Noah Lewis

This paper introduces TOIBN, a method that uses topological information from group-level brain networks to improve subject-level network estimation while preserving individual variability.

The paper introduces a voxel-centric framework using information theory to analyze multi-way brain network interactions, revealing reduced Hamming weights in auditory, visual, and motor networks in schizophrenia.

This study examines time-varying spatial propagation of brain networks in fMRI data and links propagative patterns to schizophrenia.

The paper uses a novel explainability framework to analyze sex differences in brain network structure using fMRI data, identifying modular and localized differences in temporal brain activity.

This study analyzes how cerebrovascular reactivity affects static and dynamic functional network connectivity using fMRI, finding weaker connectivity during vasodilation and incomplete recovery after CO2 inhalation.

The paper introduces a decentralized classification method that reduces network load while maintaining accuracy, applied to brain imaging data.