight teams submitted 80 fits to predict withheld measurements from human and mouse diffusion MRI datasets. Most methods predicted single-diffusion-encoding signals well, with weaker performance at extreme diffusion weightings and on double or oscillating encoding. The comparison emphasizes reporting and optimizing fitting procedures as well as choosing a signal model.",
    "findings": [
      "Generalization depended on the acquisition scheme and diffusion weighting.",
      "Fitting choices and hyperparameters substantially affected prediction performance."
    ],
    "topics": [
      "Microstructure",
      "Image Processing"
    ],
    "source": "https://pubmed.ncbi.nlm.nih.gov/34237442/",
    "summarySource": "https://pubmed.ncbi.nlm.nih.gov/34237442/",
    "publicationStatus": "Published",
    "pdf": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7615259/pdf/EMS189821.pdf",
    "metadataSource": "https://api.crossref.org/works/10.1016/j.neuroimage.2021.118367",
    "image": "/assets/papers/memento-signal-generalizability.webp",
    "imageAlt": "Measured and predicted diffusion signals compared across models and tissue configurations.",
    "caption": "Alberto De Luca et al. (2021), Figure 4. Measured and predicted diffusion signals compared across models and tissue configurations.",
    "figureSource": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7615259/",
    "imageKind": "article-figure",
    "figureLicense": "https://creativecommons.org/licenses/by/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7615259/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7615259/"
      }
    ],
    "pdfVerification": "official-link-only",
    "url": "https://www.microstructure-connectivity-lab.com/publications/memento-signal-generalizability/"
  },
  {
    "slug": "pandora-white-matter-atlas",
    "title": "Pandora: 4-D White Matter Bundle Population-Based Atlases Derived from Diffusion MRI Fiber Tractography",
    "year": 2021,
    "authors": "Colin B. Hansen, Qi Yang, Ilwoo Lyu, Francois Rheault, Cailey Kerley, Bramsh Qamar Chandio, Shreyas Fadnavis, Owen Williams, Andrea T. Shafer, Susan M. Resnick, David H. Zald, Laurie E. Cutting, Warren D. Taylor, Brian Boyd, Eleftherios Garyfallidis, Adam W. Anderson, Maxime Descoteaux, Bennett A. Landman, Kurt G. Schilling",
    "authorList": [
      "Colin B. Hansen",
      "Qi Yang",
      "Ilwoo Lyu",
      "Francois Rheault",
      "Cailey Kerley",
      "Bramsh Qamar Chandio",
      "Shreyas Fadnavis",
      "Owen Williams",
      "Andrea T. Shafer",
      "Susan M. Resnick",
      "David H. Zald",
      "Laurie E. Cutting",
      "Warren D. Taylor",
      "Brian Boyd",
      "Eleftherios Garyfallidis",
      "Adam W. Anderson",
      "Maxime Descoteaux",
      "Bennett A. Landman",
      "Kurt G. Schilling"
    ],
    "kurtPosition": "last",
    "journal": "Neuroinformatics",
    "type": "Article",
    "doi": "https://doi.org/10.1007/s12021-020-09497-1",
    "publisher": "https://doi.org/10.1007/s12021-020-09497-1",
    "summary": "Pandora provides population-based maps of 216 white matter bundles derived from 2,443 people using six automated tractography approaches. Its overlapping bundle representations support anatomical localization and comparison across studies without treating white matter as a single uniform region.",
    "description": "The Pandora atlas organizes white matter as overlapping fiber bundles rather than mutually exclusive anatomical regions. Using diffusion MRI from 2,443 participants and six automated tractography methods, the study produced 216 bundle maps in standard volumetric and surface coordinates. The resulting resource supports region definition, segmentation, and comparisons across populations while retaining differences between tractography workflows. It also highlights why a bundle atlas contains information that a conventional regional parcellation misses. These maps summarize tractography-derived population anatomy, so their spatial probabilities and boundaries should not be interpreted as direct histological ground truth or exact pathways in every individual.",
    "findings": [
      "The atlas contains 216 white matter bundles derived from six automated tractography approaches.",
      "Population maps were generated from 2,443 subjects and provided in volumetric and surface coordinates.",
      "Overlapping bundle representations preserve relationships that mutually exclusive white matter region labels cannot express."
    ],
    "topics": [
      "Tractography",
      "Image Processing"
    ],
    "source": "https://doi.org/10.1007/s12021-020-09497-1",
    "summarySource": "https://doi.org/10.1007/s12021-020-09497-1",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/pandora-white-matter-atlas.webp",
    "imageAlt": "Example white matter pathways represented in Pandora volumetric and surface atlases.",
    "caption": "Colin B. Hansen et al. (2021), Figure 3. Example white matter pathways represented in Pandora volumetric and surface atlases.",
    "figureSource": "https://doi.org/10.1007/s12021-020-09497-1",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8124084/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8124084/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/pandora-white-matter-atlas/"
  },
  {
    "slug": "tractography-dissection-variability",
    "title": "Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?",
    "year": 2021,
    "authors": "Kurt G. Schilling, François Rheault, Laurent Petit, Colin B. Hansen, Vishwesh Nath, Fang-Cheng Yeh, Gabriel Girard, Muhamed Barakovic, Jonathan Rafael-Patino, Thomas Yu, Elda Fischi-Gomez, Marco Pizzolato, Mario Ocampo-Pineda, Simona Schiavi, Erick J. Canales-Rodríguez, Alessandro Daducci, Cristina Granziera, Giorgio Innocenti, Jean-Philippe Thiran, Laura Mancini, Stephen Wastling, Sirio Cocozza, Maria Petracca, Giuseppe Pontillo, Matteo Mancini, Sjoerd B. Vos, Vejay N. Vakharia, John S. Duncan, Helena Melero, Lidia Manzanedo, Emilio Sanz-Morales, Ángel Peña-Melián, Fernando Calamante, Arnaud Attyé, Ryan P. Cabeen, Laura Korobova, Arthur W. Toga, Anupa Ambili Vijayakumari, Drew Parker, Ragini Verma, Ahmed Radwan, Stefan Sunaert, Louise Emsell, Alberto De Luca, Alexander Leemans, Claude J. Bajada, Hamied Haroon, Hojjatollah Azadbakht, Maxime Chamberland, Sila Genc, Chantal M. W. Tax, Ping-Hong Yeh, Rujirutana Srikanchana, Colin D. McKnight, Joseph Yuan-Mou Yang, Jian Chen, Claire E. Kelly, Chun-Hung Yeh, Jerome Cochereau, Jerome J. Maller, Thomas Welton, Fabien Almairac, Kiran K. Seunarine, Chris A. Clark, Fan Zhang, Nikos Makris, Alexandra Golby, Yogesh Rathi, Lauren J. O’Donnell, Yihao Xia, Dogu Baran Aydogan, Yonggang Shi, Francisco Guerreiro Fernandes, Mathijs Raemaekers, Shaun Warrington, Stijn Michielse, Alonso Ramírez-Manzanares, Luis Concha, Ramón Aranda, Mariano Rivera Meraz, Garikoitz Lerma-Usabiaga, Lucas Roitman, Lucius S. Fekonja, Navona Calarco, Michael Joseph, Hajer Nakua, Aristotle N. Voineskos, Philippe Karan, Gabrielle Grenier, Jon Haitz Legarreta, Nagesh Adluru, Veena A. Nair, Vivek Prabhakaran, Andrew L. Alexander, Koji Kamagata, Yuya Saito, Wataru Uchida, Christina Andica, Masahiro Abe, Roza G. Bayrak, Claudia A. M. Gandini Wheeler-Kingshott, Egidio D’Angelo, Fulvia Palesi, Giovanni Savini, Nicolò Rolandi, Pamela Guevara, Josselin Houenou, Narciso López-López, Jean-François Mangin, Cyril Poupon, Claudio Román, Andrea Vázquez, Chiara Maffei, Mavilde Arantes, José Paulo Andrade, Susana Maria Silva, Vince D. Calhoun, Eduardo Caverzasi, Simone Sacco, Michael Lauricella, Franco Pestilli, Daniel Bullock, Yang Zhan, Edith Brignoni-Perez, Catherine Lebel, Jess E. Reynolds, Igor Nestrasil, René Labounek, Christophe Lenglet, Amy Paulson, Stefania Aulicka, Sarah R. Heilbronner, Katja Heuer, Bramsh Qamar Chandio, Javier Guaje, Wei Tang, Eleftherios Garyfallidis, Rajikha Raja, Adam W. Anderson, Bennett A. Landman, Maxime Descoteaux",
    "authorList": [
      "Kurt G. Schilling",
      "François Rheault",
      "Laurent Petit",
      "Colin B. Hansen",
      "Vishwesh Nath",
      "Fang-Cheng Yeh",
      "Gabriel Girard",
      "Muhamed Barakovic",
      "Jonathan Rafael-Patino",
      "Thomas Yu",
      "Elda Fischi-Gomez",
      "Marco Pizzolato",
      "Mario Ocampo-Pineda",
      "Simona Schiavi",
      "Erick J. Canales-Rodríguez",
