BRAPH: A graph theory software for the analysis of brain connectivity

TLDR

The brain operates as a large‑scale complex network whose topology can be examined with graph theory, yielding connectome‑based measures of structure and function. We created BRAPH, a free MATLAB toolbox for analyzing brain connectivity from MRI, fMRI, PET, and EEG data. BRAPH builds connectivity matrices, computes global and local network metrics, performs non‑parametric permutation tests, identifies modules, compares results to random networks, supports longitudinal analyses, and offers a modular, user‑friendly interface that can be extended. In one study, MRI data revealed distinct global and nodal network alterations among healthy controls, amnestic MCI, and Alzheimer’s patients; in a second study, resting‑state fMRI showed differences between healthy controls and Parkinson’s patients with mild cognitive impairment.

Abstract

The brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH–BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer's disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinson's patients with mild cognitive impairment.