2022
Brain–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 ResearchMeSH KeywordsBrainComputer SimulationDatasets as TopicHumansIndividualityMental Status and Dementia TestsModels, BiologicalPhenotypeStereotypingConceptsBrain-phenotype relationshipsBrain functional organizationCognitive constructsIndividual differencesNeurocognitive measuresBrain activityNeurocognitive scoresStereotypical profileNeural targetsClinical interventionsNeural circuitsFunctional organizationIndividualsSuch relationshipsData-driven approachRelationshipStereotypes
2020
A hitchhiker’s guide to working with large, open-source neuroimaging datasets
Horien C, Noble S, Greene AS, Lee K, Barron DS, Gao S, O’Connor D, Salehi M, Dadashkarimi J, Shen X, Lake EMR, Constable RT, Scheinost D. A hitchhiker’s guide to working with large, open-source neuroimaging datasets. Nature Human Behaviour 2020, 5: 185-193. PMID: 33288916, PMCID: PMC7992920, DOI: 10.1038/s41562-020-01005-4.Peer-Reviewed Original Research
2019
There is no single functional atlas even for a single individual: Functional parcel definitions change with task
Salehi M, Greene AS, Karbasi A, Shen X, Scheinost D, Constable RT. There is no single functional atlas even for a single individual: Functional parcel definitions change with task. NeuroImage 2019, 208: 116366. PMID: 31740342, DOI: 10.1016/j.neuroimage.2019.116366.Peer-Reviewed Original ResearchThe 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
2017
Connectome-based predictive modeling of attention: Comparing different functional connectivity features and prediction methods across datasets
Yoo K, Rosenberg MD, Hsu WT, Zhang S, Li CR, Scheinost D, Constable RT, Chun MM. Connectome-based predictive modeling of attention: Comparing different functional connectivity features and prediction methods across datasets. NeuroImage 2017, 167: 11-22. PMID: 29122720, PMCID: PMC5845789, DOI: 10.1016/j.neuroimage.2017.11.010.Peer-Reviewed Original Research