Fei Wang
Associate Professor Adjunct, PsychiatryCards
Contact Info
About
Titles
Associate Professor Adjunct, Psychiatry
Departments & Organizations
- Yale Global Mental Health Program
- Yale Ventures
Research
Overview
My longstanding interest is identification of neural circuitry abnormalities using multimodal neuroimaging techniques and investigation of how genetic variations influence neural circuitry to produce the clinical phenotypes of mental disorders, such as schizophrenia, bipolar disorder and major depressive disorder. My primary research interests lie in developing novel multimodal magnetic resonance imaging techniques to characterize the critical neural circuitry abnormalities underlying these disorders and identifying the specific genetic variations that contribute to them. Our work in translational research will contribute to elucidating the neuropathophysiological mechanisms underlying the disorders, aid in the development of new methods for early detection, and importantly, improve treatment of debilitating psychiatric illnesses.
1. Methods of multimodal neuroimaging:
This project aims to use multimodal neuroimaging techniques to identify abnormalities of important neural circuitry in mental disorders. I developed diffusion tensor imaging (DTI) methods to study the cingulum, a white matter structure important in cortico-limbic circuitry. My work demonstrated abnormalities in the anterior cingulum in schizophrenia and bipolar disorder; however, findings demonstrate a different distribution of white matter abnormalities in the two disorders. I have also developed functional magnetic resonance imaging methods to study cortico-limbic functional connectivity to be integrated with DTI methods. Using these methodologies, I have obtained exciting findings that demonstrate altered structural and functional connectivity in cortico-limbic neural circuitry in mood disorders. Moreover, I have identified an association between structural and functional connectivity within this circuitry in bipolar disorder, providing some of the first evidence that these structural abnormalities may contribute to disruptions in the ability of the cortical region to modulate the functioning of limbic structure in mood disorders.
2. Integration of multimodal neuroimaging and molecular genetics:
This project aims to develop translational research approaches of integrating molecular genetics with multimodal neuroimaging to identify novel effects of genetic variations on cortico-limbic circuitry in mental disorders. I have reported an important finding of the influence of genetic variation in neuregulin 1 on dorsal frontotemporal white matter connection abnormalities in schizophrenia. I have also recently authored psychiatric genetic papers in mood disorders including a paper that reports a novel finding of an association between variation in the vascular endothelial growth factor gene and cortico-limbic structure and on two papers on the association between the brain-derived neurotrophic growth factor gene/serotonin transporter protein gene and cortico-limbic structure/function in bipolar disorder.
1. Methods of multimodal neuroimaging:
This project aims to use multimodal neuroimaging techniques to identify abnormalities of important neural circuitry in mental disorders. I developed diffusion tensor imaging (DTI) methods to study the cingulum, a white matter structure important in cortico-limbic circuitry. My work demonstrated abnormalities in the anterior cingulum in schizophrenia and bipolar disorder; however, findings demonstrate a different distribution of white matter abnormalities in the two disorders. I have also developed functional magnetic resonance imaging methods to study cortico-limbic functional connectivity to be integrated with DTI methods. Using these methodologies, I have obtained exciting findings that demonstrate altered structural and functional connectivity in cortico-limbic neural circuitry in mood disorders. Moreover, I have identified an association between structural and functional connectivity within this circuitry in bipolar disorder, providing some of the first evidence that these structural abnormalities may contribute to disruptions in the ability of the cortical region to modulate the functioning of limbic structure in mood disorders.
2. Integration of multimodal neuroimaging and molecular genetics:
This project aims to develop translational research approaches of integrating molecular genetics with multimodal neuroimaging to identify novel effects of genetic variations on cortico-limbic circuitry in mental disorders. I have reported an important finding of the influence of genetic variation in neuregulin 1 on dorsal frontotemporal white matter connection abnormalities in schizophrenia. I have also recently authored psychiatric genetic papers in mood disorders including a paper that reports a novel finding of an association between variation in the vascular endothelial growth factor gene and cortico-limbic structure and on two papers on the association between the brain-derived neurotrophic growth factor gene/serotonin transporter protein gene and cortico-limbic structure/function in bipolar disorder.
- Frontotemporal Neural Systems in Bipolar Disorder and Schizophrenia: This project aims to investigate differences in the distribution of brain abnormalities in bipolar disorder and schizophrenia and to determine which genes may contribute to the distinct distributions.
- Structural and Functional Connectivity of the Perigenual Anterior Cingulate in Adolescents with Bipolar Disorder: This project proposes to integrate multimodal magnetic resonance imaging techniques in order to investigate the different developmental trajectories of structural and functional connections between the amygdala and anterior cingulate cortex in adolescents with and without bipolar disorder.
- The Neural Circuitry of Adolescent Major Depressive Disorder: A Multi-modality Magnetic Resonance Imaging Study: This project proposes to integrate multimodal magnetic resonance imaging techniques in order to investigate the structural and functional connections between the ventral prefrontal cortex and the amygdala in adolescents with and without major depressive disorder.
Medical Research Interests
Bipolar Disorder; Depressive Disorder; Schizophrenia
Research at a Glance
Yale Co-Authors
Frequent collaborators of Fei Wang's published research.
Publications Timeline
A big-picture view of Fei Wang's research output by year.
Research Interests
Research topics Fei Wang is interested in exploring.
Hilary Blumberg, MD
Joel Gelernter, MD
Xingguang Luo, MD
32Publications
566Citations
Bipolar Disorder
Schizophrenia
Publications
2024
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 ResearchConceptsBipolar disorderFunctional connectivityVermis volumeDepressive disorderCognitive measuresSpectrum of schizophreniaRecent-onset schizophreniaCerebellar vermisBrain functional connectivityPsychotic psychopathologyTotal vermisMDDSchizophreniaSubcortical regionsVisual learningFrontotemporal regionsPosterior vermisExploratory analysisDisordersCanonical correlationVermisFunctional alterationsNo significant associationSubregionsSignificant diagnosesEffectiveness 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 ResearchConceptsActivation 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-interventionExploring 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 ResearchConceptsBrain 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 ResearchConceptsFeature 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 ResearchMeSH Keywords and ConceptsConceptsPsychological 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. PMID: 39355380, PMCID: PMC11442283, DOI: 10.3389/fpsyt.2024.1422020.Peer-Reviewed Original ResearchConceptsVocal 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 ResearchAltmetricConceptsAt-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 ResearchConceptsMajor 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 ResearchCitationsConceptsResponse 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 ResearchMeSH Keywords and ConceptsConceptsStudents' 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 attention
News
News
- May 29, 2015
The adolescent brain develops differently in bipolar disorder
- August 21, 2012
Seven Department of Psychiatry researchers receive Young Investigator Grants From Brain & Behavior Research Foundation
- June 01, 2010
Grants and contracts awarded to Yale School of Medicine
- January 15, 2009
A gene that helps blood vessels feed tumor growth also aids in brain plasticity
Get In Touch
Contacts
Email
Academic Office Number
Office Fax Number
Mailing Address
Psychiatry
300 George St
New Haven, CT 06511-
United States