2018
Sequence Alterations of Cortical Genes Linked to Individual Connectivity of the Human Brain
Xin Q, Ortiz-Terán L, Diez I, Perez D, Ginsburg J, Fakhri G, Sepulcre J. Sequence Alterations of Cortical Genes Linked to Individual Connectivity of the Human Brain. Cerebral Cortex 2018, 29: 3828-3835. PMID: 30307489, PMCID: PMC6686751, DOI: 10.1093/cercor/bhy262.Peer-Reviewed Original ResearchMeSH KeywordsBrainBrain MappingGene Expression ProfilingGenetic ProfileHumansMagnetic Resonance ImagingNeural PathwaysTranscriptomeConceptsGenetic sequence alterationsGene expression profilesSequence alterationsGenetic expression dataExpression profilesBrain regionsFunctional connectivity variabilityCortical genesCortical regionsAllen Brain AtlasExpression dataGenetic variationGenetic signaturesFunctional connectivity magnetic resonance imagingCortical areasDiscrete cortical regionsGenesGraph theory analysisFunctional profilesNeurobiological underpinningsSequenceDegree of individual variabilityBrain areasBrain individualizationBrain organization
2017
Frontostriatal and Dopamine Markers of Individual Differences in Reinforcement Learning: A Multi-modal Investigation
Kaiser R, Treadway M, Wooten D, Kumar P, Goer F, Murray L, Beltzer M, Pechtel P, Whitton A, Cohen A, Alpert N, Fakhri G, Normandin M, Pizzagalli D. Frontostriatal and Dopamine Markers of Individual Differences in Reinforcement Learning: A Multi-modal Investigation. Cerebral Cortex 2017, 28: 4281-4290. PMID: 29121332, PMCID: PMC6454484, DOI: 10.1093/cercor/bhx281.Peer-Reviewed Original ResearchConceptsDA transporterRL behaviourVentral striatumDA clearanceFunctional connectivityBinding potentialLevel of neurocognitive functioningIncreased intrinsic functional connectivityMulti-modal neuroimaging studiesResting-state functional connectivityPhasic DA activityStriatal DA transporterIntrinsic functional connectivityReinforcement learning behaviorMulti-modal investigationsFrontostriatal circuitsFrontostriatal connectivityFrontostriatal regionsOrbitofrontal cortexDA activityNeuroimaging studiesOrbitofrontal regionsDopamine markersNeurocognitive functionDopamineTau and amyloid β proteins distinctively associate to functional network changes in the aging brain
Sepulcre J, Sabuncu M, Li Q, Fakhri G, Sperling R, Johnson K. Tau and amyloid β proteins distinctively associate to functional network changes in the aging brain. Alzheimer's & Dementia 2017, 13: 1261-1269. PMID: 28366797, PMCID: PMC5623176, DOI: 10.1016/j.jalz.2017.02.011.Peer-Reviewed Original ResearchConceptsAging brainFunctional connectivityAlzheimer's disease-related pathologyCognitively normal individualsPositron emission tomography scanHyperconnected regionsFunctional network changesMisfolded tauDisease-related pathologyBrain areasEmission tomography scanAmyloid-bFunctional reorganizationB proteinHuman brainNeuronal circuitsTauBrainNeuronal functionNegative associationAmyloidCortical patternsNetwork changesElderly subjectsPositive association
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