2024
A multimodal vision transformer for interpretable fusion of functional and structural neuroimaging data
Bi Y, Abrol A, Fu Z, Calhoun V. A multimodal vision transformer for interpretable fusion of functional and structural neuroimaging data. Human Brain Mapping 2024, 45: e26783. PMID: 39600159, PMCID: PMC11599617, DOI: 10.1002/hbm.26783.Peer-Reviewed Original ResearchConceptsCross-attention mechanismVision transformerDeep learning modelsBrain disordersCharacteristics of schizophreniaDiagnosis of schizophreniaStructural neuroimaging dataNetwork connectivity matrixData fusion approachAttention mapsMultimodal baselinesFunctional network connectivityFuse informationDeep learningICA algorithmFusion approachGrey matter mapsAI algorithmsFunctional network connectivity matricesLeverage multiple sources of informationGray matter imagesLearning modelsMultiple sources of informationBrain imaging modalitiesNetwork connectivityA 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 datasetImaging‐genomic spatial‐modality attentive fusion for studying neuropsychiatric disorders
Rahaman A, Garg Y, Iraji A, Fu Z, Kochunov P, Hong L, Van Erp T, Preda A, Chen J, Calhoun V. Imaging‐genomic spatial‐modality attentive fusion for studying neuropsychiatric disorders. Human Brain Mapping 2024, 45: e26799. PMID: 39562310, PMCID: PMC11576332, DOI: 10.1002/hbm.26799.Peer-Reviewed Original ResearchConceptsNeural networkDilated convolutional neural networkJoint learning frameworkAttention scoresState-of-the-artDeep neural networksNeural network decisionsConvolutional neural networkAttention fusionFusion moduleDiverse data sourcesArtificial intelligence modelsLearning frameworkAttention moduleJoint learningMultimodal clusteringNetwork decisionsInput streamMultimodal learningHigh-dimensionalIntermediate fusionFused dataSZ classificationIntelligence modelsContextual patternsA simple but tough-to-beat baseline for fMRI time-series classification
Popov P, Mahmood U, Fu Z, Yang C, Calhoun V, Plis S. A simple but tough-to-beat baseline for fMRI time-series classification. NeuroImage 2024, 303: 120909. PMID: 39515403, PMCID: PMC11625415, DOI: 10.1016/j.neuroimage.2024.120909.Peer-Reviewed Original ResearchConceptsComplex machine learning modelsBlack-box natureMulti-layer perceptronMachine learning modelsPrediction accuracyBlack-box modelsFMRI classificationComplex classifiersClassification accuracySequential informationHuman fMRI dataLearning modelsBlack-boxRich modelsSuperior performanceComplex model developmentFMRI dataTime-series fMRI dataTime series dataClassifierStand-alone pieceClassificationAccuracyDesign modelSeries dataInterplay between preclinical indices of obesity and neural signatures of fluid intelligence in youth
Ward T, Schantell M, Dietz S, Ende G, Rice D, Coutant A, Arif Y, Wang Y, Calhoun V, Stephen J, Heinrichs-Graham E, Taylor B, Wilson T. Interplay between preclinical indices of obesity and neural signatures of fluid intelligence in youth. Communications Biology 2024, 7: 1285. PMID: 39379610, PMCID: PMC11461743, DOI: 10.1038/s42003-024-06924-w.Peer-Reviewed Original ResearchConceptsAbstract reasoning taskFluid intelligenceAbstract reasoningBrain regionsNeural activityReasoning tasksLeft dorsolateral prefrontal cortexLeft temporoparietal junctionDorsolateral prefrontal cortexHigher-order cognitionWhole-brain correlationHigh-density magnetoencephalographySignificant oscillatory responsesYouth aged 9Prefrontal cortexTemporoparietal junctionNeural signaturesTheta oscillationsResponse scaleWhole-brainNeurobehavioral functionNeural dynamicsAged 9CognitionReaction timeExploring nonlinear dynamics in brain functionality through phase portraits and fuzzy recurrence plots
Li Q, Calhoun V, Pham T, Iraji A. Exploring nonlinear dynamics in brain functionality through phase portraits and fuzzy recurrence plots. Chaos An Interdisciplinary Journal Of Nonlinear Science 2024, 34: 103123. PMID: 39393183, DOI: 10.1063/5.0203926.Peer-Reviewed Original ResearchConceptsFuzzy recurrence plotsPhase portraitsComplex brain networksConnectivity descriptorsLow-dimensional dynamicsField of statistical physicsNonlinear dynamicsNeural mass modelMass modelRecurrence plotsStatistical physicsNeural time seriesFunctional connectivityLimit cycle attractorNonlinear phenomenaHidden informationComplex networksLatent informationPhase trajectoriesHigh-dimensionalDynamical theoryBrain functional connectivityBrain connectivityBrain networksNeural dynamicsLocal-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, PMCID: PMC11491165, 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 profileChildhoodCommon and unique brain aging patterns between females and males quantified by large‐scale deep learning
