Yize Zhao, PhD
Associate Professor of BiostatisticsDownloadHi-Res Photo
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Biostatistics
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Associate Professor of Biostatistics
Biography
Dr. Zhao is an Associate Professor in the Department of Biostatistics at Yale School of Public Health. She is also affiliated with Yale Center for Analytical Sciences, Yale Alzheimer's Disease Research Center and Yale Computational Biology and Bioinformatics. Her main research focuses on the development of statistical and machine learning methods to analyze large-scale complex data (imaging, -omics, EHRs), Bayesian methods, feature selection, predictive modeling, data integration, missing data and network analysis. She has strong interests in biomedical research areas including mental health, mental disorders and aging, etc. Her most recent research agenda includes analytical method development and applications on brain network analyses, multimodal imaging modeling, imaging genetics, and the integration of biomedical data with EHR data. Her research is supported by multiple NIH grants.
Dr. Zhao received her Ph.D. in Biostatistics from Emory University and postdoc training at Statistical and Applied Mathematical Sciences Institute (SAMSI) and the University of North Carolina at Chapel Hill. Prior to coming to Yale, she was an Assistant Professor in Biostatistics at Cornell University, Weill Cornell Medicine.
Appointments
Biostatistics
Associate Professor TenurePrimaryBiomedical Informatics & Data Science
Associate Professor on TermSecondary
Other Departments & Organizations
- Biomedical Informatics & Data Science
- Biostatistics
- Biostatistics
- Center for Brain & Mind Health
- Computational Biology and Biomedical Informatics
- Wu Tsai Institute
- Yale Center for Analytical Sciences (YCAS)
- Yale Combined Program in the Biological and Biomedical Sciences (BBS)
- Yale School of Public Health
- Yale School of Public Health - NEW
Education & Training
- PhD
- Emory University (2014)
- BS
- Zhejiang University (2010)
Research
Overview
Medical Research Interests
Aging; Biomedical Research; Biostatistics; Electronic Health Records; Genetics; Mental Health; Neurodegenerative Diseases; Neuroimaging; Psychiatry
Public Health Interests
Aging; Bayesian Statistics; Behavioral Health; Biomarkers; Network Analysis; Mental Health; Genetics, Genomics, Epigenetics
Research at a Glance
Yale Co-Authors
Frequent collaborators of Yize Zhao's published research.
Publications Timeline
A big-picture view of Yize Zhao's research output by year.
Research Interests
Research topics Yize Zhao is interested in exploring.
Fan Li, PhD
Denise Esserman, PhD
Erich J Greene, PhD
Julian Zhao, MSPH
Can Meng, MS, MPH
Carolyn Fredericks, MD
49Publications
219Citations
Neuroimaging
Publications
2024
Sex‐specific topological structure associated with dementia via latent space estimation
Wang S, Wang Y, Xu F, Tian X, Fredericks C, Shen L, Zhao Y, Initiative F. Sex‐specific topological structure associated with dementia via latent space estimation. Alzheimer's & Dementia 2024 PMID: 39530632, DOI: 10.1002/alz.14266.Peer-Reviewed Original ResearchHeritability and genetic contribution analysis of structural-functional coupling in human brain
Dai W, Zhang Z, Song P, Zhang H, Zhao Y. Heritability and genetic contribution analysis of structural-functional coupling in human brain. Imaging Neuroscience 2024, 2: 1-19. DOI: 10.1162/imag_a_00346.Peer-Reviewed Original ResearchAltmetricConceptsSC-FCSC-FC couplingStructural connectivityHuman Connectome ProjectConnectome ProjectVisual cortexCouplingFunctional connectivityWhite matter structural connectivityCingulo-opercularDorsal attentionGenetic correlation analysisSpectrum of complex traitsAdolescent Brain Cognitive Development StudyFunctional networksComplex traitsGenetic lociCognitive Development StudyGenetic variationGenetic interplayMode networkStructural-functional couplingSpectraCollaborative Survival Analysis on Predicting Alzheimer’s Disease Progression
