2024
Exploring 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 patternsAttention-based acoustic feature fusion network for depression detection
Xu X, Wang Y, Wei X, Wang F, Zhang X. Attention-based acoustic feature fusion network for depression detection. Neurocomputing 2024, 601: 128209. DOI: 10.1016/j.neucom.2024.128209.Peer-Reviewed Original ResearchFeature fusion networkFusion networkDepression detectionAdvanced machine learning paradigmsDeep neural networksMachine learning paradigmLSTM-attention mechanismSpeech databaseFeature modelSpeech featuresNeural networkAbundance of informationBoost performanceLearning paradigmImproved detection methodAuditory dataAcoustic featuresDetection methodFeature processingAdjustment moduleNetworkLSTM-AttentionResearch directionsEffective detectionFeaturesTemporal dynamics in psychological assessments: a novel dataset with scales and response times
Su Z, Liu R, Wei Y, Zhang R, Xu X, Wang Y, Zhu Y, Wang L, Liang L, Wang F, Zhang X. Temporal dynamics in psychological assessments: a novel dataset with scales and response times. Scientific Data 2024, 11: 1046. PMID: 39333112, PMCID: PMC11437125, DOI: 10.1038/s41597-024-03888-8.Peer-Reviewed Original ResearchConceptsPsychological assessmentDiagnosis of mental disordersDomain of psychologyResponse time dataEarly diagnosis of mental disordersPrevalence of mental health issuesMental health issuesGAD-7Mental disordersPsychological screeningScale reliabilityMental healthTemporal dynamicsScreening initiativesHealth issuesPublic healthXinxiang Medical UniversityHealthResponse timeScalePsychologyMedical UniversityEnhanced 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. 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(MAEEnhancing Early Diagnosis of Bipolar Disorder in Adolescents through Multimodal Neuroimaging
Wu J, Lin K, Lu W, Zou W, Li X, Tan Y, Yang J, Zheng D, Liu X, Lam B, Xu G, Wang K, McIntyre R, Wang F, So K, Wang J. Enhancing Early Diagnosis of Bipolar Disorder in Adolescents through Multimodal Neuroimaging. Biological Psychiatry 2024 PMID: 39069165, DOI: 10.1016/j.biopsych.2024.07.018.Peer-Reviewed Original ResearchAt-risk adolescentsBipolar disorderBehavioral assessmentBD patientsDiagnosis of bipolar disorderEarly diagnosis of bipolar disordersSevere neuropsychiatric conditionMultimodal MRIAt-riskBehavioral attributesEnhancing early diagnosisSubthreshold symptomsClinical interviewPsychiatric symptomsNeuropsychiatric conditionsStructural equation modelingMultimodal neuroimagingGlobal functioningBrain healthBD diagnosisBD DiagnosticsAdvanced imaging techniquesSubgroup distinctionsAdolescentsRetrospective cohortEpigenetic 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 underpinningsExploring the relationship between response time sequence in scale answering process and severity of insomnia: A machine learning approach
Su Z, Liu R, Zhou K, Wei X, Wang N, Lin Z, Xie Y, Wang J, Wang F, Zhang S, Zhang X. Exploring the relationship between response time sequence in scale answering process and severity of insomnia: A machine learning approach. Heliyon 2024, 10: e33485. PMID: 39040408, PMCID: PMC11261114, DOI: 10.1016/j.heliyon.2024.e33485.Peer-Reviewed Original ResearchResponse time dataInsomnia Severity IndexInsomnia symptomsPsychological measuresPresence of insomnia symptomsIndividual question levelSeverity of insomniaSymptom severityPsychological evaluationResponse timeInsomniaSleep qualityMachine learning modelsSeverity IndexSymptomsQuestion levelTotal response timeParticipantsLearning modelsTime dataPotential utilityEvaluate sleep qualitySeverityMachine learning approachMobile applicationsVisual 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 attentionModule control of network analysis in psychopathology
Pan C, Zhang Q, Zhu Y, Kong S, Liu J, Zhang C, Wang F, Zhang X. Module control of network analysis in psychopathology. IScience 2024, 27: 110302. PMID: 39045106, PMCID: PMC11263636, DOI: 10.1016/j.isci.2024.110302.Peer-Reviewed Original ResearchExamining the association of family environment and children emotional/behavioral difficulties in the relationship between parental anxiety and internet addiction in youth
Wang Y, Zhou K, Wang Y, Zhang J, Xie Y, Wang X, Yang W, Zhang X, Yang J, Wang F. Examining the association of family environment and children emotional/behavioral difficulties in the relationship between parental anxiety and internet addiction in youth. Frontiers In Psychiatry 2024, 15: 1341556. PMID: 38895031, PMCID: PMC11184946, DOI: 10.3389/fpsyt.2024.1341556.Peer-Reviewed Original ResearchAdolescent Internet addictionInternet addictionFamily environmentParental anxietyAssociation of family environmentEmotional behavior problemsRisk of Internet addictionPositive family environmentBehavioral issuesIndirect relationshipCIAS-RGAD-7Behavior problemsParental anxiety levelsEmotional/behavioral difficultiesAddictionParent-child pairsAnxietyFES-CVAnxiety levelsAdolescent issuesCorrelation analysisMultiple dimensionsChildrenSDQCharacterizing 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, 1-11. 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 cortexRepetitive Transcranial Magnetic Stimulation Reversing Abnormal Brain Function in Mood Disorders with Early Life Stress: from preclinical models to clinical applications