      "Alessandro Daducci",
      "Cristina Granziera",
      "Giorgio Innocenti",
      "Jean-Philippe Thiran",
      "Laura Mancini",
      "Stephen Wastling",
      "Sirio Cocozza",
      "Maria Petracca",
      "Giuseppe Pontillo",
      "Matteo Mancini",
      "Sjoerd B. Vos",
      "Vejay N. Vakharia",
      "John S. Duncan",
      "Helena Melero",
      "Lidia Manzanedo",
      "Emilio Sanz-Morales",
      "Ángel Peña-Melián",
      "Fernando Calamante",
      "Arnaud Attyé",
      "Ryan P. Cabeen",
      "Laura Korobova",
      "Arthur W. Toga",
      "Anupa Ambili Vijayakumari",
      "Drew Parker",
      "Ragini Verma",
      "Ahmed Radwan",
      "Stefan Sunaert",
      "Louise Emsell",
      "Alberto De Luca",
      "Alexander Leemans",
      "Claude J. Bajada",
      "Hamied Haroon",
      "Hojjatollah Azadbakht",
      "Maxime Chamberland",
      "Sila Genc",
      "Chantal M. W. Tax",
      "Ping-Hong Yeh",
      "Rujirutana Srikanchana",
      "Colin D. McKnight",
      "Joseph Yuan-Mou Yang",
      "Jian Chen",
      "Claire E. Kelly",
      "Chun-Hung Yeh",
      "Jerome Cochereau",
      "Jerome J. Maller",
      "Thomas Welton",
      "Fabien Almairac",
      "Kiran K. Seunarine",
      "Chris A. Clark",
      "Fan Zhang",
      "Nikos Makris",
      "Alexandra Golby",
      "Yogesh Rathi",
      "Lauren J. O’Donnell",
      "Yihao Xia",
      "Dogu Baran Aydogan",
      "Yonggang Shi",
      "Francisco Guerreiro Fernandes",
      "Mathijs Raemaekers",
      "Shaun Warrington",
      "Stijn Michielse",
      "Alonso Ramírez-Manzanares",
      "Luis Concha",
      "Ramón Aranda",
      "Mariano Rivera Meraz",
      "Garikoitz Lerma-Usabiaga",
      "Lucas Roitman",
      "Lucius S. Fekonja",
      "Navona Calarco",
      "Michael Joseph",
      "Hajer Nakua",
      "Aristotle N. Voineskos",
      "Philippe Karan",
      "Gabrielle Grenier",
      "Jon Haitz Legarreta",
      "Nagesh Adluru",
      "Veena A. Nair",
      "Vivek Prabhakaran",
      "Andrew L. Alexander",
      "Koji Kamagata",
      "Yuya Saito",
      "Wataru Uchida",
      "Christina Andica",
      "Masahiro Abe",
      "Roza G. Bayrak",
      "Claudia A. M. Gandini Wheeler-Kingshott",
      "Egidio D’Angelo",
      "Fulvia Palesi",
      "Giovanni Savini",
      "Nicolò Rolandi",
      "Pamela Guevara",
      "Josselin Houenou",
      "Narciso López-López",
      "Jean-François Mangin",
      "Cyril Poupon",
      "Claudio Román",
      "Andrea Vázquez",
      "Chiara Maffei",
      "Mavilde Arantes",
      "José Paulo Andrade",
      "Susana Maria Silva",
      "Vince D. Calhoun",
      "Eduardo Caverzasi",
      "Simone Sacco",
      "Michael Lauricella",
      "Franco Pestilli",
      "Daniel Bullock",
      "Yang Zhan",
      "Edith Brignoni-Perez",
      "Catherine Lebel",
      "Jess E. Reynolds",
      "Igor Nestrasil",
      "René Labounek",
      "Christophe Lenglet",
      "Amy Paulson",
      "Stefania Aulicka",
      "Sarah R. Heilbronner",
      "Katja Heuer",
      "Bramsh Qamar Chandio",
      "Javier Guaje",
      "Wei Tang",
      "Eleftherios Garyfallidis",
      "Rajikha Raja",
      "Adam W. Anderson",
      "Bennett A. Landman",
      "Maxime Descoteaux"
    ],
    "kurtPosition": "first",
    "journal": "NeuroImage",
    "type": "Article",
    "doi": "https://doi.org/10.1016/j.neuroimage.2021.118502",
    "publisher": "https://doi.org/10.1016/j.neuroimage.2021.118502",
    "summary": "Forty-two teams dissected fourteen bundles from identical tractograms, revealing large differences in what researchers call the same pathway. Variation between dissection protocols exceeded within-protocol and between-person variation, demonstrating the need for clearer anatomical definitions and reproducible segmentation rules.",
    "description": "An open challenge gave 42 independent teams the same processed whole-brain tractograms from six subjects and asked them to identify fourteen white matter fascicles. The resulting 57 segmentation protocols were compared using both volume-based and streamline-based measures. Even with the underlying streamlines held fixed, differences between protocols were larger than within-protocol or between-subject differences. The study identifies nomenclature, anatomical definitions, and dissection constraints as major targets for improving reproducibility. It does not determine which reconstruction is anatomically correct: external validation is still needed. The limited subject sample and use of a shared tractogram also constrain generalization to other acquisitions and complete processing workflows.",
    "findings": [
      "Forty-two teams contributed 57 protocols for dissecting fourteen bundles in six subjects.",
      "Between-protocol variability exceeded within-protocol and between-subject variability despite identical input tractograms.",
      "Agreement alone could not establish anatomical accuracy; independent validation remained necessary."
    ],
    "topics": [
      "Tractography",
      "Image Processing"
    ],
    "source": "https://doi.org/10.1016/j.neuroimage.2021.118502",
    "summarySource": "https://doi.org/10.1016/j.neuroimage.2021.118502",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/tractography-dissection-variability.webp",
    "imageAlt": "Different bundle-dissection protocols yield different reconstructions from the same tractogram.",
    "caption": "Kurt G. Schilling et al. (2021), Figure 3. Different bundle-dissection protocols yield different reconstructions from the same tractogram.",
    "figureSource": "https://doi.org/10.1016/j.neuroimage.2021.118502",
    "imageKind": "article-figure",
    "figureLicense": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8855321/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8855321/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/tractography-dissection-variability/"
  },
  {
    "slug": "anatomical-priors-tractography-accuracy",
    "title": "Brain connections derived from diffusion MRI tractography can be highly anatomically accurate—if we know where white matter pathways start, where they end, and where they do not go",
    "year": 2020,
    "authors": "Kurt G. Schilling, Laurent Petit, Francois Rheault, Samuel Remedios, Carlo Pierpaoli, Adam W. Anderson, Bennett A. Landman, Maxime Descoteaux",
    "authorList": [
      "Kurt G. Schilling",
      "Laurent Petit",
      "Francois Rheault",
      "Samuel Remedios",
      "Carlo Pierpaoli",
      "Adam W. Anderson",
      "Bennett A. Landman",
      "Maxime Descoteaux"
    ],
    "kurtPosition": "first",
    "journal": "Brain Structure and Function",
    "type": "Article",
    "doi": "https://doi.org/10.1007/s00429-020-02129-z",
    "publisher": "https://doi.org/10.1007/s00429-020-02129-z",
    "summary": "Anatomical inclusion and exclusion constraints substantially improve tractography in a previously studied validation dataset. The results demonstrate that high sensitivity and specificity can coexist when prior pathway knowledge is available, while also showing why this best-case setting differs from discovering unknown connections.",
    "description": "The authors revisited a dataset used in influential tractography validation studies and added anatomical constraints through manually defined or template-driven regions of interest. These constraints specify where pathways should begin, end, and avoid, resembling common human bundle-dissection practice. Compared with less constrained tracking, both approaches substantially improved agreement with the reference anatomy and allowed high sensitivity and specificity simultaneously. The experiment demonstrates the value of prior information for reconstructing known bundles. However, the anatomical reference used for validation also informed the constraints, and the study represents a best-case setting. It therefore does not establish equivalent accuracy when anatomy is unknown or independent prior information is unavailable.",
    "findings": [
      "Manually placed and template-driven anatomical constraints improved agreement with reference connections.",
      "High sensitivity and specificity were achieved together for the studied pathways.",
      "The validation reference also informed the constraints, limiting interpretation as an independent test of unknown connectivity."