Xu W, Wang S, Shen L, Zhao Y. Collaborative Survival Analysis on Predicting Alzheimer’s Disease Progression. Statistics In Biosciences 2024, 1-24. DOI: 10.1007/s12561-024-09459-0.Peer-Reviewed Original ResearchConceptsAlzheimer's diseaseGenes associated with neuronal developmentAlzheimer's Disease Neuroimaging InitiativeGenetic dataAlzheimer's disease progressionGenetic variationPhenotypic varianceAD progressionGenetic featuresNeuronal developmentGenetic biomarkersAlzheimerADNI databaseMild cognitive impairmentSNPsCanonical correlation analysisGenesStructural MRI scansCombination of brain imagingBrain imagingGeneticsProgression of mild cognitive impairmentAD literatureTime-to-event outcomesTime-to-event predictionsVolume-Optimal Persistence Homological Scaffolds of Hemodynamic Networks Covary with MEG Theta-Alpha Aperiodic Dynamics
Nguyen N, Hou T, Amico E, Zheng J, Huang H, Kaplan A, Petri G, Goñi J, Kaufmann R, Zhao Y, Duong-Tran D, Shen L. Volume-Optimal Persistence Homological Scaffolds of Hemodynamic Networks Covary with MEG Theta-Alpha Aperiodic Dynamics. Lecture Notes In Computer Science 2024, 15003: 519-529. DOI: 10.1007/978-3-031-72384-1_49.Peer-Reviewed Original ResearchConceptsFunctional magnetic resonance imagingHigher-order propertiesHuman Connectome Project Young Adult datasetDistribution of cavitiesAperiodic dynamicsAperiodic activityFunctional magnetic resonance imaging dataFunctional connectomePairwise interactionsConnectomeSpatial distribution of cavitiesDynamic insightsTask-positive stateHomologous scaffoldsSpatial distributionStatePersistent homologyMagnetoencephalographyInduced connectionResting stateNeuroimaging modalitiesYoung Adult datasetCortical regionsBayesian pathway analysis over brain network mediators for survival data
Tian X, Li F, Shen L, Esserman D, Zhao Y. Bayesian pathway analysis over brain network mediators for survival data. Biometrics 2024, 80: ujae132. PMID: 39530270, PMCID: PMC11555425, DOI: 10.1093/biomtc/ujae132.Peer-Reviewed Original ResearchAltmetricMeSH Keywords and ConceptsConceptsAccelerated failure time modelFailure time modelBrain connectivityAlzheimer's Disease Neuroimaging Initiative studyMaximum information extractionResponse regressionBayesian approachInformation extractionTime modelSurvival dataNoisy componentsUnique edgeWhite matter fiber tractsNetwork configurationBrain networksInterconnection networksNetworkNetwork mediatorsBrainSelf‐reported hearing loss is associated with faster cognitive and functional decline but not diagnostic conversion in the ADNI cohort
Miller A, Sharp E, Wang S, Zhao Y, Mecca A, van Dyck C, O'Dell R, Initiative F. Self‐reported hearing loss is associated with faster cognitive and functional decline but not diagnostic conversion in the ADNI cohort. Alzheimer's & Dementia 2024, 20: 7847-7858. PMID: 39324520, DOI: 10.1002/alz.14252.Peer-Reviewed Original ResearchConceptsSelf-reported hearing lossFunctional Activities QuestionnaireHearing lossMild cognitive impairmentModifiable risk factorsMild cognitive impairment participantsFunctional declineImpairment diagnosisModified Preclinical Alzheimer Cognitive CompositeRate of functional declineRate of cognitive declinePreclinical Alzheimer Cognitive CompositeRisk factorsCognitive impairmentSignificant longitudinal associationsActivity QuestionnaireLongitudinal associationsAlzheimer's Disease Neuroimaging InitiativeLongitudinal relationshipCognitive compositeCN participantsIncreased riskCognitive declineParticipantsDiagnostic conversionA Principled Framework to Assess the Information-Theoretic Fitness of Brain Functional Sub-Circuits
