Author
Ron Borowsky
University of SaskatchewanORCID 0000-0003-0002-4021Unclaimed ORCID profile · follow to get notified of new preprints
Biology Disciplines
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2020-2023 · peak 3/yrCo-Author Collaboration Network
10 top collaborators · 30 mutual co-author links6 papers
Recent·Most cited
- NeuroscienceBrain Structure and FunctionPeer-reviewed
Comparing models of information transfer in the structural brain network and their relationship to functional connectivity: Diffusion versus shortest path routing
Josh Neudorf, Shaylyn Kress, Ron Borowsky
The paper compares diffusion and shortest path routing models for information transfer in the brain's structural network and finds the diffusion model better explains functional connectivity.
11 citations - NeuroscienceNeurosciencePeer-reviewed
Ipsilesional motor cortex activation with high-force unimanual handgrip contractions of the less-affected limb in participants with stroke
Justin W. Andrushko, Layla A. Gould, Doug W. Renshaw…·Jonathan P. Farthing
This study examines how high-force handgrip contractions of the less-affected limb affect brain activation in stroke participants and controls, finding increased ipsilesional activation in both groups.
6 citations - NeuroscienceBrain Structure and FunctionPeer-reviewed
Structure Can Predict Function in the Human Brain: A Graph Neural Network Deep Learning Model of Functional Connectivity and Centrality Based on Structural Connectivity
Josh Neudorf, Shaylyn Kress, Ron Borowsky
This paper develops a graph neural network to predict functional connectivity and centrality from structural connectivity, achieving high variance explanation in both mean and individual-level measures.
43 citations - NeuroscienceNeurosciencePeer-reviewed
Brain structural connectivity predicts brain functional complexity: DTI derived centrality accounts for variance in fractal properties of fMRI signal
Josh Neudorf, Chelsea Ekstrand, Shaylyn Kress, Ron Borowsky
The paper investigates how brain structural connectivity, measured by DTI-derived centrality, relates to functional complexity in fMRI signals, finding that degree centrality best predicts the Hurst exponent.
17 citations