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
2024
Medial 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 ResearchAssociated 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 symptoms
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.ChaptersConnectome-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 ResearchData 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 ResearchConceptsPopulation-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 ResearchConnectome-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 ResearchDefault 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
2022
Sex differences in default mode network connectivity in healthy aging adults
Ficek-Tani B, Horien C, Ju S, Xu W, Li N, Lacadie C, Shen X, Scheinost D, Constable T, Fredericks C. Sex differences in default mode network connectivity in healthy aging adults. Cerebral Cortex 2022, 33: 6139-6151. PMID: 36563018, PMCID: PMC10183749, DOI: 10.1093/cercor/bhac491.Peer-Reviewed Original ResearchConceptsDefault mode networkPreclinical Alzheimer's diseaseAlzheimer's diseaseSex differencesBrain connectivity changesDefault mode network connectivityIntrinsic connectivity distributionSeed-based analysisMode network connectivityMedial prefrontal cortexPosterior DMN nodesHealthy aging adultsImpact of sexLifetime riskDMN connectivityWhole brainPosterior cingulateDMN nodesSignificant sex differencesPrefrontal cortexConnectivity changesAging AdultsHealthy participantsDMN functionMode networkRobust prediction of memory and neuroticism in men and women using connectome‐based predictive modeling
Ju S, Horien C, Constable T, Fredericks C. Robust prediction of memory and neuroticism in men and women using connectome‐based predictive modeling. Alzheimer's & Dementia 2022, 18 DOI: 10.1002/alz.063015.Peer-Reviewed Original ResearchConnectome-based predictive modelingAlzheimer's diseaseRAVLT measuresFunctional MRI scansBackground Alzheimer's diseaseHealthy womenNeurobehavioral scoresHealthy subjectsHigh riskMemory performanceMRI scansBrain circuitryBrain connectivityWomenMenSexBrain connectomeDiseasePearson correlationScoresPredictorsConnectivity matrixBrain-based predictorsBehavioral measuresSubjectsFunctional network properties derived from wide-field calcium imaging differ with wakefulness and across cell type
O’Connor D, Mandino F, Shen X, Horien C, Ge X, Herman P, Hyder F, Crair M, Papademetris X, Lake E, Constable. Functional network properties derived from wide-field calcium imaging differ with wakefulness and across cell type. NeuroImage 2022, 264: 119735. PMID: 36347441, PMCID: PMC9808917, DOI: 10.1016/j.neuroimage.2022.119735.Peer-Reviewed Original ResearchAn analysis-ready and quality controlled resource for pediatric brain white-matter research.
Richie-Halford A, Cieslak M, Ai L, Caffarra S, Covitz S, Franco AR, Karipidis II, Kruper J, Milham M, Avelar-Pereira B, Roy E, Sydnor VJ, Yeatman JD, Fibr Community Science Consortium., Satterthwaite TD, Rokem A. An analysis-ready and quality controlled resource for pediatric brain white-matter research. Sci Data 2022, 9: 616. PMID: 36224186, DOI: 10.1038/s41597-022-01695-7.Peer-Reviewed Original ResearchAssociations among Household and Neighborhood Socioeconomic Disadvantages, Resting-state Frontoamygdala Connectivity, and Internalizing Symptoms in Youth
Ip KI, Sisk LM, Horien C, Conley MI, Rapuano KM, Rosenberg MD, Greene AS, Scheinost D, Constable RT, Casey BJ, Baskin-Sommers A, Gee DG. Associations among Household and Neighborhood Socioeconomic Disadvantages, Resting-state Frontoamygdala Connectivity, and Internalizing Symptoms in Youth. Journal Of Cognitive Neuroscience 2022, 34: 1810-1841. PMID: 35104356, DOI: 10.1162/jocn_a_01826.Peer-Reviewed Original ResearchConceptsSocioeconomic disadvantageNeighbourhood deprivationResting-state functional connectivityInternalizing symptomsSymptoms 1 yearHigher neighborhood deprivationNeighborhood socioeconomic disadvantageCognitive Development StudyAdolescent Brain Cognitive Development (ABCD) studyEarly interventionBilateral amygdalaElevated symptomsNegative connectivitySymptomsFunctional connectivityMental healthPositive connectivityBaselineHigher internalizing symptomsFrontoparietal networkOFC regionsFunctional couplingDeleterious effectsHigh disadvantageNeeds ratioArousal impacts distributed hubs modulating the integration of brain functional connectivity
Lee K, Horien C, O’Connor D, Garand-Sheridan B, Tokoglu F, Scheinost D, Lake EMR, Constable RT. Arousal impacts distributed hubs modulating the integration of brain functional connectivity. NeuroImage 2022, 258: 119364. PMID: 35690257, PMCID: PMC9341222, DOI: 10.1016/j.neuroimage.2022.119364.Peer-Reviewed Original ResearchThe lateral entorhinal cortex is a hub for local and global dysfunction in early Alzheimer’s disease states
Mandino F, Yeow LY, Bi R, Sejin L, Bae HG, Baek SH, Lee CY, Mohammad H, Horien C, Teoh CL, Lee JH, Lai MK, Jung S, Fu Y, Olivo M, Gigg J, Grandjean J. The lateral entorhinal cortex is a hub for local and global dysfunction in early Alzheimer’s disease states. Cerebrovascular And Brain Metabolism Reviews 2022, 42: 1616-1631. PMID: 35466772, PMCID: PMC9441719, DOI: 10.1177/0271678x221082016.Peer-Reviewed Original ResearchConceptsAlzheimer's diseaseEntorhinal cortexFunctional connectivity lossEarly AD stagesEarly Alzheimer's diseaseLateral entorhinal cortexVentral networkSynaptic hyperexcitabilityAD progressionGlobal dysfunctionMouse modelOptogenetic activationProjection targetsPathophysiological modelAD stagesMice showNetwork alterationsActive phenotypeNeuronal facilitationEarly hallmarkDiseaseActivity alterationsFMRI signalsNeuronal underpinningsTauopathiesA protocol for working with open-source neuroimaging datasets
Horien C, Lee K, Westwater ML, Noble S, Tejavibulya L, Kayani T, Constable RT, Scheinost D. A protocol for working with open-source neuroimaging datasets. STAR Protocols 2022, 3: 101077. PMID: 35036958, PMCID: PMC8749295, DOI: 10.1016/j.xpro.2021.101077.Peer-Reviewed Original Research
2021
Sex differences in connectivity in the default mode network in healthy aging adults
Ficek B, Horien C, Lacadie C, Shen X, Scheinost D, Constable T, Fredericks C. Sex differences in connectivity in the default mode network in healthy aging adults. Alzheimer's & Dementia 2021, 17 DOI: 10.1002/alz.056050.Peer-Reviewed Original ResearchIntrinsic connectivity distributionDefault mode networkAlzheimer's diseaseHealthy aging adultsElevated riskAging AdultsLarge cross-sectional studyPosterior default mode networkSex differencesMode networkCross-sectional cohortCross-sectional studyPreclinical Alzheimer's diseaseSymptomatic Alzheimer's diseaseResting-state scansSex-based differencesAnterior nodeAD showDMN connectivityHealthy adultsFunctional MRI dataNormal individualsResults FemalesZ-scoreFunctional connectivity