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 abnormalitiesAlcoholBrainThe 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 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 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 connectivitySearching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities
Fu Z, Batta I, Wu L, Abrol A, Agcaoglu O, Salman M, Du Y, Iraji A, Shultz S, Sui J, Calhoun V. Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities. NeuroImage 2024, 292: 120617. PMID: 38636639, PMCID: PMC11416721, DOI: 10.1016/j.neuroimage.2024.120617.Peer-Reviewed Original ResearchConceptsFunctional MRIStructural MRIResting-state scanSpatial similarity analysisMental health researchBrain markersDiffusion MRIAge differencesBrain featuresNeuromarkersBrain disordersYoung adult cohortBrain developmentWell-replicatedHuman brainBrainDiffusion MRI dataData-driven analysisDisordersSimilarity analysisAge cohortsGeneralizabilityPopulation-based researchAdult cohortAge-specific adaptationSMART (Splitting-Merging Assisted Reliable) Independent Component Analysis for Extracting Accurate Brain Functional Networks
He X, Calhoun V, Du Y. SMART (Splitting-Merging Assisted Reliable) Independent Component Analysis for Extracting Accurate Brain Functional Networks. Neuroscience Bulletin 2024, 40: 905-920. PMID: 38491231, DOI: 10.1007/s12264-024-01184-4.Peer-Reviewed Original ResearchConceptsIndependent component analysisFunctional magnetic resonance imagingClustering independent componentsFunctional networksIndependent component analysis methodMulti-subject fMRI dataIndependent componentsBrain functional networksFMRI dataSubject-specific functional networksFunctional magnetic resonance imaging dataOptimal model orderSmartComponent analysis
2023
How Does Aging Affect Whole-brain Functional Network Connectivity? Evidence from An ICA Method
Du Y, Guo Y, Calhoun V. How Does Aging Affect Whole-brain Functional Network Connectivity? Evidence from An ICA Method. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083384, DOI: 10.1109/embc40787.2023.10340189.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAgingBrainBrain MappingHumansMagnetic Resonance ImagingMiddle AgedNeural PathwaysElevated C-reactive protein mediates the liver-brain axis: a preliminary study
Jiang R, Wu J, Rosenblatt M, Dai W, Rodriguez R, Sui J, Qi S, Liang Q, Xu B, Meng Q, Calhoun V, Scheinost D. Elevated C-reactive protein mediates the liver-brain axis: a preliminary study. EBioMedicine 2023, 93: 104679. PMID: 37356206, PMCID: PMC10320521, DOI: 10.1016/j.ebiom.2023.104679.Peer-Reviewed Original ResearchConceptsRegional gray matter volumeGray matter volumeCognitive functioningMost cognitive measuresUnderlying neurobiological factorsEffect sizeLarge effect sizesProspective memoryVisual memoryCognitive measuresExecutive functionTrail MakingCognitive performanceNeurobiological factorsSmall effect sizesProcessing speedVentral striatumParahippocampal gyrusCognitive declineCognitive impairmentMatter volumeMemoryFunctioningCross-sectional associationsLimited researchAssociations 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 ResearchMeSH KeywordsAgedBiological Specimen BanksBrainFrailtyHumansMiddle AgedOutcome Assessment, Health CareUnited KingdomConceptsPopulation-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