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Ariel Rokem

Researcher at University of Washington

Publications -  161
Citations -  6898

Ariel Rokem is an academic researcher from University of Washington. The author has contributed to research in topics: Diffusion MRI & Computer science. The author has an hindex of 34, co-authored 143 publications receiving 4976 citations. Previous affiliations of Ariel Rokem include Humboldt State University & University of California, Berkeley.

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The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments.

TL;DR: The Brain Imaging Data Structure (BIDS) is developed, a standard for organizing and describing MRI datasets that uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.
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Dipy, a library for the analysis of diffusion MRI data

TL;DR: Dipy aims to provide transparent implementations for all the different steps of dMRI analysis with a uniform programming interface, and has implemented classical signal reconstruction techniques, such as the diffusion tensor model and deterministic fiber tractography.
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GABA Concentration Is Reduced in Visual Cortex in Schizophrenia and Correlates with Orientation-Specific Surround Suppression

TL;DR: The results suggest that a neocortical GABA deficit in subjects with schizophrenia leads to impaired cortical inhibition and that GABAergic synaptic transmission in visual cortex plays a critical role in OSSS.
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Deep-learning based, automated segmentation of macular edema in optical coherence tomography

TL;DR: A convolutional neural network (CNN) is developed that detects intraretinal fluid (IRF) on OCT in a manner indistinguishable from clinicians and can be trained to perform automated segmentations of clinically relevant image features.
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Evaluation and statistical inference for human connectomes

TL;DR: Linear fascicle evaluation (LiFE) takes any connectome as input and predicts diffusion measurements as output, using the difference between the measured and predicted diffusion signals to quantify the prediction error.