Oktay Agcaoglu

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.

This paper introduces pmg-jICA, a method that fuses structural and functional brain data to identify structure-function links in Alzheimer's disease, revealing disrupted brain regions and network connectivity.

The paper develops a method to estimate time-resolved spatial networks and applies it to study sex-specific schizophrenia disruptions and their genetic links.

This paper uses ml-jICA to analyze multimodal MRI data from Alzheimer's patients and controls, finding significant function/structure coupling in brain regions like the default mode network and hippocampus.

The paper analyzes resting-state fMRI data across different frequency bins to assess functional network connectivity and finds distinct patterns at higher frequencies.