2025
Penetrance of neurodevelopmental copy number variants is associated with variations in cortical morphology
Silva A, Sønderby I, Kirov G, Abdellaoui A, Agartz I, Ames D, Armstrong N, Artiges E, Banaschewski T, Bassett A, Bearden C, Blangero J, Boen R, Boomsma D, Bülow R, Butcher N, Calhoun V, Campbell L, Chow E, Ciufolini S, Craig M, Crespo-Farroco B, Cunningham A, Dalvie S, Daly E, Dazzan P, de Geus E, de Zubicaray G, Doherty J, Donohoe G, Drakesmith M, Espeseth T, Frouin V, Garavan H, Glahn D, Goodrich-Hunsaker N, Gowland P, Grabe H, Grigis A, Gudbrandsen M, Gutman B, Haavik J, Håberg A, Hall J, Heinz A, Hohmann S, Hottenga J, Jacquemont S, Jahanshad N, Jonas R, Jones D, Jönsson E, Koops S, Kumar K, Le Hellard S, Lemaitre H, Liu J, Lundervold A, Martinot J, Mather K, McDonald-McGinn D, McMahon K, McRae A, Medland S, Moreau C, Murphy K, Murphy D, Murray R, Nees F, Owen M, Martinot M, Orfanos D, Paus T, Poustka L, Marques T, Roalf D, Sachdev P, Scheffler F, Schmitt J, Schumann G, Steen V, Stein D, Strike L, Teumer A, Thalamuthu A, Thomopoulos S, Tordesillas-Gutiérrez D, Trollor J, Uhlmann A, Vajdi A, van ’t Ent D, van Amelsvoort T, van den Bree M, van der Meer D, Vázquez-Bourgon J, Villalón-Reina J, Völker U, Völzke H, Vorstman J, Westlye L, Williams N, Wittfeld K, Wright M, Thompson P, Andreassen O, Linden D, group E. Penetrance of neurodevelopmental copy number variants is associated with variations in cortical morphology. Biological Psychiatry Cognitive Neuroscience And Neuroimaging 2025 PMID: 40414598, DOI: 10.1016/j.bpsc.2025.05.010.Peer-Reviewed Original ResearchCopy number variantsDevelopmental disordersNeurobiological mechanismsPenetration scoresMechanisms of genetic riskAssociated with variationBrain magnetic resonance imagingCohort of patientsCortical surface areaT1-weighted brain magnetic resonance imagingMagnetic resonance imagingCortical morphometric featuresGenetic dataLingual gyrusClinical phenotypeSubcortical morphologyIncreased riskNeuroimaging dataSchizophreniaBrain abnormalitiesNeurodevelopmental conditionsIntracranial volumeCerebral cortexResonance imagingCortical morphology
2024
Anxiety symptoms are differentially associated with facial expression processing in boys and girls
Doucet G, Kruse J, Keefe A, Rice D, Coutant A, Pulliam H, Smith O, Calhoun V, Stephen J, Wang Y, White S, Picci G, Taylor B, Wilson T. Anxiety symptoms are differentially associated with facial expression processing in boys and girls. Social Cognitive And Affective Neuroscience 2024, 19: nsae085. PMID: 39587034, PMCID: PMC11631531, DOI: 10.1093/scan/nsae085.Peer-Reviewed Original ResearchFacial expression processingAssociated with psychiatric disordersExpression processingFacial expressionsFunctional magnetic resonance imagingFace processing taskMedial temporal cortexTypically-developing youthLevels of anxietyEmotional facesNeutral contrastAnxiety symptomsPosterior networkPsychiatric disordersFacial emotionsBrain responsesTemporal cortexNeural mechanismsHigher anxietyFMRI dataAnxietySocial informationAnxiety levelsBehavioral changesMagnetic resonance imagingMultimodal predictive modeling: Scalable imaging informed approaches to predict future brain health
Ajith M, Spence J, Chapman S, Calhoun V. Multimodal predictive modeling: Scalable imaging informed approaches to predict future brain health. Journal Of Neuroscience Methods 2024, 414: 110322. PMID: 39608579, PMCID: PMC11687617, DOI: 10.1016/j.jneumeth.2024.110322.Peer-Reviewed Original ResearchStatic functional network connectivityHealth constructsNeuroimaging dataBrain healthResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingSupport vector regressionFunctional network connectivityRandom forestCognitive performanceAssessment-onlyRs-fMRINeural patternsBehavioral outcomesBehavioral dataDiverse data sourcesNeural connectionsPsychological stateTraining stageMagnetic resonance imagingLongitudinal changesNetwork connectivityBrainPerformance evaluationVector regressionA 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 datasetAssessing Pediatric Cognitive Development via Multisensory Brain Imaging Analysis
