2025
Tensor dictionary-based heterogeneous transfer learning to study emotion-related gender differences in brain
Yang L, Qiao C, Kanamori T, Calhoun V, Stephen J, Wilson T, Wang Y. Tensor dictionary-based heterogeneous transfer learning to study emotion-related gender differences in brain. Neural Networks 2025, 183: 106974. DOI: 10.1016/j.neunet.2024.106974.Peer-Reviewed Original ResearchFeature spaceClassification performanceHeterogeneous transfer learningTensor dictionary learningHeterogeneous knowledge sharingTransfer learning frameworkReduce training costsDictionary learningKnowledge sharing strategyHeterogeneous transferGender classificationTransfer learningLearning frameworkConnectivity dataHeterogeneous dataHeterogeneous knowledgeBrain activity dataPriori knowledgeTraining costsSharing strategyProblem of insufficient sample sizeKnowledge sharingEEG dataExperimental resultsDictionary
2024
Large-Scale Independent Vector Analysis (IVA-G) via Coresets
Gabrielson B, Yang H, Vu T, Calhoun V, Adali T. Large-Scale Independent Vector Analysis (IVA-G) via Coresets. IEEE Transactions On Signal Processing 2024, PP: 1-13. DOI: 10.1109/tsp.2024.3517323.Peer-Reviewed Original ResearchJoint blind source separationIndependent vector analysisBlind source separationSubset selection methodJoint diagonalizationMultivariate Gaussian modelSource separationSignificant scalabilityComputational costCoresetMultiple datasetsSelection methodDatasetMeasure of discrepancyGaussian modelVector analysisNumerous extensionsScalabilityMethodConsistent frontal-limbic-occipital connections in distinguishing treatment-resistant and non-treatment-resistant schizophrenia
Zhang Y, Gao S, Liang C, Bustillo J, Kochunov P, Turner J, Calhoun V, Wu L, Fu Z, Jiang R, Zhang D, Jiang J, Wu F, Peng T, Xu X, Qi S. Consistent frontal-limbic-occipital connections in distinguishing treatment-resistant and non-treatment-resistant schizophrenia. NeuroImage Clinical 2024, 45: 103726. PMID: 39700898, DOI: 10.1016/j.nicl.2024.103726.Peer-Reviewed Original ResearchNon-treatment-resistant schizophreniaTreatment-resistant schizophreniaFunctional connectivityDiagnosis of SZHealthy controlsFrontal-parietalResting-state functional connectivityAutomated anatomical labelingDysfunctional brain connectivityBrain functional connectivityAffiliated Brain Hospital of Nanjing Medical UniversityFrontal limbBrain connectivitySchizophreniaMedication dosageTreatment resistanceNeural pathwaysNanjing Medical UniversityDisease progressionMedical UniversityClinical practiceSpecific biomarkersDiagnosisAnatomical labelingA multimodal Neuroimaging-Based risk score for mild cognitive impairment
Zendehrouh E, Sendi M, Abrol A, Batta I, Hassanzadeh R, Calhoun V. A multimodal Neuroimaging-Based risk score for mild cognitive impairment. NeuroImage Clinical 2024, 45: 103719. PMID: 39637673, DOI: 10.1016/j.nicl.2024.103719.Peer-Reviewed Original ResearchMild cognitive impairment riskMild cognitive impairmentMild cognitive impairment groupRisk of mild cognitive impairmentRisk scoreUK Biobank participantsFunctional network connectivityCognitive impairmentPrecursor to ADSignificant cognitive declineBiobank participantsUK BiobankMild cognitive impairment individualsGenetic risk factorsAlzheimer's diseaseFunctional MRIHigh-risk groupStructural MRIAD riskRisk factorsCognitive declineFeatures of CNGray matterDifferentiate CNParticipantsAnxiety symptoms are differentially associated with facial expression processing in boys and girls
