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

Mijalkov, M.
Kakaei, E.
Pereira, J. B.
Westman, E.
Volpe, G.
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Public Library of Science
1 - 23
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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. © 2017 Mijalkov et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Adult, Aged, Alzheimer disease, Article, Brain region, Computer interface, Connectome, Controlled study, Data analysis software, Electroencephalogram, Female, Functional magnetic resonance imaging, Graph theory, Human, Information processing, Major clinical study, Male, Mild cognitive impairment, Nuclear magnetic resonance imaging, Parkinson disease, Positron emission tomography, Software design, Algorithm, Amnesia, Brain, Case control study, Cluster analysis, Cognitive defect, Cohort analysis, Connectome, Diagnostic imaging, Electroencephalography, Image processing, Nerve cell network, Nerve tract, Pathophysiology, Physiology, Procedures, Software, Very elderly, Aged, Aged, 80 and over, Algorithms, Alzheimer disease, Amnesia, Brain, Case-control studies, Cluster analysis, Cognitive dysfunction, Cohort studies, Connectome, Electroencephalography, Female, Humans, Image processing, computer-assisted, Magnetic resonance imaging, Male, Nerve net, Neural pathways, Parkinson disease, Positron-emission tomography, Software
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