Research

Our work is devoted to advancing understanding of the brain through data-driven discovery, focusing on the role of brain networks in a range of behaviors and on how these networks change across the lifespan, through experience, and in neurological and psychiatric diseases.

Our approach stems from a conviction that scientific progress requires not only isolated discoveries but also integrative infrastructure and supportive community practices enabling knowledge to be shared, verified, and built upon. We take a highly collaborative approach, working with researchers across complementary domains to translate advances in artificial intelligence and machine learning into impactful insights about the brain.

Our commitment to transparent and reproducible science means that all of our research outputs – from data and software, through publications and educational materials – are openly available. This ensures that publicly funded research benefits the broadest possible audience while maintaining potential for clinical and commercial translation.

Human Connectomics

Networks of brain regions and their joint activity give rise to coordinated information processing and to the complex adaptive behavior that characterizes human cognition. The proper function of brain networks is also crucial to neurological, cognitive, and psychiatric health. Therefore, a better understanding of brain networks is a major goal of contemporary neuroscience. Diffusion MRI (dMRI) is the only currently available method to measure and delineate white matter connections in vivo in a non-invasive matter.

We focus on a dMRI analysis approach known as ``tractometry’’. This approach uses dMRI measurements to assess the physical properties of long-range brain connections within every individual. We develop and maintain cutting edge methods to perform tractometry and we have established an integrative ecosystem of software that implements all of the steps of tractometry: post-processing of dMRI data, delineation of major white matter pathways, and modeling of the tissue properties within them.

Tools and publications:

Free water elimination tractometry for aging brains
Kelly Chang, Luke Burke, Nina LaPiana, Bradley Howlett, David Hunt, Margaret Dezelar, Jalal B. Andre, Patti Curl, James D. Ralston, Ariel Rokem, Christine L. Mac Donald
Imaging Neuroscience 3: IMAG.a.991

What needs to be standardized for reliable, reproducible, and robust tractography?
Jon Haitz Legarreta, Simona Schiavi, Wei Tang, Garrett Banks, Matthew Cieslak, Kurt Schilling, Alberto De Luca, Jacques-Donald Tournier, John Kruper, Francois Rheault, Stamatios N Sotiropoulos, Franco Pestilli, Jelle Veraart, Joseph Yuan-Mou Yang, Maxime Descoteaux, Sarah Heilbronner, Ariel Rokem
GigaScience, 15: giag034

Microstructural Alterations of the Cerebellum-Ventral Tegmental Area Pathways in First-Episode Psychosis
Halil Aziz Velioglu, Teresa Gomez, Juan A. Gallego, Todd Lencz, Anil K. Malhotra, Ariel Rokem, Hengyi Cao
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, in press

A software ecosystem for brain tractometry processing, analysis, and insight
John Kruper, Adam Richie-Halford, Joanna Qiao, Asa Gilmore, Kelly Chang, Mareike Grotheer, Ethan Roy, Sendy Caffarra, Teresa Gomez, Sam Chou, Matthew Cieslak, Serge Koudoro, Eleftherios Garyfallidis, Theodore D. Satterthwaite, Jason D. Yeatman, Ariel Rokem
PLoS Comput Biol 21(8): e1013323.

Convolutional neural network-based classification of glaucoma using optic radiation tissue properties
John Kruper, Adam Richie-Halford, Noah C Benson, Sendy Caffarra, Julia Owen, Yue Wu, Catherine Egan, Aaron Y Lee, Cecilia S Lee, Jason D Yeatman, Ariel Rokem & UK Biobank Eye and Vision Consortium
Communications Medicine volume 4, Article number: 72 (2024)

Tractometry of Human Visual White Matter Pathways in Health and Disease
Hiromasa Takemura, John Kruper, Toshikazu Miyata, & Ariel Rokem
Magnetic Resonance in Medical Sciences, 23(3), 2024

