Shawn Rhoads
@shawnrhoadsphd.com
Assistant Professor @ ISMMS
NIH Director's Early Independence Awardee
Lindau Nobel Laureate Young Scientist
PI @ sinclaboratory.com
Using computational models, fMRI, & intracranial EEG to study social inference, learning, empathy, loneliness, & well-being
NIH Director's Early Independence Awardee
Lindau Nobel Laureate Young Scientist
PI @ sinclaboratory.com
Using computational models, fMRI, & intracranial EEG to study social inference, learning, empathy, loneliness, & well-being
Reposted by Shawn Rhoads
And the next step? Full voxel-level modeling.
Recent numerical advances cracked the scalability barrier. Voxel-level hierarchical modeling is now feasible, revealing just how punishing traditional multiple-comparison adjustments really are.
arxiv.org/abs/2511.12825
Recent numerical advances cracked the scalability barrier. Voxel-level hierarchical modeling is now feasible, revealing just how punishing traditional multiple-comparison adjustments really are.
arxiv.org/abs/2511.12825
SIMBA: Scalable Image Modeling using a Bayesian Approach, A Consistent Framework for Including Spatial Dependencies in fMRI Studies
Bayesian spatial modeling provides a flexible framework for whole-brain fMRI analysis by explicitly incorporating spatial dependencies, overcoming the limitations of traditional massive univariate app...
arxiv.org
November 18, 2025 at 10:13 PM
And the next step? Full voxel-level modeling.
Recent numerical advances cracked the scalability barrier. Voxel-level hierarchical modeling is now feasible, revealing just how punishing traditional multiple-comparison adjustments really are.
arxiv.org/abs/2511.12825
Recent numerical advances cracked the scalability barrier. Voxel-level hierarchical modeling is now feasible, revealing just how punishing traditional multiple-comparison adjustments really are.
arxiv.org/abs/2511.12825