2024
Local-structure-preservation and redundancy-removal-based feature selection method and its application to the identification of biomarkers for schizophrenia
Xing Y, Pearlson G, Kochunov P, Calhoun V, Du Y. Local-structure-preservation and redundancy-removal-based feature selection method and its application to the identification of biomarkers for schizophrenia. NeuroImage 2024, 299: 120839. PMID: 39251116, DOI: 10.1016/j.neuroimage.2024.120839.Peer-Reviewed Original ResearchConceptsSelection methodClassification accuracy gainsGraph-based regularizationHigh-dimensional dataFeature selection methodLocal structural informationSparse regularizationAblation studiesFeature subsetPublic datasetsFeature selectionClassification accuracyExperimental evaluationAccuracy gainsSelection techniquesNetwork connectivityData transformationSuperior performanceDatasetConvergence analysisStructural informationClassificationRegularizationFeaturesDisorder predictionNeurodevelopmental subtypes of functional brain organization in the ABCD study using a rigorous analytic framework
DeRosa J, Friedman N, Calhoun V, Banich M. Neurodevelopmental subtypes of functional brain organization in the ABCD study using a rigorous analytic framework. NeuroImage 2024, 299: 120827. PMID: 39245397, DOI: 10.1016/j.neuroimage.2024.120827.Peer-Reviewed Original ResearchConceptsResting-state functional connectivityAdolescent Brain Cognitive DevelopmentIndividual’s resting-state functional connectivityAdolescent Brain Cognitive Development StudyFunctional brain organizationMental health profilesMental health characteristicsRsFC dataBrain organizationFunctional connectivityDevelopmental trajectoriesChildren aged 9Emotional functioningCognitive developmentLate childhoodAged 9SubtypesAdolescentsHealth characteristicsHealth profileChildhoodJoint multi-site domain adaptation and multi-modality feature selection for the diagnosis of psychiatric disorders
Ji Y, Silva R, Adali T, Wen X, Zhu Q, Jiang R, Zhang D, Qi S, Calhoun V. Joint multi-site domain adaptation and multi-modality feature selection for the diagnosis of psychiatric disorders. NeuroImage Clinical 2024, 43: 103663. PMID: 39226701, DOI: 10.1016/j.nicl.2024.103663.Peer-Reviewed Original ResearchAssociations of alcohol and tobacco use with psychotic, depressive and developmental disorders revealed via multimodal neuroimaging
Qiu L, Liang C, Kochunov P, Hutchison K, Sui J, Jiang R, Zhi D, Vergara V, Yang X, Zhang D, Fu Z, Bustillo J, Qi S, Calhoun V. Associations of alcohol and tobacco use with psychotic, depressive and developmental disorders revealed via multimodal neuroimaging. Translational Psychiatry 2024, 14: 326. PMID: 39112461, PMCID: PMC11306356, DOI: 10.1038/s41398-024-03035-2.Peer-Reviewed Original ResearchConceptsFronto-limbic networkSalience networkAssociated with cognitionFronto-basal gangliaDevelopmental disordersBrain networksLimbic systemAlcohol useAssociated with alcohol useMultimodal brain networksTobacco useAssociation of alcoholPsychiatric disordersMultimodal neuroimagingDMNBrain featuresCognitionAlcohol/tobacco useDisordersAssociated with tobacco useDepressionSymptomsFunctional abnormalitiesAlcoholBrain4D dynamic spatial brain networks at rest linked to cognition show atypical variability and coupling in schizophrenia
Pusuluri K, Fu Z, Miller R, Pearlson G, Kochunov P, Van Erp T, Iraji A, Calhoun V. 4D dynamic spatial brain networks at rest linked to cognition show atypical variability and coupling in schizophrenia. Human Brain Mapping 2024, 45: e26773. PMID: 39045900, PMCID: PMC11267451, DOI: 10.1002/hbm.26773.Peer-Reviewed Original ResearchConceptsBrain networksFunctional magnetic resonance imagingAssociated with cognitive performanceDynamics of functional brain networksAssociated with cognitionFunctional brain networksVoxel-wise changesVolumetric couplingDynamical variablesCognitive performanceTypical controlsSchizophreniaCognitive impairmentNetwork pairsMagnetic resonance imagingPair of networksCognitionAtypical variabilityResonance imagingCouplingNetwork connectivityNetwork growthImpairmentBrainStatic networksEffects of endogenous testosterone on oscillatory activity during verbal working memory in youth
Killanin A, Ward T, Embury C, Calhoun V, Wang Y, Stephen J, Picci G, Heinrichs‐Graham E, Wilson T. Effects of endogenous testosterone on oscillatory activity during verbal working memory in youth. Human Brain Mapping 2024, 45: e26774. PMID: 38949599, PMCID: PMC11215982, DOI: 10.1002/hbm.26774.Peer-Reviewed Original ResearchConceptsVerbal working memoryVerbal working memory processingWorking memory processesWorking memoryEffects of chronological ageEndogenous testosterone levelsMemory processesOscillatory activitySternberg verbal working memory taskEffects of testosteroneLeft-lateralized language networkVerbal working memory taskAlpha oscillationsSalivary testosterone samplesWorking memory encodingWorking memory taskLeft temporal cortexRight cerebellar cortexNeural oscillatory activitySignificant oscillatory responsesNeural oscillatory dynamicsHuman brain structureCerebellar cortexYouth aged 6Chronological ageEstimation of complete mutual information exploiting nonlinear magnitude-phase dependence: Application to spatial FNC for complex-valued fMRI data
Li W, Lin Q, Zhang C, Han Y, Li H, Calhoun V. Estimation of complete mutual information exploiting nonlinear magnitude-phase dependence: Application to spatial FNC for complex-valued fMRI data. Journal Of Neuroscience Methods 2024, 409: 110207. PMID: 38944128, DOI: 10.1016/j.jneumeth.2024.110207.Peer-Reviewed Original ResearchConceptsComplex-valued fMRI dataMutual informationJoint entropyNetwork connectivityComplex-valued signalsFunctional network connectivityMagnitude-phase dependenceDensity estimation methodMI estimationHistogram-basedKernel density estimation methodFMRI dataEstimation accuracyProbability density functionJoint probability density functionSimulated signalsChain rulePhase dependenceEstimation methodHigh-orderDensity functionControl networkInaccurate estimationNonlinear dependenceDependenceThe brain structure, inflammatory, and genetic mechanisms mediate the association between physical frailty and depression
Jiang R, Noble S, Rosenblatt M, Dai W, Ye J, Liu S, Qi S, Calhoun V, Sui J, Scheinost D. The brain structure, inflammatory, and genetic mechanisms mediate the association between physical frailty and depression. Nature Communications 2024, 15: 4411. PMID: 38782943, PMCID: PMC11116547, DOI: 10.1038/s41467-024-48827-8.Peer-Reviewed Original ResearchConceptsIncident depressionPre-frailPhysical frailtyFrail individualsPopulation attributable fraction analysisRisk factors of depressionMendelian randomization analysisFactors of depressionPotential causal effectModifiable risk factorsNon-frail individualsCross-sectional studyEffect of frailtyHigher disease burdenUK BiobankRandomization analysisBrain volumeDepression casesDisease burdenFrailtyRegional brain volumesIncreased riskDepressionHigh riskFollow-upA confounder controlled machine learning approach: Group analysis and classification of schizophrenia and Alzheimer’s disease using resting-state functional network connectivity
