Vince Calhoun, PhD
About
Research
Publications
2024
Networks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls
Kinsey S, Kazimierczak K, Camazón P, Chen J, Adali T, Kochunov P, Adhikari B, Ford J, van Erp T, Dhamala M, Calhoun V, Iraji A. Networks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls. Nature Mental Health 2024, 1-12. DOI: 10.1038/s44220-024-00341-y.Peer-Reviewed Original ResearchSelf-referential cognitionFunctional magnetic resonance imaging connectivityFunctional brain connectivityCingulo-opercularDefault-modeSchizophrenia diagnosisExecutive regionsFMRI connectivityFunctional connectivityConnectivity analysisSchizophreniaSensitive to differencesBrain connectivityFunctional connectivity structureWidespread alterationsImaging connectivityIndependent component analysisBrain phenomenaNetwork integrationHypoconnectivityPsychosisCognitionCore regionNonlinear networksCase-control datasetA 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 datasetAdolescent brain maturation associated with environmental factors: a multivariate analysis
Ray B, Jensen D, Suresh P, Thapaliya B, Sapkota R, Farahdel B, Fu Z, Chen J, Calhoun V, Liu J. Adolescent brain maturation associated with environmental factors: a multivariate analysis. Frontiers In Neuroimaging 2024, 3: 1390409. DOI: 10.3389/fnimg.2024.1390409.Peer-Reviewed Original ResearchLeft medial orbitofrontal cortexReduced gray matter densityMedial orbitofrontal cortexMiddle temporal gyrusGray matter densityIncreased functional connectivityIncrease cognitive performanceSusceptible to environmental influencesBilateral occipital regionsDelayed brain maturationIncreased cortical thicknessOrbitofrontal cortexAdolescent brainHuman adolescentsTemporal gyrusBrain characteristicsCognitive performanceBrain regionsBrain age estimationBrain metricsFunctional connectivityBrain agingBrain maturationBrain variationBrain modalitiesImaging‐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 patternsMultimodal MRI accurately identifies amyloid status in unbalanced cohorts in Alzheimer’s disease continuum
Dolci G, Ellis C, Cruciani F, Brusini L, Abrol A, Galazzo I, Menegaz G, Calhoun V. Multimodal MRI accurately identifies amyloid status in unbalanced cohorts in Alzheimer’s disease continuum. Network Neuroscience 2024, 1-21. DOI: 10.1162/netn_a_00423.Peer-Reviewed Original ResearchNeuropathological hallmarks of Alzheimer's diseaseHallmarks of Alzheimer's diseaseHyperphosphorylated tau proteinAmyloid-bTau proteinNeurofibrillary tanglesNeuropathological hallmarksAmyloid accumulationAlzheimer's diseaseAb accumulationDepositional signatureIdentification of individualsAmyloid statusAccumulationAmyloidShed lightTanglesAlzheimer's disease continuumProteinA spatially constrained independent component analysis jointly informed by structural and functional network connectivity
Fouladivanda M, Iraji A, Wu L, van Erp T, Belger A, Hawamdeh F, Pearlson G, Calhoun V. A spatially constrained independent component analysis jointly informed by structural and functional network connectivity. Network Neuroscience 2024, 1-31. DOI: 10.1162/netn_a_00398.Peer-Reviewed Original ResearchIntrinsic connectivity networksFunctional brain connectivityBrain connectivityStructural connectivityFunctional connectivityIndependent component analysisResting-state functional MRIAnalysis of group differencesBrain functional organizationFunctional network connectivityStructural-functional connectivityNeuroimaging studiesFunctional MRIWhole-brain tractographyGroup differencesRs-fMRIBrain disordersFunctional couplingSchizophreniaStatistical analysis of group differencesSubject levelFunctional organizationConnectivity networksBrainDiffusion-weighted MRIA 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, 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 dataENIGMA-Meditation: Worldwide consortium for neuroscientific investigations of meditation practices
Ganesan S, Barrios F, Batta I, Bauer C, Braver T, Brewer J, Brown K, Cahn R, Cain J, Calhoun V, Cao L, Chetelat G, Ching C, Creswell J, Dagnino P, Davanger S, Davidson R, Deco G, Dutcher J, Escrichs A, Eyler L, Fani N, Farb N, Fialoke S, Fresco D, Garg R, Garland E, Goldin P, Hafeman D, Jahanshad N, Kang Y, Khalsa S, Kirlic N, Lazar S, Lutz A, McDermott T, Pagnoni G, Piguet C, Prakash R, Rahrig H, Reggente N, Saccaro L, Sacchet M, Siegle G, Tang Y, Thomopoulos S, Thompson P, Torske A, Treves I, Tripathi V, Tsuchiyagaito A, Turner M, Vago D, Valk S, Zeidan F, Zalesky A, Turner J, King A. ENIGMA-Meditation: Worldwide consortium for neuroscientific investigations of meditation practices. Biological Psychiatry Cognitive Neuroscience And Neuroimaging 2024 PMID: 39515581, DOI: 10.1016/j.bpsc.2024.10.015.Peer-Reviewed Original ResearchMeditation practiceMeditation interventionNeuroscientific investigationsNeuroimaging methodsNon-clinical populationsNeuroscientific mechanismsContemplative neuroscienceMega-analysesNeuroscientific modelsPsychological processesNeuroscientific accountsMental statesMind-body practicesNeuroscientific insightsCognitive attributesTherapeutic actionNeuroimaging datasetsMeditationNeuroimagingClinical scienceGeneralizabilityStatistical powerImprove statistical powerAddictionAnxietyFunctional imaging derived ADHD biotypes based on deep clustering: a study on personalized medication therapy guidance
Feng A, Zhi D, Feng Y, Jiang R, Fu Z, Xu M, Zhao M, Yu S, Stevens M, Sun L, Calhoun V, Sui J. Functional imaging derived ADHD biotypes based on deep clustering: a study on personalized medication therapy guidance. EClinicalMedicine 2024, 77: 102876. DOI: 10.1016/j.eclinm.2024.102876.Peer-Reviewed Original ResearchAttention deficit hyperactivity disorderAttention deficit hyperactivity disorder patientsFunctional network connectivityAttention Deficit Hyperactivity Disorder AdolescentTreatment of attention deficit hyperactivity disorderPeking University Sixth HospitalBackground Attention deficit hyperactivity disorderDeficit hyperactivity disorderTreated with methylphenidateADHD subtypesHyperactivity/impulsivity symptomsAdolescent brainMedical treatment effectsHyperactivity disorderCognitive performanceABCD studyProblem scoresNeuroimaging markersNeurodevelopmental disordersMedical treatmentChildhood onsetChina Postdoctoral Science FoundationNational Natural Science Foundation of ChinaNatural Science Foundation of ChinaPrimary medicationA telescopic independent component analysis on functional magnetic resonance imaging dataset
Mirzaeian S, Faghiri A, Calhoun V, Iraji A. A telescopic independent component analysis on functional magnetic resonance imaging dataset. Network Neuroscience 2024, 1-16. DOI: 10.1162/netn_a_00421.Peer-Reviewed Original ResearchRight frontoparietal networkVisual networkIndependent component analysisBrain functionExtraction of informationFunctional magnetic resonance imaging datasetsImage datasetsFrontoparietal networkMagnetic resonance imaging datasetFMRI dataGroup differencesLeverage informationSmall networksDMNNetworkComponent analysisIncomplete viewAbstract Brain functionFunctional source
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