Corey Horien
DownloadHi-Res Photo
About
Biography
I grew up in Thief River Falls, MN and went to Concordia College in Moorhead, MN. I spent a year at the University of Oxford studying various aspects of Neisseria type four pili molecular biology. I obtained my PhD in the lab of Todd Constable through the Interdepartmental Neuroscience program, studying individual differences in the developing functional connectome.
Fun fact about me: obsessed with bikram yoga--I do a killer triangle pose.
Education & Training
- BA
- Concordia College (MN), Biology (2013)
Research
Research at a Glance
Yale Co-Authors
Frequent collaborators of Corey Horien's published research.
Publications Timeline
A big-picture view of Corey Horien's research output by year.
Todd Constable, PhD
Abigail Greene
Dustin Scheinost, PhD, BS
Carolyn Fredericks, MD
Francesca Mandino
Kangjoo Lee, PhD
41Publications
1,346Citations
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 ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsAttention 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 ResearchCitationsAltmetricMeSH KeywordsBrain–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 ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsBrain-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 StatementsCitationsAltmetricMeSH Keywords and ConceptsThe 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 ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsHigh ID ratesIndividual differencesFunctional connectomeIndividual functional connectomesStable individual differencesID rateResting-state fMRI datasetsFrontoparietal networkFunctional connectivityParietal cortexFMRI datasetsIdiosyncratic aspectsConnectomeHead motionEntire brainFMRIBrainCortexSpecific datasetDifferencesConnectivity
2023
Functional Connectivity MR Imaging
Horien C, Shen X, Scheinost D, Constable R, Hampson M. Functional Connectivity MR Imaging. 2023, 521-541. DOI: 10.1007/978-3-031-10909-6_24.Peer-Reviewed Original ResearchConnectome-based machine learning models are vulnerable to subtle data manipulations
Rosenblatt M, Rodriguez R, Westwater M, Dai W, Horien C, Greene A, Constable R, Noble S, Scheinost D. Connectome-based machine learning models are vulnerable to subtle data manipulations. Patterns 2023, 4: 100756. PMID: 37521052, PMCID: PMC10382940, DOI: 10.1016/j.patter.2023.100756.Peer-Reviewed Original ResearchCitationsAltmetricConceptsData manipulationNoise attacksPrediction performanceMachine learning modelsManipulated dataLearning modelHigh trustworthinessConnectome dataTrustworthinessAttacksModel performancePredictive modelDownstream analysisPerformanceAcademic researchMachineRobustnessModelConnectomeConnectome-based modelsFunctional connectomeManipulationAssociations of physical frailty with health outcomes and brain structure in 483 033 middle-aged and older adults: a population-based study from the UK Biobank
Jiang R, Noble S, Sui J, Yoo K, Rosenblatt M, Horien C, Qi S, Liang Q, Sun H, Calhoun V, Scheinost D. Associations of physical frailty with health outcomes and brain structure in 483 033 middle-aged and older adults: a population-based study from the UK Biobank. The Lancet Digital Health 2023, 5: e350-e359. PMID: 37061351, PMCID: PMC10257912, DOI: 10.1016/s2589-7500(23)00043-2.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsPopulation-based studyPhysical frailtyHealth-related outcomesBrain structuresMental healthHealth outcomesHealth measuresTotal white matter hyperintensitiesIndicators of frailtySeverity of frailtyLower gray matter volumePoor physical fitnessWhite matter hyperintensitiesGray matter volumeUK BiobankHealth-related measuresPoor mental healthMental health measuresDirection of associationMatter hyperintensitiesUnhealthy lifestyleEarly-life risksPsychiatric disordersNumerous confoundersPreventative strategiesA consensus protocol for functional connectivity analysis in the rat brain