Belyaeva I, Wang Y, Wilson T, Calhoun V, Stephen J, Adali T. Assessing Pediatric Cognitive Development via Multisensory Brain Imaging Analysis. 2015 23rd European Signal Processing Conference (EUSIPCO) 2024, 1362-1366. DOI: 10.23919/eusipco63174.2024.10714926.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingFunctional magnetic resonance imaging dataMultisensory integrationSensory stimuliEffect of multisensory integrationMultisensory integration effectsMultiple sensory stimuliBrain imaging modalitiesCognitive developmentBrain image analysisBrain developmental patternsSensory modalitiesBrain componentsLearning paradigmMagnetoencephalographyMagnetic resonance imagingBrainDevelopmental patternsStimuliMultiple sensesCanonical polyadic tensor decompositionMultimodal data fusion frameworkAdolescentsMultitask learning paradigmPolyadic tensor decompositionData augmentation for schizophrenia diagnosis via vision transformer-based latent diffusion model
Yang Y, Ma S, Cao S, Jia S, Bi Y, Calhoun V. Data augmentation for schizophrenia diagnosis via vision transformer-based latent diffusion model. Proceedings Of SPIE--the International Society For Optical Engineering 2024, 13252: 1325214-1325214-7. DOI: 10.1117/12.3044654.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingFunctional network connectivity matricesIndependent component analysisVision Transformer (ViTAdvanced artificial intelligence techniquesTraditional U-NetArtificial intelligence techniquesFunctional magnetic resonance imaging dataGroup independent component analysisNetwork connectivity matrixDenoising functionData augmentationImage generationIntelligence techniquesU-NetSmall datasetsDiagnosed schizophreniaSchizophrenia diagnosisGeneration taskNeuroimaging dataSchizophreniaComputational burdenConnectivity matrixMagnetic resonance imagingRelevant information4D 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 networksA new transfer entropy method for measuring directed connectivity from complex-valued fMRI data
Li W, Lin Q, Zhang C, Han Y, Calhoun V. A new transfer entropy method for measuring directed connectivity from complex-valued fMRI data. Frontiers In Neuroscience 2024, 18: 1423014. PMID: 39050665, PMCID: PMC11266018, DOI: 10.3389/fnins.2024.1423014.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingFMRI dataBrain regionsAnatomical Automatic LabelingTransfer entropyFunctional magnetic resonance imaging dataConnectivity of brain regionsFrontal-parietal regionsConsistent with previous findingsSignificant group differencesRight frontal-parietal regionPartial transfer entropyPredicting mental disordersMental disordersParietal regionsGroup differencesMagnitude effectExperimental fMRI dataDirectional connectivityComplex-valued fMRI dataSchizophreniaMagnetic resonance imagingComplex-valued approachEntropyMagnitude dataNeuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade
Du Y, Niu J, Xing Y, Li B, Calhoun V. Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade. Schizophrenia Bulletin 2024, 51: 325-342. PMID: 38982882, PMCID: PMC11908864, DOI: 10.1093/schbul/sbae110.Peer-Reviewed Original ResearchArtificial intelligenceSemi-supervised learning methodArtificial intelligence techniquesAccurate diagnosis of SZMultimodal fusionAccurate diagnosis of schizophreniaIntelligence techniquesAI methodsLearning methodsDiagnosis of SZMental disordersSelection methodUnsupervised clusteringMagnetic resonance imagingBiomarker extractionDiagnosis of schizophreniaCopula 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-dataSensorimotorNetworkCerebellumIdentifying Canonical multi-scale Intrinsic Connectivity Networks in Infant resting-state fMRI and their Association with Age
