2019
Multi-Modal Signatures of Tau Pathology, Neuronal Fiber Integrity, and Functional Connectivity in Traumatic Brain Injury
Wooten D, Ortiz-Terán L, Zubcevik N, Zhang X, Huang C, Sepulcre J, Atassi N, Johnson K, Zafonte R, Fakhri G. Multi-Modal Signatures of Tau Pathology, Neuronal Fiber Integrity, and Functional Connectivity in Traumatic Brain Injury. Journal Of Neurotrauma 2019, 36: 3233-3243. PMID: 31210098, PMCID: PMC6857466, DOI: 10.1089/neu.2018.6178.Peer-Reviewed Original ResearchConceptsTraumatic brain injuryTraumatic brain injury subjectsDistribution volume ratioWhite matter integrityFunctional connectivityDiffusion tensor imagingPositron emission tomographyTBI subjectsResting-state functional magnetic resonance imagingResting state functional connectivityFunctional magnetic resonance imagingFractional anisotropyDecreased white matter integrityGroup of TBI subjectsIncreased functional connectivityFunctional MRI resultsGraph theory metricsBrain injuryLiving human brainMagnetic resonance imagingNoninvasive neuroimaging techniquesBrain regionsFunctional MRINeuroimaging techniquesDiffusion tractography imaging
2017
Partial volume correction for PET quantification and its impact on brain network in Alzheimer’s disease
Yang J, Hu C, Guo N, Dutta J, Vaina L, Johnson K, Sepulcre J, Fakhri G, Li Q. Partial volume correction for PET quantification and its impact on brain network in Alzheimer’s disease. Scientific Reports 2017, 7: 13035. PMID: 29026139, PMCID: PMC5638902, DOI: 10.1038/s41598-017-13339-7.Peer-Reviewed Original ResearchConceptsQuantitative accuracy of PET imagesSpatial resolution of PET scannersAccuracy of PET imagesPET scannerBrain networksPET imagingQuantitative accuracyPartial volume effectsClassification performanceImage registrationPositron emission tomography quantificationPartial volume correctionSpatial resolutionJoint entropyVolume correctionNetwork structure analysisCorrected imagesVolume effectClinical datasetsParameter settingsPositron emission tomographyClassification testsCompare network propertiesNoise sensitivityBrain circuit–gene expression relationships and neuroplasticity of multisensory cortices in blind children
Ortiz-Terán L, Diez I, Ortiz T, Perez D, Aragón J, Costumero V, Pascual-Leone A, Fakhri G, Sepulcre J. Brain circuit–gene expression relationships and neuroplasticity of multisensory cortices in blind children. Proceedings Of The National Academy Of Sciences Of The United States Of America 2017, 114: 6830-6835. PMID: 28607055, PMCID: PMC5495230, DOI: 10.1073/pnas.1619121114.Peer-Reviewed Original ResearchConceptsBlind childrenFunctional connectivity analysisMultisensory integration areasSensory deprivationFunctional connectivity relationshipsProtein gene familyNetwork connectivity profilesPrimary sensory cortexSystems-level analysisNeuroplastic adaptationsMultisensory cortexFunctional connectivityConnectivity analysisHuman neuroplasticityConnectivity changesGene expression profilesConnectivity profilesNeural reorganizationSensory cortexGene familySighted controlsHuman brainCortexBiological basisGenetic underpinnings
2014
Evaluating Structural Symmetry of Weighted Brain Networks via Graph Matching
Hu C, El Fakhri G, Li Q. Evaluating Structural Symmetry of Weighted Brain Networks via Graph Matching. Lecture Notes In Computer Science 2014, 17: 733-740. PMID: 25485445, DOI: 10.1007/978-3-319-10470-6_91.Peer-Reviewed Original ResearchConceptsAttention deficit hyperactivity disorderNetwork symmetryBrain networksType of Attention Deficit Hyperactivity DisorderWeighted brain networksConsistent with former findingsDeficit hyperactivity disorderRs-fMRI networksResting state fMRISymmetry levelInattentive typeHyperactivity disorderBrain areasSymmetryStructural symmetryBrain connectivityFunctional networksResting stateLarger thresholdsBrainFMRIHigher levelsGraph matchingNode pairsNormal subjects
2013
Matched Signal Detection on Graphs: Theory and Application to Brain Network Classification
Hu C, Cheng L, Sepulcre J, El Fakhri G, Lu Y, Li Q. Matched Signal Detection on Graphs: Theory and Application to Brain Network Classification. Lecture Notes In Computer Science 2013, 23: 1-12. PMID: 24683953, DOI: 10.1007/978-3-642-38868-2_1.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAlzheimer DiseaseAniline CompoundsBenzothiazolesBrainBrain MappingConnectomeHumansImage EnhancementImage Interpretation, Computer-AssistedNerve NetNeural PathwaysPattern Recognition, AutomatedPositron-Emission TomographyReproducibility of ResultsSensitivity and SpecificityThiazolesTissue DistributionConceptsBrain network classificationNetwork classification problemWeighted energy detectorPrinciple component analysisSub-manifold structureTraditional principle component analysisSubspace detectionTraining dataEnergy detectorGraph structureProblem of Alzheimer's diseaseGraph LaplacianNetwork classificationNoise varianceLevel of smoothnessWeighted graphSignal detectionIntrinsic structureSignal modelGraphSubspaceIsing modelNoiseSignal variationsComponent analysis