    ],
    "topics": [
      "Tractography"
    ],
    "source": "https://doi.org/10.1007/s00429-020-02129-z",
    "summarySource": "https://doi.org/10.1007/s00429-020-02129-z",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/anatomical-priors-tractography-accuracy.webp",
    "imageAlt": "Anatomical constraints improve tractography sensitivity and specificity relative to earlier validation results.",
    "caption": "Kurt G. Schilling et al. (2020), Figure 6. Anatomical constraints improve tractography sensitivity and specificity relative to earlier validation results.",
    "figureSource": "https://doi.org/10.1007/s00429-020-02129-z",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7554161/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7554161/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/anatomical-priors-tractography-accuracy/"
  },
  {
    "slug": "challenges-biophysical-modeling",
    "title": "Challenges for biophysical modeling of microstructure",
    "year": 2020,
    "authors": "Ileana O. Jelescu, Marco Palombo, Francesca Bagnato, Kurt G. Schilling",
    "authorList": [
      "Ileana O. Jelescu",
      "Marco Palombo",
      "Francesca Bagnato",
      "Kurt G. Schilling"
    ],
    "kurtPosition": "last",
    "journal": "Journal of Neuroscience Methods",
    "type": "Review",
    "doi": "https://doi.org/10.1016/j.jneumeth.2020.108861",
    "publisher": "https://doi.org/10.1016/j.jneumeth.2020.108861",
    "summary": "This review follows diffusion MRI biophysical models from design to clinical application. It explains how model assumptions, acquisition choices, parameter estimation, and validation determine what can be inferred about tissue, and why interpreting model parameters becomes especially difficult in disease.",
    "description": "The review examines the steps required to turn diffusion MRI measurements into defensible estimates of tissue microstructure. It considers which tissue properties can be identified, how acquisitions should be designed, how numerical simulations and experimental measurements test performance, and how complementary methods can validate estimates. Examples from tumors, ischemia, and demyelinating disease illustrate why assumptions suitable for healthy tissue may fail in pathology. Four unresolved challenges concern microstructural ground truth, parameters inaccessible to complementary techniques, a broadly applicable model across brain regions and diseases, and communication between model developers and clinical users. The paper offers a critical framework rather than a universally validated model.",
    "findings": [
      "Acquisition design, model identifiability, and fitting strategy must be evaluated together.",
      "Pathological tissue may require adaptations to assumptions developed for healthy tissue.",
      "The review identifies four unresolved challenges involving ground truth, validation, model generalization, and communication."
    ],
    "topics": [
      "Microstructure",
      "Reviews & Consensus"
    ],
    "source": "https://doi.org/10.1016/j.jneumeth.2020.108861",
    "summarySource": "https://doi.org/10.1016/j.jneumeth.2020.108861",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/challenges-biophysical-modeling.webp",
    "imageAlt": "A roadmap for developing, estimating, and validating diffusion MRI models of tissue microstructure.",
    "caption": "Ileana O. Jelescu et al. (2020), Figure 3. A roadmap for developing, estimating, and validating diffusion MRI models of tissue microstructure.",
    "figureSource": "https://doi.org/10.1016/j.jneumeth.2020.108861",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10163379/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10163379/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/challenges-biophysical-modeling/"
  },
  {
    "slug": "synb0-without-fieldmaps",
    "title": "Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps",
    "year": 2020,
    "authors": "Kurt G. Schilling, Justin Blaber, Colin Hansen, Leon Cai, Baxter Rogers, Adam W. Anderson, Seth Smith, Praitayini Kanakaraj, Tonia Rex, Susan M. Resnick, Andrea T. Shafer, Laurie E. Cutting, Neil Woodward, David Zald, Bennett A. Landman",
    "authorList": [
      "Kurt G. Schilling",
      "Justin Blaber",
      "Colin Hansen",
      "Leon Cai",
      "Baxter Rogers",
      "Adam W. Anderson",
      "Seth Smith",
      "Praitayini Kanakaraj",
      "Tonia Rex",
      "Susan M. Resnick",
      "Andrea T. Shafer",
      "Laurie E. Cutting",
      "Neil Woodward",
      "David Zald",
      "Bennett A. Landman"
    ],
    "kurtPosition": "first",
    "journal": "PLOS ONE",
    "type": "Article",
    "doi": "https://doi.org/10.1371/journal.pone.0236418",
    "publisher": "https://doi.org/10.1371/journal.pone.0236418",
    "summary": "A second-generation Synb0 pipeline uses three-dimensional deep learning to create an undistorted diffusion reference from available MRI data. It supports distortion correction when reverse phase-encoding scans or field maps are missing, extending practical preprocessing options for historical and heterogeneous datasets.",
    "description": "The study develops a three-dimensional U-net approach to synthesize an undistorted b0 image with structural T1-weighted geometry and diffusion-like contrast. A heterogeneous training dataset was used to improve applicability across acquisition conditions, and performance was evaluated on withheld data against standard reversed phase-encoding correction and intensity-based registration. The synthesized target enabled correction of geometric distortions without collecting an additional field map or opposite-encoding scan. This extends the earlier Synb0-DisCo method and reduces reliance on computationally intensive registration alone. Because the target is synthesized, unusual anatomy, contrast, or acquisition conditions still require quality control; successful correction in the test set is not a guarantee for every new scan.",
    "findings": [
      "The pipeline used three-dimensional U-nets and heterogeneous training acquisitions to synthesize an undistorted b0 image.",
      "Withheld test data showed successful geometric distortion correction without additional correction scans.",
      "The proposed approach was quantitatively compared with intensity-based registration and FSL TOPUP."
    ],
    "topics": [
      "Image Processing"
    ],
    "source": "https://doi.org/10.1371/journal.pone.0236418",
    "summarySource": "https://doi.org/10.1371/journal.pone.0236418",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/synb0-without-fieldmaps.webp",
    "imageAlt": "Deep learning synthesizes an undistorted b0 image from a distorted b0 and an anatomical T1 image.",
    "caption": "Kurt G. Schilling et al. (2020), Figure 1. Deep learning synthesizes an undistorted b0 image from a distorted b0 and an anatomical T1 image.",
    "figureSource": "https://doi.org/10.1371/journal.pone.0236418",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7394453/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7394453/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/synb0-without-fieldmaps/"
  },
  {
    "slug": "fiber-coherence-index",
    "title": "A fiber coherence index for quality control of B-table orientation in diffusion MRI scans",
    "year": 2019,
    "authors": "Kurt G. Schilling, Fang-Cheng Yeh, Vishwesh Nath, Colin Hansen, Owen Williams, Susan Resnick, Adam W. Anderson, Bennett A. Landman",
    "authorList": [
      "Kurt G. Schilling",
      "Fang-Cheng Yeh",
      "Vishwesh Nath",
      "Colin Hansen",
      "Owen Williams",
      "Susan Resnick",
      "Adam W. Anderson",
      "Bennett A. Landman"
    ],
    "kurtPosition": "first",
    "journal": "Magnetic Resonance Imaging",
    "type": "Article",
    "doi": "https://doi.org/10.1016/j.mri.2019.01.018",
    "publisher": "https://linkinghub.elsevier.com/retrieve/pii/S0730725X1830540X",
    "summary": "A fiber coherence index detects incorrectly flipped or permuted diffusion gradient tables by testing whether neighboring fiber orientations agree. Validation across research, clinical, and animal scans supports its use as an automated quality-control check.",
    "description": "Incorrect b-vector orientation can invalidate diffusion reconstruction and tractography. This study evaluates a coherence-based method that selects the gradient-table configuration producing the most consistent neighboring fiber orientations. It succeeds across thousands of human scans and a smaller animal dataset; the failures are associated with substantial motion or signal dropout.",
    "findings": [
      "Local fiber coherence can identify gradient-table flips and permutations.",
      "Severe image artifacts accounted for the observed failures."
    ],
    "topics": [
      "Image Processing",
      "Tractography"
    ],
    "source": "https://pubmed.ncbi.nlm.nih.gov/30682379/",
    "summarySource": "https://pubmed.ncbi.nlm.nih.gov/30682379/",
    "publicationStatus": "Published",
    "pdf": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6401245/pdf/nihms-1519694.pdf",
    "metadataSource": "https://api.crossref.org/works/10.1016/j.mri.2019.01.018",
    "image": "/assets/papers/fiber-coherence-index.webp",
    "imageAlt": "Correct, flipped, and permuted diffusion gradient tables produce different orientation patterns.",
    "caption": "Kurt G. Schilling et al. (2019), Figure 1. Correct, flipped, and permuted diffusion gradient tables produce different orientation patterns.",
    "figureSource": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6401245/",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6401245/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6401245/"
      }
    ],
    "pdfVerification": "official-link-only",
    "url": "https://www.microstructure-connectivity-lab.com/publications/fiber-coherence-index/"
  },
  {
    "slug": "chiasm-squirrel-monkey-atlas",
    "title": "A Web-Based Atlas Combining MRI and Histology of the Squirrel Monkey Brain",
    "year": 2019,
    "authors": "Kurt G. Schilling, Yurui Gao, Matthew Christian, Vaibhav Janve, Iwona Stepniewska, Bennett A. Landman, Adam W. Anderson",
    "authorList": [
      "Kurt G. Schilling",
      "Yurui Gao",
      "Matthew Christian",
      "Vaibhav Janve",
      "Iwona Stepniewska",
      "Bennett A. Landman",
      "Adam W. Anderson"
    ],
    "kurtPosition": "first",
    "journal": "Neuroinformatics",
    "type": "Article",
    "doi": "https://doi.org/10.1007/s12021-018-9391-z",
    "publisher": "https://doi.org/10.1007/s12021-018-9391-z",
    "summary": "CHIASM combines structural MRI, diffusion MRI, histological stains, and motor-cortex tracing in a common squirrel monkey reference space. The integrated atlas lets researchers compare tissue appearance across modalities and use labeled cortical regions and white matter pathways for anatomical localization.",
    "description": "The Combined Histology-MRI Integrated Atlas of the Squirrel Monkey aligns in vivo and ex vivo MRI acquired at 9.4 T with Nissl and myelin histology. A tracer injection in primary motor cortex adds a specific anatomical connectivity reference. The atlas includes diffusion tensor glyphs, 57 tractography-defined white matter labels, and 18 cortical regions identified from cytoarchitecture, accessible through a multimodal web viewer. Bringing these contrasts into one coordinate system supports anatomical interpretation and validation across imaging methods. The reference remains tied to its specimen, registration accuracy, and motor-cortex injection; its tractography labels and histological measurements provide complementary rather than interchangeable evidence.",
    "findings": [
      "The atlas combines MRI, Nissl staining, myelin staining, and primary motor-cortex tracer information in one space.",
      "It includes 57 tractography-defined white matter pathways and 18 histologically defined cortical regions.",
      "A web-based viewer supports comparisons between user-selected contrasts and resolutions."
