This package enables users to generate biophysically realistic simulations of task-modulated fMRI time series with predefined task designs and customized synaptic weight matrices for each condition (task or rest).

HORME: Handler for the Observable Rotating Motion of the Encephalos

We provide both Matlab and Python versions of netNorm. In this folder you find the Maltab version of the code.

The analysis pipeline for our paper 'Functional connectivity fingerprints of the frontal eye field and inferior frontal junction suggest spatial versus nonspatial processing in the prefrontal cortex'.

Learning-guided Graph Dual Adversarial Domain Alignment (LG-DADA) framework for predicting a target graph from a source graph.

ML on FC matrices. Supporting code for diploma thesis at Charles Uni.

One-representative shot learning for graph classification.

PyPDC is a Python package to perform asymptotic Partial Directed Coherence estimations for brain connectivity analysis.

Graph SuperResolution Network using geometric deep learning.

Sparsely Reconstructed Graphs (SpaRG) for robust and interpretable fMRI analysis using GNNs and VAEs.

Repository for Masharipov, R. et al. Comparison of whole-brain task-modulated functional connectivity methods for fMRI task connectomics. Commun Biol 7, 1402 (2024). https://doi.org/10.1038/s42003-024-07088-3

Predicting the multi-trajectory evolution of multimodal brain connectivity.

Napari-based 3D cell segmentation software using machine learning predictions as inputs, especially for EM image stacks of brains

Multigraph fusion and classification network using graph neural network

Publicly available code for "Distributed network processes account for the majority of variance in localized visual category selectivity", Cocuzza et al., 2022.

Topology-guided cyclic graph generation using GCNs.

Publicly available code for "A flexible hub connectivity mechanism for cognitive control". Manuscript in-preparation. Part of C. Cocuzza's dissertation (Aim 3).

NAG-FS (Network Atlas-Guided Feature Selection) for a fast and accurate graph data classification.

Diffusion-based Graph Super-resolution