2024
Local-structure-preservation and redundancy-removal-based feature selection method and its application to the identification of biomarkers for schizophrenia
Xing Y, Pearlson G, Kochunov P, Calhoun V, Du Y. Local-structure-preservation and redundancy-removal-based feature selection method and its application to the identification of biomarkers for schizophrenia. NeuroImage 2024, 299: 120839. PMID: 39251116, PMCID: PMC11491165, DOI: 10.1016/j.neuroimage.2024.120839.Peer-Reviewed Original ResearchMeSH KeywordsAdultBiomarkersBrainFemaleHumansMagnetic Resonance ImagingMaleNeuroimagingSchizophreniaConceptsSelection methodClassification accuracy gainsGraph-based regularizationHigh-dimensional dataFeature selection methodLocal structural informationSparse regularizationAblation studiesFeature subsetPublic datasetsFeature selectionClassification accuracyExperimental evaluationAccuracy gainsSelection techniquesNetwork connectivityData transformationSuperior performanceDatasetConvergence analysisStructural informationClassificationRegularizationFeaturesDisorder predictionAssociations of alcohol and tobacco use with psychotic, depressive and developmental disorders revealed via multimodal neuroimaging
Qiu L, Liang C, Kochunov P, Hutchison K, Sui J, Jiang R, Zhi D, Vergara V, Yang X, Zhang D, Fu Z, Bustillo J, Qi S, Calhoun V. Associations of alcohol and tobacco use with psychotic, depressive and developmental disorders revealed via multimodal neuroimaging. Translational Psychiatry 2024, 14: 326. PMID: 39112461, PMCID: PMC11306356, DOI: 10.1038/s41398-024-03035-2.Peer-Reviewed Original ResearchConceptsFronto-limbic networkSalience networkAssociated with cognitionFronto-basal gangliaDevelopmental disordersBrain networksLimbic systemAlcohol useAssociated with alcohol useMultimodal brain networksTobacco useAssociation of alcoholPsychiatric disordersMultimodal neuroimagingDMNBrain featuresCognitionAlcohol/tobacco useDisordersAssociated with tobacco useDepressionSymptomsFunctional abnormalitiesAlcoholBrainSearching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities
Fu Z, Batta I, Wu L, Abrol A, Agcaoglu O, Salman M, Du Y, Iraji A, Shultz S, Sui J, Calhoun V. Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities. NeuroImage 2024, 292: 120617. PMID: 38636639, PMCID: PMC11416721, DOI: 10.1016/j.neuroimage.2024.120617.Peer-Reviewed Original ResearchConceptsFunctional MRIStructural MRIResting-state scanSpatial similarity analysisMental health researchBrain markersDiffusion MRIAge differencesBrain featuresNeuromarkersBrain disordersYoung adult cohortBrain developmentWell-replicatedHuman brainBrainDiffusion MRI dataData-driven analysisDisordersSimilarity analysisAge cohortsGeneralizabilityPopulation-based researchAdult cohortAge-specific adaptationMultimodal active subspace analysis for computing assessment oriented subspaces from neuroimaging data
Batta I, Abrol A, Calhoun V, Initiative A. Multimodal active subspace analysis for computing assessment oriented subspaces from neuroimaging data. Journal Of Neuroscience Methods 2024, 406: 110109. PMID: 38494061, PMCID: PMC11100582, DOI: 10.1016/j.jneumeth.2024.110109.Peer-Reviewed Original ResearchConceptsBrain subspacesStandard machine learning algorithmsHigh-dimensional neuroimaging dataTrain machine learning modelsUnsupervised decompositionMachine learning algorithmsMachine learning modelsFunctional sub-systemsSubspace analysisSubspace componentsLearning algorithmsSupervised approachBiological traitsLearning modelsSub-systemsAlzheimer's diseaseSubspaceComputational frameworkActive subspaceCross-validation procedureNeuroimaging dataAD-related brain regionsAutomated identificationPredictive performanceOrientation subspaces
2023
Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links
Fedorov A, Geenjaar E, Wu L, Sylvain T, DeRamus T, Luck M, Misiura M, Mittapalle G, Hjelm R, Plis S, Calhoun V. Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links. NeuroImage 2023, 285: 120485. PMID: 38110045, PMCID: PMC10872501, DOI: 10.1016/j.neuroimage.2023.120485.Peer-Reviewed Original ResearchConceptsBrain regionsMultimodal neuroimaging dataNeuroimaging dataBrain disordersComplex brain disordersMRI dataNeuroimaging researchGroup inferencesDeep InfoMaxSupervised modelsDiagnostic labelsDisordersBrainState-of-the-art unsupervised methodsAlzheimer's phenotypeNovel self-supervised frameworkSelf-supervised frameworkSelf-supervised methodologyCanonical correlation analysisSelf-supervised representationsState-of-the-artDeep learning approachSingle-modal dataMultimode linksComplex brainsChromatic fusion: Generative multimodal neuroimaging data fusion provides multi‐informed insights into schizophrenia
