2024
A Trifecta of Deep Learning Models: Assessing Brain Health by Integrating Assessment and Neuroimaging Data
Ajith M, M. Aycock D, B. Tone E, Liu J, B. Misiura M, Ellis R, M. Plis S, Z. King T, M. Dotson V, Calhoun V. A Trifecta of Deep Learning Models: Assessing Brain Health by Integrating Assessment and Neuroimaging Data. Aperture Neuro 2024, 4 DOI: 10.52294/001c.118576.Peer-Reviewed Original ResearchStatic functional network connectivityBrain health indexBrain healthResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingPsychological assessment measuresAssessment dataFunctional network connectivityMental health disordersBrain systemsEvaluating brain healthNeuroimaging dataRs-fMRINeural patternsPhysical well-beingCognitive declineAssessment measuresHealth disordersVariational autoencoderNeuroimagingHealthy brainBrainMagnetic resonance imagingTesting phaseWell-beingMultimodal 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 subspacesA 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 disordersA Multi-dimensional Joint ICA Model with Gaussian Copula
Agcaoglu O, Silva R, Alacam D, Calhoun V. A Multi-dimensional Joint ICA Model with Gaussian Copula. Lecture Notes In Computer Science 2024, 14366: 152-163. DOI: 10.1007/978-3-031-51026-7_14.Peer-Reviewed Original ResearchIndependent component analysisBivariate distributionMarginal distributionsGaussian copulaLogistic distributionJoint ICAImage data miningSuper-Gaussian distributionImage datasetsFunctional magnetic resonance imaging datasetsInfomax principleAlzheimer's Disease Neuroimaging InitiativeProposed algorithmData miningIdentical marginalsMagnetic resonance imaging datasetICA modelMultimodal versionICA methodJoint independent component analysisCopulasDatasetMaximum likelihoodMixing matrixNeuroimaging data
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 brainsA 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 noiseSubjectsFusion of Multi-Modal Neuroimaging Data and Association With Cognitive Data
LoPresto M, Akhonda M, Calhoun V, Adali T. Fusion of Multi-Modal Neuroimaging Data and Association With Cognitive Data. 2023, 00: 1-5. DOI: 10.1109/icasspw59220.2023.10193147.Peer-Reviewed Original ResearchHopkins Verbal Learning TestNeuropsychological assessment batteryCognitive dataNeuroimaging modalitiesBrief Assessment of CognitionSchizophrenia composite scoreVerbal Learning TestAssessment of CognitionMultiple neuroimaging modalitiesBrief AssessmentCross-modal connectionsLearning TestAssessment batteryCross-modal relationshipsNeuroimaging dataBrain benefitsCognitive scoresComposite scoreCognitionData-driven analysisIndependent vector analysisSchizophreniaNeuropsychologyFusion frameworkScoresAging brain shows joint declines in brain within-network connectivity and between-network connectivity: a large-sample study (N > 6,000)
Du Y, Guo Y, Calhoun V. Aging brain shows joint declines in brain within-network connectivity and between-network connectivity: a large-sample study (N > 6,000). Frontiers In Aging Neuroscience 2023, 15: 1159054. PMID: 37273655, PMCID: PMC10233064, DOI: 10.3389/fnagi.2023.1159054.Peer-Reviewed Original ResearchFunctional network connectivityWithin-network connectivityCognitive control networkFunctional networksIncreased within-network connectivityDecreased within-network connectivityWhole-brain functional networksControl networkReduced functional network connectivityBetween-network connectivityWhole-brain levelNon-pathological agingSub-cortical networksIndependent component analysisBrain functional networksMode networkAging-related changesSensorimotor networkNeuroimaging dataConnectivity declineVisual networkWhole-brainBrain agingAging brainEffects of ageMulti-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 ResearchConceptsMood stabilizersMood disordersDSM-based diagnosesBipolar disorder patientsDepressive disorderDisorder patientsManic stateResponders to treatmentDepressive stateNeuroimaging dataMoodTreatment responseAntidepressantsDisordersDSMMedication classesComplex symptomsGold standardPatientsMDDSupport vector machineDiagnosisTreatmentComplex casesGeneralizabilityFederated Linear Mixed Effects Modeling for Voxel-Based Morphometry
Basodi S, Raja R, Gazula H, Romero J, Panta S, Calhoun V. Federated Linear Mixed Effects Modeling for Voxel-Based Morphometry. 2023, 00: 1-4. DOI: 10.1109/isbi53787.2023.10230684.Peer-Reviewed Original ResearchAn Adaptive Semi-Supervised Deep Clustering and Its Application to Identifying Biotypes of Psychiatric Disorders
Du Y, Wu F, Niu J, Calhoun V. An Adaptive Semi-Supervised Deep Clustering and Its Application to Identifying Biotypes of Psychiatric Disorders. 2023, 00: 1-4. DOI: 10.1109/isbi53787.2023.10230805.Peer-Reviewed Original ResearchFashion-MNIST dataDeep clustering methodsFunctional magnetic resonance imagingMNIST dataAutism spectrum disorderClustering methodPsychiatric disordersSemi-supervised clusteringPsychiatric disorder symptomsUnlabeled samplesClustering performanceDeep clusteringLabeled samplesDeep learningClustering techniqueDisorder symptomsSpectrum disorderNeuroimaging dataUseful informationSchizophreniaTraditional methodsMagnetic resonance imagingDisordersResonance imagingHigh confidence level