    ],
    "topics": [
      "Image Processing",
      "Tractography",
      "Microstructure"
    ],
    "source": "https://doi.org/10.1007/s12021-018-9391-z",
    "summarySource": "https://doi.org/10.1007/s12021-018-9391-z",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/chiasm-squirrel-monkey-atlas.webp",
    "imageAlt": "MRI contrasts and matched histological modalities included in the squirrel monkey atlas.",
    "caption": "Kurt G. Schilling et al. (2019), Figure 3. MRI contrasts and matched histological modalities included in the squirrel monkey atlas.",
    "figureSource": "https://doi.org/10.1007/s12021-018-9391-z",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6330248/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6330248/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/chiasm-squirrel-monkey-atlas/"
  },
  {
    "slug": "ai-in-mri-grassroots",
    "title": "AI in MRI: A case for grassroots deep learning",
    "year": 2019,
    "authors": "Kurt G. Schilling, Bennett A. Landman",
    "authorList": [
      "Kurt G. Schilling",
      "Bennett A. Landman"
    ],
    "kurtPosition": "first",
    "journal": "Magnetic Resonance Imaging",
    "type": "Commentary",
    "doi": "https://doi.org/10.1016/j.mri.2019.07.004",
    "publisher": "https://linkinghub.elsevier.com/retrieve/pii/S0730725X19304370",
    "summary": "This commentary introduces a special issue on machine learning in MRI, bringing together eighteen perspectives spanning image acquisition, processing, and modeling.",
    "description": "Schilling and Landman discuss the growing role of data-driven methods in MRI while introducing eighteen contributions to a special issue. The commentary frames developments across acquisition, image processing, and modeling from the perspective of researchers applying machine learning to practical imaging problems.",
    "findings": [
      "The special issue spans machine learning approaches to MRI acquisition, processing, and modeling."
    ],
    "topics": [
      "Image Processing",
      "Reviews & Consensus"
    ],
    "source": "https://pubmed.ncbi.nlm.nih.gov/31283972/",
    "summarySource": "https://pubmed.ncbi.nlm.nih.gov/31283972/",
    "publicationStatus": "Published",
    "pdf": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8278255/pdf/nihms-1721243.pdf",
    "metadataSource": "https://api.crossref.org/works/10.1016/j.mri.2019.07.004",
    "image": "/assets/papers/ai-in-mri-grassroots.webp",
    "imageAlt": "Article preview; this letter contains no figures.",
    "caption": "Kurt G. Schilling et al. (2019), Article preview. Article preview; this letter contains no figures.",
    "figureSource": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8278255/",
    "imageKind": "article-preview",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8278255/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8278255/"
      }
    ],
    "pdfVerification": "official-link-only",
    "url": "https://www.microstructure-connectivity-lab.com/publications/ai-in-mri-grassroots/"
  },
  {
    "slug": "motor-system-tractography-validation",
    "title": "Anatomical accuracy of standard-practice tractography algorithms in the motor system - A histological validation in the squirrel monkey brain",
    "year": 2019,
    "authors": "Kurt G. Schilling, Yurui Gao, Iwona Stepniewska, Vaibhav Janve, Bennett A. Landman, Adam W. Anderson",
    "authorList": [
      "Kurt G. Schilling",
      "Yurui Gao",
      "Iwona Stepniewska",
      "Vaibhav Janve",
      "Bennett A. Landman",
      "Adam W. Anderson"
    ],
    "kurtPosition": "first",
    "journal": "Magnetic Resonance Imaging",
    "type": "Article",
    "doi": "https://doi.org/10.1016/j.mri.2018.09.004",
    "publisher": "https://doi.org/10.1016/j.mri.2018.09.004",
    "summary": "Forty commonly used tractography configurations were tested against motor-cortex tracer anatomy in squirrel monkeys. Accuracy depended strongly on reconstruction, tracking, and seeding choices, and deteriorated with distance from the seed; some methods missed substantial parts of known pathways.",
    "description": "The authors compared forty tractography outputs with histological tracer distributions originating in primary motor cortex, evaluating both voxel-level pathway coverage and regional connectivity. Although all methods used the same MRI inputs, their reconstructions varied substantially, and no configuration succeeded on every accuracy metric. Reconstruction model, tracking algorithm, and seed definition were major determinants of performance. Errors often arose when streamlines left gray matter, and anatomical accuracy decreased farther from the seed. The validation involved one injection site in two squirrel monkey brains and a relatively modest acquisition, so the results characterize common implementations in this motor-system setting rather than all pathways or imaging regimes.",
    "findings": [
      "No tested configuration achieved the best performance across all validation metrics.",
      "Accuracy depended on reconstruction, tracking, and how the seed region was used.",
      "Tractography accuracy decreased with distance from the seed, with frequent failures near the cortical boundary."
    ],
    "topics": [
      "Tractography"
    ],
    "source": "https://doi.org/10.1016/j.mri.2018.09.004",
    "summarySource": "https://doi.org/10.1016/j.mri.2018.09.004",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/motor-system-tractography-validation.webp",
    "imageAlt": "Variation among standard-practice reconstructions of motor pathways in the squirrel monkey brain.",
    "caption": "Kurt G. Schilling et al. (2019), Figure 3. Variation among standard-practice reconstructions of motor pathways in the squirrel monkey brain.",
    "figureSource": "https://doi.org/10.1016/j.mri.2018.09.004",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6855403/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6855403/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/motor-system-tractography-validation/"
  },
  {
    "slug": "tractography-benchmark-lessons",
    "title": "Challenges in diffusion MRI tractography – Lessons learned from international benchmark competitions",
    "year": 2019,
    "authors": "Kurt G. Schilling, Alessandro Daducci, Klaus Maier-Hein, Cyril Poupon, Jean-Christophe Houde, Vishwesh Nath, Adam W. Anderson, Bennett A. Landman, Maxime Descoteaux",
    "authorList": [
      "Kurt G. Schilling",
      "Alessandro Daducci",
      "Klaus Maier-Hein",
      "Cyril Poupon",
      "Jean-Christophe Houde",
      "Vishwesh Nath",
      "Adam W. Anderson",
      "Bennett A. Landman",
      "Maxime Descoteaux"
    ],
    "kurtPosition": "first",
    "journal": "Magnetic Resonance Imaging",
    "type": "Review",
    "doi": "https://doi.org/10.1016/j.mri.2018.11.014",
    "publisher": "https://doi.org/10.1016/j.mri.2018.11.014",
    "summary": "This review synthesizes a decade of tractography benchmark competitions. Physical phantoms, simulated data, and anatomical references reveal recurring tradeoffs between finding true pathways and avoiding false connections, while showing how acquisition, reconstruction, seeding, and evaluation choices shape apparent performance.",
    "description": "International tractography challenges provide shared datasets and reference standards for comparing algorithms under common conditions. This paper reviews lessons from a decade of such competitions, spanning local orientation reconstruction and whole-pathway connectivity. Across benchmarks, plausible-looking bundles can coexist with incomplete coverage, incorrect branching, and false connections. Denoising, sharper orientation estimates, and spatial constraints can help, but seeding and tracking choices change the balance between sensitivity and specificity. Deterministic and probabilistic methods often favor different aspects of that balance. Because each benchmark captures only part of tissue complexity and has its own reference limitations, the paper argues for interpreting competition results in context rather than treating one ranking as universal.",
    "findings": [
      "Benchmark studies repeatedly revealed incomplete bundle coverage and false-positive connections.",
      "Seeding, noise, local orientation estimation, and tracking strategy materially affected results.",
      "Different reference datasets probe complementary limitations and cannot establish one universally optimal algorithm."
