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 datasetInterplay 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 timeLocal-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 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 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 dependenceDependenceStructural 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 abnormalitiesTopological state-space estimation of functional human brain networks
Chung M, Huang S, Carroll I, Calhoun V, Goldsmith H. Topological state-space estimation of functional human brain networks. PLOS Computational Biology 2024, 20: e1011869. PMID: 38739671, PMCID: PMC11115255, DOI: 10.1371/journal.pcbi.1011869.Peer-Reviewed Original ResearchCross‐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 imagingThe risk of cannabis use disorder is mediated by altered brain connectivity: A chronnectome study
Fazio G, Olivo D, Wolf N, Hirjak D, Schmitgen M, Werler F, Witteman M, Kubera K, Calhoun V, Reith W, Wolf R, Sambataro F. The risk of cannabis use disorder is mediated by altered brain connectivity: A chronnectome study. Addiction Biology 2024, 29: e13395. PMID: 38709211, PMCID: PMC11072977, DOI: 10.1111/adb.13395.Peer-Reviewed Original ResearchConceptsRisk of cannabis use disorderCannabis use disorderDynamic functional connectivityFunctional connectivityUse disorderTreatment of cannabis use disorderAt-risk individualsResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingCannabis-related problemsDefault-mode networkPatterns of FCCognitive-controlCUDIT-RBrain mechanismsSubcortical functionBrain networksSelf-screening questionnaireBrain connectivityBrain functionSensory-motorNeurostimulation treatmentsMagnetic resonance imagingBrainCluster statesGenetic variants for head size share genes and pathways with cancer
Knol M, Poot R, Evans T, Satizabal C, Mishra A, Sargurupremraj M, van der Auwera S, Duperron M, Jian X, Hostettler I, van Dam-Nolen D, Lamballais S, Pawlak M, Lewis C, Carrion-Castillo A, van Erp T, Reinbold C, Shin J, Scholz M, Håberg A, Kämpe A, Li G, Avinun R, Atkins J, Hsu F, Amod A, Lam M, Tsuchida A, Teunissen M, Aygün N, Patel Y, Liang D, Beiser A, Beyer F, Bis J, Bos D, Bryan R, Bülow R, Caspers S, Catheline G, Cecil C, Dalvie S, Dartigues J, DeCarli C, Enlund-Cerullo M, Ford J, Franke B, Freedman B, Friedrich N, Green M, Haworth S, Helmer C, Hoffmann P, Homuth G, Ikram M, Jack C, Jahanshad N, Jockwitz C, Kamatani Y, Knodt A, Li S, Lim K, Longstreth W, Macciardi F, Consortium T, Amouyel P, Arfanakis K, Aribisala B, Bastin M, Chauhan G, Chen C, Cheng C, de Jager P, Deary I, Fleischman D, Gottesman R, Gudnason V, Hilal S, Hofer E, Janowitz D, Jukema J, Liewald D, Lopez L, Lopez O, Luciano M, Martinez O, Niessen W, Nyquist P, Rotter J, Rundek T, Sacco R, Schmidt H, Tiemeier H, Trompet S, van der Grond J, Völzke H, Wardlaw J, Yanek L, Yang J, Consortium T, Agartz I, Alhusaini S, Almasy L, Ames D, Amunts K, Andreassen O, Armstrong N, Bernard M, Blangero J, Blanken L, Boks M, Boomsma D, Brickman A, Brodaty H, Buckner R, Buitelaar J, Cannon D, Carr V, Catts S, Chakravarty M, Chen Q, Ching C, Corvin A, Crespo-Facorro B, Curran J, Davies G, de Geus E, de Zubicaray G, Braber A, Desrivières S, Dillman A, Djurovic S, Drevets W, Duggirala R, Ehrlich S, Erk S, Espeseth T, Fedko I, Fernández G, Fisher S, Foroud T, Ge T, Giddaluru S, Glahn D, Goldman A, Green R, Greven C, Grimm O, Hansell N, Hartman C, Hashimoto R, Heinz A, Henskens F, Hibar D, Ho B, Hoekstra P, Holmes A, Hoogman M, Hottenga J, Pol H, Jablensky A, Jenkinson M, Jia T, Jöckel K, Jönsson E, Kim S, Klein M, Kochunov P, Kwok J, Lawrie S, Le Hellard S, Lemaître H, Loughland C, Marquand A, Martin N, Martinot J, Matarin M, Mathalon D, Mather K, Mattay V, McDonald C, McMahon F, McMahon K, Rebekah E, McWhirter, Mecocci P, Melle I, Meyer-Lindenberg A, Michie P, Milaneschi Y, Morris D, Mowry B, Nho K, Nichols T, Nöthen M, Olvera R, Oosterlaan J, Ophoff R, Pandolfo M, Pantelis C, Pappa I, Penninx B, Pike G, Rasser P, Rentería M, Reppermund S, Rietschel M, Risacher S, Romanczuk-Seiferth N, Rose E, Sachdev P, Sämann P, Saykin A, Schall U, Schofield P, Schramm S, Schumann G, Scott R, Shen L, Sisodiya S, Soininen H, Sprooten E, Srikanth V, Steen V, Strike L, Thalamuthu A, Toga A, Tooney P, Tordesillas-Gutiérrez D, Turner J, del C. Valdés Hernández M, van der Meer D, Van der Wee N, Van Haren N, van 't Ent D, Veltman D, Walter H, Weinberger D, Weiner M, Wen W, Westlye L, Westman E, Winkler A, Woldehawariat G, Wright M, Wu J, Mäkitie O, Mazoyer B, Medland S, Miyamoto S, Moebus S, Mosley T, Muetzel R, Mühleisen T, Nagata M, Nakahara S, Palmer N, Pausova Z, Preda A, Quidé Y, Reay W, Roshchupkin G, Schmidt R, Schreiner P, Setoh K, Shapland C, Sidney S, St Pourcain B, Stein J, Tabara Y, Teumer A, Uhlmann A, van der Lugt A, Vernooij M, Werring D, Windham B, Witte A, Wittfeld K, Yang Q, Yoshida K, Brunner H, Le Grand Q, Sim K, Stein D, Bowden D, Cairns M, Hariri A, Cheung C, Andersson S, Villringer A, Paus T, Cichon S, Calhoun V, Crivello F, Launer L, White T, Koudstaal P, Houlden H, Fornage M, Matsuda F, Grabe H, Ikram M, Debette S, Thompson P, Seshadri S, Adams H. Genetic variants for head size share genes and pathways with cancer. Cell Reports Medicine 2024, 5: 101529. PMID: 38703765, PMCID: PMC11148644, DOI: 10.1016/j.xcrm.2024.101529.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesGene set enrichmentErbB signaling pathwayNear genesGenetic lociAssociation studiesCancer genesGenetic variantsSyndrome geneShared genesGenetic driversSize variantsGenesSignaling pathwayIntermediate progenitor cellsHeight variantsHead sizeVariantsNeural cellsPathwayWidespread effectsCellsLociP53Early brainMRI 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 MRI