2024
Brain-phenotype predictions of language and executive function can survive across diverse real-world data: Dataset shifts in developmental populations
Adkinson B, Rosenblatt M, Dadashkarimi J, Tejavibulya L, Jiang R, Noble S, Scheinost D. Brain-phenotype predictions of language and executive function can survive across diverse real-world data: Dataset shifts in developmental populations. Developmental Cognitive Neuroscience 2024, 70: 101464. PMID: 39447452, PMCID: PMC11538622, DOI: 10.1016/j.dcn.2024.101464.Peer-Reviewed Original ResearchBrain-phenotype associationsConnectome-based predictive modelingBrain-behavior associationsPrediction of languagePhiladelphia Neurodevelopmental CohortHealthy Brain NetworkClinical symptom burdenFMRI taskHuman Connectome ProjectExecutive functionBehavioral measuresDevelopmental populationsNeurodevelopmental CohortBrain networksDevelopmental sampleConnectome ProjectResearch settingsGeneralizabilitySymptom burdenExternal validationFMRIClinical settingAssociationEthnic minority representationTaskOvercoming Atlas Heterogeneity in Federated Learning for Cross-Site Connectome-Based Predictive Modeling
Liang Q, Adkinson B, Jiang R, Scheinost D. Overcoming Atlas Heterogeneity in Federated Learning for Cross-Site Connectome-Based Predictive Modeling. Lecture Notes In Computer Science 2024, 15010: 579-588. DOI: 10.1007/978-3-031-72117-5_54.Peer-Reviewed Original ResearchPower 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 samplesRescuing missing data in connectome-based predictive modeling
Liang Q, Jiang R, Adkinson B, Rosenblatt M, Mehta S, Foster M, Dong S, You C, Negahban S, Zhou H, Chang J, Scheinost D. Rescuing missing data in connectome-based predictive modeling. Imaging Neuroscience 2024, 2: 1-16. DOI: 10.1162/imag_a_00071.Peer-Reviewed Original Research
2023
Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for different atlases when raw data is not available
Dadashkarimi J, Karbasi A, Liang Q, Rosenblatt M, Noble S, Foster M, Rodriguez R, Adkinson B, Ye J, Sun H, Camp C, Farruggia M, Tejavibulya L, Dai W, Jiang R, Pollatou A, Scheinost D. Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for different atlases when raw data is not available. Medical Image Analysis 2023, 88: 102864. PMID: 37352650, PMCID: PMC10526726, DOI: 10.1016/j.media.2023.102864.Peer-Reviewed Original ResearchConceptsDifferent atlasesRaw data accessWeb applicationData accessOpen source dataSource codePatient privacyOptimal transportRaw dataStorage concernsLarge-scale data collection effortsOriginal counterpartsExtensive setData collection effortsProcessing effortPredictive modelNeuroimaging dataDownstream analysisPrivacyAtlasesCollection effortsComputationalTime seriesDatasetConnectome
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply