2024
Cognitive and psychiatric relevance of dynamic functional connectivity states in a large (N > 10,000) children population
Fu Z, Sui J, Iraji A, Liu J, Calhoun V. Cognitive and psychiatric relevance of dynamic functional connectivity states in a large (N > 10,000) children population. Molecular Psychiatry 2024, 1-12. PMID: 39085394, DOI: 10.1038/s41380-024-02683-6.Peer-Reviewed Original ResearchDynamic functional connectivity statesDynamic functional connectivityAdolescent Brain Cognitive DevelopmentCognitive performanceDynamic functional connectivity patternsSensory networksAnalysis of dynamic functional connectivityFunctional connectivity statesDefault-modeNeurological underpinningsAttention problemsPsychiatric relevanceFunctional connectivitySensorimotor networkMediation analysisCognitive developmentChild's brainBrain statesMental healthMental problemsBrain dynamicsSliding-window approachMental behaviorBrainCerebellumCross-continental environmental and genome-wide association study on children and adolescent anxiety and depression
Thapaliya B, Ray B, Farahdel B, Suresh P, Sapkota R, Holla B, Mahadevan J, Chen J, Vaidya N, Perrone-Bizzozero N, Benegal V, Schumann G, Calhoun V, Liu J. Cross-continental environmental and genome-wide association study on children and adolescent anxiety and depression. Frontiers In Psychiatry 2024, 15: 1384298. PMID: 38827440, PMCID: PMC11141390, DOI: 10.3389/fpsyt.2024.1384298.Peer-Reviewed Original ResearchEarly life stressAdolescent anxietyLife stressMega-analysisCognitive Development StudyHypothalamic-pituitary-adrenal axisSupport IndexAssociated with anxietyRisk of anxietyExternalizing disordersAdolescent brainGenome-wide association studiesGenetic vulnerabilityAnxietyGenome-wide association analysisPublic health concernDepressionMental healthLinear mixed-effects modelsEnvironmental factorsMixed-effects modelsAssociation studiesTissue enrichment analysisGenetic associationGenomic significanceA deep learning approach for mental health quality prediction using functional network connectivity and assessment data
Ajith M, Aycock D, Tone E, Liu J, Misiura M, Ellis R, Plis S, King T, Dotson V, Calhoun V. A deep learning approach for mental health quality prediction using functional network connectivity and assessment data. Brain Imaging And Behavior 2024, 18: 630-645. PMID: 38340285, DOI: 10.1007/s11682-024-00857-y.Peer-Reviewed Original ResearchStatic functional network connectivityMental health qualityFunctional network connectivityMental health categoriesRs-fMRIMental healthPatterns of abnormal connectivityHealth categoriesHealth qualityDevelopment of personalized interventionsManagement of mental healthResting-state fMRIMeasure mental healthUK Biobank datasetNeural patternsBrain healthVisual domainAbnormal connectionPersonalized interventionsBiobank datasetTreatment responseHealthNetwork connectivityBehavioral aspectsAssessment data
2023
Triple Interactions Between the Environment, Brain, and Behavior in Children: An ABCD Study
Zhi D, Jiang R, Pearlson G, Fu Z, Qi S, Yan W, Feng A, Xu M, Calhoun V, Sui J. Triple Interactions Between the Environment, Brain, and Behavior in Children: An ABCD Study. Biological Psychiatry 2023, 95: 828-838. PMID: 38151182, PMCID: PMC11006588, DOI: 10.1016/j.biopsych.2023.12.019.Peer-Reviewed Original ResearchFunctional network connectivityCognitive abilitiesABCD studyMental healthLongitudinal predictionBehavioral developmentSleep problemsBrain functional network connectivityHigher cognitive abilitiesDefault mode networkChildren's behavioral developmentUnique protective factorsBrain functional connectivityCognitive controlTriple interactionFamily conflictMode networkMediation analysisFunctional connectivityInfluence behaviorFunctional networksProtective factorsSchool environmentChildhood developmentEnvironmental exposuresAddressing Global Environmental Challenges to Mental Health Using Population Neuroscience
Schumann G, Andreassen O, Banaschewski T, Calhoun V, Clinton N, Desrivieres S, Brandlistuen R, Feng J, Hese S, Hitchen E, Hoffmann P, Jia T, Jirsa V, Marquand A, Nees F, Nöthen M, Novarino G, Polemiti E, Ralser M, Rapp M, Schepanski K, Schikowski T, Slater M, Sommer P, Stahl B, Thompson P, Twardziok S, van der Meer D, Walter H, Westlye L, Heinz A, Lett T, Vaidya N, Serin E, Neidhart M, Jentsch M, Eils R, Taron U, Schütz T, Banks J, Meyer-Lindenberg A, Tost H, Holz N, Schwarz E, Stringaris A, Christmann N, Jansone K, Siehl S, Ask H, Fernández-Cabello S, Kjelkenes R, Tschorn M, Böttger S, Bernas A, Marr L, Feixas Viapiana G, Eiroa-Orosa F, Gallego J, Pastor A, Forstner A, Claus I, Miller A, Heilmann-Heimbach S, Boye M, Wilbertz J, Schmitt K, Petkoski S, Pitel S, Otten L, Athanasiadis A, Pearmund C, Spanlang B, Alvarez E, Sanchez M, Giner A, Renner P, Gong Y, Dai Y, Xia Y, Chang X, Liu J, Young A, Ogoh G. Addressing Global Environmental Challenges to Mental Health Using Population Neuroscience. JAMA Psychiatry 2023, 80: 1066-1074. PMID: 37610741, DOI: 10.1001/jamapsychiatry.2023.2996.Peer-Reviewed Original ResearchConceptsMental illnessMechanisms of mental illnessSymptoms of depressionEvidence-based interventionsBrain mechanismsPopulation neuroscienceSocioeconomic inequalitiesEnvironmental adversityMental healthSubstance misuseBrain healthPsychosocial effectsDigital healthCohort dataDeep phenotyping dataObjective biomarkersHealthIllnessBrainDevelopment of objective biomarkersImprove outcomesPopulation levelCOVID-19 pandemicPollution measurementsResearch strategyFunctional Network Connectivity Based Mental Health Category Prediction from Rest-fMRI Data
Ajith M, Calhoun V. Functional Network Connectivity Based Mental Health Category Prediction from Rest-fMRI Data. 2023, 00: 1-5. DOI: 10.1109/isbi53787.2023.10230721.Peer-Reviewed Original ResearchFunctional network connectivityMental healthResting fMRIStatic functional network connectivityMental health classesMental health categoriesBrain networksMental health scoresBehavioral changesBehavior modificationSignificant health issueBrainHealthy habitsHealth scoresHealth categoriesHealth classesHealth issuesIndividual levelFMRICategory predictionNetwork connectivityNeuroimagingHealthAssociations 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