A Reproducible Workflow for Structural and Functional Connectome Ensemble Learning

Easy and comprehensive assessment of predictive power, with support for neuroimaging features

Minimal scripts available for predictions on multiple datasets

Methods for estimating time-varying functional connectivity (TVFC)

Seed-based resting-state functional connectivity with Nilearn.

Bachelor thesis project analyzing fMRI connectivity and BOLD variability for CRCI biomarker discovery.

Drop-in extra features for Nilearn

Codes for pre-processing, dataloader creation, and Self-Supervised Learning evaluation over current ENIGMA-PTSD dataset. If queries about code or accessing the data contact Dr. Xi Zhu at xi.zhu@uta.edu

fMRI MVPA and searchlight analysis of emotion and language using Nilearn

Predicting risky behavior in structural brain volume using the UK Biobank

fMRI data analysis of long-covid patients