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

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      Author(s)
      Mijalkov, M.
      Kakaei, E.
      Pereira, J. B.
      Westman, E.
      Volpe, G.
      Date
      2017
      Source Title
      PLoS ONE
      Electronic ISSN
      1932-6203
      Publisher
      Public Library of Science
      Volume
      12
      Issue
      8
      Pages
      1 - 23
      Language
      English
      Type
      Article
      Item Usage Stats
      262
      views
      196
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      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. © 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.
      Keywords
      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
      Permalink
      http://hdl.handle.net/11693/37018
      Published Version (Please cite this version)
      http://dx.doi.org/10.1371/journal.pone.0178798
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      • Institute of Materials Science and Nanotechnology (UNAM) 2098
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