Geenjaar E, Lewis N, Fedorov A, Wu L, Ford J, Preda A, Plis S, Calhoun V. Chromatic fusion: Generative multimodal neuroimaging data fusion provides multi‐informed insights into schizophrenia. Human Brain Mapping 2023, 44: 5828-5845. PMID: 37753705, PMCID: PMC10619380, DOI: 10.1002/hbm.26479.Peer-Reviewed Original ResearchMeSH KeywordsBrainDiffusion Magnetic Resonance ImagingHumansMagnetic Resonance ImagingNeuroimagingSchizophreniaA Deep Learning Approach for Psychosis Spectrum Label Noise Detection from Multimodal Neuroimaging Data
Rokham H, Falakshahi H, Calhoun V. A Deep Learning Approach for Psychosis Spectrum Label Noise Detection from Multimodal Neuroimaging Data. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38082903, DOI: 10.1109/embc40787.2023.10339949.Peer-Reviewed Original ResearchConceptsStructural MRI dataResting-state functional MRI dataFunctional MRI dataFunctional magnetic resonance imaging dataMRI dataMagnetic resonance imaging dataSchizophrenia patientsFunctional connectivity featuresBrain imaging modalitiesMental disordersNeuroimaging dataNeuroimaging techniquesBorderline subjectsHealthy control groupSchizophrenia datasetSchizophreniaConnectivity featuresBrainPsychosisMoodNosologyControl groupDisordersLabel noiseSubjectsDecentralized Parallel Independent Component Analysis for Multimodal, Multisite Data
Panichvatana C, Chen J, Baker B, Thapaliya B, Calhoun V, Liu J. Decentralized Parallel Independent Component Analysis for Multimodal, Multisite Data. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083130, DOI: 10.1109/embc40787.2023.10340070.Peer-Reviewed Original ResearchTowards a multimodal neuroimaging-based risk score for Alzheimer’s disease by combining clinical and large N>37000 population data
Zendehrouh E, Sendi M, Calhoun V. Towards a multimodal neuroimaging-based risk score for Alzheimer’s disease by combining clinical and large N>37000 population data. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083709, DOI: 10.1109/embc40787.2023.10340414.Peer-Reviewed Original ResearchNetwork Differential in Gaussian Graphical Models from Multimodal Neuroimaging Data*
Falakshahi H, Rokham H, Miller R, Liu J, Calhoun V. Network Differential in Gaussian Graphical Models from Multimodal Neuroimaging Data*. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-6. PMID: 38083176, DOI: 10.1109/embc40787.2023.10340856.Peer-Reviewed Original ResearchConceptsStatic functional network connectivityGaussian graphical modelsBrain disordersBrain graphsModel of schizophreniaMiddle temporal gyrusMechanisms of brain disordersFunctional network connectivityGray matter featuresBrain network analysisTemporal gyrusGroup graphPath-based analysisCerebellar regionsGraph theory approachSchizophreniaMultimodal studiesGraphical modelsNetwork connectivityNetwork differentiationGray matterGraphical metricsControl graphPairwise edgesBrainMulti-study evaluation of neuroimaging-based prediction of medication class in mood disorders
Salman M, Verner E, Bockholt H, Fu Z, Misiura M, Baker B, Osuch E, Sui J, Calhoun V. Multi-study evaluation of neuroimaging-based prediction of medication class in mood disorders. Psychiatry Research Neuroimaging 2023, 333: 111655. PMID: 37201216, PMCID: PMC10330565, DOI: 10.1016/j.pscychresns.2023.111655.Peer-Reviewed Original ResearchMeSH KeywordsAntipsychotic AgentsBipolar DisorderDepressive Disorder, MajorHumansMood DisordersNeuroimagingConceptsMood stabilizersMood disordersDSM-based diagnosesBipolar disorder patientsDepressive disorderDisorder patientsManic stateResponders to treatmentDepressive stateNeuroimaging dataMoodTreatment responseAntidepressantsDisordersDSMMedication classesComplex symptomsGold standardPatientsMDDSupport vector machineDiagnosisTreatmentComplex casesGeneralizabilityA systematic review of neuroimaging epigenetic research: calling for an increased focus on development
Walton E, Baltramonaityte V, Calhoun V, Heijmans B, Thompson P, Cecil C. A systematic review of neuroimaging epigenetic research: calling for an increased focus on development. Molecular Psychiatry 2023, 28: 2839-2847. PMID: 37185958, PMCID: PMC10615743, DOI: 10.1038/s41380-023-02067-2.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentBrainChildDNA MethylationEpigenesis, GeneticHumansNeuroimagingProspective StudiesConceptsFunctional neuroimaging measuresBrain-based disordersBrain imaging measuresBrain alterationsNeuroimaging measuresBrain outcomesInvestigate environmental influencesBehavioral outcomesDevelopmental periodEnvironmental influencesSample characteristicsDNA methylationSystematically review evidenceBrainTime of lifeDisordersMeasurement of DNA methylationContext of healthImaging measurementsCandidate-gene approachEpigenetic mechanismsEpigenetic researchMeta-analysisDNA methylation markersYears of age