Bajracharya P, Faghiri A, Fu Z, Calhoun V, Shultz S, Iraji A. Identifying Canonical multi-scale Intrinsic Connectivity Networks in Infant resting-state fMRI and their Association with Age. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039283, DOI: 10.1109/embc53108.2024.10782404.Peer-Reviewed Original ResearchConceptsIntrinsic connectivity networksStatic functional network connectivitySubject-specific intrinsic connectivity networksResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingFunctional brain organizationResting-state fMRIFunctional network connectivityConnectivity networksCognitive domainsCognitive processesBrain organizationSub-corticalRsfMRI dataIndependent component analysisMagnetic resonance imagingNeuromarkersDistinct patternsMotor controlNeurodevelopmental disabilitiesResonance imagingEarly identificationSensory perceptionAssociated with ageFMRIParallel Multilink Joint ICA for Multimodal Fusion of Gray Matter and Multiple Resting fMRI Networks
Khalilullah K, Agcaoglu O, Duda M, Calhoun V. Parallel Multilink Joint ICA for Multimodal Fusion of Gray Matter and Multiple Resting fMRI Networks. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039683, DOI: 10.1109/embc53108.2024.10782528.Peer-Reviewed Original ResearchConceptsFunctional magnetic resonance imagingJoint independent component analysisAssociated with Alzheimer's diseaseFalse discovery rateMultimodal fusion approachGray matterAssess group differencesHealthy controlsMultimodal fusionIndependent component analysisFusion approachSensorimotor domainBrain regionsSMRI dataGroup differencesParacentral lobuleBrain functionAD pathologyConnectivity patternsDiscovery rateJoint ICAJoint relationshipAlzheimer's diseaseActivity patternsMagnetic resonance imagingA 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 ResearchA 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-beingCoupling between Time-Varying EEG Spectral Bands and Spatial Dynamic FMRI Networks
Phadikar S, Pusuluri K, Jensen K, Wu L, Iraji A, Calhoun V. Coupling between Time-Varying EEG Spectral Bands and Spatial Dynamic FMRI Networks. 2024, 00: 1-4. DOI: 10.1109/isbi56570.2024.10635622.Peer-Reviewed Original ResearchFunctional brain networksDynamic brain networksBrain networksSpectral propertiesDynamics of functional brain networksFMRI networksSpectral bandsSpatial dimensionsResting-state functional magnetic resonance imagingConnectivity matrixCouplingBandFunctional magnetic resonance imagingDynamic networksSimultaneous electroencephalographyPersonalized treatment approachesElectroencephalography spectral powerResting stateMagnetic resonance imagingA 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 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 statesSubgroup Identification Through Multiplex Community Structure Within Functional Connectivity Networks
Yang H, Ortiz-Bouza M, Vu T, Laport F, Calhoun V, Aviyente S, Adali T. Subgroup Identification Through Multiplex Community Structure Within Functional Connectivity Networks. 2024, 00: 2141-2145. DOI: 10.1109/icassp48485.2024.10446076.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingFunctional networksResting-state fMRI dataMultiplex networksMulti-subject functional magnetic resonance imagingNature of psychiatric disordersFunctional connectivity networksDiagnostic heterogeneityPsychotic patientsIndividual functional networksPsychiatric disordersCommunity detectionGroup differencesFMRI dataData-driven methodMultiple networksConnectivity networksMagnetic resonance imagingIdentified subgroupsNetworkSubgroup identificationResonance imagingSubject correlationSubgroup structureNeuroimaging alterations and relapse in early-stage psychosis
Mihaljevic M, Nagpal A, Etyemez S, Narita Z, Ross A, Schaub R, Cascella N, Coughlin J, Nestadt G, Nucifora F, Sedlak T, Calhoun V, Faria A, Yang K, Sawa A. Neuroimaging alterations and relapse in early-stage psychosis. Journal Of Psychiatry And Neuroscience 2024, 49: e135-e142. PMID: 38569725, PMCID: PMC10980532, DOI: 10.1503/jpn.230115.Peer-Reviewed Original ResearchConceptsEarly-stage psychosisNeuroimaging alterationsResting-state functional MRI dataNo-relapse groupFunctional connectivity changesFunctional MRI dataHealthy controlsMagnetic resonance imagingRelapse groupPsychotic disordersFunctional connectivity estimatesBrain changesPsychotic eventsPsychosisConnectivity changesSymptom exacerbationConnectivity estimatesComparison correctionNo relapseLongitudinal studyThalamusNeuroimagingMRI dataClinical confounding factorsControl group
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