2024
A 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 dataDeficitsMore 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