Corey Horien
About
Research
Publications
Featured Publications
A generalizable connectome-based marker of in-scan sustained attention in neurodiverse youth
Horien C, Greene A, Shen X, Fortes D, Brennan-Wydra E, Banarjee C, Foster R, Donthireddy V, Butler M, Powell K, Vernetti A, Mandino F, O'Connor D, Lake E, McPartland J, Volkmar F, Chun M, Chawarska K, Rosenberg M, Scheinost D, Constable R. A generalizable connectome-based marker of in-scan sustained attention in neurodiverse youth. Cerebral Cortex 2022, 33: 6320-6334. PMID: 36573438, PMCID: PMC10183743, DOI: 10.1093/cercor/bhac506.Peer-Reviewed Original ResearchConceptsAttention taskAttentional stateConnectome-based predictive modelingNeurodiverse conditionsSustained attention taskAttention network modelSample of youthNeurotypical participantsSustained attentionBrain correlatesNeurobiological correlatesAttention networkIndividual participantsSeparate samplesYouthParticipantsHead motionTaskCorrelatesAttentionAutismConfoundsNetwork modelGeneralizesHealthcare settingsWhy is everyone talking about brain state?
Greene A, Horien C, Barson D, Scheinost D, Constable R. Why is everyone talking about brain state? Trends In Neurosciences 2023, 46: 508-524. PMID: 37164869, PMCID: PMC10330476, DOI: 10.1016/j.tins.2023.04.001.Peer-Reviewed Original ResearchBrain–phenotype models fail for individuals who defy sample stereotypes
Greene AS, Shen X, Noble S, Horien C, Hahn CA, Arora J, Tokoglu F, Spann MN, Carrión CI, Barron DS, Sanacora G, Srihari VH, Woods SW, Scheinost D, Constable RT. Brain–phenotype models fail for individuals who defy sample stereotypes. Nature 2022, 609: 109-118. PMID: 36002572, PMCID: PMC9433326, DOI: 10.1038/s41586-022-05118-w.Peer-Reviewed Original ResearchConceptsBrain-phenotype relationshipsBrain functional organizationCognitive constructsIndividual differencesNeurocognitive measuresBrain activityNeurocognitive scoresStereotypical profileNeural targetsClinical interventionsNeural circuitsFunctional organizationIndividualsSuch relationshipsData-driven approachRelationshipStereotypesFunctional Connectome–Based Predictive Modeling in Autism
Horien C, Floris DL, Greene AS, Noble S, Rolison M, Tejavibulya L, O'Connor D, McPartland JC, Scheinost D, Chawarska K, Lake EMR, Constable RT. Functional Connectome–Based Predictive Modeling in Autism. Biological Psychiatry 2022, 92: 626-642. PMID: 35690495, PMCID: PMC10948028, DOI: 10.1016/j.biopsych.2022.04.008.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsThe individual functional connectome is unique and stable over months to years
Horien C, Shen X, Scheinost D, Constable RT. The individual functional connectome is unique and stable over months to years. NeuroImage 2019, 189: 676-687. PMID: 30721751, PMCID: PMC6422733, DOI: 10.1016/j.neuroimage.2019.02.002.Peer-Reviewed Original ResearchConceptsHigh ID ratesIndividual differencesFunctional connectomeIndividual functional connectomesStable individual differencesID rateResting-state fMRI datasetsFrontoparietal networkFunctional connectivityParietal cortexFMRI datasetsIdiosyncratic aspectsConnectomeHead motionEntire brainFMRIBrainCortexSpecific datasetDifferencesConnectivity
2026
Optimizing functional connectivity scanning conditions for predicting autistic traits
Horien C, Mandino F, Greene A, Shen X, Powell K, Vernetti A, O’Connor D, Adkinson B, Tejavibulya L, McPartland J, Volkmar F, Chun M, Chawarska K, Lake E, Rosenberg M, Satterthwaite T, Scheinost D, Finn E, Constable R. Optimizing functional connectivity scanning conditions for predicting autistic traits. Nature Mental Health 2026, 4: 792-805. PMID: 42137910, PMCID: PMC13167459, DOI: 10.1038/s44220-026-00623-7.Peer-Reviewed Original ResearchSustained attention taskAutistic traitsAttention taskConnectome-based predictive modelingAutism Brain Imaging Data ExchangeBrain-phenotype relationshipsScanning conditionsMagnetic resonance imaging-based studiesMeasures of attentionSocial attention taskOptimal brain stateMeasure of social responsibilityHealthy Brain NetworkSample of youthResting-state conditionNeurobiological correlatesAttentional challengesNeurotypical adultsNeurotypical participantsBrain networksAutistic featuresBrain statesAutismClinically relevant phenotypesImaging-based studiesFeature selection leads to divergent neurobiological interpretations of brain-based machine learning biomarkers