Doucet G, Kruse J, Keefe A, Rice D, Coutant A, Pulliam H, Smith O, Calhoun V, Stephen J, Wang Y, White S, Picci G, Taylor B, Wilson T. Anxiety symptoms are differentially associated with facial expression processing in boys and girls. Social Cognitive And Affective Neuroscience 2024, 19: nsae085. PMID: 39587034, PMCID: PMC11631531, DOI: 10.1093/scan/nsae085.Peer-Reviewed Original ResearchFacial expression processingAssociated with psychiatric disordersExpression processingFacial expressionsFunctional magnetic resonance imagingFace processing taskMedial temporal cortexTypically-developing youthLevels of anxietyEmotional facesNeutral contrastAnxiety symptomsPosterior networkPsychiatric disordersFacial emotionsBrain responsesTemporal cortexNeural mechanismsHigher anxietyFMRI dataAnxietySocial informationAnxiety levelsBehavioral changesMagnetic resonance imagingA multimodal vision transformer for interpretable fusion of functional and structural neuroimaging data
Bi Y, Abrol A, Fu Z, Calhoun V. A multimodal vision transformer for interpretable fusion of functional and structural neuroimaging data. Human Brain Mapping 2024, 45: e26783. PMID: 39600159, PMCID: PMC11599617, DOI: 10.1002/hbm.26783.Peer-Reviewed Original ResearchConceptsCross-attention mechanismVision transformerDeep learning modelsBrain disordersCharacteristics of schizophreniaDiagnosis of schizophreniaStructural neuroimaging dataNetwork connectivity matrixData fusion approachAttention mapsMultimodal baselinesFunctional network connectivityFuse informationDeep learningICA algorithmFusion approachGrey matter mapsAI algorithmsFunctional network connectivity matricesLeverage multiple sources of informationGray matter imagesLearning modelsMultiple sources of informationBrain imaging modalitiesNetwork connectivityMultimodal predictive modeling: Scalable imaging informed approaches to predict future brain health
Ajith M, Spence J, Chapman S, Calhoun V. Multimodal predictive modeling: Scalable imaging informed approaches to predict future brain health. Journal Of Neuroscience Methods 2024, 414: 110322. PMID: 39608579, DOI: 10.1016/j.jneumeth.2024.110322.Peer-Reviewed Original ResearchStatic functional network connectivityHealth constructsNeuroimaging dataBrain healthResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingSupport vector regressionFunctional network connectivityRandom forestCognitive performanceAssessment-onlyRs-fMRINeural patternsBehavioral outcomesBehavioral dataDiverse data sourcesNeural connectionsPsychological stateTraining stageMagnetic resonance imagingLongitudinal changesNetwork connectivityBrainPerformance evaluationVector regressionNetworks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls
Kinsey S, Kazimierczak K, Camazón P, Chen J, Adali T, Kochunov P, Adhikari B, Ford J, van Erp T, Dhamala M, Calhoun V, Iraji A. Networks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls. Nature Mental Health 2024, 1-12. DOI: 10.1038/s44220-024-00341-y.Peer-Reviewed Original ResearchSelf-referential cognitionFunctional magnetic resonance imaging connectivityFunctional brain connectivityCingulo-opercularDefault-modeSchizophrenia diagnosisExecutive regionsFMRI connectivityFunctional connectivityConnectivity analysisSchizophreniaSensitive to differencesBrain connectivityFunctional connectivity structureWidespread alterationsImaging connectivityIndependent component analysisBrain phenomenaNetwork integrationHypoconnectivityPsychosisCognitionCore regionNonlinear networksCase-control datasetA Method for Multimodal IVA Fusion Within a MISA Unified Model Reveals Markers of Age, Sex, Cognition, and Schizophrenia in Large Neuroimaging Studies