    ],
    "topics": [
      "Tractography",
      "Reviews & Consensus"
    ],
    "source": "https://doi.org/10.1016/j.mri.2018.11.014",
    "summarySource": "https://doi.org/10.1016/j.mri.2018.11.014",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/tractography-benchmark-lessons.webp",
    "imageAlt": "Overview of benchmark datasets and anatomical references used in tractography challenges.",
    "caption": "Kurt G. Schilling et al. (2019), Figure 1. Overview of benchmark datasets and anatomical references used in tractography challenges.",
    "figureSource": "https://doi.org/10.1016/j.mri.2018.11.014",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6331218/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6331218/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/tractography-benchmark-lessons/"
  },
  {
    "slug": "cervical-cord-microstructure-validation",
    "title": "Diffusion MRI microstructural models in the cervical spinal cord – Application, normative values, and correlations with histological analysis",
    "year": 2019,
    "authors": "Kurt G. Schilling, Samantha By, Haley R. Feiler, Bailey A. Box, Kristin P. O’Grady, Atlee Witt, Bennett A. Landman, Seth A. Smith",
    "authorList": [
      "Kurt G. Schilling",
      "Samantha By",
      "Haley R. Feiler",
      "Bailey A. Box",
      "Kristin P. O’Grady",
      "Atlee Witt",
      "Bennett A. Landman",
      "Seth A. Smith"
    ],
    "kurtPosition": "first",
    "journal": "NeuroImage",
    "type": "Article",
    "doi": "https://doi.org/10.1016/j.neuroimage.2019.116026",
    "publisher": "https://doi.org/10.1016/j.neuroimage.2019.116026",
    "summary": "DTI, NODDI, and spherical mean techniques were evaluated in the cervical spinal cords of 21 healthy participants. Comparisons with a histological template showed sensitivity to tissue differences but limited specificity for the biological properties that model parameters are often assumed to measure.",
    "description": "This study applied diffusion tensor imaging, NODDI, and the spherical mean technique to cervical spinal cord MRI from 21 healthy controls. It reports reference measurements across ascending and descending white matter pathways and gray matter regions, then compares those measurements with microstructural features from a histological template. Tensor indices reflected several tissue properties without isolating one mechanism. More elaborate microstructural models also showed associations, but did not specifically recover the properties their parameters were intended to represent. Some correlations were driven by broad white-versus-gray matter differences. Because the reference was a template rather than matched histology from each participant, the work motivates cord-specific validation rather than definitive individual tissue quantification.",
    "findings": [
      "Normative DTI, NODDI, and SMT measurements were reported for 21 healthy cervical spinal cords.",
      "Microstructural model parameters did not specifically capture the histological properties explicitly modeled.",
      "Broad differences between white and gray matter contributed strongly to several observed correlations."
    ],
    "topics": [
      "Spinal Cord",
      "Microstructure"
    ],
    "source": "https://doi.org/10.1016/j.neuroimage.2019.116026",
    "summarySource": "https://doi.org/10.1016/j.neuroimage.2019.116026",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/cervical-cord-microstructure-validation.webp",
    "imageAlt": "Population-average DTI, NODDI, and spherical mean technique parameter maps in the cervical spinal cord.",
    "caption": "Kurt G. Schilling et al. (2019), Figure 6. Population-average DTI, NODDI, and spherical mean technique parameter maps in the cervical spinal cord.",
    "figureSource": "https://doi.org/10.1016/j.neuroimage.2019.116026",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6765439/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6765439/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/cervical-cord-microstructure-validation/"
  },
  {
    "slug": "harfi-functional-tractography",
    "title": "Functional tractography of white matter by high angular resolution functional-correlation imaging (HARFI)",
    "year": 2019,
    "authors": "Kurt G. Schilling, Yurui Gao, Muwei Li, Tung-Lin Wu, Justin Blaber, Bennett A. Landman, Adam W. Anderson, Zhaohua Ding, John C. Gore",
    "authorList": [
      "Kurt G. Schilling",
      "Yurui Gao",
      "Muwei Li",
      "Tung-Lin Wu",
      "Justin Blaber",
      "Bennett A. Landman",
      "Adam W. Anderson",
      "Zhaohua Ding",
      "John C. Gore"
    ],
    "kurtPosition": "first",
    "journal": "Magnetic Resonance in Medicine",
    "type": "Article",
    "doi": "https://doi.org/10.1002/mrm.27512",
    "publisher": "https://doi.org/10.1002/mrm.27512",
    "summary": "HARFI estimates the directional structure of correlations in white matter BOLD signals without restricting them to a tensor. Its sampling approach reduces orientation bias and represents bending or fanning patterns, providing a functional MRI complement to diffusion-based descriptions of white matter pathways.",
    "description": "The study introduces high-angular-resolution functional-correlation imaging to characterize how resting-state BOLD correlations vary with direction and distance in white matter. Conventional functional-correlation tensors use nearest neighbors and impose an ellipsoidal, symmetric shape, creating orientation biases and limiting the patterns they can describe. HARFI uses broader radial and angular sampling to estimate more complex distributions, including asymmetric bending and fanning. The resulting orientation information reconstructed known white matter pathways and supported proposed functional and asymmetry indices. This is a proof of concept for spatial BOLD organization: correlation-derived trajectories should not be equated with direct measurements of axons, neural signaling direction, or causal connectivity.",
    "findings": [
      "HARFI sampling removed orientation biases demonstrated in nearest-neighbor tensor models.",
      "Estimated functional orientation distributions supported reconstruction of known white matter pathways.",
      "The representation allowed asymmetric bending and fanning patterns excluded by a symmetric tensor."
    ],
    "topics": [
      "Functional Connectivity",
      "Tractography"
    ],
    "source": "https://doi.org/10.1002/mrm.27512",
    "summarySource": "https://doi.org/10.1002/mrm.27512",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/harfi-functional-tractography.webp",
    "imageAlt": "HARFI functional pathways compared with familiar structural white matter pathways.",
    "caption": "Kurt G. Schilling et al. (2019), Figure 4. HARFI functional pathways compared with familiar structural white matter pathways.",
    "figureSource": "https://doi.org/10.1002/mrm.27512",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6347525/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6347525/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/harfi-functional-tractography/"
  },
  {
    "slug": "histological-fiber-response-functions",
    "title": "Histologically derived fiber response functions for diffusion MRI vary across white matter fibers—An ex vivo validation study in the squirrel monkey brain",
    "year": 2019,
    "authors": "Kurt G. Schilling, Yurui Gao, Iwona Stepniewska, Vaibhav Janve, Bennett A. Landman, Adam W. Anderson",
    "authorList": [
      "Kurt G. Schilling",
      "Yurui Gao",
      "Iwona Stepniewska",
      "Vaibhav Janve",
      "Bennett A. Landman",
      "Adam W. Anderson"
    ],
    "kurtPosition": "first",
    "journal": "NMR in Biomedicine",
    "type": "Article",
    "doi": "https://doi.org/10.1002/nbm.4090",
    "publisher": "https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/nbm.4090",
    "summary": "Histology from three squirrel monkey brains shows that diffusion MRI fiber response functions vary across white matter regions. Choosing a different response function changes reconstructed fiber orientations and can alter tractography accuracy.",
    "description": "Spherical deconvolution commonly assumes one fiber response function throughout the brain. This study derives reference functions from three-dimensional histology and matched ex vivo diffusion MRI. The functions differ across regions and from conventional estimates, showing that the choice of response function can affect both fiber orientation distributions and reconstructed pathways.",
    "findings": [
      "Histologically derived response functions varied across white matter regions.",
      "Response-function choice changed orientation reconstruction and tractography results."
    ],
    "topics": [
      "Microstructure",
      "Tractography"
    ],
    "source": "https://pubmed.ncbi.nlm.nih.gov/30908803/",
    "summarySource": "https://pubmed.ncbi.nlm.nih.gov/30908803/",
    "publicationStatus": "Published",
    "pdf": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6525086/pdf/nihms-1013269.pdf",
    "metadataSource": "https://api.crossref.org/works/10.1002/nbm.4090",
    "image": "/assets/papers/histological-fiber-response-functions.webp",
    "imageAlt": "Histologically derived fiber response functions vary across regions of the same brain.",
    "caption": "Kurt G. Schilling et al. (2019), Figure 3. Histologically derived fiber response functions vary across regions of the same brain.",
    "figureSource": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6525086/",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6525086/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6525086/"
      }
    ],
    "pdfVerification": "official-link-only",
    "url": "https://www.microstructure-connectivity-lab.com/publications/histological-fiber-response-functions/"
  },
  {
    "slug": "modern-tractography-accuracy-limits",
    "title": "Limits to anatomical accuracy of diffusion tractography using modern approaches",
    "year": 2019,
    "authors": "Kurt G. Schilling, Vishwesh Nath, Colin Hansen, Prasanna Parvathaneni, Justin Blaber, Yurui Gao, Peter Neher, Dogu Baran Aydogan, Yonggang Shi, Mario Ocampo-Pineda, Simona Schiavi, Alessandro Daducci, Gabriel Girard, Muhamed Barakovic, Jonathan Rafael-Patino, David Romascano, Gaëtan Rensonnet, Marco Pizzolato, Alice Bates, Elda Fischi, Jean-Philippe Thiran, Erick J. Canales-Rodríguez, Chao Huang, Hongtu Zhu, Liming Zhong, Ryan Cabeen, Arthur W. Toga, Francois Rheault, Guillaume Theaud, Jean-Christophe Houde, Jasmeen Sidhu, Maxime Chamberland, Carl-Fredrik Westin, Tim B. Dyrby, Ragini Verma, Yogesh Rathi, M. Okan Irfanoglu, Cibu Thomas, Carlo Pierpaoli, Maxime Descoteaux, Adam W. Anderson, Bennett A. Landman",
    "authorList": [
      "Kurt G. Schilling",
      "Vishwesh Nath",
      "Colin Hansen",
      "Prasanna Parvathaneni",
      "Justin Blaber",
      "Yurui Gao",
      "Peter Neher",
      "Dogu Baran Aydogan",
      "Yonggang Shi",
      "Mario Ocampo-Pineda",
      "Simona Schiavi",
      "Alessandro Daducci",
      "Gabriel Girard",
      "Muhamed Barakovic",
      "Jonathan Rafael-Patino",
      "David Romascano",
      "Gaëtan Rensonnet",
      "Marco Pizzolato",
      "Alice Bates",
      "Elda Fischi",
      "Jean-Philippe Thiran",
      "Erick J. Canales-Rodríguez",
      "Chao Huang",
      "Hongtu Zhu",
      "Liming Zhong",
      "Ryan Cabeen",
      "Arthur W. Toga",
      "Francois Rheault",
      "Guillaume Theaud",
      "Jean-Christophe Houde",
      "Jasmeen Sidhu",
      "Maxime Chamberland",
      "Carl-Fredrik Westin",
      "Tim B. Dyrby",
      "Ragini Verma",
      "Yogesh Rathi",
      "M. Okan Irfanoglu",
      "Cibu Thomas",
      "Carlo Pierpaoli",
      "Maxime Descoteaux",
      "Adam W. Anderson",
      "Bennett A. Landman"
    ],
    "kurtPosition": "first",
    "journal": "NeuroImage",
    "type": "Article",
    "doi": "https://doi.org/10.1016/j.neuroimage.2018.10.029",
    "publisher": "https://doi.org/10.1016/j.neuroimage.2018.10.029",
    "summary": "The 3D-VoTEM challenge tested 176 tractography submissions against a physical phantom and two ex vivo brain references. Despite modern modeling and tracking methods, substantial anatomical errors persisted across these complementary tests, demonstrating that algorithmic sophistication alone does not guarantee accurate connectivity.",
    "description": "The 3-D Validation of Tractography with Experimental MRI challenge evaluated 176 submissions from nine research groups using three independent reference datasets: a physical phantom and two ex vivo primate brains. These datasets tested tractography under different fiber geometries, acquisition conditions, and definitions of anatomical accuracy. The central finding was consistent across the sub-challenges: contemporary approaches still faced substantial errors, and anatomical accuracy had not improved dramatically compared with established limitations. The study exposes tradeoffs that are difficult to see from visually plausible streamlines alone. Its conclusions concern the tested implementations and reference conditions, rather than establishing that every future or anatomically constrained method must perform similarly.",
    "findings": [
      "Nine groups submitted 176 tractography results across three independent validation datasets.",
      "Persistent anatomical inaccuracies were observed in both phantom and ex vivo brain tests.",
      "Modern methodological advances did not produce a dramatic overall improvement in the evaluated anatomical accuracy."
