2024
A 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 survey of brain functional network extraction methods using fMRI data
Du Y, Fang S, He X, Calhoun V. A survey of brain functional network extraction methods using fMRI data. Trends In Neurosciences 2024, 47: 608-621. PMID: 38906797, DOI: 10.1016/j.tins.2024.05.011.Peer-Reviewed Original ResearchGray matters: ViT-GAN framework for identifying schizophrenia biomarkers linking structural MRI and functional network connectivity
Bi Y, Abrol A, Jia S, Sui J, Calhoun V. Gray matters: ViT-GAN framework for identifying schizophrenia biomarkers linking structural MRI and functional network connectivity. NeuroImage 2024, 297: 120674. PMID: 38851549, DOI: 10.1016/j.neuroimage.2024.120674.Peer-Reviewed Original ResearchFunctional network connectivityMedial prefrontal cortexBrain structuresFunctional network connectivity matricesPrefrontal cortexStructural MRINetwork connectivityGray matterSelf-attention mechanismGenerative adversarial networkDeep learning architectureBrain disordersDorsolateral prefrontal cortexResearch of schizophreniaNeural signal processingIdentified functional connectivityCross-domain analysisAttention mapsStructural biomarkersAdversarial networkLearning architectureDL-PFCICA algorithmSchizophrenia patientsHigh-dimensional fMRI dataSearching 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 adaptationA Novel Deep Subspace Learning Framework to Automatically Uncover Assessment-Specific Independent Brain Networks
Batta I, Abrol A, Calhoun V. A Novel Deep Subspace Learning Framework to Automatically Uncover Assessment-Specific Independent Brain Networks. 2024, 00: 1-6. DOI: 10.1109/ciss59072.2024.10480204.Peer-Reviewed Original ResearchLearning frameworkBrain subsystemsSubspace learning frameworkBrain networksHigh-dimensional neuroimaging dataConvolutional neural networkLow-dimensional subspaceSupervised learning approachDeep learning frameworkStructural brain featuresPredictive performanceUnsupervised approachNeural networkAutomated frameworkDimensional subspaceAlzheimer's diseaseLearning approachBrain changesFeature importanceTraining procedureNeuroimaging dataBrain featuresSalient networkNetworkBrain disorders
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 brainsREGRESSION-ASSISTED INDEPENDENT VECTOR ANALYSIS: A SOLUTION TO LARGE-SCALE FMRI DATA ANALYSIS
Yang H, Gabrielson B, Calhoun V, Adali T. REGRESSION-ASSISTED INDEPENDENT VECTOR ANALYSIS: A SOLUTION TO LARGE-SCALE FMRI DATA ANALYSIS. 2023, 00: 1443-1447. DOI: 10.1109/ieeeconf59524.2023.10476796.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 edgesBrainTopological Characteristics of 5d Spatially Dynamic Brain Networks in Schizophrenia
Salman M, Iraji A, Lewis N, Calhoun V. Topological Characteristics of 5d Spatially Dynamic Brain Networks in Schizophrenia. 2023, 00: 1-5. DOI: 10.1109/isbi53787.2023.10230513.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingSchizophrenia patientsIntrinsic connectivity networksFMRI dataIndependent component analysisResting-state fMRI studiesAnalysis of fMRI dataSpatial independent component analysisHuman brain functionDynamic brain networksFMRI studyBrain networksBrain functionAberrant behaviorBrain disordersBrain statesSchizophreniaConnectivity networksMagnetic resonance imagingMulti-subject fMRI dataData-driven analysisResonance imagingDynamics of controlSpatial activityDisorders