Human Neuroscience

INCF Portal

The INCF portal is the guide to the INCF activities and its community resources. INCF advances data reuse and reproducibility in brain research by coordinating the development of Open, FAIR, and Citable tools and resources for neuroscience

INCF TrainingSpace

TrainingSpace is an online hub that aims to make neuroscience educational materials more accessible to the global neuroscience community developed by the Training and Education Committee composed of members from the INCF network, HBP, SfN, FENS, IBRO, IEEE, BD2K, and iNeuro Initiative.

Open Source Brain

Open Source Brain, a platform for sharing, viewing, analyzing, and simulating standardized models from different brain regions and species. Model structure and parameters can be automatically visualized and their dynamical properties explored through browser-based simulations.

DIPY

DIPY is the paragon 3D/4D+ imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.

OpenNeuro

A free and open platform for sharing MRI, MEG, EEG, iEEG, and ECoG data. With OpenNeuro, you can: Browse and explore public datasets and analyses from a wide range of global contributors.

MNE-Python

MNE is a software package for processing electrophysiological signals primarily from magnetoencephalographic (MEG) and electroencephalographic (EEG) recordings, and more recently sEEG, ECoG and fNIRS. It provides a comprehensive solution for data preprocessing, forward modeling (with boundary element models), distributed source imaging, time–frequency analysis, non-parametric multivariate statistics, multivariate pattern analysis, and connectivity estimation.

Nilearn

Nilearn is a Python module for fast and easy statistical learning on NeuroImaging data. It leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.