Hassanzadeh R, Abrol A, Pearlson G, Turner J, Calhoun V. A confounder controlled machine learning approach: Group analysis and classification of schizophrenia and Alzheimer’s disease using resting-state functional network connectivity. PLOS ONE 2024, 19: e0293053. PMID: 38768123, PMCID: PMC11104643, DOI: 10.1371/journal.pone.0293053.Peer-Reviewed Original ResearchConceptsResting-state functional network connectivityFunctional network connectivityResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingAlzheimer's diseaseClassification of schizophreniaNetwork pairsPatients to healthy controlsSchizophrenia patientsNeurobiological mechanismsSZ patientsSubcortical networksCerebellum networkSchizophreniaRs-fMRIDisorder developmentMotor networkCompare patient groupsSubcortical domainSZ disorderHealthy controlsMagnetic resonance imagingDisordersNetwork connectivityFunctional abnormalitiesCross‐cohort replicable resting‐state functional connectivity in predicting symptoms and cognition of schizophrenia
Zhao C, Jiang R, Bustillo J, Kochunov P, Turner J, Liang C, Fu Z, Zhang D, Qi S, Calhoun V. Cross‐cohort replicable resting‐state functional connectivity in predicting symptoms and cognition of schizophrenia. Human Brain Mapping 2024, 45: e26694. PMID: 38727014, PMCID: PMC11083889, DOI: 10.1002/hbm.26694.Peer-Reviewed Original ResearchConceptsFunctional magnetic resonance imagingNegative symptomsFunctional connectivityCognitive impairmentPrediction of negative symptomsResting-state functional connectivityAssociated with reduced cognitive functionDebilitating mental illnessHealthy controlsPredicting functional connectivityEarly adulthood onsetPositive symptomsNeural underpinningsSchizophreniaCognitive functionSensorimotor networkPredicting symptomsMental illnessConnectivity patternsClinical interventionsMagnetic resonance imagingAdulthood onsetSymptomsImpairmentResonance imagingMRI morphometry of the anterior and posterior cerebellar vermis and its relationship to sensorimotor and cognitive functions in children
Hodgdon E, Anderson R, Al Azzawi H, Wilson T, Calhoun V, Wang Y, Solis I, Greve D, Stephen J, Ciesielski K. MRI morphometry of the anterior and posterior cerebellar vermis and its relationship to sensorimotor and cognitive functions in children. Developmental Cognitive Neuroscience 2024, 67: 101385. PMID: 38713999, PMCID: PMC11096723, DOI: 10.1016/j.dcn.2024.101385.Peer-Reviewed Original ResearchConceptsLobules I-VCognitive functionCerebellar vermis volumePosterior brain structuresCerebellar vermisVI-VIIAnterior cerebellar vermisLobules VI-VIIWASI-IIEmotional processingVermis volumeNeuropsychological scoresPosterior cerebellar vermisBrain structuresDevelopmental trajectoriesMRI morphometryCerebellar anatomyPosterior vermisSensorimotorDevelopmental studiesHuman cerebellumHealthy adultsAdult volumeVermisHigh-resolution MRIThe overlap across psychotic disorders: A functional network connectivity analysis
Dini H, Bruni L, Ramsøy T, Calhoun V, Sendi M. The overlap across psychotic disorders: A functional network connectivity analysis. International Journal Of Psychophysiology 2024, 201: 112354. PMID: 38670348, PMCID: PMC11163820, DOI: 10.1016/j.ijpsycho.2024.112354.Peer-Reviewed Original ResearchConceptsFunctional network connectivitySchizoaffective disorderPsychotic disordersHealthy controlsBipolar-Schizophrenia NetworkFunctional network connectivity analysisStatic functional network connectivityResting-state fMRINetwork connectivity analysisPatterns of activityPsychiatric disordersDisorder groupSchizophreniaConnectivity analysisHC groupBipolarConnectivity patternsDisordersPatient groupSymptom scoresGroup of patientsPANSSSchizoaffectiveFMRINetwork connectivitySearching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities
