Publications

For a full list see below or go to Google Scholar

Highlights

Multidimensional analysis and detection of informative features in diffusion MRI measurements of human white matter

The white matter contains long-range connections between different brain regions and the organization of these connections holds important implications for brain function in health and disease. Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) data to quantify tissue properties along the trajectories of these connections. In the present work, we developed a method based on the sparse group lasso (SGL) that takes into account tissue properties measured along all of the bundles, and selects informative features by enforcing sparsity, not only at the level of individual bundles, but also across the entire set of bundles and all of the measured tissue properties. The sparsity penalties for each of these constraints is identified using a nested cross-validation scheme that guards against over-fitting and simultaneously identifies the correct level of sparsity. SGL makes it possible to leverage the multivariate relationship between diffusion properties measured along multiple bundles to make accurate predictions of subject characteristics while simultaneously discovering the most relevant features of the white matter for the characteristic of interest.

Adam Richie-Halford, Jason Yeatman, Noah Simon & Ariel Rokem

BiorXiv preprint

Combining citizen science and deep learning to amplify expertise in neuroimaging

Combining citizen science and deep learning can generalize and scale expert decision making; this is particularly important in disciplines where specialized, automated tools do not yet exist. In Braindr, expert-labeled data were amplified by citizen scientists through a simple web interface. A deep learning algorithm was then trained to predict data quality, based on citizen scientist labels. Deep learning performed as well as specialized algorithms for quality control (AUC = 0.99).

Anisha Keshavan, Jason Yeatman & Ariel Rokem

Frontiers in Neuroinformatics, 13: 29

Cloudknot: A Python library to run your existing code on AWS Batch

In the quest to minimize time-to-first-result, many computational scientists are turning to cloud-based distributed computing with commercial vendors like Amazon to run their computational workloads. Yet cloud computing remains inaccessible to many researchers. Cloudknot takes as input a Python function, Dockerizes it for use in an Amazon ECS instance, and creates all the necessary AWS Batch constituent resources to submit jobs. You can then use cloudknot to submit and view jobs for a range of inputs.

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

Human neuroscience research faces several challenges with regards to reproducibility. While scientists are generally aware that data sharing is important, it is not always clear how to share data in a manner that allows other labs to understand and reproduce published findings. Here we report a new open source tool, AFQ-Browser, that builds an interactive website as a companion to a diffusion MRI study. Because AFQ-Browser is portable—it runs in any web-browser—it can facilitate transparency and data sharing. Moreover, by leveraging new web-visualization technologies to create linked views between different dimensions of the dataset (anatomy, diffusion metrics, subject metadata), AFQ-Browser facilitates exploratory data analysis, fueling new discoveries based on previously published datasets. In an era where Big Data is playing an increasingly prominent role in scientific discovery, so will browser-based tools for exploring high-dimensional datasets, communicating scientific discoveries, aggregating data across labs, and publishing data alongside manuscripts.

Jason Yeatman, Adam Richie-Halford, Josh Smith, Anisha Keshavan & Ariel Rokem

Nature Communications: 9, Article number: 940

Hack weeks as a model for data science education and collaboration.

As scientific disciplines grapple with more datasets of rapidly increasing complexity and size, new approaches are urgently required to introduce new statistical and computational tools into research communities and improve the cross-disciplinary exchange of ideas. In this paper, we introduce a type of scientific workshop, called a hack week, which allows for fast dissemination of new methodologies into scientific communities and fosters exchange and collaboration within and between disciplines. We present implementations of this concept in astronomy, neuroscience, and geoscience and show that hack weeks produce positive learning outcomes, foster lasting collaborations, yield scientific results, and promote positive attitudes toward open science.

Daniela Huppenkothen, Anthony Arendt, David W. Hogg, Karthik Ram, Jacob T. VanderPlas, & Ariel Rokem

PNAS, 115: 8872-8877

A model of ganglion axon pathways accounts for percepts elicited by retinal implants

Degenerative retinal diseases such as retinitis pigmentosa and macular degeneration cause irreversible vision loss in more than 10 million people worldwide. Retinal prostheses, now implanted in over 250 patients worldwide, electrically stimulate surviving cells in order to evoke neuronal responses that are interpreted by the brain as visual percepts (‘phosphenes’). However, instead of seeing focal spots of light, current implant users perceive highly distorted phosphenes that vary in shape both across subjects and electrodes. We characterized these distortions by asking users of the Argus retinal prosthesis system (Second Sight Medical Products Inc.) to draw electrically elicited percepts on a touchscreen. Using ophthalmic fundus imaging and computational modeling, we show that elicited percepts can be accurately predicted by the topographic organization of optic nerve fiber bundles in each subject’s retina, successfully replicating visual percepts ranging from ‘blobs’ to oriented ‘streaks’ and ‘wedges’ depending on the retinal location of the stimulating electrode. This provides the first evidence that activation of passing axon fibers accounts for the rich repertoire of phosphene shape commonly reported in psychophysical experiments, which can severely distort the quality of the generated visual experience. Overall our findings argue for more detailed modeling of biological detail across neural engineering applications.

Michael Beyeler, Devyani Nanduri, James D Weiland, Ariel Rokem, Geoffrey M Boynton & Ione Fine

Scientific Reports, 9:9199

`pulse2percept`: A Python-based simulation framework for bionic vision

By 2020 roughly 20 million people worldwide will suffer from photoreceptor diseases such as retinitis pigmentosa and age-related macular degeneration, and a variety of retinal sight restoration technologies are being developed to target these diseases. One technology, analogous to cochlear implants, uses a grid of electrodes to stimulate remaining retinal cells. Two brands of retinal prostheses are currently approved for implantation in patients with late stage photoreceptor disease. Clinical experience with these implants has made it apparent that the vision restored by these devices differs substantially from normal sight. To better understand the outcomes of this technology, we developed pulse2percept, an open-source Python implementation of a computational model that predicts the perceptual experience of retinal prosthesis patients across a wide range of implant configurations. A modular and extensible user interface exposes the different building blocks of the software, making it easy for users to simulate novel implants, stimuli, and retinal models. We hope that this library will contribute substantially to the field of medicine by providing a tool to accelerate the development of visual prostheses.

Michael Beyeler, Geoffrey M. Boynton, Ione Fine & Ariel Rokem

Proceedings of the 16th Python in Science Conference (2017): 81 - 88

 

Full List

Multidimensional analysis and detection of informative features in diffusion MRI measurements of human white matter
Adam Richie-Halford, Jason Yeatman, Noah Simon & Ariel Rokem
BiorXiv preprint

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

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

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

pulse2percept: A Python-based simulation framework for bionic vision
Michael Beyeler, Geoffrey M. Boynton, Ione Fine & Ariel Rokem
Proceedings of the 16th Python in Science Conference (2017): 81 - 88