    ],
    "topics": [
      "Tractography"
    ],
    "source": "https://doi.org/10.1016/j.neuroimage.2018.10.029",
    "summarySource": "https://doi.org/10.1016/j.neuroimage.2018.10.029",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/modern-tractography-accuracy-limits.webp",
    "imageAlt": "Tractography submissions reconstructed the same validation pathways with markedly different spatial extents.",
    "caption": "Kurt G. Schilling et al. (2019), Figure 2. Tractography submissions reconstructed the same validation pathways with markedly different spatial extents.",
    "figureSource": "https://doi.org/10.1016/j.neuroimage.2018.10.029",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6551229/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6551229/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/modern-tractography-accuracy-limits/"
  },
  {
    "slug": "synb0-disco",
    "title": "Synthesized b0 for diffusion distortion correction (Synb0-DisCo)",
    "year": 2019,
    "authors": "Kurt G. Schilling, Justin Blaber, Yuankai Huo, Allen Newton, Colin Hansen, Vishwesh Nath, Andrea T. Shafer, Owen Williams, Susan M. Resnick, Baxter Rogers, Adam W. Anderson, Bennett A. Landman",
    "authorList": [
      "Kurt G. Schilling",
      "Justin Blaber",
      "Yuankai Huo",
      "Allen Newton",
      "Colin Hansen",
      "Vishwesh Nath",
      "Andrea T. Shafer",
      "Owen Williams",
      "Susan M. Resnick",
      "Baxter Rogers",
      "Adam W. Anderson",
      "Bennett A. Landman"
    ],
    "kurtPosition": "first",
    "journal": "Magnetic Resonance Imaging",
    "type": "Article",
    "doi": "https://doi.org/10.1016/j.mri.2019.05.008",
    "publisher": "https://doi.org/10.1016/j.mri.2019.05.008",
    "summary": "Synb0-DisCo uses a structural MRI scan to synthesize an undistorted diffusion reference image. This enables susceptibility-distortion correction for datasets lacking reverse phase-encoded scans, improving anatomical alignment and reducing variation in diffusion modeling in the evaluated data.",
    "description": "Many historical or abbreviated diffusion MRI protocols lack the reversed phase-encoding images needed for standard susceptibility correction. Synb0-DisCo addresses this gap by using deep learning to synthesize a non-diffusion-weighted image from a structural scan, providing an undistorted anatomical target for TOPUP-based processing. The evaluated pipeline improved agreement with structural geometry and reduced variation in diffusion-derived measurements, with performance comparable to paired phase-encoding references in the study. The method makes additional datasets usable, but the synthesized reference is a model prediction rather than an acquired opposite-encoding image. Its reliability therefore depends on synthesis quality and the similarity of new data to the evaluated conditions.",
    "findings": [
      "A structural image was used to synthesize an undistorted b0-like reference.",
      "Correction improved geometric correspondence with anatomical images and reduced diffusion-model variation.",
      "The approach enabled TOPUP-like correction without an acquired reversed phase-encoding scan."
    ],
    "topics": [
      "Image Processing"
    ],
    "source": "https://doi.org/10.1016/j.mri.2019.05.008",
    "summarySource": "https://doi.org/10.1016/j.mri.2019.05.008",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/synb0-disco.webp",
    "imageAlt": "Synthesized b0 distortion correction compared with uncorrected and reversed-encoding results.",
    "caption": "Kurt G. Schilling et al. (2019), Figure 3. Synthesized b0 distortion correction compared with uncorrected and reversed-encoding results.",
    "figureSource": "https://doi.org/10.1016/j.mri.2019.05.008",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6834894/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6834894/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/synb0-disco/"
  },
  {
    "slug": "gyral-bias-validation",
    "title": "Confirmation of a gyral bias in diffusion MRI fiber tractography",
    "year": 2018,
    "authors": "Kurt Schilling, Yurui Gao, Vaibhav Janve, Iwona Stepniewska, Bennett A. Landman, Adam W. Anderson",
    "authorList": [
      "Kurt Schilling",
      "Yurui Gao",
      "Vaibhav Janve",
      "Iwona Stepniewska",
      "Bennett A. Landman",
      "Adam W. Anderson"
    ],
    "kurtPosition": "first",
    "journal": "Human Brain Mapping",
    "type": "Article",
    "doi": "https://doi.org/10.1002/hbm.23936",
    "publisher": "https://doi.org/10.1002/hbm.23936",
    "summary": "Comparing tractography with myelin-stained histology confirms that streamlines preferentially terminate on gyral crowns. The bias persists across tracking algorithms, diffusion models, weightings, and high-resolution acquisitions, complicating interpretation of cortical connectivity from streamline density.",
    "description": "The study compared axonal orientation and density near the white matter–gray matter boundary with diffusion MRI orientations and tractography in corresponding tissue locations. Histological observations showed that tractography overrepresented terminations at gyral crowns relative to sulcal banks. The effect appeared with deterministic and probabilistic tracking, tensor and higher-angular-resolution models, and multiple diffusion weightings, including very high-resolution data. This demonstrates a systematic anatomical bias rather than a peculiarity of one tracking configuration. The histological reference was two-dimensional and sensitive to myelinated axons, and the analysis did not directly identify full cortico-cortical connections; these limits matter when extending the results to connectivity estimates.",
    "findings": [
      "Tractography preferentially terminated on gyral crowns compared with histological axon distributions.",
      "The bias persisted across deterministic/probabilistic tracking and multiple diffusion models and weightings.",
      "Higher spatial resolution did not eliminate the observed gyral bias."
    ],
    "topics": [
      "Tractography",
      "Microstructure"
    ],
    "source": "https://doi.org/10.1002/hbm.23936",
    "summarySource": "https://doi.org/10.1002/hbm.23936",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/gyral-bias-validation.webp",
    "imageAlt": "Histology and diffusion MRI workflow for testing tractography bias across gyral blades.",
    "caption": "Kurt Schilling et al. (2018), Figure 1. Histology and diffusion MRI workflow for testing tractography bias across gyral blades.",
    "figureSource": "https://doi.org/10.1002/hbm.23936",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5807146/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5807146/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/gyral-bias-validation/"
  },
  {
    "slug": "histological-fod-dispersion-validation",
    "title": "Histological validation of diffusion MRI fiber orientation distributions and dispersion",
    "year": 2018,
    "authors": "Kurt G. Schilling, Vaibhav Janve, Yurui Gao, Iwona Stepniewska, Bennett A. Landman, Adam W. Anderson",
    "authorList": [
      "Kurt G. Schilling",
      "Vaibhav Janve",
      "Yurui Gao",
      "Iwona Stepniewska",
      "Bennett A. Landman",
      "Adam W. Anderson"
    ],
    "kurtPosition": "first",
    "journal": "NeuroImage",
    "type": "Article",
    "doi": "https://doi.org/10.1016/j.neuroimage.2017.10.046",
    "publisher": "https://doi.org/10.1016/j.neuroimage.2017.10.046",
    "summary": "Histological validation compares several diffusion reconstruction methods against three-dimensional fiber geometry. Models captured overall angular structure better than discrete peak counts or low-angle crossings, and no method led across all accuracy criteria; dispersion estimates also showed limited agreement with histology.",
    "description": "Three-dimensional histological fiber orientation distributions were compared with constrained spherical deconvolution, q-ball imaging, diffusion orientation transform, persistent angular structure, and NODDI-based measurements. The evaluation tested overall distribution shape, fiber count, orientation accuracy, and dispersion across acquisition settings and tissue geometries. Continuous angular structure was generally reproduced with median angular correlations above 0.70, but identifying discrete peaks remained difficult. Median errors were approximately 10 degrees for the primary direction and 20 degrees for a secondary direction. No method adequately resolved crossings below 60 degrees, and dispersion agreement was limited. The comparison therefore identifies tradeoffs rather than a single universally superior reconstruction method.",
    "findings": [
      "No reconstruction method performed best across all evaluated accuracy measures.",
      "Median angular errors were approximately 10 degrees for primary and 20 degrees for secondary fiber directions.",
      "All tested methods struggled with crossings below 60 degrees and with accurate dispersion estimation."
