2025
Covariance-on-covariance regression
Zhao Y, Zhao Y. Covariance-on-covariance regression. Biometrics 2025, 81: ujaf097. PMID: 40742449, PMCID: PMC12312406, DOI: 10.1093/biomtc/ujaf097.Peer-Reviewed Original ResearchSupervised brain node and network construction under voxel-level functional imaging
Xu W, Wang S, Gao S, Tian X, Tan C, Shen X, Luo W, Constable T, Li T, Zhao Y. Supervised brain node and network construction under voxel-level functional imaging. Imaging Neuroscience 2025, 3: imag.a.56. PMID: 40800928, PMCID: PMC12319940, DOI: 10.1162/imag.a.56.Peer-Reviewed Original ResearchAdolescent Brain Cognitive DevelopmentHuman Connectome ProjectResting-stateFunctional time-coursesBrain functional architectureBrain nodesConnectome ProjectCognitive tasksBrain regionsBrain parcellationFMRI dataBrain connectivityParcellation schemesBehavioral outcomesCognitive developmentConnectivity matrixFunctional architectureDownstream prediction tasksNode partitionBrainFunctional imagingBrain atlasesPrediction taskTwo-step processTime courseCost Efficiency of fMRI Studies Using Resting‐State Vs. Task‐Based Functional Connectivity
Zhang X, Hulvershorn L, Constable T, Zhao Y, Wang S. Cost Efficiency of fMRI Studies Using Resting‐State Vs. Task‐Based Functional Connectivity. Human Brain Mapping 2025, 46: e70260. PMID: 40543060, PMCID: PMC12182254, DOI: 10.1002/hbm.70260.Peer-Reviewed Original ResearchConceptsNeuroimaging studiesNeuropsychological outcomesFMRI conditionsGradual-onset continuous performance taskCognitive control outcomesN-back memory taskContinuous performance taskNegative emotional outcomesFMRI taskMemory taskFMRI studyPerformance tasksTask conditionsEmotional outcomesFMRITaskFunctional fingerprintsPower differentialsSociabilityOutcomesControl outcomesscMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links
Wang G, Zhao J, Lin Y, Liu T, Zhao Y, Zhao H. scMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links. Nature Communications 2025, 16: 4994. PMID: 40442129, PMCID: PMC12122792, DOI: 10.1038/s41467-025-60333-z.Peer-Reviewed Original ResearchConceptsDeep learning frameworkSingle-cell multi-omics researchSingle-cell multi-omics dataLearning frameworkMulti-omics dataGenerative adversarial networkSingle-cell technologiesData alignmentSingle-cell resolutionMulti-omics researchDownstream analysisCellular statesOmics datasetsAdversarial networkNeural networkProteomic profilingCorrelated featuresBiological informationOmics perspectiveDiverse datasetsFeature topologyDisease mechanismsCell embeddingData resourcesRelationship inferenceBayesian Longitudinal Network Regression With Application to Brain Connectome Genetics
Li C, Tian X, Gao S, Wang S, Wang G, Zhao Y, Zhao Y. Bayesian Longitudinal Network Regression With Application to Brain Connectome Genetics. Statistics In Medicine 2025, 44: e70069. PMID: 40277222, DOI: 10.1002/sim.70069.Peer-Reviewed Original ResearchConceptsSample relatednessLongitudinal genome-wide association studiesGenome-wide association studiesBrain imaging genetic studiesMultivariate phenotypesGenetic signalsImaging genetics studiesAssociation studiesGenetic studiesGenetic variantsGenetic underpinningsGenetic contributionGenetic effectsRelatednessAdolescent Brain Cognitive DevelopmentBrain functional connectivityFunctional organizationBiological architectureFunctional connectivityRobust inferenceGeneticsPhenotypeAnalytical challengesPosterior inferenceBrain network configurationA genetically informed brain atlas for enhancing brain imaging genomics
Bao J, Wen J, Chang C, Mu S, Chen J, Shivakumar M, Cui Y, Erus G, Yang Z, Yang S, Wen Z, Zhao Y, Kim D, Duong-Tran D, Saykin A, Zhao B, Davatzikos C, Long Q, Shen L. A genetically informed brain atlas for enhancing brain imaging genomics. Nature Communications 2025, 16: 3524. PMID: 40229250, PMCID: PMC11997130, DOI: 10.1038/s41467-025-57636-6.Peer-Reviewed Original ResearchConceptsBrain imaging genomicsImaging genomicsComplex traits/diseasesSNP heritabilityFunctional annotationGenetic architecturePolygenic risk scoresGenomic investigationsGenetic ancestryDiscovery powerBrain atlasesHuman brain structureGenetic determinantsNeuroanatomical heterogeneityGenomeNeuroanatomical variationImaging endophenotypesBrain structuresMolecular levelBrain voxelsHeritabilityPhenotypic correlationsGiant regionGeneticsBrain conditionsSemiparametric joint modeling for biomarker trajectory before disease onset
Sun Y, Zhao X, Chan K, Xu W, Allore H, Zhao Y. Semiparametric joint modeling for biomarker trajectory before disease onset. Biometrics 2025, 81: ujaf064. PMID: 40433774, PMCID: PMC12117339, DOI: 10.1093/biomtc/ujaf064.Peer-Reviewed Original ResearchConceptsSemiparametric joint modelFinite-sample performanceLarge-sample propertiesConduct simulation studiesBiomarker trajectoriesNatural time scaleJoint modelEstimated regression coefficientsTime-on-studySimulation studyTrajectory functionRegression coefficientsEquationsKernelTime scalesProfile kernel
2024
Bayesian thresholded modeling for integrating brain node and network predictors