Adkinson B, Rosenblatt M, Sun H, Dadashkarimi J, Tejavibulya L, Horien C, Westwater M, Rodriguez R, Noble S, Scheinost D. Feature selection leads to divergent neurobiological interpretations of brain-based machine learning biomarkers. Nature Human Behaviour 2026, 1-15. PMID: 41986741, DOI: 10.1038/s41562-026-02447-y.Peer-Reviewed Original ResearchNeurobiological interpretationNeurobiological basisBrain-behavior associationsBrain connectivity dataHuman neuroimagingPsychiatric phenotypesBehavioral phenotypesFunctional MRIFunctional connectivityNeuroimaging dataMental healthLarge-scale neuroimaging datasetsConnectivity dataNeuroimaging datasetsNeurobiologyMRI studiesNeuroimagingExternal validationCognitionConnectomeGeneralizabilityParticipants
2025
Investigating Eye Movement Metrics and Traditional Visuospatial Task Performance in Heterogeneous Alzheimer's Disease
Yi H, Ficek‐Tani B, Horien C, Fredericks C. Investigating Eye Movement Metrics and Traditional Visuospatial Task Performance in Heterogeneous Alzheimer's Disease. Alzheimer's & Dementia 2025, 21: e100357. PMCID: PMC12738021, DOI: 10.1002/alz70857_100357.Peer-Reviewed Original ResearchEye movement metricsTests of visuospatial functionVisuospatial task performanceEye-tracking metricsAD participantsFixation durationVisuospatial functionTask performanceMeasures of spatial functionsNeuropsychological test performanceVisuospatial function scoresEye-tracking dataVisual search paradigmBlock Design scoresLonger fixation durationsMovement metricsNo group differencesAlzheimer's diseaseEye movement performanceCognitive test resultsNeutral scenesNeuropsychological metricsHC participantsNeuropsychological assessmentStimulus categoriesImpulsivity and neuroticism share distinct functional connectivity signatures with alcohol-use risk in youth
Cheng A, Lichenstein S, Chaarani B, Liang Q, Babaeianjelodar M, Riley S, Luo W, Horien C, Greene A, Banaschewski T, Bokde A, Desrivières S, Flor H, Grigis A, Gowland P, Heinz A, Brühl R, Martinot J, Martinot M, Artiges E, Nees F, Papadopoulos Orfanos D, Poustka L, Hohmann S, Holz N, Baeuchl C, Smolka M, Vaidya N, Walter H, Whelan R, Schumann G, Constable R, Pearlson G, Garavan H, Yip S. Impulsivity and neuroticism share distinct functional connectivity signatures with alcohol-use risk in youth. Molecular Psychiatry 2025, 31: 953-962. PMID: 40913110, PMCID: PMC12815650, DOI: 10.1038/s41380-025-03196-6.Peer-Reviewed Original ResearchFunctional connectivity signaturesNeural signaturesConnectivity signaturesConnectome-based predictive modelingUnique neural mechanismsFunctional connectivity dataStudy of adolescent developmentDimensional traitsNegative affectDimensional phenotypesDefault modeNeural mechanismsNeuroticismAlcohol-useCerebellar networkYouth impulsivityAdolescent developmentCanonical networksAssociated with riskConnectivity dataNetwork anatomyComplex neural networksImpulseRisk behaviorsTraitsWhole-brain functional connectivity predicts regional tau PET in preclinical Alzheimer’s disease
Abuwarda H, Trainer A, Horien C, Shen X, Moret S, Ju S, Constable R, Fredericks C. Whole-brain functional connectivity predicts regional tau PET in preclinical Alzheimer’s disease. Brain Communications 2025, 7: fcaf274. PMID: 40735265, PMCID: PMC12305425, DOI: 10.1093/braincomms/fcaf274.Peer-Reviewed Original ResearchAlzheimer's disease pathologyAlzheimer's diseaseTau elevationTau-PET signalTau-PETTau modelDisease pathologyAbnormal accumulationPreclinical Alzheimer's diseaseAmyloid-bWhole-brain FCTau pathologyAssociated with tauFunctional connectivityRegional tauTauFunctional connectomeFC-based modelsWhole-brainPET signalAlzheimerConnectome-based predictive modelingWhole-brain functional connectivityDisease clinical spectrumTarget region