Tractometry of the Human Connectome Project: resources and insights
John Kruper, McKenzie P Hagen, François Rheault, Isaac Crane, Asa Gilmore, Manjari Narayan, Keshav Motwani, Eardi Lila, Chris Rorden, Jason D Yeatman & Ariel Rokem
Front. Neurosci., 11 June 2024

Human white matter myelinates faster in utero than ex utero
Mareike Grotheer, David Bloom, John Kruper, Adam Richie-Halford, Stephanie Zika, Vicente A. Aguilera González, Jason D. Yeatman, Kalanit Grill-Spector & Ariel Rokem
PNAS 120 (33) e2303491120

Optic radiations representing different eccentricities age differently
John Kruper, Noah C. Benson, Sendy Caffarra, Julia Owen, Yue Wu, Aaron Y. Lee, Cecilia S. Lee, Jason D. Yeatman, Ariel Rokem & UK Biobank Eye and Vision Consortium
Human Brain Mapping 44 (8), 3123-3135

An open, analysis-ready, and quality controlled resource for pediatric brain white-matter research
Adam Richie-Halford, Matthew Cieslak, Lei Ai, Sendy Caffarra, Sydney Covitz, Alexandre R. Franco, Iliana I. Karipidis, John Kruper, Michael Milham, Bárbara Avelar-Pereira, Ethan Roy, Valerie J. Sydnor, Jason Yeatman, The Fibr Community Science Consortium, Theodore D. Satterthwaite, Ariel Rokem
Scientific Data 9, 616

Evaluating the reliability of human brain white matter tractometry
John Kruper, Jason Yeatman, Adam Richie-Halford, David Bloom, Mareike Grotheer, Sendy Caffarra, Gregory Kiar, Iliana Karipidis, Ethan Roy & Ariel Rokem
Aperture Neuro 1:1-25

Multidimensional analysis and detection of informative features in diffusion MRI measurements of human white matter
Adam Richie-Halford, Jason Yeatman, Noah Simon & Ariel Rokem
PLoS Computational Biology: 17(6): e1009136

Combining citizen science and deep learning to amplify expertise in neuroimaging
Anisha Keshavan, Jason Yeatman & Ariel Rokem
Frontiers in Neuroinformatics, 13: 29

A browser-based tool for visualization and analysis of diffusion MRI data
Jason Yeatman, Adam Richie-Halford, Josh Smith, Anisha Keshavan & Ariel Rokem
Nature Communications: 9, Article number: 940

Developing interpretable AI methods for neuroimaging

To make progress in addressing the range of disorders and conditions that affect the aging human brain, including neurodegenerative disorders, it is vital that we capitalize on the rapid advances that are happening in artificial intelligence and machine learning. However, it is also crucial that we develop tools that are transparent and amenable to interpretation.

Tools and publications:

Combining citizen science and deep learning to amplify expertise in neuroimaging
Anisha Keshavan, Jason Yeatman & Ariel Rokem
Frontiers in Neuroinformatics, 13: 29

Building open-source infrastructure for reproducible neuroscience

We are embedded in a network of

Tools and publications:

The benefits of prefetching for large-scale cloud-based neuroimaging analysis workflows
Valerie Hayot-Sasson, Tristan Glatard & Ariel Rokem
2021 IEEE Workshop on Workflows in Support of Large-Scale Science (WORKS)

Combining citizen science and deep learning to amplify expertise in neuroimaging
Anisha Keshavan, Jason Yeatman & Ariel Rokem
Frontiers in Neuroinformatics, 13: 29

Cloudknot: A Python library to run your existing code on AWS Batch
Adam Richie-Halford & Ariel Rokem
Proceedings of the 17th Python in Science Conference (2018): 8 - 14

A browser-based tool for visualization and analysis of diffusion MRI data
Jason Yeatman, Adam Richie-Halford, Josh Smith, Anisha Keshavan & Ariel Rokem
Nature Communications: 9, Article number: 940