    ],
    "topics": [
      "Tractography",
      "Microstructure"
    ],
    "source": "https://doi.org/10.1016/j.neuroimage.2017.10.046",
    "summarySource": "https://doi.org/10.1016/j.neuroimage.2017.10.046",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/histological-fod-dispersion-validation.webp",
    "imageAlt": "Histological fiber orientations compared with eight diffusion MRI reconstruction methods.",
    "caption": "Kurt G. Schilling et al. (2018), Figure 3. Histological fiber orientations compared with eight diffusion MRI reconstruction methods.",
    "figureSource": "https://doi.org/10.1016/j.neuroimage.2017.10.046",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5732036/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5732036/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/histological-fod-dispersion-validation/"
  },
  {
    "slug": "resolution-crossing-fibers",
    "title": "Can increased spatial resolution solve the crossing fiber problem for diffusion MRI?",
    "year": 2017,
    "authors": "Kurt Schilling, Yurui Gao, Vaibhav Janve, Iwona Stepniewska, Bennett A. Landman, Adam W. Anderson",
    "authorList": [
      "Kurt Schilling",
      "Yurui Gao",
      "Vaibhav Janve",
      "Iwona Stepniewska",
      "Bennett A. Landman",
      "Adam W. Anderson"
    ],
    "kurtPosition": "first",
    "journal": "NMR in Biomedicine",
    "type": "Article",
    "doi": "https://doi.org/10.1002/nbm.3787",
    "publisher": "https://doi.org/10.1002/nbm.3787",
    "summary": "Ex vivo diffusion MRI and matched histology challenge the assumption that smaller voxels eliminate crossing fibers. Across the tested scales, finer resolution revealed more complex orientations, indicating that tissue geometry remains a fundamental obstacle even when image resolution improves.",
    "description": "The study combined ex vivo macaque diffusion MRI with histological analysis of the same specimen to measure crossing-fiber prevalence as spatial resolution increased. Histology extended the analysis below practical MRI voxel sizes. Contrary to the usual expectation, the proportion of voxels with multiple orientations increased with finer sampling. Even at a histological voxel size of 32 micrometers, more than half of white matter voxels contained crossing configurations. The results show that improved resolution can reveal previously averaged structure without removing the ambiguity faced by tractography or microstructural models. These conclusions derive from the studied specimen and methods, rather than proving performance for every possible reconstruction.",
    "findings": [
      "Crossing-fiber prevalence increased as spatial resolution increased in both MRI and histology.",
      "More than 50% of white matter voxels still showed crossing fibers at a 32-micrometer histological scale.",
      "Signal-to-noise analyses indicated that noise alone did not explain the observed resolution effect."
    ],
    "topics": [
      "Tractography",
      "Microstructure"
    ],
    "source": "https://doi.org/10.1002/nbm.3787",
    "summarySource": "https://doi.org/10.1002/nbm.3787",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/resolution-crossing-fibers.webp",
    "imageAlt": "Histology and diffusion MRI reveal fiber geometry and crossing-fiber estimates across spatial scales.",
    "caption": "Kurt Schilling et al. (2017), Figure 1. Histology and diffusion MRI reveal fiber geometry and crossing-fiber estimates across spatial scales.",
    "figureSource": "https://doi.org/10.1002/nbm.3787",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5685916/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5685916/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/resolution-crossing-fibers/"
  },
  {
    "slug": "qball-directions-bvalue",
    "title": "Effects of b-Value and Number of Gradient Directions on Diffusion MRI Measures Obtained with Q-ball Imaging",
    "year": 2017,
    "authors": "Kurt G. Schilling, Vishwesh Nath, Justin Blaber, Robert L. Harrigan, Zhaohua Ding, Adam W. Anderson, Bennett A. Landman",
    "authorList": [
      "Kurt G. Schilling",
      "Vishwesh Nath",
      "Justin Blaber",
      "Robert L. Harrigan",
      "Zhaohua Ding",
      "Adam W. Anderson",
      "Bennett A. Landman"
    ],
    "kurtPosition": "first",
    "journal": "Proceedings of SPIE: Medical Imaging 2017, Image Processing",
    "type": "Conference paper",
    "doi": "https://doi.org/10.1117/12.2254545",
    "publisher": "https://doi.org/10.1117/12.2254545",
    "summary": "This full conference paper uses eleven repeated diffusion acquisitions to examine q-ball measurement precision. It shows that directional sampling and the spherical harmonic model can bias tissue and orientation metrics, helping explain why acquisition choices must be evaluated together with the reconstruction method.",
    "description": "A high-angular-resolution diffusion dataset acquired across multiple b-values and repeated eleven times in one participant was used to test q-ball imaging measurements. The study varied the number of gradient directions and spherical harmonic representation to evaluate precision and reproducibility of tissue and fiber-orientation indices. Derived quantities were sensitive to sampling and model order, and undersampling often introduced systematic bias rather than only increasing random variability. The findings support choosing acquisition and reconstruction settings together when shortening a protocol. This eleven-page proceedings paper provides an empirical methods analysis, with the single-participant design limiting direct extrapolation to other populations and acquisition systems.",
    "findings": [
      "Q-ball tissue and orientation measures were sensitive to the number of diffusion directions.",
      "The spherical harmonic representation affected measurement precision and bias.",
      "Undersampled acquisition/model combinations could produce biased q-ball metrics."
    ],
    "topics": [
      "Image Processing",
      "Microstructure"
    ],
    "source": "https://doi.org/10.1117/12.2254545",
    "summarySource": "https://doi.org/10.1117/12.2254545",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/qball-directions-bvalue.webp",
    "imageAlt": "Q-ball diffusion orientation estimates across diffusion weightings and gradient-direction counts.",
    "caption": "Kurt G. Schilling et al. (2017), Figure 1. Q-ball diffusion orientation estimates across diffusion weightings and gradient-direction counts.",
    "figureSource": "https://doi.org/10.1117/12.2254545",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5571896/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5571896/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/qball-directions-bvalue/"
  },
  {
    "slug": "clinically-feasible-qball",
    "title": "Empirical consideration of the effects of acquisition parameters and analysis model on clinically feasible q-ball imaging",
    "year": 2017,
    "authors": "Kurt G. Schilling, Vishwesh Nath, Justin A. Blaber, Prasanna Parvathaneni, Adam W. Anderson, Bennett A. Landman",
    "authorList": [
      "Kurt G. Schilling",
      "Vishwesh Nath",
      "Justin A. Blaber",
      "Prasanna Parvathaneni",
      "Adam W. Anderson",
      "Bennett A. Landman"
    ],
    "kurtPosition": "first",
    "journal": "Magnetic Resonance Imaging",
    "type": "Article",
    "doi": "https://doi.org/10.1016/j.mri.2017.04.007",
    "publisher": "https://doi.org/10.1016/j.mri.2017.04.007",
    "summary": "Repeated scans test how short clinical q-ball acquisitions balance diffusion weighting, direction count, and model complexity. The analysis shows that spherical harmonic order strongly affects derived metrics and that adding directions can be more useful than repeating measurements within a fixed scan time.",
    "description": "Using eleven repeats of a five-b-value dataset from one participant at 3 T, this study evaluated q-ball imaging protocols lasting less than five minutes. Both diffusion weighting and direction count affected derived tissue and orientation measures, but the spherical harmonic representation had a particularly strong influence. Insufficient directional sampling inflated anisotropy and generated false orientation peaks. The authors recommend collecting at least 8–12 more directions than estimated harmonic coefficients and found that greater directional coverage was preferable to repeated observations at equal scan time. Because the empirical evaluation used one participant, these recommendations address the tested low-SNR acquisition regime rather than every population or scanner.",
    "findings": [
      "Spherical harmonic order strongly influenced q-ball indices and numerical stability.",
      "The study recommends oversampling by at least 8–12 directions beyond the number of fitted harmonic coefficients.",
      "At equal scan time, increasing directional sampling was preferable to repeating observations."
