Microstructure & Connectivity Lab
Publications
Microstructure & Connectivity Lab
Publications
Embedded Files
Publications
Below we feature some of our Recent Work and Notable Lab Publications
*for a full list of publications by year, please go to our Publications (List) page.
Recent Work
Recent Work
Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI:
Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI:
Part 1: In vivo small-animal imaging
Part 1: In vivo small-animal imaging
Part 2: Ex vivo imaging: Added value and acquisition
Part 2: Ex vivo imaging: Added value and acquisition
Part 3: Ex vivo imaging: Data processing, comparisons with microscopy, and tractography
Part 3: Ex vivo imaging: Data processing, comparisons with microscopy, and tractography
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
Journal: Magnetic Resonance in Medicine (2025)
Summary: These three papers from the ISMRM Diffusion Study Group provide best practices and recommendations for preclinical diffusion MRI (dMRI). Part 1 focuses on in vivo small-animal imaging, detailing considerations for species selection, imaging protocols, and analysis to enhance rigor and reproducibility. Part 2 explores ex vivo imaging, highlighting its advantages—such as higher resolution, improved signal-to-noise ratio (SNR), and direct histological comparisons—while addressing challenges in tissue preparation and acquisition. Part 3 covers ex vivo data processing, discussing preprocessing steps, diffusion model fitting, and comparisons with microscopy and tractography, aiming to improve methodological validation and open science efforts. Together, these works provide a comprehensive guide to optimizing preclinical dMRI research.
Head Motion in Diffusion Magnetic Resonance Imaging: Quantification, Mitigation, and Structural Associations in Large, Cross-Sectional Datasets Across the Lifespan
Head Motion in Diffusion Magnetic Resonance Imaging: Quantification, Mitigation, and Structural Associations in Large, Cross-Sectional Datasets Across the Lifespan
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
Journal: Human Brain Mapping (2025)
Summary: Head motion in diffusion MRI (dMRI) introduces artifacts that bias structural connectivity analyses. This study quantifies motion across 13 cohorts (16,995 sessions, ages 0.1–100 years) and evaluates preprocessing strategies for mitigating motion-induced biases. Findings show that modern pipelines effectively reduce these biases, ensuring reliable dMRI-derived metrics across diverse populations.
Large-Scale High-Resolution Probabilistic Maps of theHuman Superior Longitudinal Fasciculus Subdivisionsand their Cortical Terminations
Large-Scale High-Resolution Probabilistic Maps of theHuman Superior Longitudinal Fasciculus Subdivisionsand their Cortical Terminations
Authors: Matt Amandola, Katherine Farber, Roma Kidambi, Hoi-Chung Leung
Journal: The Journal of Neuroscience (2025)
Summary: The superior longitudinal fasciculus (SLF) is the major white after association tract connecting the frontal and parietal cortices. While it is described in great detail in the monkey brain due to tract-tracing studies, the organizational properties of the SLF in the human brain are unclear. In this study, we created large-scale, high-resolution probabilistic maps of the SLF subdivisions in the human brain, using imaging data from 700+ individuals via the Human Connectome Project. Our findings suggest that the human SLF closely resembles the non-human primate SLF in both dorsomedial-to-ventrolateral organization and cortical terminations. We also attempt to distinguish the SLF-I and the cingulum as two separable tracts, which is inconsistent in recent literature.
Notable Lab Publications
Notable Lab Publications
Brain connections derived from difusion MRI tractography can be highly anatomically accurate—if we know where white matter pathways start, where they end, and where they do not go
Brain connections derived from difusion MRI tractography can be highly anatomically accurate—if we know where white matter pathways start, where they end, and where they do not go
Authors: Kurt G. Schilling, Laurent Petit, Francois Rheault, Samuel Remedios, Carlo Pierpaoli, Adam W. Anderson, Bennett A. Landman, Maxime Descoteaux
Journal: Brain Structure and Function (2020)
Summary: Diffusion tractography suffers from a number of limitations, challenges, and biases. By re-analyzing several well-established datasets and benchmarks, we show that incorporating anatomical constraints into the tractography process can significantly improves the sensitivity and specificity of white matter bundle segmentation, enabling tractography to closely approximate true anatomical connectivity.
Why this is a Notable Lab publication: As the manuscript title optimistically states, we show that tractography can indeed be very highly anatomically accurate, and thus an invaluable resource to study the human brain
Whole-brain, gray, and white matter time-locked functional signal changes with simple tasks and model-free analysis
Whole-brain, gray, and white matter time-locked functional signal changes with simple tasks and model-free analysis
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
Journal: Proceedings of the National Academy of Sciences (2023)
Summary: This study challenges conventional fMRI approaches by demonstrating that both gray and white matter exhibit widespread, time-locked BOLD signal changes in response to simple tasks. Using a model-free analysis, we show that nearly all brain tissue is functionally engaged, contradicting the assumption that white matter BOLD signals are artifacts. These findings suggest that traditional fMRI studies may significantly underestimate the extent of brain activation, highlighting the need to reconsider how functional signals are interpreted.
Why this is a Notable Lab publication: This study challenges the traditional view of brain activity by showing that both gray and white matter actively respond to tasks, suggesting that half of the brain’s signals have been overlooked in standard MRI research.
Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?
Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?
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 Attyéw, 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
Journal: NeuroImage (2021)
Summary: This study examines the variability in white matter tractography by analyzing how 42 research groups segmented 14 major white matter bundles from the same dataset. Despite using identical data, significant differences emerged in how pathways were identified, highlighting the impact of methodological choices on tractography outcomes. The findings underscore the need for standardized protocols to ensure reproducibility and reliability in neuroimaging research.
Why this is a Notable Lab publication: This paper reveals that different research groups can produce widely varying results when identifying the same white matter pathways, emphasizing the urgent need for standardized methods in brain imaging to improve scientific consistency and clinical applications.
Synthesized b0 for diffusion distortion correction (Synb0-DisCo)
Synthesized b0 for diffusion distortion correction (Synb0-DisCo)
Authors: urt 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
Journal: Magnetic Resonance Imaging (2019)
Summary: Diffusion MRI is commonly distorted by magnetic field inhomogeneities, leading to misalignment with anatomical images. This study introduces Synb0-DisCo, a deep-learning-based method that synthesizes undistorted diffusion images from structural scans, enabling distortion correction without specialized acquisitions. The method significantly improves image alignment and diffusion model accuracy, making advanced correction techniques accessible to a broader range of clinical and research applications.
Why this is a Notable Lab publication: This paper introduces an AI-powered solution to correct distortions in brain scans, allowing researchers and clinicians to improve MRI accuracy even when specialized correction scans are unavailable.
White matter tract microstructure, macrostructure, and associated cortical gray matter morphology across the lifespan
White matter tract microstructure, macrostructure, and associated cortical gray matter morphology across the lifespan
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
Journal: Imaging Neuroscience (2023)
Summary: This study provides a comprehensive analysis of white matter pathways across the human lifespan, using data from 2,789 imaging sessions spanning ages 0 to 100 years. By examining white matter microstructure, macrostructure, and associated cortical features, we identify unique developmental and aging trajectories that vary across different brain pathways. These findings establish normative benchmarks for studying brain maturation and degeneration, with implications for understanding neurodevelopmental and neurodegenerative disorders.
Why this is a Notable Lab publication: This study maps how white matter in the brain changes from infancy to old age, providing crucial insights into brain development and aging that can help detect early signs of neurological diseases.
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