Du Y, Yuan Z, Sui J, Calhoun V. Common and unique brain aging patterns between females and males quantified by large‐scale deep learning. Human Brain Mapping 2024, 45: e70005. PMID: 39225381, PMCID: PMC11369911, DOI: 10.1002/hbm.70005.Peer-Reviewed Original ResearchConceptsBrain functional changesFunctional connectivityCognitive controlBrain agingBrain functionPatterns of brain agingResting-state brain functional connectivityBrain functional interactionsBrain functional connectivityHuman brain functionBrain aging patternsGender commonalitiesAge-related changesDeep learningHealthy participantsNormal agingNegative connectionFunctional changesBrainPositive connectionDeep learning modelsFunctional domainsAge effectsFunctional interactionsCross-validation schemeJoint 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, PMCID: PMC11639356, 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 abnormalitiesAlcoholBrainExplainable spatio-temporal graph evolution learning with applications to dynamic brain network analysis during development
Chen L, Qiao C, Ren K, Qu G, Calhoun V, Stephen J, Wilson T, Wang Y. Explainable spatio-temporal graph evolution learning with applications to dynamic brain network analysis during development. NeuroImage 2024, 298: 120771. PMID: 39111376, PMCID: PMC11533345, DOI: 10.1016/j.neuroimage.2024.120771.Peer-Reviewed Original ResearchConceptsSpatio-temporal dependenciesSpatial neighborhoodGraph learning methodsBrain network analysisNode representationsEvolution mechanisms of complex networksAdjacency informationDynamic brain network analysisModel explainabilityLanguage processingGraph evolutionEvolution learningLearning methodsLocal informationMechanism of complex networksDynamic evolutionModel dynamic interactionsDynamic functional connectivityNetwork componentsNested subgraphsLearning moduleExperimental resultsNetworkNetwork transitionsBrain development studies4D 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 networksNeurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm
Jiang Y, Luo C, Wang J, Palaniyappan L, Chang X, Xiang S, Zhang J, Duan M, Huang H, Gaser C, Nemoto K, Miura K, Hashimoto R, Westlye L, Richard G, Fernandez-Cabello S, Parker N, Andreassen O, Kircher T, Nenadić I, Stein F, Thomas-Odenthal F, Teutenberg L, Usemann P, Dannlowski U, Hahn T, Grotegerd D, Meinert S, Lencer R, Tang Y, Zhang T, Li C, Yue W, Zhang Y, Yu X, Zhou E, Lin C, Tsai S, Rodrigue A, Glahn D, Pearlson G, Blangero J, Karuk A, Pomarol-Clotet E, Salvador R, Fuentes-Claramonte P, Garcia-León M, Spalletta G, Piras F, Vecchio D, Banaj N, Cheng J, Liu Z, Yang J, Gonul A, Uslu O, Burhanoglu B, Uyar Demir A, Rootes-Murdy K, Calhoun V, Sim K, Green M, Quidé Y, Chung Y, Kim W, Sponheim S, Demro C, Ramsay I, Iasevoli F, de Bartolomeis A, Barone A, Ciccarelli M, Brunetti A, Cocozza S, Pontillo G, Tranfa M, Park M, Kirschner M, Georgiadis F, Kaiser S, Van Rheenen T, Rossell S, Hughes M, Woods W, Carruthers S, Sumner P, Ringin E, Spaniel F, Skoch A, Tomecek D, Homan P, Homan S, Omlor W, Cecere G, Nguyen D, Preda A, Thomopoulos S, Jahanshad N, Cui L, Yao D, Thompson P, Turner J, van Erp T, Cheng W, Feng J. Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm. Nature Communications 2024, 15: 5996. PMID: 39013848, PMCID: PMC11252381, DOI: 10.1038/s41467-024-50267-3.Peer-Reviewed Original ResearchConceptsGray matter changesDisorder constructsEnlarged striatumPsychiatric conditionsMental disordersSubcortical regionsSchizophreniaBiological foundationsMatter changesBrain imagingStriatumDisordersBiological factorsIndividualsSubtypesHealthy subjectsCross-sectional brain imagingHippocampusTemporal trajectoriesInternational cohortSubgroup 2Subgroup 1SubgroupsEffects 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 dependenceDependenceA survey of brain functional network extraction methods using fMRI data
Du Y, Fang S, He X, Calhoun V. A survey of brain functional network extraction methods using fMRI data. Trends In Neurosciences 2024, 47: 608-621. PMID: 38906797, DOI: 10.1016/j.tins.2024.05.011.Peer-Reviewed Original ResearchStructural white matter abnormalities in Schizophrenia and associations with neurocognitive performance and symptom severity
Male A, Goudzwaard E, Nakahara S, Turner J, Calhoun V, Mueller B, Lim K, Bustillo J, Belger A, Voyvodic J, O'Leary D, Mathalon D, Ford J, Potkin S, Preda A, van Erp T. Structural white matter abnormalities in Schizophrenia and associations with neurocognitive performance and symptom severity. Psychiatry Research Neuroimaging 2024, 342: 111843. PMID: 38896909, DOI: 10.1016/j.pscychresns.2024.111843.Peer-Reviewed Original ResearchConceptsSymptom severityFractional anisotropyDiffusion tensor imagingNeurocognitive performanceCognitive performanceAssociated with speed of processingLeft inferior fronto-occipital fasciculusWM abnormalitiesAssociated with neurocognitive performancePathophysiology of schizophreniaCognitive performance deficitsInferior fronto-occipital fasciculusSpeed of processingStructural white matter abnormalitiesMean diffusivityAxial diffusivityFronto-occipital fasciculusHealthy controlsRadial diffusivityRegional WM abnormalitiesNegative symptomsPerformance deficitsWhite matterWhite matter abnormalitiesAssociated with speedThe 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 abnormalities