Sample code in python for model identification is provided. Model identification is the process we use to identify structural connections related to functional connections assigned with a probability. The precision matrix is estimated based on the Ledoit-Wolf estimator.

This repository provides the code that could be used to train a neural network using a dataset comprising of structural and functional connectivity matrices. Refer to the paper for selection of the hyper-parameters.

Mar 14, 2025 · Seed-based resting-state functional connectivity with Nilearn. Simulations of different functional connectivity measures. This Repository is a part of my thesis for analyzing connectivity of EEG channels, but codes are extendable to other fields and studies.

siibra provides access to parcellation-averaged connectivity matrices. Several types of connectivity are supported. As of now, these include “StreamlineCounts”, “StreamlineLengths”, “FunctionalConnectivity”, and “AnatomoFunctionalConnectivity” (F-Tract). We start by selecting an atlas parcellation.

Here we show how to extract activation time-series to compute functional connectomes. 6.1.1. Time-series from a brain parcellation or “MaxProb” atlas ¶. 6.1.1.1. Brain parcellations ¶. Regions used to extract the signal can be defined by a “hard” parcellation.

Demonstration of how to generate functional connectivity maps with Neurosynth. Note that you can threshold the image to only show correlations above a certain value, and you can also download the correlation map to use as a mask.

Oct 1, 2020 · NeuroPycon is an open-source multi-modal brain data analysis toolkit which provides Python-based template pipelines for advanced multi-processing of MEG, EEG, functional and anatomical MRI data, with a focus on connectivity and graph theoretical analyses.

Oct 10, 2021 · In this article, we presented a python module, called dyconnmap, for static and dynamic brain network construction in multiple ways, mining time‐resolved function brain networks, network comparison and brain network classification. 1. INTRODUCTION.

In this notebook you will learn: - Download an open EEG dataset and explore the files - Pre-process the EEG signal with Medusa filters - Know the different phase-based connectivity methods - Know...

Brain connectivity measures are designed to probe how brain regions (or nodes) interact as a network. A distinction is made between structural (fiber pathways), functional (non-directed statistical associations) and effective (causal interactions, or "directed functional connectivity") connectivity between regions.