Sun Z, Xu W, Li T, Kang J, Alanis-Lobato G, Zhao Y. Bayesian thresholded modeling for integrating brain node and network predictors. Biostatistics 2024, 26: kxae048. PMID: 39780514, PMCID: PMC11823287, DOI: 10.1093/biostatistics/kxae048.Peer-Reviewed Original ResearchConceptsPrediction mechanismNetwork-level metricsExtensive simulationsNetwork predictorPrior modelsSub-networksVector-variantPosterior inferenceNodesSignal patternsPredictable componentBrain nodesSpatial contiguityBayesian regression modelsImagesHierarchyLiterature gapNetworkMetricsCommunicationAlternative approachOut-of-sample predictionsInferenceModelBayesian subtyping for multi-state brain functional connectome with application on preadolescent brain cognition
Chen T, Zhao H, Tan C, Constable T, Yip S, Zhao Y. Bayesian subtyping for multi-state brain functional connectome with application on preadolescent brain cognition. Biostatistics 2024, 26: kxae045. PMID: 39656842, PMCID: PMC11823269, DOI: 10.1093/biostatistics/kxae045.Peer-Reviewed Original ResearchAdolescent Brain Cognitive DevelopmentVariational inference algorithmApproximate posterior inferenceFunctional connectivityMultiple cognitive statesInference algorithmExtensive simulationsNetwork topologyNetwork featuresFunctional network patternsBrain functional connectomeBrain functional connectivityEstimation accuracySubgroups of individualsNeurobiological heterogeneityCognitive profileCognitive statesConverging evidencePosterior inferenceDetection alternativesNeuroscience literatureBrain cognitionFunctional connectomeNetworkCognitive developmentEffect of brain network scale on Persistence Cycles: An ADNI comparative study
Garai S, Liu M, Xu F, Goñi J, Duong‐Tran D, Zhao Y, Shen L, for the ADNI. Effect of brain network scale on Persistence Cycles: An ADNI comparative study. Alzheimer's & Dementia 2024, 20: e092343. PMCID: PMC11716291, DOI: 10.1002/alz.092343.Peer-Reviewed Original ResearchStructural connectomeFunctional connectomeBOLD signal fluctuationsBrain networksHomologation cycleAlzheimer's diseaseDiffusion tensor imagingStages of AD progressionAverage persistenceFMRI neuroimagingTopological featuresAlzheimer's Disease Neuroimaging InitiativeStages of disease progressionSignal fluctuationsConnectomeGroup differencesResolution scaleAD progressionImage resolutionResolution imagesTopological pointIncrease image resolutionDeath timeRegion-of-interestBarcodingSex‐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, 20: 8387-8401. PMID: 39530632, PMCID: PMC11667551, DOI: 10.1002/alz.14266.Peer-Reviewed Original ResearchCaudal and Thalamic Segregation in White Matter Brain Network Communities in Alzheimer's Disease Population
Xu F, Duong-Tran D, Zhao Y, Shen L. Caudal and Thalamic Segregation in White Matter Brain Network Communities in Alzheimer's Disease Population. 2022 IEEE-EMBS International Conference On Biomedical And Health Informatics (BHI) 2024, 00: 1-8. PMID: 40814337, PMCID: PMC12345622, DOI: 10.1109/bhi62660.2024.10913835.Peer-Reviewed Original ResearchStructural brain networksMild cognitive impairmentBrain networksDiffusion tensor imagingNeural correlates of cognitive deficitsCorrelates of cognitive deficitsConsensus communitiesAlzheimer's diseaseDecision-making regionsWhite matter connectivityHealthy controlsResolution parameterNeural correlatesCognitive deficitsNeuroimaging studiesBrain regionsDiagnosed AD subjectsDisconnection syndromeClinically diagnosed ADCognitive impairmentAD groupTensor imagingAD subjectsGray matterAD relationshipsHeritability 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: imag-2-00346. PMID: 40800503, PMCID: PMC12290780, DOI: 10.1162/imag_a_00346.Peer-Reviewed Original ResearchSC-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 ResearchAlzheimer'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. PMID: 39949393, PMCID: PMC11816146, DOI: 10.1007/978-3-031-72384-1_49.Peer-Reviewed Original ResearchFunctional 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 ResearchConceptsAccelerated 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, PMCID: PMC11567835, DOI: 10.1002/alz.14252.Peer-Reviewed Original ResearchSelf-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 ResearchStochastic 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 levelsPartitioningA 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: 40822446, PMCID: PMC12352546, DOI: 10.3390/math12192967.Peer-Reviewed Original ResearchStochastic block modelLevels of granularityFunctional networksThreshold methodThreshold strategyFunctional connectomeSub-circuitsGranularityHuman brain connectomeNetworkHuman Connectome ProjectBlock modelBrain connectome analysisFunctional sub-circuitsTopological featuresConnectome ProjectBrain connectomeFrameworkNetwork neuroscienceThreshold valuePartitioningConnectome analysisConnectomeEstablishing 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, PMCID: PMC11609031, DOI: 10.1016/j.media.2024.103309.Peer-Reviewed Original ResearchBrain 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 model
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply