2024
Power and reproducibility in the external validation of brain-phenotype predictions
Rosenblatt M, Tejavibulya L, Sun H, Camp C, Khaitova M, Adkinson B, Jiang R, Westwater M, Noble S, Scheinost D. Power and reproducibility in the external validation of brain-phenotype predictions. Nature Human Behaviour 2024, 8: 2018-2033. PMID: 39085406, DOI: 10.1038/s41562-024-01931-7.Peer-Reviewed Original ResearchHuman Connectome ProjectAdolescent Brain Cognitive Development StudyConnectome ProjectCognitive Development StudyPhiladelphia Neurodevelopmental CohortHealthy Brain NetworkStructural connectivity dataMatrix reasoningWorking memoryAnxiety/depression symptomsAttention problemsNeurodevelopmental CohortBrain networksBrain-phenotype associationsEffect sizeConnectivity dataExternal validationRelated processesValidation studySample sizeBrain ProjectDevelopment studiesTraining sample sizeGeneralizability of modelsExternal samples
2023
Transdiagnostic Connectome-Based Prediction of Craving
Garrison K, Sinha R, Potenza M, Gao S, Liang Q, Lacadie C, Scheinost D. Transdiagnostic Connectome-Based Prediction of Craving. American Journal Of Psychiatry 2023, 180: 445-453. PMID: 36987598, DOI: 10.1176/appi.ajp.21121207.Peer-Reviewed Original ResearchConceptsConnectome-based predictive modelingImagery conditionFunctional connectomeSelf-reported cravingStudy of motivationDefault mode networkFunctional connectivity dataIndependent samplesKey phenomenological featuresNeural signaturesTransdiagnostic sampleTransdiagnostic perspectiveMode networkMotivated behaviorCentral constructAddictive disordersHuman behaviorConnectivity dataPhenomenological featuresStrongest predictorCravingTaskSubstance use-related disordersConnectomeIndividuals
2017
An exemplar-based approach to individualized parcellation reveals the need for sex specific functional networks
Salehi M, Karbasi A, Shen X, Scheinost D, Constable RT. An exemplar-based approach to individualized parcellation reveals the need for sex specific functional networks. NeuroImage 2017, 170: 54-67. PMID: 28882628, PMCID: PMC5905726, DOI: 10.1016/j.neuroimage.2017.08.068.Peer-Reviewed Original ResearchConceptsIndividualized parcellationParcellation techniqueFunctional networksCross-validated predictive modelSpecific functional networksCerebral cortexPatient subgroupsFunctional connectivity dataFunctional organizationBrainParcellation schemesClinical applicationParcellation approachParcellationSexSubgroupsConnectivity dataIndividualized studyNetwork organizationIndividualsAmple evidencePatientsCortexUsing connectome-based predictive modeling to predict individual behavior from brain connectivity
Shen X, Finn ES, Scheinost D, Rosenberg MD, Chun MM, Papademetris X, Constable RT. Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nature Protocols 2017, 12: 506-518. PMID: 28182017, PMCID: PMC5526681, DOI: 10.1038/nprot.2016.178.Peer-Reviewed Original Research
2014
Coupled Intrinsic Connectivity Distribution Analysis: A Method for Exploratory Connectivity Analysis of Paired fMRI Data
Scheinost D, Shen X, Finn E, Sinha R, Constable RT, Papademetris X. Coupled Intrinsic Connectivity Distribution Analysis: A Method for Exploratory Connectivity Analysis of Paired fMRI Data. PLOS ONE 2014, 9: e93544. PMID: 24676034, PMCID: PMC3968179, DOI: 10.1371/journal.pone.0093544.Peer-Reviewed Original ResearchConceptsHealthy controlsIntrinsic connectivity distributionCocaine-dependent subjectsResting-state scansSeed-based analysisConnectivity analysisSimilar brain regionsBasic neuroscience researchMagnetic resonance imaging dataPsychiatric diseasesVoxel-based resultsConnectivity differencesBrain regionsClinical toolFunctional changesFunctional magnetic resonance imaging (fMRI) dataScansVoxel-based methodConnectivity approachNeuroscience researchConnectivity data