    ],
    "topics": [
      "Image Processing",
      "Microstructure",
      "Tractography"
    ],
    "source": "https://doi.org/10.1016/j.mri.2017.04.007",
    "summarySource": "https://doi.org/10.1016/j.mri.2017.04.007",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/clinically-feasible-qball.webp",
    "imageAlt": "Q-ball orientation estimates across diffusion weightings, gradient counts, and spherical harmonic orders.",
    "caption": "Kurt G. Schilling et al. (2017), Figure 2. Q-ball orientation estimates across diffusion weightings, gradient counts, and spherical harmonic orders.",
    "figureSource": "https://doi.org/10.1016/j.mri.2017.04.007",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5500983/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5500983/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/clinically-feasible-qball/"
  },
  {
    "slug": "squirrel-monkey-diffusion-reproducibility",
    "title": "Reproducibility and variation of diffusion measures in the squirrel monkey brain, in vivo and ex vivo",
    "year": 2017,
    "authors": "Kurt Schilling, Yurui Gao, Iwona Stepniewska, Ann S. Choe, Bennett A. Landman, Adam W. Anderson",
    "authorList": [
      "Kurt Schilling",
      "Yurui Gao",
      "Iwona Stepniewska",
      "Ann S. Choe",
      "Bennett A. Landman",
      "Adam W. Anderson"
    ],
    "kurtPosition": "first",
    "journal": "Magnetic Resonance Imaging",
    "type": "Article",
    "doi": "https://doi.org/10.1016/j.mri.2016.08.015",
    "publisher": "https://doi.org/10.1016/j.mri.2016.08.015",
    "summary": "Repeated in vivo and ex vivo imaging of three squirrel monkeys establishes reference diffusion measurements and their variability. Mean diffusivity was relatively reproducible, while fractional anisotropy varied more across regions and subjects; fixation substantially changed both measures.",
    "description": "Three healthy squirrel monkeys were each scanned twice in vivo and once ex vivo to measure reproducibility, regional variation, and agreement across tissue conditions. The analysis examined fractional anisotropy, mean diffusivity, and principal diffusion orientation in anatomically defined regions. Mean diffusivity showed coefficients of variation below 10%, while fractional anisotropy was more variable. Ex vivo mean diffusivity decreased by 30–50% and fractional anisotropy increased by 30–39% relative to in vivo values. These reference measurements support validation studies using the squirrel monkey model. The small sample and possible residual alignment errors between histological regions and MRI limit generalization and precision.",
    "findings": [
      "Mean diffusivity coefficients of variation were below 10% for both within-subject and between-subject comparisons.",
      "Ex vivo mean diffusivity fell by 30–50%, while fractional anisotropy rose by 30–39%.",
      "The modal angular difference between in vivo and ex vivo principal eigenvectors was 12 degrees."
    ],
    "topics": [
      "Microstructure",
      "Image Processing"
    ],
    "source": "https://doi.org/10.1016/j.mri.2016.08.015",
    "summarySource": "https://doi.org/10.1016/j.mri.2016.08.015",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/squirrel-monkey-diffusion-reproducibility.webp",
    "imageAlt": "Repeated in vivo and ex vivo diffusion orientation and anisotropy maps of the same squirrel monkey brain.",
    "caption": "Kurt Schilling et al. (2017), Figure 1. Repeated in vivo and ex vivo diffusion orientation and anisotropy maps of the same squirrel monkey brain.",
    "figureSource": "https://doi.org/10.1016/j.mri.2016.08.015",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5125845/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5125845/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/squirrel-monkey-diffusion-reproducibility/"
  },
  {
    "slug": "validate29-squirrel-monkey-atlas",
    "title": "The VALiDATe29 MRI Based Multi-Channel Atlas of the Squirrel Monkey Brain",
    "year": 2017,
    "authors": "Kurt G. Schilling, Yurui Gao, Iwona Stepniewska, Tung-Lin Wu, Feng Wang, Bennett A. Landman, John C. Gore, Li Min Chen, Adam W. Anderson",
    "authorList": [
      "Kurt G. Schilling",
      "Yurui Gao",
      "Iwona Stepniewska",
      "Tung-Lin Wu",
      "Feng Wang",
      "Bennett A. Landman",
      "John C. Gore",
      "Li Min Chen",
      "Adam W. Anderson"
    ],
    "kurtPosition": "first",
    "journal": "Neuroinformatics",
    "type": "Article",
    "doi": "https://doi.org/10.1007/s12021-017-9334-0",
    "publisher": "https://doi.org/10.1007/s12021-017-9334-0",
    "summary": "VALiDATe29 provides a common anatomical reference built from MRI of 29 squirrel monkeys. Structural, diffusion, and ex vivo templates are paired with cortical and white matter labels to support registration, regional measurements, and comparisons across animals and experiments.",
    "description": "The authors built a population-averaged squirrel monkey brain atlas using unbiased nonlinear registration of MRI from 29 animals. The atlas combines proton-density, T1-weighted, and T2*-weighted templates with fractional anisotropy and mean diffusivity maps, plus ex vivo structural and diffusion templates. Histologically defined cortical regions and tractography-defined white matter labels extend the reference beyond image intensity alone. Demonstrated applications include spatial normalization, anatomical localization, and propagation of regional labels to new scans. The atlas provides a shared coordinate system for experiments, but population averaging and registration cannot capture every individual anatomical feature, and tractography labels are not direct measurements of all axonal connections.",
    "findings": [
      "The population template was constructed from MRI of 29 squirrel monkeys.",
      "Multiple structural and diffusion contrasts were combined with cortical and white matter labels.",
      "The resource supports spatial normalization and label propagation across experimental datasets."
    ],
    "topics": [
      "Image Processing",
      "Tractography",
      "Microstructure"
    ],
    "source": "https://doi.org/10.1007/s12021-017-9334-0",
    "summarySource": "https://doi.org/10.1007/s12021-017-9334-0",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/validate29-squirrel-monkey-atlas.webp",
    "imageAlt": "Surface renderings of cortical and white matter labels in the VALiDATe29 squirrel monkey atlas.",
    "caption": "Kurt G. Schilling et al. (2017), Figure 3. Surface renderings of cortical and white matter labels in the VALiDATe29 squirrel monkey atlas.",
    "figureSource": "https://doi.org/10.1007/s12021-017-9334-0",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5671902/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5671902/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/validate29-squirrel-monkey-atlas/"
  },
  {
    "slug": "confocal-diffusion-orientation-validation",
    "title": "Comparison of 3D Orientation Distribution Functions Measured with Confocal Microscopy and Diffusion MRI",
    "year": 2016,
    "authors": "Kurt Schilling, Vaibhav Janve, Yurui Gao, Iwona Stepniewska, Bennett A. Landman, Adam W. Anderson",
    "authorList": [
      "Kurt Schilling",
      "Vaibhav Janve",
      "Yurui Gao",
      "Iwona Stepniewska",
      "Bennett A. Landman",
      "Adam W. Anderson"
    ],
    "kurtPosition": "first",
    "journal": "NeuroImage",
    "type": "Article",
    "doi": "https://doi.org/10.1016/j.neuroimage.2016.01.022",
    "publisher": "https://doi.org/10.1016/j.neuroimage.2016.01.022",
    "summary": "Three-dimensional confocal microscopy provides a direct reference for testing diffusion MRI estimates of fiber orientation. Comparisons with constrained spherical deconvolution found good agreement for nearly parallel fibers but larger errors in crossing regions, including failure to resolve crossings below 60 degrees in this dataset.",
    "description": "This study developed a method to extract three-dimensional fiber orientation distributions from confocal microscopy z-stacks and compare them with diffusion MRI in the same postmortem tissue. Unlike validation confined to a two-dimensional section, the microscopy approach measures orientations throughout a small tissue volume. Constrained spherical deconvolution estimated nearly parallel fibers with approximately 6-degree orientation error, increasing to about 10–11 degrees in crossing regions. Crossings below 60 degrees were not resolved in the studied dataset. The work establishes a practical validation framework while showing that accurate local orientation estimation depends on fiber geometry; its numerical results describe the acquisition and reconstruction tested.",
    "findings": [
      "Orientation error was approximately 6 degrees for nearly parallel fibers and 10–11 degrees in crossing regions.",
      "Constrained spherical deconvolution did not resolve crossings below 60 degrees in the evaluated dataset.",
      "Confocal z-stacks enabled a three-dimensional histological reference rather than a two-dimensional comparison."
    ],
    "topics": [
      "Tractography",
      "Microstructure"
    ],
    "source": "https://doi.org/10.1016/j.neuroimage.2016.01.022",
    "summarySource": "https://doi.org/10.1016/j.neuroimage.2016.01.022",
    "pdf": "",
    "publicationStatus": "Published",
    "image": "/assets/papers/confocal-diffusion-orientation-validation.webp",
    "imageAlt": "Comparison of histological and diffusion MRI fiber orientations in crossing and fanning regions.",
    "caption": "Kurt Schilling et al. (2016), Figure 9. Comparison of histological and diffusion MRI fiber orientations in crossing and fanning regions.",
    "figureSource": "https://doi.org/10.1016/j.neuroimage.2016.01.022",
    "imageKind": "article-figure",
    "fullText": "https://pmc.ncbi.nlm.nih.gov/articles/PMC4803575/",
    "links": [
      {
        "label": "Open full text",
        "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC4803575/"
      }
    ],
    "url": "https://www.microstructure-connectivity-lab.com/publications/confocal-diffusion-orientation-validation/"
  }
]