2024
Neuroimaging alterations and relapse in early-stage psychosis
Mihaljevic M, Nagpal A, Etyemez S, Narita Z, Ross A, Schaub R, Cascella N, Coughlin J, Nestadt G, Nucifora F, Sedlak T, Calhoun V, Faria A, Yang K, Sawa A. Neuroimaging alterations and relapse in early-stage psychosis. Journal Of Psychiatry And Neuroscience 2024, 49: e135-e142. PMID: 38569725, PMCID: PMC10980532, DOI: 10.1503/jpn.230115.Peer-Reviewed Original ResearchConceptsEarly-stage psychosisNeuroimaging alterationsResting-state functional MRI dataNo-relapse groupFunctional connectivity changesFunctional MRI dataHealthy controlsMagnetic resonance imagingRelapse groupPsychotic disordersFunctional connectivity estimatesBrain changesPsychotic eventsPsychosisConnectivity changesSymptom exacerbationConnectivity estimatesComparison correctionNo relapseLongitudinal studyThalamusNeuroimagingMRI dataClinical confounding factorsControl groupDynamic functional connectivity in anorexia nervosa: alterations in states of low connectivity and state transitions
Boehm I, Mennigen E, Geisler D, Poller N, Gramatke K, Calhoun V, Roessner V, King J, Ehrlich S. Dynamic functional connectivity in anorexia nervosa: alterations in states of low connectivity and state transitions. Journal Of Child Psychology And Psychiatry 2024, 65: 1299-1310. PMID: 38480007, DOI: 10.1111/jcpp.13970.Peer-Reviewed Original ResearchConceptsAnorexia nervosaFunctional connectivityResting state functional connectivityResting-state functional MRI dataInternalizing mental disordersAssociated with preoccupationOnset of anorexia nervosaFunctional MRI dataFemale healthy controlsHealthy controlsDynamic functional connectivityDynamics of functional connectivityTemporal dynamics of functional connectivityFunctional connectivity statesMental disordersStatic analytical approachesGroup differencesNervosaFractional timeMRI dataAdolescentsConnectivity statesFemale patientsClinical featuresTemporal dynamics
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 ResearchA 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 noiseSubjects