2024
Brain networks and intelligence: A graph neural network based approach to resting state fMRI data
Thapaliya B, Akbas E, Chen J, Sapkota R, Ray B, Suresh P, Calhoun V, Liu J. Brain networks and intelligence: A graph neural network based approach to resting state fMRI data. Medical Image Analysis 2024, 101: 103433. PMID: 39708510, PMCID: PMC11877132, DOI: 10.1016/j.media.2024.103433.Peer-Reviewed Original ResearchConceptsGraph neural networksNeural networkGraph isomorphism networkGraph convolutional layersGraph convolutional networkMachine learning modelsNetwork connectivity matrixCognitive processesConvolutional layersConvolutional networkPrediction taskModel architectureGraph architectureAdolescent Brain Cognitive Development datasetResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingLearning modelsMiddle frontal gyrusPredicting individual differencesResting state fMRI dataPredictive intelligenceIntelligenceNetworkFunctional network connectivity matricesArchitectureA 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, PMCID: PMC11779700, 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 ageNeural Complexity Unveiled: Doubly Functionally Independent Primitives (dFIPs) in Psychiatric Risk Score Assessment
Soleimani N, Calhoun V. Neural Complexity Unveiled: Doubly Functionally Independent Primitives (dFIPs) in Psychiatric Risk Score Assessment. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039582, DOI: 10.1109/embc53108.2024.10781623.Peer-Reviewed Original ResearchConceptsFunctional network connectivityAutism spectrum disorderBipolar disorderPsychiatric disordersDepressive disorderAdolescent brainNeural underpinningsPolygenic risk scoresPsychiatric riskSpectrum disorderDifferential contributionsDisordersMDDHigh-risk scoreSchizophreniaHealthy controlsRisk scoreScoresIndividualsPsychiatricAutismNetwork connectivityNeuroimagingRisk score assessmentElevated risk scoresCopula linked parallel ICA jointly estimates linked structural and functional MRI brain networks
Agcaoglu O, Alacam D, Adalı T, Calhoun V, Silva R, Plis S, Bostami B. Copula linked parallel ICA jointly estimates linked structural and functional MRI brain networks. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40040121, DOI: 10.1109/embc53108.2024.10781658.Peer-Reviewed Original ResearchConceptsFunctional magnetic resonance imagingStructural MRIAmplitude of low frequency fluctuationsBrain imaging methodsStructural MRI dataFunctional network connectivityLow frequency fluctuationsEstimated independent sourcesBrain networksRegional homogeneityFMRI networksTemporal informationMagnetic resonance imagingFrequency fluctuationsAlzheimer's studiesBrainResonance imagingFusion approachUnmixing matrixNetwork connectivityReal-dataSensorimotorNetworkCerebellumInter-modality source coupling: a fully automated whole-brain data-driven structure-function fingerprint shows replicable links to reading in large-scale (N~8K) analysis
Kotoski A, Morris R, Calhoun V. Inter-modality source coupling: a fully automated whole-brain data-driven structure-function fingerprint shows replicable links to reading in large-scale (N~8K) analysis. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039662, DOI: 10.1109/embc53108.2024.10781720.Peer-Reviewed Original ResearchPath-based Differential Analysis in Near-centenarians and Centenarians Brain Network
Falakshahi H, Rokham H, Calhoun V. Path-based Differential Analysis in Near-centenarians and Centenarians Brain Network. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039654, DOI: 10.1109/embc53108.2024.10781732.Peer-Reviewed Original ResearchConceptsCognitive control domainsBrain networksCognitive functionPreservation of cognitive functionPromote cognitive healthInvestigate brain networksGaussian graphical modelsControl domainCognitive agingNeural mechanismsGraph theory techniquesGraphical modelsCognitive healthIntricate informationBrain graphsNear-centenariansGroup graphGraphNetworkGraph metricsGraph theoryAging StudyBrainTargeted interventionsYounger group
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