2024
A Method for Multimodal IVA Fusion Within a MISA Unified Model Reveals Markers of Age, Sex, Cognition, and Schizophrenia in Large Neuroimaging Studies
Silva R, Damaraju E, Li X, Kochunov P, Ford J, Mathalon D, Turner J, van Erp T, Adali T, Calhoun V. A Method for Multimodal IVA Fusion Within a MISA Unified Model Reveals Markers of Age, Sex, Cognition, and Schizophrenia in Large Neuroimaging Studies. Human Brain Mapping 2024, 45: e70037. PMID: 39560198, PMCID: PMC11574741, DOI: 10.1002/hbm.70037.Peer-Reviewed Original ResearchConceptsMultimodal neuroimaging datasetSchizophrenia patientsNeuroimaging studiesCognitive performanceGroup differencesSchizophreniaSex effectsNeuroimaging datasetsMagnetic resonance imagingCognitionAge-associated declineControl subjectsMarkers of agingResonance imagingNon-imaging variablesSubject profilesSexNeuroimagingUK Biobank datasetENIGMA-Meditation: Worldwide consortium for neuroscientific investigations of meditation practices
Ganesan S, Barrios F, Batta I, Bauer C, Braver T, Brewer J, Brown K, Cahn R, Cain J, Calhoun V, Cao L, Chetelat G, Ching C, Creswell J, Dagnino P, Davanger S, Davidson R, Deco G, Dutcher J, Escrichs A, Eyler L, Fani N, Farb N, Fialoke S, Fresco D, Garg R, Garland E, Goldin P, Hafeman D, Jahanshad N, Kang Y, Khalsa S, Kirlic N, Lazar S, Lutz A, McDermott T, Pagnoni G, Piguet C, Prakash R, Rahrig H, Reggente N, Saccaro L, Sacchet M, Siegle G, Tang Y, Thomopoulos S, Thompson P, Torske A, Treves I, Tripathi V, Tsuchiyagaito A, Turner M, Vago D, Valk S, Zeidan F, Zalesky A, Turner J, King A. ENIGMA-Meditation: Worldwide consortium for neuroscientific investigations of meditation practices. Biological Psychiatry Cognitive Neuroscience And Neuroimaging 2024 PMID: 39515581, DOI: 10.1016/j.bpsc.2024.10.015.Peer-Reviewed Original ResearchMeditation practiceMeditation interventionNeuroscientific investigationsNeuroimaging methodsNon-clinical populationsNeuroscientific mechanismsContemplative neuroscienceMega-analysesNeuroscientific modelsPsychological processesNeuroscientific accountsMental statesMind-body practicesNeuroscientific insightsCognitive attributesTherapeutic actionNeuroimaging datasetsMeditationNeuroimagingClinical scienceGeneralizabilityStatistical powerImprove statistical powerAddictionAnxietyA Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia
Blair D, Miller R, Calhoun V. A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia. Entropy 2024, 26: 545. PMID: 39056908, PMCID: PMC11275472, DOI: 10.3390/e26070545.Peer-Reviewed Original ResearchSubjective trajectoriesBrain connectivity measuresPatient’s brain functionCognitive performancePsychiatric diseasesCourse of developmentBrain functionInformation theoryCortical hierarchyInformation processingConnectivity measuresSchizophreniaHealthy controlsDynamical systems theoryFunctional imagingTransit alterationsTransitionConnectivity statesPerspective of dynamical systemsStateTheoryEntropy generationDynamical systemsDynamicsNeuroimagingA 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-beingCortical similarities in psychiatric and mood disorders identified in federated VBM analysis via COINSTAC
Rootes-Murdy K, Panta S, Kelly R, Romero J, Quidé Y, Cairns M, Loughland C, Carr V, Catts S, Jablensky A, Green M, Henskens F, Kiltschewskij D, Michie P, Mowry B, Pantelis C, Rasser P, Reay W, Schall U, Scott R, Watkeys O, Roberts G, Mitchell P, Fullerton J, Overs B, Kikuchi M, Hashimoto R, Matsumoto J, Fukunaga M, Sachdev P, Brodaty H, Wen W, Jiang J, Fani N, Ely T, Lorio A, Stevens J, Ressler K, Jovanovic T, van Rooij S, Federmann L, Jockwitz C, Teumer A, Forstner A, Caspers S, Cichon S, Plis S, Sarwate A, Calhoun V. Cortical similarities in psychiatric and mood disorders identified in federated VBM analysis via COINSTAC. Patterns 2024, 5: 100987. PMID: 39081570, PMCID: PMC11284501, DOI: 10.1016/j.patter.2024.100987.Peer-Reviewed Original ResearchPsychiatric disordersStructural neuroimaging studiesPattern of gray matterAutism spectrum disorderGray matterDepressive disorderMood disordersNeuroimaging studiesNeuroanatomical basisSubcortical regionsGM alterationsSpectrum disorderVBM analysisMental illnessGM patternsDisordersCollaborative InformaticsSchizophreniaMoodNeuroimaging Suite ToolkitAutismNeuroimagingVulnerabilityLarge-scale dataDeficitsNeuroimaging 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 groupMore 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
Identifying Neuropsychiatric Disorder Subtypes and Subtype-Dependent Variation in Diagnostic Deep Learning Classifier Performance
Ellis C, Miller R, Calhoun V. Identifying Neuropsychiatric Disorder Subtypes and Subtype-Dependent Variation in Diagnostic Deep Learning Classifier Performance. 2023, 00: 1-4. DOI: 10.1109/isbi53787.2023.10230384.Peer-Reviewed Original ResearchClinical decision support systemsDynamic functional network connectivityDeep learning classifier’s performanceDisorder subtypesDeep learning classifierDecision support systemClassifier performanceLearning classifiersNetwork connectivityClassifierFunctional network connectivitySupport systemSchizophrenia subtypesStudy disordersPerformanceDisordersSchizophreniaSubtypesNeuropsychiatricSystemNeuroimagingSubtype-dependentCapabilityFunctional Network Connectivity Based Mental Health Category Prediction from Rest-fMRI Data
Ajith M, Calhoun V. Functional Network Connectivity Based Mental Health Category Prediction from Rest-fMRI Data. 2023, 00: 1-5. DOI: 10.1109/isbi53787.2023.10230721.Peer-Reviewed Original ResearchFunctional network connectivityMental healthResting fMRIStatic functional network connectivityMental health classesMental health categoriesBrain networksMental health scoresBehavioral changesBehavior modificationSignificant health issueBrainHealthy habitsHealth scoresHealth categoriesHealth classesHealth issuesIndividual levelFMRICategory predictionNetwork connectivityNeuroimagingHealthIndividualizing TMS treatment targets for PTSD using neuroimaging: Preliminary findings from an ongoing clinical trial
van Rooij S, Teye-Botchway L, Hinojosa C, Minton S, Job G, Riva-Posse P, Rauch S, Ressler K, Jovanovic T, Holtzheimer P, Calhoun V, Camprodon J, McDonald W. Individualizing TMS treatment targets for PTSD using neuroimaging: Preliminary findings from an ongoing clinical trial. Brain Stimulation 2023, 16: 3. DOI: 10.1016/j.brs.2023.03.020.Peer-Reviewed Original Research