2024
More reliable biomarkers and more accurate prediction for mental disorders using a label-noise filtering-based dimensional prediction method
Xing Y, van Erp T, Pearlson G, Kochunov P, Calhoun V, Du Y. More reliable biomarkers and more accurate prediction for mental disorders using a label-noise filtering-based dimensional prediction method. IScience 2024, 27: 109319. PMID: 38482500, PMCID: PMC10933544, DOI: 10.1016/j.isci.2024.109319.Peer-Reviewed Original ResearchDiagnosis of mental disordersMental disordersDiagnostic labelsIntegration of neuroimagingSchizophrenia patientsNeuroimaging measuresNeuroimaging perspectiveFMRI dataStable abnormalitiesNeuroimagingDisordersHealthy controlsIndependent subjectsSchizophreniaFMRIDimensional predictionsSubjectsAccurate diagnosisClassification accuracy
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 brains