Fu Z, Batta I, Wu L, Abrol A, Agcaoglu O, Salman M, Du Y, Iraji A, Shultz S, Sui J, Calhoun V. Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities. NeuroImage 2024, 292: 120617. PMID: 38636639, DOI: 10.1016/j.neuroimage.2024.120617.Peer-Reviewed Original ResearchConceptsFunctional MRIStructural MRIResting-state scanSpatial similarity analysisMental health researchBrain markersDiffusion MRIAge differencesBrain featuresNeuromarkersBrain disordersYoung adult cohortBrain developmentWell-replicatedHuman brainBrainDiffusion MRI dataData-driven analysisDisordersSimilarity analysisAge cohortsGeneralizabilityPopulation-based researchAdult cohortAge-specific adaptationMultimodal 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 subspacesSMART (Splitting-Merging Assisted Reliable) Independent Component Analysis for Extracting Accurate Brain Functional Networks
He X, Calhoun V, Du Y. SMART (Splitting-Merging Assisted Reliable) Independent Component Analysis for Extracting Accurate Brain Functional Networks. Neuroscience Bulletin 2024, 40: 905-920. PMID: 38491231, DOI: 10.1007/s12264-024-01184-4.Peer-Reviewed Original ResearchConceptsIndependent component analysisFunctional magnetic resonance imagingClustering independent componentsFunctional networksIndependent component analysis methodMulti-subject fMRI dataIndependent componentsBrain functional networksFMRI dataSubject-specific functional networksFunctional magnetic resonance imaging dataOptimal model orderSmartComponent analysis
2023
Dehydroepiandrosterone mediates associations between trauma‐related symptoms and anterior pituitary volume in children and adolescents
Picci G, Casagrande C, Ott L, Petro N, Christopher‐Hayes N, Johnson H, Willett M, Okelberry H, Wang Y, Stephen J, Calhoun V, Wilson T. Dehydroepiandrosterone mediates associations between trauma‐related symptoms and anterior pituitary volume in children and adolescents. Human Brain Mapping 2023, 44: 6388-6398. PMID: 37853842, PMCID: PMC10681633, DOI: 10.1002/hbm.26516.Peer-Reviewed Original ResearchConceptsTrauma-related symptomsAnxiety symptomsPituitary volumeAnterior pituitary volumeConsistent with adult findingsHigh-resolution T1-weighted MRI scansPG volumeEarly life stressLevels of dehydroepiandrosteroneT1-weighted MRI scansDehydroepiandrosterone levelsPituitary glandResponse to stressComorbid anxietyNeurobiological sequelaeSubclinical anxietyLife stressAnxiety/depression symptomsElevated anxietyAdult findingsAssociated with disruptionDevelopmental sampleHealthy youthTrained ratersPubertal windowDevelopmental changes in endogenous testosterone have sexually‐dimorphic effects on spontaneous cortical dynamics
Picci G, Ott L, Penhale S, Taylor B, Johnson H, Willett M, Okelberry H, Wang Y, Calhoun V, Stephen J, Wilson T. Developmental changes in endogenous testosterone have sexually‐dimorphic effects on spontaneous cortical dynamics. Human Brain Mapping 2023, 44: 6043-6054. PMID: 37811842, PMCID: PMC10619376, DOI: 10.1002/hbm.26496.Peer-Reviewed Original ResearchConceptsCortical activityRobust sex differencesFunctional brain developmentTypically-developing youthSpontaneous cortical activityLowest relative powerCortical dynamicsEndogenous testosteronePrefrontal cortexExecutive functionFrontal cortexPubertal hormonesNeural circuitryResting-stateStructural MRIEffects of testosteroneSex differencesEffects of endogenous testosteroneDevelopmental changesBrain developmentReverse patternBehavioral changesGamma activityDevelopmental windowDevelopmental patternsAutomated Interpretation of Clinical Electroencephalograms Using Artificial Intelligence
