The involvement of the cerebellar vermis across the psychotic-affective spectrum in enriched samples of recent-onset schizophrenia, bipolar disorder, and major depressive disorder
Xiao Y, Kandala S, Huang J, Liu J, McGonigle T, Barch D, Tang Y, Fan G, Wang F, Womer F. The involvement of the cerebellar vermis across the psychotic-affective spectrum in enriched samples of recent-onset schizophrenia, bipolar disorder, and major depressive disorder. Journal Of Psychiatric Research 2024, 181: 14-22. PMID: 39577028, DOI: 10.1016/j.jpsychires.2024.11.023.Peer-Reviewed Original ResearchBipolar disorderFunctional connectivityVermis volumeDepressive disorderCognitive measuresSpectrum of schizophreniaRecent-onset schizophreniaCerebellar vermisBrain functional connectivityPsychotic psychopathologyTotal vermisMDDSchizophreniaSubcortical regionsVisual learningFrontotemporal regionsPosterior vermisExploratory analysisDisordersCanonical correlationVermisFunctional alterationsNo significant associationSubregionsSignificant diagnosesExploring Spatio-temporal Interpretable Dynamic Brain Function with Transformer for Brain Disorder Diagnosis
Li L, Zhang L, Cao P, Yang J, Wang F, Zaiane O. Exploring Spatio-temporal Interpretable Dynamic Brain Function with Transformer for Brain Disorder Diagnosis. Lecture Notes In Computer Science 2024, 15002: 195-205. DOI: 10.1007/978-3-031-72069-7_19.Peer-Reviewed Original ResearchBrain functional modulesState-of-the-art performanceMajor depressive disorderTransformer-based frameworkSelf-attention mechanismState-of-the-artEnd-to-endSpatio-temporal representationBrain disorder diagnosisBipolar disorderBrain disordersDiagnosis of major depressive disorderPatterns of brain activityClustering strategyDynamic brain functionAssociated with brain disordersDepressive disorderExperimental resultsDisorder diagnosisBrain activitySpatio-temporal characteristicsBrain functionFunctional modulesDisordersSpatio-temporal patternsEnhanced 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(MAEEpigenetic molecular underpinnings of brain structural-functional connectivity decoupling in patients with major depressive disorder
Tang L, Zhao P, Pan C, Song Y, Zheng J, Zhu R, Wang F, Tang Y. Epigenetic molecular underpinnings of brain structural-functional connectivity decoupling in patients with major depressive disorder. Journal Of Affective Disorders 2024, 363: 249-257. PMID: 39029702, DOI: 10.1016/j.jad.2024.07.110.Peer-Reviewed Original ResearchMajor depressive disorderMajor depressive disorder patientsStructural-functional connectivityHPA axisDepressive disorderIncreased susceptibility to MDDSusceptibility to major depressive disorderMajor depressive disorder treatmentHealthy controlsStress-related disordersBrain network dynamicsMultimodal neuroimaging dataGender-matched healthy controlsSubcortical regionsNeuroimaging dataChronic stressCortical regionsNodal levelMedical statusFunctional networksCRHR1FKBP5CpG sitesDisordersMolecular underpinningsCharacterizing 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 cortexFrom Connectivity to Controllability: Unraveling the Brain Biomarkers of Major Depressive Disorder
Pan C, Ma Y, Wang L, Zhang Y, Wang F, Zhang X. From Connectivity to Controllability: Unraveling the Brain Biomarkers of Major Depressive Disorder. Brain Sciences 2024, 14: 509. PMID: 38790487, PMCID: PMC11119370, DOI: 10.3390/brainsci14050509.Peer-Reviewed Original ResearchMajor depressive disorderFunctional magnetic resonance imagingDepressive disorderTreatment of Major Depressive DisorderBiomarkers of major depressive disorderBrain functional networksDevelopment of precision medicine strategiesBrain regionsNetwork topology perspectiveNetwork neuroscienceBrain biomarkersBrain's abilityBrain statesPersonalized interventionsFunctional networksBrainMagnetic resonance imagingEfficacy of treatmentDisordersResonance imagingTopological perspectivePathological profileComplex dynamicsNeuroscienceLearningDysregulated cerebral blood flow, rather than gray matter Volume, exhibits stronger correlations with blood inflammatory and lipid markers in depression
Kang L, Wang W, Nie Z, Gong Q, Yao L, Xiang D, Zhang N, Tu N, Feng H, Zong X, Bai H, Wang G, Wang F, Bu L, Liu Z. Dysregulated cerebral blood flow, rather than gray matter Volume, exhibits stronger correlations with blood inflammatory and lipid markers in depression. NeuroImage Clinical 2024, 41: 103581. PMID: 38430800, PMCID: PMC10944186, DOI: 10.1016/j.nicl.2024.103581.Peer-Reviewed Original ResearchConceptsGray matter volumeCerebral blood flowMatter volumeArterial spin labelingRight middle temporal gyrusPredictors of MDDMiddle temporal gyrusImmune markersBrain functional changesProportion of MDDExploratory correlation analysisTumor necrosis factor-alphaBlood flowMDD patientsDepressive disorderAngular gyrusTemporal gyrusNecrosis factor-alphaMDDBrain regionsCerebral blood flow changesBlood lipid levelsInferior temporalCase-control comparisonCD4 countIntegrative omics analysis reveals epigenomic and transcriptomic signatures underlying brain structural deficits in major depressive disorder
Zheng J, Womer F, Tang L, Guo H, Zhang X, Tang Y, Wang F. Integrative omics analysis reveals epigenomic and transcriptomic signatures underlying brain structural deficits in major depressive disorder. Translational Psychiatry 2024, 14: 17. PMID: 38195555, PMCID: PMC10776753, DOI: 10.1038/s41398-023-02724-8.Peer-Reviewed Original ResearchConceptsGray matter volumeBrain structural deficitsFrontal cortexGMV changesStructural deficitsDecreased GMVGray matter volume abnormalitiesInferior frontal cortexAnterior cingulate cortexAllen Human Brain AtlasDifferentially methylated CpG positionsGray matter abnormalitiesHuman Brain AtlasRegionally specific correlationsDepressive disorderCingulate cortexMatter volumeMorphological deficitsMDDBetween-group differencesCortex regionsCortexSynaptic transmission processesDeficitsHealthy controls