nilearn

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

fMRI Imaging Analysis

Repository for the Brainhack School 2020 team working with fMRI and ABIDE data to train machine learning models.

Python extension backed by a multi-threaded Rust implementation of Dynamic Time Warping (DTW).

Introduction to neuroimaging machine learning tool Nilearn

group sequential tests for neuroimaging

Learn Python for neuroscience in 12 short Colab notebooks, from basics to real fMRI analysis.

Methods for estimating time-varying functional connectivity (TVFC)

plot fMRI ROIs with different colors

Applying CNNs, Decoders, and Transfer Learning to distinguish the MRIs of heavy cannabis users vs. controls

Kurs zu fMRT-Datenanalyse mit Python (Sommersemester 2019). Eigenständige Erstellung von MRT-Viewern und DIY-Analyse von fMRT-Zeitverläufen und Aktivierungskarten mit Python.

Atlas methods and classes for neuroimaging

Reproducible analysis of cortical geometry, structural connectivity, and resting-state functional connectivity in the Dallas Lifespan Brain Study

Seed-based resting-state functional connectivity with Nilearn.

A deep learning and neuroscience project for processing fMRI data. Uses Nilearn for functional connectivity mapping and PyTorch for model training, specifically optimized for Apple Silicon (M1/M2/M3) via MPS acceleration. Features automated preprocessing of 48x48 connectivity matrices into 2,304-feature vectors. Built with Python 3.13.

Analyzing functional brain connectivity and network efficiency from fMRI data using Graph Theory & Nilearn.

ASD classification from resting-state fMRI using functional brain connectivity using Random Forest and 3D brain visualisation on ABIDE I.

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