2018
Neurogenetic profiles delineate large-scale connectivity dynamics of the human brain
Diez I, Sepulcre J. Neurogenetic profiles delineate large-scale connectivity dynamics of the human brain. Nature Communications 2018, 9: 3876. PMID: 30250030, PMCID: PMC6155203, DOI: 10.1038/s41467-018-06346-3.Peer-Reviewed Original ResearchConceptsWhole-brain functional connectivityCognitive statesDepression-related genesDynamic connectivity patternsLong-term potentiationResting stateSynaptic long-term potentiationNeurobiological basisGraph theory-based analysisHeteromodal cortexFunctional connectivityPrimary sensory areasNeural activityAttention areasConnectivity patternsConnectivity dynamicsHuman brainSensory areasGenetic transcription levelsDMNTheory-based analysisDynamic connectivityDynamic streamsLocal networkTask
2015
A Spectral Graph Regression Model for Learning Brain Connectivity of Alzheimer’s Disease
Hu C, Cheng L, Sepulcre J, Johnson K, Fakhri G, Lu Y, Li Q. A Spectral Graph Regression Model for Learning Brain Connectivity of Alzheimer’s Disease. PLOS ONE 2015, 10: e0128136. PMID: 26024224, PMCID: PMC4449104, DOI: 10.1371/journal.pone.0128136.Peer-Reviewed Original ResearchConceptsNetwork featuresAlzheimer's diseaseConsistent with known pathologyUnknown graphConnection weightsReconstruction networkCortical hubsDegree statisticsData modelSmooth signalsFeatures of brain pathologyOptimization frameworkAmyloid-bPartial correlation estimationImage dataNetworkGraphGlobal connectivity measuresPositron emission tomographyConnectivity measuresNeurodegenerative diseasesConnectivity patternsSample correlationClinical ADSimulated data
2011
Modeling the effector - regulatory T cell cross-regulation reveals the intrinsic character of relapses in Multiple Sclerosis
Vélez de Mendizábal N, Carneiro J, Solé R, Goñi J, Bragard J, Martinez-Forero I, Martinez-Pasamar S, Sepulcre J, Torrealdea J, Bagnato F, Garcia-Ojalvo J, Villoslada P. Modeling the effector - regulatory T cell cross-regulation reveals the intrinsic character of relapses in Multiple Sclerosis. BMC Systems Biology 2011, 5: 114. PMID: 21762505, PMCID: PMC3155504, DOI: 10.1186/1752-0509-5-114.Peer-Reviewed Original ResearchConceptsCross-regulationT cellsAutoimmune diseasesImmune systemMultiple sclerosisEffector T cellsRegulatory T cellsT cell memoryTissue damageEffects of such therapyPathogenesis of autoimmune diseasesT cell activationPredicting disease courseModulating effectorsBiological knowledgeMolecular mechanismsIrreversible tissue damageClinical relapseStochastic eventsRegulatory populationsCentral toleranceAutoimmune activityDisease courseNegative feedbackEffector
2008
A computational analysis of protein-protein interaction networks in neurodegenerative diseases
Goñi J, Esteban F, de Mendizábal N, Sepulcre J, Ardanza-Trevijano S, Agirrezabal I, Villoslada P. A computational analysis of protein-protein interaction networks in neurodegenerative diseases. BMC Systems Biology 2008, 2: 52. PMID: 18570646, PMCID: PMC2443111, DOI: 10.1186/1752-0509-2-52.Peer-Reviewed Original ResearchMeSH KeywordsAlzheimer DiseaseBrainComputational BiologyComputer SimulationGene Expression RegulationModels, BiologicalMultiple SclerosisProtein BindingConceptsProtein-protein interactionsAnalysis of protein-protein interaction networksProtein-protein interaction informationProtein-protein interaction networkAlzheimer's diseaseNeurodegenerative diseasesProtein-protein interaction analysisSeed proteinStudy of biological networksGene expression studiesDNA array experimentsProperties of proteinsMultifactorial neurodegenerative diseaseAD brainInteraction networkExpression studiesBiological networksGene expressionBackgroundRecent developmentsMolecular pathwaysGenesArray experimentsComputational analysisParameters of degreePathway