2022
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 settingsBrain–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 approachRelationshipStereotypes
2020
How Tasks Change Whole-Brain Functional Organization to Reveal Brain-Phenotype Relationships
Greene AS, Gao S, Noble S, Scheinost D, Constable RT. How Tasks Change Whole-Brain Functional Organization to Reveal Brain-Phenotype Relationships. Cell Reports 2020, 32: 108066. PMID: 32846124, PMCID: PMC7469925, DOI: 10.1016/j.celrep.2020.108066.Peer-Reviewed Original Research
2019
Combining multiple connectomes improves predictive modeling of phenotypic measures
Gao S, Greene AS, Constable RT, Scheinost D. Combining multiple connectomes improves predictive modeling of phenotypic measures. NeuroImage 2019, 201: 116038. PMID: 31336188, PMCID: PMC6765422, DOI: 10.1016/j.neuroimage.2019.116038.Peer-Reviewed Original ResearchMeSH KeywordsAdultAlgorithmsConnectomeFemaleForecastingHumansMaleModels, NeurologicalPhenotypeYoung AdultConceptsMultiple connectomesLarge open-source datasetOpen-source datasetNovel prediction frameworkPredictive modelingSingle predictive modelPredictive modelArt algorithmsPrediction frameworkMultiple tasksPredictive model approachPrincipled waySpecific algorithmsFunctional connectivity matricesConnectivity matrixDifferent tasksPrediction performanceConnectome-based predictive modelingHuman Connectome ProjectTaskSuperior performanceAlgorithmComplementary informationNaïve extensionsConnectome Project