Machine learning for NeuroImaging in Python

Workflows and interfaces for neuroimaging packages

Python package to access a cacophony of neuro-imaging file formats

fMRIPrep is a robust and easy-to-use pipeline for preprocessing of diverse fMRI data. The transparent workflow dispenses of manual intervention, thereby ensuring the reproducibility of the results.

Learn about the Neumorphic engineering process of creating large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures.

A toolbox for comparing brain maps

Graph theory analysis of brain MRI data

A NIfTI (nii.gz) 3D Visualizer using VTK and Qt5

Framework for the reproducible processing of neuroimaging data with deep learning methods

TE-dependent analysis of multi-echo fMRI

Automated anatomical brain label/shape analysis software (+ website)

BIDScoin converts your source-level neuroimaging data to BIDS

PyTorch Implementation of QuickNAT and Bayesian QuickNAT, a fast brain MRI segmentation framework with segmentation Quality control using structure-wise uncertainty

Easy to use web database for statistical maps.

Code supporting 'Geometric constraints on human brain function'

A python package which aligns histology to the Allen Brain Atlas and Waxholm rat atlas using deep learning.

The TemplateFlow Archive - A DataLad super-dataset

Public release of The Cole-Anticevic Brain-wide Network Partition (CAB-NP)

3D Unet Equipped with Advanced Deep Learning Methods

Useful tools from the Network Neuroscience Lab