Effectiveness of non-invasive brain stimulation on depressive symptoms targeting prefrontal cortex in functional magnetic resonance imaging studies: a combined systematic review and meta-analysis
Xiao Y, Dong S, Pan C, Guo H, Tang L, Zhang X, Wang F. Effectiveness of non-invasive brain stimulation on depressive symptoms targeting prefrontal cortex in functional magnetic resonance imaging studies: a combined systematic review and meta-analysis. Psychoradiology 2024, kkae025. DOI: 10.1093/psyrad/kkae025.Peer-Reviewed Original ResearchActivation likelihood estimationNon-invasive brain stimulationFunctional magnetic resonance imagingPrefrontal cortexEffect of non-invasive brain stimulationActivation likelihood estimation meta-analysisFunctional magnetic resonance imaging studyModerate depressive symptomsBrain stimulationMagnetic resonance imaging studiesMeta-regressionTreatment-resistant conditionClinical moderatorsMeta-analysisDepressive symptomsTreating depressionActivity post-interventionSignificant moderatorEffect sizePooled effect sizeGender differencesUnivariate meta-regressionRandomized controlled trailsMagnetic resonance imagingPost-interventionEnhanced classification and severity prediction of major depressive disorder using acoustic features and machine learning
Liang L, Wang Y, Ma H, Zhang R, Liu R, Zhu R, Zheng Z, Zhang X, Wang F. Enhanced classification and severity prediction of major depressive disorder using acoustic features and machine learning. Frontiers In Psychiatry 2024, 15: 1422020. PMID: 39355380, PMCID: PMC11442283, DOI: 10.3389/fpsyt.2024.1422020.Peer-Reviewed Original ResearchVocal acoustic featuresHealthy control groupSeverity of depressive symptomsTotal depression scoreAcoustic featuresClassification accuracyMDD groupDepressive disorderAnxiety comorbiditiesDepression prediction modelDeep learning methodsDepressive symptomsDepression scoresHC groupSpeech characteristicsMean Absolute Error(MAEDepressionNeural networkEnhanced classificationControl groupLearning methodsMachine learningClassification modelOpen-source algorithmAbsolute error(MAEVisual environment in schools and student depressive symptoms: Insights from a prospective study across multiple cities in eastern China
Zhang X, Tang J, Wang Y, Yang W, Wang X, Zhang R, Yang J, Lu W, Wang F. Visual environment in schools and student depressive symptoms: Insights from a prospective study across multiple cities in eastern China. Environmental Research 2024, 258: 119490. PMID: 38925465, DOI: 10.1016/j.envres.2024.119490.Peer-Reviewed Original ResearchConceptsStudents' depressive symptomsDepressive symptomsHigh-intensity exercise sessionCombination of physical activityCohort studyHealth Cohort StudyOccurrence of depressive symptomsExercise sessionsPhysical activityFollow-up cohortOne-year follow-upFollow-upOne-year outcomesImpairment groupPhysical examination indicatorsSchool-related factorsModify behaviorPositive effectRR valuesData collectionConsecutive follow-upsPersonal factorsVisual impairmentProspective studyAppropriate attentionCharacterizing the distinct imaging phenotypes, clinical behavior, and genetic vulnerability of brain maturational subtypes in mood disorders.
Zheng J, Zong X, Tang L, Guo H, Zhao P, Womer F, Zhang X, Tang Y, Wang F. Characterizing the distinct imaging phenotypes, clinical behavior, and genetic vulnerability of brain maturational subtypes in mood disorders. Psychological Medicine 2024, 54: 2774-2784. PMID: 38804091, DOI: 10.1017/s0033291724000886.Peer-Reviewed Original ResearchGray matter volumeMood disordersGenetic vulnerabilityDepressive disorderHeterogeneity of mood disordersRegional gray matter volumeDrug-free patientsClinical behaviorIncreased genetic vulnerabilityGenetic riskSevere depressive symptomsClinical manifestationsBipolar disorderDrug-naiveFrontal cortexPolygenic risk scoresMatter volumeDepressive symptomsNeurocognitive assessmentCognitive impairmentPrimary motor cortexBehavioral termsDisordersHealthy controlsMotor cortex