Zhao T, Guo H, Yang J, Cai A, Liu J, Zheng J, Xiao Y, Zhao P, Li Y, Luo X, Zhang X, Zhu R, Wang J, Wang F. Repetitive Transcranial Magnetic Stimulation Reversing Abnormal Brain Function in Mood Disorders with Early Life Stress: from preclinical models to clinical applications. Asian Journal Of Psychiatry 2024, 97: 104092. PMID: 38823081, DOI: 10.1016/j.ajp.2024.104092.Peer-Reviewed Original ResearchEarly life stressFunctional magnetic resonance imagingRepetitive transcranial magnetic stimulationMood disorder patientsChronic unpredictable mild stressMood disordersDisorder patientsAbnormal functional activityLife stressCross-species translational studiesImpact of early life stressFunctional magnetic resonance imaging analysisPrimary cortexRisk of mood disordersUnpredictable mild stressTargeting rTMSRTMS interventionAbnormal brain functionIncreased activityDecreased activityDepression-relatedAdolescent ratsCUMS ratsFrontal cortexMild stressFrom 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 dynamicsNeuroscienceLearningDigital Dietary Behaviors in Individuals With Depression: Real-World Behavioral Observation
Zhu Y, Zhang R, Yin S, Sun Y, Womer F, Liu R, Zeng S, Zhang X, Wang F. Digital Dietary Behaviors in Individuals With Depression: Real-World Behavioral Observation. JMIR Public Health And Surveillance 2024, 10: e47428. PMID: 38648087, PMCID: PMC11074900, DOI: 10.2196/47428.Peer-Reviewed Original ResearchConceptsModerate-severe depressionDietary behaviorsMultivariate logistic regression analysisDepression severityMild depressionIrregular eating patternsLogistic regression analysisDetect depressionDinner patternEating breakfastMeal patternsEating patternsDaily lunchSelf-reported dataSupport vector machine analysisBehavior monitoringNaturalistic settingsVector machine analysisFood choicesDepressionProfile of changesBehavioral dataTowards Disease-Aware Self-Supervised Dynamic Brain Network Learning For Mental Diagnosis
Jin Z, Wen G, Cao P, Liu L, Yang J, Zhu X, Zaiane O, Wang F. Towards Disease-Aware Self-Supervised Dynamic Brain Network Learning For Mental Diagnosis. 2024, 00: 2270-2274. DOI: 10.1109/icassp48485.2024.10446417.Peer-Reviewed Original ResearchState-of-the-art methodsRepresentation learning frameworkSupervised learning schemeSelf-attention mechanismState-of-the-artNetwork learning methodReconstruction lossContrastive lossPoor generalizationLearning schemeLearning frameworkGraph structureLearning methodsTopological informationLearning modelsCross-decodingDiagnosis resultsBrain network analysisDynamic brain network analysisMajor depressive disorderAutism spectrum disorderInformationDynamic brain networksBipolar disorderDecodingPrediction of the efficacy of group cognitive behavioral therapy using heart rate variability based smart wearable devices: a randomized controlled study
Lin Z, Zheng J, Wang Y, Su Z, Zhu R, Liu R, Wei Y, Zhang X, Wang F. Prediction of the efficacy of group cognitive behavioral therapy using heart rate variability based smart wearable devices: a randomized controlled study. BMC Psychiatry 2024, 24: 187. PMID: 38448895, PMCID: PMC10916138, DOI: 10.1186/s12888-024-05638-x.Peer-Reviewed Original ResearchConceptsGroup cognitive behavioral therapyWait-list controlCognitive behavioral therapyBehavioral therapyEfficacy of group cognitive behavioral therapyGroup cognitive behavioural therapy groupDisabling mental health problemPredictors of treatment responseAssociated with greater improvementTreatment outcomesAssociated with depressionHigher heart rate variabilityMental health problemsHeart rate variabilityAnxious symptomsAnxiety symptomsParticipants' symptomsCollege studentsHRV levelsAnxietyTrial registrationThe trialSignificant public health concernDepressionGreater improvementHeart rate variability parametersDysregulated 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 countA role for the cerebellum in motor-triggered alleviation of anxiety
Zhang X, Wu W, Shen L, Ji M, Zhao P, Yu L, Yin J, Xie S, Xie Y, Zhang Y, Li H, Zhang Q, Yan C, Wang F, De Zeeuw C, Wang J, Zhu J. A role for the cerebellum in motor-triggered alleviation of anxiety. Neuron 2024, 112: 1165-1181.e8. PMID: 38301648, DOI: 10.1016/j.neuron.2024.01.007.Peer-Reviewed Original ResearchAlleviation of anxietyAnxiolytic effectsDentate neuronsBrain mechanismsOrexinergic projectionsAmygdalar neuronsEmotional systemsCerebellar dentate nucleusAnxietyReduce anxietyPhysical exerciseMotor systemRotarodHypothalamic neuronsDentate nucleusNeuronsAmygdalaSubject animalsDentateCerebellumBrainAnimalsState- and trait-related dysfunctions in bipolar disorder across different mood states: a graph theory study
Chen Y, Zhao P, Pan C, Chang M, Zhang X, Duan J, Wei Y, Tang Y, Wang F. State- and trait-related dysfunctions in bipolar disorder across different mood states: a graph theory study. Journal Of Psychiatry And Neuroscience 2024, 49: e11-e22. PMID: 38238036, PMCID: PMC10803102, DOI: 10.1503/jpn.230069.Peer-Reviewed Original ResearchConceptsDepressed BDBipolar disorderEuthymic BDMood statesManic BDDiffusion-tensor imagingHealthy controlsGenetic riskWhite matter networksGlobal efficiencyFrontolimbic circuitsCross-sectional natureStructural topological propertiesGraph theory approachSecondary analysisMedication effectsLocal network changesMoodLocal efficiencyClinical variablesEpisode countsNetwork changesDisordersStructural networkHigh-risk individualsIntegrative 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