Silva R, Damaraju E, Li X, Kochunov P, Ford J, Mathalon D, Turner J, van Erp T, Adali T, Calhoun V. A Method for Multimodal IVA Fusion Within a MISA Unified Model Reveals Markers of Age, Sex, Cognition, and Schizophrenia in Large Neuroimaging Studies. Human Brain Mapping 2024, 45: e70037. PMID: 39560198, PMCID: PMC11574741, DOI: 10.1002/hbm.70037.Peer-Reviewed Original ResearchConceptsMultimodal neuroimaging datasetSchizophrenia patientsNeuroimaging studiesCognitive performanceGroup differencesSchizophreniaSex effectsNeuroimaging datasetsMagnetic resonance imagingCognitionAge-associated declineControl subjectsMarkers of agingResonance imagingNon-imaging variablesSubject profilesSexNeuroimagingUK Biobank datasetAdolescent brain maturation associated with environmental factors: a multivariate analysis
Ray B, Jensen D, Suresh P, Thapaliya B, Sapkota R, Farahdel B, Fu Z, Chen J, Calhoun V, Liu J. Adolescent brain maturation associated with environmental factors: a multivariate analysis. Frontiers In Neuroimaging 2024, 3: 1390409. DOI: 10.3389/fnimg.2024.1390409.Peer-Reviewed Original ResearchLeft medial orbitofrontal cortexReduced gray matter densityMedial orbitofrontal cortexMiddle temporal gyrusGray matter densityIncreased functional connectivityIncrease cognitive performanceSusceptible to environmental influencesBilateral occipital regionsDelayed brain maturationIncreased cortical thicknessOrbitofrontal cortexAdolescent brainHuman adolescentsTemporal gyrusBrain characteristicsCognitive performanceBrain regionsBrain age estimationBrain metricsFunctional connectivityBrain agingBrain maturationBrain variationBrain modalitiesImaging‐genomic spatial‐modality attentive fusion for studying neuropsychiatric disorders
Rahaman A, Garg Y, Iraji A, Fu Z, Kochunov P, Hong L, Van Erp T, Preda A, Chen J, Calhoun V. Imaging‐genomic spatial‐modality attentive fusion for studying neuropsychiatric disorders. Human Brain Mapping 2024, 45: e26799. PMID: 39562310, PMCID: PMC11576332, DOI: 10.1002/hbm.26799.Peer-Reviewed Original ResearchConceptsNeural networkDilated convolutional neural networkJoint learning frameworkAttention scoresState-of-the-artDeep neural networksNeural network decisionsConvolutional neural networkAttention fusionFusion moduleDiverse data sourcesArtificial intelligence modelsLearning frameworkAttention moduleJoint learningMultimodal clusteringNetwork decisionsInput streamMultimodal learningHigh-dimensionalIntermediate fusionFused dataSZ classificationIntelligence modelsContextual patternsMultimodal MRI accurately identifies amyloid status in unbalanced cohorts in Alzheimer’s disease continuum
Dolci G, Ellis C, Cruciani F, Brusini L, Abrol A, Galazzo I, Menegaz G, Calhoun V. Multimodal MRI accurately identifies amyloid status in unbalanced cohorts in Alzheimer’s disease continuum. Network Neuroscience 2024, 1-21. DOI: 10.1162/netn_a_00423.Peer-Reviewed Original ResearchNeuropathological hallmarks of Alzheimer's diseaseHallmarks of Alzheimer's diseaseHyperphosphorylated tau proteinAmyloid-bTau proteinNeurofibrillary tanglesNeuropathological hallmarksAmyloid accumulationAlzheimer's diseaseAb accumulationDepositional signatureIdentification of individualsAmyloid statusAccumulationAmyloidShed lightTanglesAlzheimer's disease continuumProteinA spatially constrained independent component analysis jointly informed by structural and functional network connectivity
