Research

The brain is a tremendously complex system. In order to understand it we are going to need large amounts of data from many different kinds of measurements. We use data science methods to integrate the information provided by these measurements into a coherent picture. In particular, we develop image processing pipelines, statistical analysis techniques, and visualization tools to decipher the role of networks of brain areas in development, in complex behaviors and in brain disorders. We implement these methods in robust, efficient, and openly-available computer software.

Human Connectomics

White matter connections between brain regions form a network that integrates information across the brain, comprising approximately 45% of the total cortical volume. Diffusion MRI (dMRI) is a technique that non-invasively measures properties of the white matter, assessing individual differences in the properties of the major tracts. We develop computational tools for analysis of dMRI data, and apply these tools in large-scale datasets of human neuroimaging data to understand the brain and the complex relationships between the properties of brain networks, complex behavior and brain health.

Tools and publications:

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

Incremental improvements in tractometry-based brain-age modeling with deep learning
Ariel Rokem, Joanna Qiao, Jason D. Yeatman & Adam Richie-Halford
biorxiv

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

Vision science

We work on modeling and analysis of the biology of the early visual system. In some projects, we created phenomenological models of retinal processing, that simulate prosthetic vision. These models, implemented in software are useful for assessing the utility of these devices and as tools for designing new devices and stimulation protocols. In other projects, we analyze data about retinal health from large-scale databases. We are also interested to understand how the biology of networks in the early visual system evolves over time, and in response to disease.

Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal images
Parmita Mehta, Christine A Petersen, Joanne C Wen, Michael R Banitt, Philip P Chen, Karine D Bojikian, Catherine Egan, Su-In Lee, Magdalena Balazinska, Aaron Y Lee & Ariel Rokem (last two authors contributed equally)
American Journal of Ophthalmology 231:154-169

A model of ganglion axon pathways accounts for percepts elicited by retinal implants
Michael Beyeler, Devyani Nanduri, James D Weiland, Ariel Rokem, Geoffrey M Boynton & Ione Fine
Scientific Reports, 9:9199

Cloud-enabled data-driven discovery

Progress in understanding the brain depends on sophisticated analysis of massive neuroscience datasets. This requires the adoption of data science technologies that are emerging in industry, such as cloud computing. One of the objectives of our work is to reduce the barriers to wider adoption of these technologies. We are exploring multiple aspects of cloud computing. This includes development of software for deployment of computations to cloud systems, as well as work on web-based tools for data visualization and apps for citizen science.

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)

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

Data science

As we apply tools from statistical learning to problems in neuroscience, we also end up developing general purpose open-source statistical computing tools. Our group is also involved in a variety of ways in data science education and training.

Tools and publications:

Incremental improvements in tractometry-based brain-age modeling with deep learning
Ariel Rokem, Joanna Qiao, Jason D. Yeatman & Adam Richie-Halford
biorxiv

Groupyr: Sparse Group Lasso in Python
Adam Richie-Halford, Manjari Narayan, Jason Yeatman, Noah Simon & Ariel Rokem
Journal of Open Source Software, 6(58), 3024

Hack weeks as a model for data science education and collaboration.
Daniela Huppenkothen, Anthony Arendt, David W. Hogg, Karthik Ram, Jacob T. VanderPlas, & Ariel Rokem
PNAS, 115: 8872-8877