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dc.contributor.authorMijalkov, M.en_US
dc.contributor.authorKakaei, E.en_US
dc.contributor.authorPereira, J. B.en_US
dc.contributor.authorWestman, E.en_US
dc.contributor.authorVolpe, G.en_US
dc.date.accessioned2018-04-12T11:00:13Z
dc.date.available2018-04-12T11:00:13Z
dc.date.issued2017en_US
dc.identifier.urihttp://hdl.handle.net/11693/37018
dc.description.abstractThe 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.en_US
dc.language.isoEnglishen_US
dc.source.titlePLoS ONEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1371/journal.pone.0178798en_US
dc.subjectAdulten_US
dc.subjectAgeden_US
dc.subjectAlzheimer diseaseen_US
dc.subjectArticleen_US
dc.subjectBrain regionen_US
dc.subjectComputer interfaceen_US
dc.subjectConnectomeen_US
dc.subjectControlled studyen_US
dc.subjectData analysis softwareen_US
dc.subjectElectroencephalogramen_US
dc.subjectFemaleen_US
dc.subjectFunctional magnetic resonance imagingen_US
dc.subjectGraph theoryen_US
dc.subjectHumanen_US
dc.subjectInformation processingen_US
dc.subjectMajor clinical studyen_US
dc.subjectMaleen_US
dc.subjectMild cognitive impairmenten_US
dc.subjectNuclear magnetic resonance imagingen_US
dc.subjectParkinson diseaseen_US
dc.subjectPositron emission tomographyen_US
dc.subjectSoftware designen_US
dc.subjectAlgorithmen_US
dc.subjectAmnesiaen_US
dc.subjectBrainen_US
dc.subjectCase control studyen_US
dc.subjectCluster analysisen_US
dc.subjectCognitive defecten_US
dc.subjectCohort analysisen_US
dc.subjectConnectomeen_US
dc.subjectDiagnostic imagingen_US
dc.subjectElectroencephalographyen_US
dc.subjectImage processingen_US
dc.subjectNerve cell networken_US
dc.subjectNerve tracten_US
dc.subjectPathophysiologyen_US
dc.subjectPhysiologyen_US
dc.subjectProceduresen_US
dc.subjectSoftwareen_US
dc.subjectVery elderlyen_US
dc.subjectAgeden_US
dc.subjectAged, 80 and overen_US
dc.subjectAlgorithmsen_US
dc.subjectAlzheimer diseaseen_US
dc.subjectAmnesiaen_US
dc.subjectBrainen_US
dc.subjectCase-control studiesen_US
dc.subjectCluster analysisen_US
dc.subjectCognitive dysfunctionen_US
dc.subjectCohort studiesen_US
dc.subjectConnectomeen_US
dc.subjectElectroencephalographyen_US
dc.subjectFemaleen_US
dc.subjectHumansen_US
dc.subjectImage processing, computer-assisteden_US
dc.subjectMagnetic resonance imagingen_US
dc.subjectMaleen_US
dc.subjectNerve neten_US
dc.subjectNeural pathwaysen_US
dc.subjectParkinson diseaseen_US
dc.subjectPositron-emission tomographyen_US
dc.subjectSoftwareen_US
dc.titleBRAPH: A graph theory software for the analysis of brain connectivityen_US
dc.typeArticleen_US
dc.departmentUNAM - Institute of Materials Science and Nanotechnology
dc.citation.spage1en_US
dc.citation.epage23en_US
dc.citation.volumeNumber12en_US
dc.citation.issueNumber8en_US
dc.identifier.doi10.1371/journal.pone.0178798en_US
dc.publisherPublic Library of Scienceen_US
dc.identifier.eissn1932-6203en_US


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