Tveit J, Aurlien H, Plis S, Calhoun V, Tatum W, Schomer D, Arntsen V, Cox F, Fahoum F, Gallentine W, Gardella E, Hahn C, Husain A, Kessler S, Kural M, Nascimento F, Tankisi H, Ulvin L, Wennberg R, Beniczky S. Automated Interpretation of Clinical Electroencephalograms Using Artificial Intelligence. JAMA Neurology 2023, 80: 805-812. PMID: 37338864, PMCID: PMC10282956, DOI: 10.1001/jamaneurol.2023.1645.Peer-Reviewed Original ResearchConceptsPublishing AI modelsAI modelsArtificial intelligenceTesting data setsHuman expertsAutomated interpretationConvolutional neural network modelHuman expert level performanceElectroencephalogram data setsData setsRoutine electroencephalogramNeural network modelExpert-level performanceMulticenter diagnostic accuracy studyReference standardAbnormal EEG recordingsVideo-EEG recordingsDetection of epileptiform abnormalitiesRecords of patientsReceiver operating characteristic curveSingle-center dataArea under the receiver operating characteristic curveDevelopment dataDiagnostic accuracy studiesNetwork modelDevelopmental differences in functional organization of multispectral networks
Petro N, Picci G, Embury C, Ott L, Penhale S, Rempe M, Johnson H, Willett M, Wang Y, Stephen J, Calhoun V, Doucet G, Wilson T. Developmental differences in functional organization of multispectral networks. Cerebral Cortex 2023, 33: 9175-9185. PMID: 37279931, PMCID: PMC10505424, DOI: 10.1093/cercor/bhad193.Peer-Reviewed Original ResearchConceptsSpontaneous cortical activityPhase coherenceImaginary partFunctional magnetic resonance imaging measuresConsistent with previous workFunctional brain organizationLimbic cortical regionsFunctional organizationBrain connectivityFunction of increasing ageEyes-closed restGamma bandSpectral specificityConnectivity matrixBrain regionsCanonical deltaBrain organizationBrain activityFunctional connectivityDevelopmental differencesBandCortical regionsCanonical networksCortical activityMagnetic resonance imaging measuresLarge-scale analysis of structural brain asymmetries in schizophrenia via the ENIGMA consortium
Schijven D, Postema M, Fukunaga M, Matsumoto J, Miura K, de Zwarte S, van Haren N, Cahn W, Pol H, Kahn R, Ayesa-Arriola R, de la Foz V, Tordesillas-Gutierrez D, Vázquez-Bourgon J, Crespo-Facorro B, Alnæs D, Dahl A, Westlye L, Agartz I, Andreassen O, Jönsson E, Kochunov P, Bruggemann J, Catts S, Michie P, Mowry B, Quidé Y, Rasser P, Schall U, Scott R, Carr V, Green M, Henskens F, Loughland C, Pantelis C, Weickert C, Weickert T, de Haan L, Brosch K, Pfarr J, Ringwald K, Stein F, Jansen A, Kircher T, Nenadić I, Krämer B, Gruber O, Satterthwaite T, Bustillo J, Mathalon D, Preda A, Calhoun V, Ford J, Potkin S, Chen J, Tan Y, Wang Z, Xiang H, Fan F, Bernardoni F, Ehrlich S, Fuentes-Claramonte P, Garcia-Leon M, Guerrero-Pedraza A, Salvador R, Sarró S, Pomarol-Clotet E, Ciullo V, Piras F, Vecchio D, Banaj N, Spalletta G, Michielse S, van Amelsvoort T, Dickie E, Voineskos A, Sim K, Ciufolini S, Dazzan P, Murray R, Kim W, Chung Y, Andreou C, Schmidt A, Borgwardt S, McIntosh A, Whalley H, Lawrie S, du Plessis S, Luckhoff H, Scheffler F, Emsley R, Grotegerd D, Lencer R, Dannlowski U, Edmond J, Rootes-Murdy K, Stephen J, Mayer A, Antonucci L, Fazio L, Pergola G, Bertolino A, Díaz-Caneja C, Janssen J, Lois N, Arango C, Tomyshev A, Lebedeva I, Cervenka S, Sellgren C, Georgiadis F, Kirschner M, Kaiser S, Hajek T, Skoch A, Spaniel F, Kim M, Bin Kwak Y, Oh S, Kwon J, James A, Bakker G, Knöchel C, Stäblein M, Oertel V, Uhlmann A, Howells F, Stein D, Temmingh H, Diaz-Zuluaga A, Pineda-Zapata J, López-Jaramillo C, Homan S, Ji E, Surbeck W, Homan P, Fisher S, Franke B, Glahn D, Gur R, Hashimoto R, Jahanshad N, Luders E, Medland S, Thompson P, Turner J, van Erp T, Francks C. Large-scale analysis of structural brain asymmetries in schizophrenia via the ENIGMA consortium. Proceedings Of The National Academy Of Sciences Of The United States Of America 2023, 120: e2213880120. PMID: 36976765, PMCID: PMC10083554, DOI: 10.1073/pnas.2213880120.Peer-Reviewed Original Research