Duong-Tran D, Nguyen N, Mu S, Chen J, Bao J, Xu F, Garai S, Cadena-Pico J, Kaplan A, Chen T, Zhao Y, Shen L, Goñi J. A Principled Framework to Assess the Information-Theoretic Fitness of Brain Functional Sub-Circuits. Mathematics 2024, 12: 2967. PMID: 38979488, PMCID: PMC11230349, DOI: 10.3390/math12192967.Peer-Reviewed Original ResearchAltmetricConceptsStochastic block modelMultiple levels of granularityLevels of granularityFunctional networksInformation-theoreticThreshold methodThreshold strategyFunctional connectomeSub-circuitsGranularityHuman brain connectomeNetworkHuman Connectome ProjectBlock modelBrain connectome analysisFunctional sub-circuitsTopological featuresFrameworkPrinciples frameworkConnectome ProjectBrain connectomeNetwork neuroscienceThreshold valueMultiple levelsPartitioningEstablishing group-level brain structural connectivity incorporating anatomical knowledge under latent space modeling
Wang S, Wang Y, Xu F, Shen L, Zhao Y, Initiative A. Establishing group-level brain structural connectivity incorporating anatomical knowledge under latent space modeling. Medical Image Analysis 2024, 99: 103309. PMID: 39243600, DOI: 10.1016/j.media.2024.103309.Peer-Reviewed Original ResearchCitationsConceptsBrain structural connectivityBrain connectivityStructural connectivityBrain connectivity matricesDiffusion MRITopological propertiesGenerative network modelsWhite matter fiber tractsAttributes of nodesConnectivity estimatesBrain networksGroup-level connectivityConnectivity matrixBrain regionsConnectivity architectureKnowledge of nodesAlzheimer's diseaseLatent space modelAnatomical informationGroup-level effectsImprove biological interpretationExtensive simulationsNetwork modelABC modelMedial amygdalar tau is associated with mood symptoms in preclinical Alzheimer’s disease
Li J, Tun S, Ficek-Tani B, Xu W, Wang S, Horien C, Toyonaga T, Nuli S, Zeiss C, Powers A, Zhao Y, Mormino E, Fredericks C. Medial amygdalar tau is associated with mood symptoms in preclinical Alzheimer’s disease. Biological Psychiatry Cognitive Neuroscience And Neuroimaging 2024 PMID: 39059466, DOI: 10.1016/j.bpsc.2024.07.012.Peer-Reviewed Original ResearchAltmetricConceptsAssociated with mood symptomsMood symptomsAmyloid-positive individualsMedial amygdalaSeed-based functional connectivity analysisAssociated with anxiety symptomsSelf-reported mood symptomsPost hoc correlation analysisAlzheimer's diseaseFunctional connectivity analysisPreclinical Alzheimer's diseaseTau bindingOrbitofrontal cortexTau depositionAmygdalar connectivityEmotional processingAnxiety symptomsRetrosplenial cortexBetween-group differencesAmygdalaFunctional connectivityLateral amygdalaConnectivity analysisDepression scoresNeuropsychiatric symptomsMultiscale Estimation of Morphometricity for Revealing Neuroanatomical Basis of Cognitive Traits
Wen Z, Bao J, Yang S, Wen J, Zhan Q, Cui Y, Erus G, Yang Z, Thompson P, Zhao Y, Davatzikos C, Shen L. Multiscale Estimation of Morphometricity for Revealing Neuroanatomical Basis of Cognitive Traits. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2024, 00: 1-5. PMID: 39371474, PMCID: PMC11452152, DOI: 10.1109/isbi56570.2024.10635581.Peer-Reviewed Original Research
Academic Achievements & Community Involvement
activity Biometrics
Journal ServiceAssociate EditorDetails2022 - Presentactivity BMC Medical Research Methodology
Journal ServiceAssociate EditorDetails2019 - Presenthonor YSPH Investigator Research Award
Yale University AwardYSPHDetails05/01/2024activity Regional Advisory Board
Advisory BoardsMemberDetailsInternational Biometric Society/Eastern North American Region2019 - 2022honor Elected member
International AwardInternational Statistical InstituteDetails07/01/2021Netherlands
News & Links
News
- December 20, 2024
Interdisciplinary Collaboration Yields Greater Impact
- August 29, 2024
Yale Researchers Awarded $20.6M Grant for Wide-Ranging Study of Mental Illness
- May 23, 2024
2024 Top Research Awards Announced
- January 10, 2023
Award-winning Associate Professor Yize Zhao Applies Innovative Statistical Methods to Advance Medical Science
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