Machine learning for NeuroImaging in Python
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Updated
Mar 24, 2026 - Python
Machine learning for NeuroImaging in Python
Frequency domain estimation and functional and directed connectivity analysis tools for electrophysiological data
A Reproducible Workflow for Structural and Functional Connectome Ensemble Learning
Regression Graph Neural Network (regGNN) for cognitive score prediction.
Python Machine Learning Toolbox for Brain Network Classification. Source codes are included of the top 20 teams in the Kaggle competition.
The Brain Activity Flow ("Actflow") Toolbox. Tools to quantify the relationship between connectivity and task activity through network simulations and machine learning prediction. Helps determine how connections contribute to specific brain functions.
How to fuse a population of graphs into a single one using graph neural networks?
Pythonic implementation of the Phase Transfer Entropy method using NumPy and SciPy
MGN-Net: A novel Graph Neural Network for integrating heterogenous graph population derived from multiple sources.
MultiGraphGAN for predicting multiple target graphs from a source graph using geometric deep learning.
Federated multigraph integration with application to connectional brain template estimation.
Federated time-dependent graph evolution prediction with missing timepoints.
netNorm (network normalization) framework for multi-view network integration (or fusion), recoded up in Python by Ahmed Nebli.
HCAE (HyperConnectome AutoEncoder) for brain state identification.
NAGFS (Network Atlas-Guided Feature Selection) for a fast and accurate graph data classification code, recoded by Dogu Can ELCI.
Brain Graph Super-Resolution: how to generate high-resolution graphs from low-resolution graphs? (Python3 version)
ABMT (Adversarial Brain Multiplex Translator) for brain graph translation using geometric generative adversarial network (gGAN).
Multigraph generation from a source graph.
Python package for analysis and visualization of functional ultrasound imaging data.
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