2024
Associations 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 abnormalitiesAlcoholBrainDynamic Functional Connectivity Correlates of Trait Mindfulness in Early Adolescence
Treves I, Marusak H, Decker A, Kucyi A, Hubbard N, Bauer C, Leonard J, Grotzinger H, Giebler M, Torres Y, Imhof A, Romeo R, Calhoun V, Gabrieli J. Dynamic Functional Connectivity Correlates of Trait Mindfulness in Early Adolescence. Biological Psychiatry Global Open Science 2024, 4: 100367. PMID: 39286525, PMCID: PMC11402920, DOI: 10.1016/j.bpsgos.2024.100367.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingTrait mindfulnessFunctional connectivity analysisDynamic functional connectivity analysisBrain statesConnectivity analysisSelf-reported trait mindfulnessResting-state fMRI scansHigher trait mindfulnessPresent-moment experienceFunctional connectivity correlatesDynamic brain statesStatic functional connectivityState-of-mindTest-retest reliabilityAdolescent anxietyFMRI scanningNeural basisPsychiatric disordersDepressive symptomsNeural mechanismsLower anxietyFunctional connectivityEarly adolescenceConnectivity correlationsCortical similarities in psychiatric and mood disorders identified in federated VBM analysis via COINSTAC
Rootes-Murdy K, Panta S, Kelly R, Romero J, Quidé Y, Cairns M, Loughland C, Carr V, Catts S, Jablensky A, Green M, Henskens F, Kiltschewskij D, Michie P, Mowry B, Pantelis C, Rasser P, Reay W, Schall U, Scott R, Watkeys O, Roberts G, Mitchell P, Fullerton J, Overs B, Kikuchi M, Hashimoto R, Matsumoto J, Fukunaga M, Sachdev P, Brodaty H, Wen W, Jiang J, Fani N, Ely T, Lorio A, Stevens J, Ressler K, Jovanovic T, van Rooij S, Federmann L, Jockwitz C, Teumer A, Forstner A, Caspers S, Cichon S, Plis S, Sarwate A, Calhoun V. Cortical similarities in psychiatric and mood disorders identified in federated VBM analysis via COINSTAC. Patterns 2024, 5: 100987. PMID: 39081570, PMCID: PMC11284501, DOI: 10.1016/j.patter.2024.100987.Peer-Reviewed Original ResearchPsychiatric disordersStructural neuroimaging studiesPattern of gray matterAutism spectrum disorderGray matterDepressive disorderMood disordersNeuroimaging studiesNeuroanatomical basisSubcortical regionsGM alterationsSpectrum disorderVBM analysisMental illnessGM patternsDisordersCollaborative InformaticsSchizophreniaMoodNeuroimaging Suite ToolkitAutismNeuroimagingVulnerabilityLarge-scale dataDeficitsThe overlap across psychotic disorders: A functional network connectivity analysis
Dini H, Bruni L, Ramsøy T, Calhoun V, Sendi M. The overlap across psychotic disorders: A functional network connectivity analysis. International Journal Of Psychophysiology 2024, 201: 112354. PMID: 38670348, PMCID: PMC11163820, DOI: 10.1016/j.ijpsycho.2024.112354.Peer-Reviewed Original ResearchConceptsFunctional network connectivitySchizoaffective disorderPsychotic disordersHealthy controlsBipolar-Schizophrenia NetworkFunctional network connectivity analysisStatic functional network connectivityResting-state fMRINetwork connectivity analysisPatterns of activityPsychiatric disordersDisorder groupSchizophreniaConnectivity analysisHC groupBipolarConnectivity patternsDisordersPatient groupSymptom scoresGroup of patientsPANSSSchizoaffectiveFMRINetwork connectivitySubgroup 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 structureComplexity Measures of Psychotic Brain Activity In The FMRI Signal
Li Q, Seraji M, Calhoun V, Iraji A. Complexity Measures of Psychotic Brain Activity In The FMRI Signal. 2024, 00: 9-12. DOI: 10.1109/ssiai59505.2024.10508702.Peer-Reviewed Original ResearchBrain activityPsychotic brainBrain regionsFunctional connectivityCharacteristics of psychiatric disordersAtypical brain activityNeural activity patternsFuzzy recurrence plotsPsychiatric disordersFMRI signalsComplexity measuresRecurrence plotsActivity patternsBrainEntropySample entropyFMRIImage analysisNonlinear dynamical systemsA whole-brain neuromark resting-state fMRI analysis of first-episode and early psychosis: Evidence of aberrant cortical-subcortical-cerebellar functional circuitry
Jensen K, Calhoun V, Fu Z, Yang K, Faria A, Ishizuka K, Sawa A, Andrés-Camazón P, Coffman B, Seebold D, Turner J, Salisbury D, Iraji A. A whole-brain neuromark resting-state fMRI analysis of first-episode and early psychosis: Evidence of aberrant cortical-subcortical-cerebellar functional circuitry. NeuroImage Clinical 2024, 41: 103584. PMID: 38422833, PMCID: PMC10944191, DOI: 10.1016/j.nicl.2024.103584.Peer-Reviewed Original ResearchConceptsFunctional network connectivityFirst-episodeEarly psychosisAberrant functional network connectivityResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingCorrelates of psychosisResting-state fMRI analysisWhole-brain approachPsychiatric disordersPsychiatric illnessSubcortical regionsCerebellar regionsFMRI analysisPsychosisControl participantsCognitive functionRs-fMRICerebellar connectivityMulti-site datasetFunctional circuitryMagnetic resonance imagingCircuitryResonance imagingProminent patternSexual dimorphism in cortical theta rhythms relates to elevated internalizing symptoms during adolescence
