nilearn-fmri

What problem does it solve?

This Skill helps you analyze task-based and resting-state fMRI data end-to-end, producing statistical maps, functional connectivity, ICA components, and MVPA decoding results without stitching together many separate tools by hand.

Core Features & Use Cases

• Task fMRI GLM (First- and Second-Level): Build first-level design matrices, fit GLMs, compute z/t contrasts, and run group-level one-sample analyses.
• Resting-State Connectivity & Parcellation: Extract ROI time series from common atlases (Schaefer or AAL), compute connectivity matrices, and compare connectivity across groups.
• ICA + MVPA Decoding: Run CanICA for spatial ICA decomposition and support classification via SVM-based MVPA (including examples using FC-derived features).

Quick Start

Ask the AI to run a first-level GLM contrast on your NIfTI task fMRI data with events and confounds, then compute a group-level z-map and visualize the results.