Fouladivanda M, Iraji A, Wu L, van Erp T, Belger A, Hawamdeh F, Pearlson G, Calhoun V. A spatially constrained independent component analysis jointly informed by structural and functional network connectivity. Network Neuroscience 2024, 1-31. DOI: 10.1162/netn_a_00398.Peer-Reviewed Original ResearchIntrinsic connectivity networksFunctional brain connectivityBrain connectivityStructural connectivityFunctional connectivityIndependent component analysisResting-state functional MRIAnalysis of group differencesBrain functional organizationFunctional network connectivityStructural-functional connectivityNeuroimaging studiesFunctional MRIWhole-brain tractographyGroup differencesRs-fMRIBrain disordersFunctional couplingSchizophreniaStatistical analysis of group differencesSubject levelFunctional organizationConnectivity networksBrainDiffusion-weighted MRIA simple but tough-to-beat baseline for fMRI time-series classification
Popov P, Mahmood U, Fu Z, Yang C, Calhoun V, Plis S. A simple but tough-to-beat baseline for fMRI time-series classification. NeuroImage 2024, 303: 120909. PMID: 39515403, DOI: 10.1016/j.neuroimage.2024.120909.Peer-Reviewed Original ResearchConceptsComplex machine learning modelsBlack-box natureMulti-layer perceptronMachine learning modelsPrediction accuracyBlack-box modelsFMRI classificationComplex classifiersClassification accuracySequential informationHuman fMRI dataLearning modelsBlack-boxRich modelsSuperior performanceComplex model developmentFMRI dataTime-series fMRI dataTime series dataClassifierStand-alone pieceClassificationAccuracyDesign modelSeries dataENIGMA-Meditation: Worldwide consortium for neuroscientific investigations of meditation practices
Ganesan S, Barrios F, Batta I, Bauer C, Braver T, Brewer J, Brown K, Cahn R, Cain J, Calhoun V, Cao L, Chetelat G, Ching C, Creswell J, Dagnino P, Davanger S, Davidson R, Deco G, Dutcher J, Escrichs A, Eyler L, Fani N, Farb N, Fialoke S, Fresco D, Garg R, Garland E, Goldin P, Hafeman D, Jahanshad N, Kang Y, Khalsa S, Kirlic N, Lazar S, Lutz A, McDermott T, Pagnoni G, Piguet C, Prakash R, Rahrig H, Reggente N, Saccaro L, Sacchet M, Siegle G, Tang Y, Thomopoulos S, Thompson P, Torske A, Treves I, Tripathi V, Tsuchiyagaito A, Turner M, Vago D, Valk S, Zeidan F, Zalesky A, Turner J, King A. ENIGMA-Meditation: Worldwide consortium for neuroscientific investigations of meditation practices. Biological Psychiatry Cognitive Neuroscience And Neuroimaging 2024 PMID: 39515581, DOI: 10.1016/j.bpsc.2024.10.015.Peer-Reviewed Original ResearchMeditation practiceMeditation interventionNeuroscientific investigationsNeuroimaging methodsNon-clinical populationsNeuroscientific mechanismsContemplative neuroscienceMega-analysesNeuroscientific modelsPsychological processesNeuroscientific accountsMental statesMind-body practicesNeuroscientific insightsCognitive attributesTherapeutic actionNeuroimaging datasetsMeditationNeuroimagingClinical scienceGeneralizabilityStatistical powerImprove statistical powerAddictionAnxietyFunctional imaging derived ADHD biotypes based on deep clustering: a study on personalized medication therapy guidance
Feng A, Zhi D, Feng Y, Jiang R, Fu Z, Xu M, Zhao M, Yu S, Stevens M, Sun L, Calhoun V, Sui J. Functional imaging derived ADHD biotypes based on deep clustering: a study on personalized medication therapy guidance. EClinicalMedicine 2024, 77: 102876. DOI: 10.1016/j.eclinm.2024.102876.Peer-Reviewed Original ResearchAttention deficit hyperactivity disorderAttention deficit hyperactivity disorder patientsFunctional network connectivityAttention Deficit Hyperactivity Disorder AdolescentTreatment of attention deficit hyperactivity disorderPeking University Sixth HospitalBackground Attention deficit hyperactivity disorderDeficit hyperactivity disorderTreated with methylphenidateADHD subtypesHyperactivity/impulsivity symptomsAdolescent brainMedical treatment effectsHyperactivity disorderCognitive performanceABCD studyProblem scoresNeuroimaging markersNeurodevelopmental disordersMedical treatmentChildhood onsetChina Postdoctoral Science FoundationNational Natural Science Foundation of ChinaNatural Science Foundation of ChinaPrimary medicationA telescopic independent component analysis on functional magnetic resonance imaging dataset
