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

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

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

Atlas methods and classes for neuroimaging

Seed-based resting-state functional connectivity with Nilearn.

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

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

This repository explores functional brain connectivity using fMRI data and methods for constructing and visualizing functional connectomes. This work was developed by Ana Silva, Catarina Finuras, João Mata (me) and Tomás Serra from Técnico Lisboa.

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.