Grandjean J, Desrosiers-Gregoire G, Anckaerts C, Angeles-Valdez D, Ayad F, Barrière D, Blockx I, Bortel A, Broadwater M, Cardoso B, Célestine M, Chavez-Negrete J, Choi S, Christiaen E, Clavijo P, Colon-Perez L, Cramer S, Daniele T, Dempsey E, Diao Y, Doelemeyer A, Dopfel D, Dvořáková L, Falfán-Melgoza C, Fernandes F, Fowler C, Fuentes-Ibañez A, Garin C, Gelderman E, Golden C, Guo C, Henckens M, Hennessy L, Herman P, Hofwijks N, Horien C, Ionescu T, Jones J, Kaesser J, Kim E, Lambers H, Lazari A, Lee S, Lillywhite A, Liu Y, Liu Y, López -Castro A, López-Gil X, Ma Z, MacNicol E, Madularu D, Mandino F, Marciano S, McAuslan M, McCunn P, McIntosh A, Meng X, Meyer-Baese L, Missault S, Moro F, Naessens D, Nava-Gomez L, Nonaka H, Ortiz J, Paasonen J, Peeters L, Pereira M, Perez P, Pompilus M, Prior M, Rakhmatullin R, Reimann H, Reinwald J, Del Rio R, Rivera-Olvera A, Ruiz-Pérez D, Russo G, Rutten T, Ryoke R, Sack M, Salvan P, Sanganahalli B, Schroeter A, Seewoo B, Selingue E, Seuwen A, Shi B, Sirmpilatze N, Smith J, Smith C, Sobczak F, Stenroos P, Straathof M, Strobelt S, Sumiyoshi A, Takahashi K, Torres-García M, Tudela R, van den Berg M, van der Marel K, van Hout A, Vertullo R, Vidal B, Vrooman R, Wang V, Wank I, Watson D, Yin T, Zhang Y, Zurbruegg S, Achard S, Alcauter S, Auer D, Barbier E, Baudewig J, Beckmann C, Beckmann N, Becq G, Blezer E, Bolbos R, Boretius S, Bouvard S, Budinger E, Buxbaum J, Cash D, Chapman V, Chuang K, Ciobanu L, Coolen B, Dalley J, Dhenain M, Dijkhuizen R, Esteban O, Faber C, Febo M, Feindel K, Forloni G, Fouquet J, Garza-Villarreal E, Gass N, Glennon J, Gozzi A, Gröhn O, Harkin A, Heerschap A, Helluy X, Herfert K, Heuser A, Homberg J, Houwing D, Hyder F, Ielacqua G, Jelescu I, Johansen-Berg H, Kaneko G, Kawashima R, Keilholz S, Keliris G, Kelly C, Kerskens C, Khokhar J, Kind P, Langlois J, Lerch J, López-Hidalgo M, Manahan-Vaughan D, Marchand F, Mars R, Marsella G, Micotti E, Muñoz-Moreno E, Near J, Niendorf T, Otte W, Pais-Roldán P, Pan W, Prado-Alcalá R, Quirarte G, Rodger J, Rosenow T, Sampaio-Baptista C, Sartorius A, Sawiak S, Scheenen T, Shemesh N, Shih Y, Shmuel A, Soria G, Stoop R, Thompson G, Till S, Todd N, Van Der Linden A, van der Toorn A, van Tilborg G, Vanhove C, Veltien A, Verhoye M, Wachsmuth L, Weber-Fahr W, Wenk P, Yu X, Zerbi V, Zhang N, Zhang B, Zimmer L, Devenyi G, Chakravarty M, Hess A. A consensus protocol for functional connectivity analysis in the rat brain. Nature Neuroscience 2023, 26: 673-681. PMID: 36973511, PMCID: PMC10493189, DOI: 10.1038/s41593-023-01286-8.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConnectome-based predictive modeling shows sex differences in brain-based predictors of memory performance
Ju S, Horien C, Shen X, Abuwarda H, Trainer A, Constable R, Fredericks C. Connectome-based predictive modeling shows sex differences in brain-based predictors of memory performance. Frontiers In Dementia 2023, 2: 1126016. PMID: 39082002, PMCID: PMC11285565, DOI: 10.3389/frdem.2023.1126016.Peer-Reviewed Original ResearchCitationsAltmetricConceptsDefault mode networkConnectome-based predictive modelingMemory performanceMemory taskMemory scoresShort-term memory performanceBrain-based predictorsShort-term memoryPosterior default mode networkAmnestic Alzheimer's diseaseRecollective memoryDMN activityMode networkVisual networkPast researchSex-specific modelsVisual circuitryAlzheimer's diseaseSex differencesMemoryDifferent circuitryTaskElevated riskMachine learning approachesNetwork activity