2017
Spatiotemporal Network Markers of Individual Variability in the Human Functional Connectome
Peña-Gómez C, Avena-Koenigsberger A, Sepulcre J, Sporns O. Spatiotemporal Network Markers of Individual Variability in the Human Functional Connectome. Cerebral Cortex 2017, 28: 2922-2934. PMID: 28981611, PMCID: PMC6041986, DOI: 10.1093/cercor/bhx170.Peer-Reviewed Original ResearchMeSH KeywordsAdultAlgorithmsBrainConnectomeDermatoglyphicsFemaleHumansIndividualityMaleNeural PathwaysReproducibility of ResultsYoung Adult
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
2011
Computational classifiers for predicting the short-term course of Multiple sclerosis
Bejarano B, Bianco M, Gonzalez-Moron D, Sepulcre J, Goñi J, Arcocha J, Soto O, Carro U, Comi G, Leocani L, Villoslada P. Computational classifiers for predicting the short-term course of Multiple sclerosis. BMC Neurology 2011, 11: 67. PMID: 21649880, PMCID: PMC3118106, DOI: 10.1186/1471-2377-11-67.Peer-Reviewed Original ResearchConceptsClinical end pointsCentral motor conduction timeMotor evoked potentialsMultiple sclerosisDiagnostic accuracyEnd pointsMRI lesion loadCourse of multiple sclerosisShort-term prognosisCourse of MSPrognosis of multiple sclerosisMotor conduction timeEDSS changeDisability progressionShort-term disabilityProspective cohortDisease courseClinical dataBaseline disabilityShort-term courseClinical variablesIndependent cohortMS patientsGray matter volumeBackgroundThe aim