Petro N, Picci G, Ott L, Rempe M, Embury C, Penhale S, Wang Y, Stephen J, Calhoun V, Taylor B, Wilson T. Sexual dimorphism in cortical theta rhythms relates to elevated internalizing symptoms during adolescence. Imaging Neuroscience 2024, 2: 1-13. DOI: 10.1162/imag_a_00062.Peer-Reviewed Original ResearchSpontaneous neural activityInternalizing symptomsNeural activityPsychiatric disordersEmergence of psychiatric disordersTheta activityEmergence of psychiatric symptomsAbstract Psychiatric disordersElevated internalizing symptomsStructural brain differencesTypically-developing youthSpontaneous theta activityEyes-closed restTheta frequency bandNegative ruminationCingulate cortexTemporoparietal junctionBrain differencesExternalizing symptomsPsychiatric symptomsAssociation cortexCortical regionsSex differencesCortical activityAssociation regions
2023
A Brainwide Risk Score for Psychiatric Disorder Evaluated in a Large Adolescent Population Reveals Increased Divergence Among Higher-Risk Groups Relative to Control Participants
Yan W, Pearlson G, Fu Z, Li X, Iraji A, Chen J, Sui J, Volkow N, Calhoun V. A Brainwide Risk Score for Psychiatric Disorder Evaluated in a Large Adolescent Population Reveals Increased Divergence Among Higher-Risk Groups Relative to Control Participants. Biological Psychiatry 2023, 95: 699-708. PMID: 37769983, PMCID: PMC10942727, DOI: 10.1016/j.biopsych.2023.09.017.Peer-Reviewed Original ResearchFunctional network connectivityHealthy control individualsPsychiatric disordersRisk scoreEarly psychosisPsychiatric riskControl individualsStudy participantsHigh-risk groupMajor depressive disorderHigh-risk patternsPsychiatric risk assessmentCognitive Development StudyUnaffected adolescentsAdolescent Brain Cognitive Development (ABCD) studyLarge adolescent populationDepressive disorderHigh riskPsychosis scoresBipolar disorderPotential biomarkersEarly screeningPsychiatric vulnerabilityAdolescent populationDisordersMulti-scale convolutional recurrent neural network for psychiatric disorder identification in resting-state EEG
Yan W, Yu L, Liu D, Sui J, Calhoun V, Lin Z. Multi-scale convolutional recurrent neural network for psychiatric disorder identification in resting-state EEG. Frontiers In Psychiatry 2023, 14: 1202049. PMID: 37441141, PMCID: PMC10333510, DOI: 10.3389/fpsyt.2023.1202049.Peer-Reviewed Original ResearchConvolutional recurrent neural networkRecurrent neural networkResting-state EEGNeural networkPsychiatric disordersDeep learning classification modelLow-dimensional subspaceTwo-class classificationDesigning individualized treatmentLearning classification modelsEEG backgroundClassification modelHealthy controlsDepressive disorderSpatiotemporal informationClinical observationsDisease severityAccurate classificationIndividualized treatmentBiomarkersDisorder classificationDisorder identificationDisordersClassificationNeuroimaging biomarkersAn Adaptive Semi-Supervised Deep Clustering and Its Application to Identifying Biotypes of Psychiatric Disorders
Du Y, Wu F, Niu J, Calhoun V. An Adaptive Semi-Supervised Deep Clustering and Its Application to Identifying Biotypes of Psychiatric Disorders. 2023, 00: 1-4. DOI: 10.1109/isbi53787.2023.10230805.Peer-Reviewed Original ResearchFashion-MNIST dataDeep clustering methodsFunctional magnetic resonance imagingMNIST dataAutism spectrum disorderClustering methodPsychiatric disordersSemi-supervised clusteringPsychiatric disorder symptomsUnlabeled samplesClustering performanceDeep clusteringLabeled samplesDeep learningClustering techniqueDisorder symptomsSpectrum disorderNeuroimaging dataUseful informationSchizophreniaTraditional methodsMagnetic resonance imagingDisordersResonance imagingHigh confidence levelAssociations of physical frailty with health outcomes and brain structure in 483 033 middle-aged and older adults: a population-based study from the UK Biobank
Jiang R, Noble S, Sui J, Yoo K, Rosenblatt M, Horien C, Qi S, Liang Q, Sun H, Calhoun V, Scheinost D. Associations of physical frailty with health outcomes and brain structure in 483 033 middle-aged and older adults: a population-based study from the UK Biobank. The Lancet Digital Health 2023, 5: e350-e359. PMID: 37061351, PMCID: PMC10257912, DOI: 10.1016/s2589-7500(23)00043-2.Peer-Reviewed Original ResearchConceptsPopulation-based studyPhysical frailtyHealth-related outcomesBrain structuresMental healthHealth outcomesHealth measuresTotal white matter hyperintensitiesIndicators of frailtySeverity of frailtyLower gray matter volumePoor physical fitnessWhite matter hyperintensitiesGray matter volumeUK BiobankHealth-related measuresPoor mental healthMental health measuresDirection of associationMatter hyperintensitiesUnhealthy lifestyleEarly-life risksPsychiatric disordersNumerous confoundersPreventative strategies