Mirzaeian S, Faghiri A, Calhoun V, Iraji A. A telescopic independent component analysis on functional magnetic resonance imaging dataset. Network Neuroscience 2024, 1-16. DOI: 10.1162/netn_a_00421.Peer-Reviewed Original ResearchRight frontoparietal networkVisual networkIndependent component analysisBrain functionExtraction of informationFunctional magnetic resonance imaging datasetsImage datasetsFrontoparietal networkMagnetic resonance imaging datasetFMRI dataGroup differencesLeverage informationSmall networksDMNNetworkComponent analysisIncomplete viewAbstract Brain functionFunctional sourceLinking neuroimaging and mental health data from the ABCD Study to UrbanSat measurements of macro environmental factors
Goldblatt R, Holz N, Tate G, Sherman K, Ghebremicael S, Bhuyan S, Al-Ajlouni Y, Santillanes S, Araya G, Abad S, Herting M, Thompson W, Thapaliya B, Sapkota R, Xu J, Liu J, Schumann G, Calhoun V. Linking neuroimaging and mental health data from the ABCD Study to UrbanSat measurements of macro environmental factors. Nature Mental Health 2024, 2: 1285-1297. DOI: 10.1038/s44220-024-00318-x.Peer-Reviewed Original ResearchSymptoms of mental illnessAdolescent Brain Cognitive DevelopmentResidential addressesAdolescent Brain Cognitive Development StudyMental illnessMental healthSubject's residential addressMental health dataDevelopmental periods of childhoodChild healthHealth dataEnvironmental factorsBaseline visitPeriod of childhoodObservational studyPopulation characteristicsHealthIndividual symptomsStudy dataUrban livingIllnessNeurobehavioral researchBrain structuresCognitive developmentAdolescentsBrain networks and intelligence: A graph neural network based approach to resting state fMRI data
Thapaliya B, Akbas E, Chen J, Sapkota R, Ray B, Suresh P, Calhoun V, Liu J. Brain networks and intelligence: A graph neural network based approach to resting state fMRI data. Medical Image Analysis 2024, 101: 103433. PMID: 37986729, PMCID: PMC10659448, DOI: 10.1016/j.media.2024.103433.Peer-Reviewed Original ResearchGraph neural networksNeural networkGraph isomorphism networkGraph convolutional layersGraph convolutional networkMachine learning modelsMean square errorNetwork connectivity matrixCognitive processesConvolutional layersConvolutional networkPrediction taskModel architectureGraph architectureAdolescent Brain Cognitive Development datasetResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingLearning modelsMiddle frontal gyrusPredicting individual differencesResting state fMRI dataPredictive intelligenceIntelligenceNetworkFunctional network connectivity matricesAssessing Pediatric Cognitive Development via Multisensory Brain Imaging Analysis
Belyaeva I, Wang Y, Wilson T, Calhoun V, Stephen J, Adali T. Assessing Pediatric Cognitive Development via Multisensory Brain Imaging Analysis. 2015 23rd European Signal Processing Conference (EUSIPCO) 2024, 1362-1366. DOI: 10.23919/eusipco63174.2024.10714926.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingFunctional magnetic resonance imaging dataMultisensory integrationSensory stimuliEffect of multisensory integrationMultisensory integration effectsMultiple sensory stimuliBrain imaging modalitiesCognitive developmentBrain image analysisBrain developmental patternsSensory modalitiesBrain componentsLearning paradigmMagnetoencephalographyMagnetic resonance imagingBrainDevelopmental patternsStimuliMultiple sensesCanonical polyadic tensor decompositionMultimodal data fusion frameworkAdolescentsMultitask learning paradigmPolyadic tensor decomposition