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 settingsFunctional 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 StatementsMeSH KeywordsAutism Spectrum DisorderAutistic DisorderBrainConnectomeForecastingHumansMagnetic Resonance ImagingThe 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
2023
A 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 Research
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 ResearchMeSH KeywordsAdultBrainConnectomeDefault Mode NetworkFemaleHealthy AgingHumansMagnetic Resonance ImagingMaleNerve NetNeuropsychological TestsSex CharacteristicsConceptsDefault 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 networkArousal 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 Research
2021
Genetic variation in endocannabinoid signaling is associated with differential network‐level functional connectivity in youth
Sisk LM, Rapuano KM, Conley MI, Greene AS, Horien C, Rosenberg MD, Scheinost D, Constable RT, Glatt CE, Casey BJ, Gee DG. Genetic variation in endocannabinoid signaling is associated with differential network‐level functional connectivity in youth. Journal Of Neuroscience Research 2021, 100: 731-743. PMID: 34496065, PMCID: PMC8866205, DOI: 10.1002/jnr.24946.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAmygdalaAnxietyAnxiety DisordersEndocannabinoidsFemaleHumansMagnetic Resonance ImagingMalePolymorphism, Single NucleotideConceptsEndocannabinoid signalingAllele carriersLower anxiety symptomsC385A polymorphismNetwork-level functional connectivityEnhanced endocannabinoid signalingLarge-scale resting-state brain networksAnxiety symptomsResting-state brain networksGenotype-associated differencesBrain networksFronto-amygdala connectivityFunctional connectionsCognitive Development StudyNetwork-level changesPotential protective factorsAdolescent Brain Cognitive Development (ABCD) studyEndocannabinoid systemNetwork-level differencesYounger ageFunctional connectivityProtective factorsNeural phenotypesAnxiety disordersNeural connectivityImaging and Reimagining the Mind: fMRI and Psychiatric Illness
Horien C, Constable RT, Ross DA. Imaging and Reimagining the Mind: fMRI and Psychiatric Illness. Biological Psychiatry 2021, 89: e45-e47. PMID: 33858592, PMCID: PMC8695861, DOI: 10.1016/j.biopsych.2021.02.013.Peer-Reviewed Original Research
2020
Low-motion fMRI data can be obtained in pediatric participants undergoing a 60-minute scan protocol
Horien C, Fontenelle S, Joseph K, Powell N, Nutor C, Fortes D, Butler M, Powell K, Macris D, Lee K, Greene AS, McPartland JC, Volkmar FR, Scheinost D, Chawarska K, Constable RT. Low-motion fMRI data can be obtained in pediatric participants undergoing a 60-minute scan protocol. Scientific Reports 2020, 10: 21855. PMID: 33318557, PMCID: PMC7736342, DOI: 10.1038/s41598-020-78885-z.Peer-Reviewed Original ResearchConceptsPediatric participantsMRI protocolMagnetic resonance imaging (MRI) scansFunctional magnetic resonance imaging (fMRI) scansShorter MRI protocolsScan protocolResonance imaging scansImaging scansMRI sessionsFMRI connectivity analysisFMRI dataFMRI findingsSignificant confoundScansReplication groupConnectivity analysisAutism spectrum disorderMock scanSpectrum disorderParticipantsHead motionProtocolConnectome-based models can predict early symptom improvement in major depressive disorder
Ju Y, Horien C, Chen W, Guo W, Lu X, Sun J, Dong Q, Liu B, Liu J, Yan D, Wang M, Zhang L, Guo H, Zhao F, Zhang Y, Shen X, Constable RT, Li L. Connectome-based models can predict early symptom improvement in major depressive disorder. Journal Of Affective Disorders 2020, 273: 442-452. PMID: 32560939, DOI: 10.1016/j.jad.2020.04.028.Peer-Reviewed Original ResearchMeSH KeywordsAntidepressive AgentsBrainConnectomeDepressive Disorder, MajorHumansMagnetic Resonance ImagingConceptsMajor depressive disorderSymptom improvementAntidepressant treatmentMDD patientsDepressive disorderTreatment outcomesEarly symptom improvementIndividual therapeutic responseInitial MR scansUntreated MDD patientsResting-state functional connectivity patternsFirst-line treatmentThree-month time pointTime pointsAntidepressant treatment outcomeBaseline functional connectivityHamilton Rating ScaleSeverity of depressionResting-state connectivityFunctional connectivity patternsResting-state fMRI dataDifferent time pointsTherapeutic responseClinical practiceFunctional brain networks
2019
Regions and Connections: Complementary Approaches to Characterize Brain Organization and Function
Horien C, Greene AS, Constable RT, Scheinost D. Regions and Connections: Complementary Approaches to Characterize Brain Organization and Function. The Neuroscientist 2019, 26: 117-133. PMID: 31304866, PMCID: PMC7079335, DOI: 10.1177/1073858419860115.Peer-Reviewed Original ResearchTen simple rules for predictive modeling of individual differences in neuroimaging
Scheinost D, Noble S, Horien C, Greene AS, Lake EM, Salehi M, Gao S, Shen X, O’Connor D, Barron DS, Yip SW, Rosenberg MD, Constable RT. Ten simple rules for predictive modeling of individual differences in neuroimaging. NeuroImage 2019, 193: 35-45. PMID: 30831310, PMCID: PMC6521850, DOI: 10.1016/j.neuroimage.2019.02.057.Peer-Reviewed Original ResearchMeSH KeywordsBrainConnectomeHumansMachine LearningMagnetic Resonance ImagingModels, NeurologicalNeuroimagingConceptsBrain-behavior associations