2024
Data leakage inflates prediction performance in connectome-based machine learning models
Rosenblatt M, Tejavibulya L, Jiang R, Noble S, Scheinost D. Data leakage inflates prediction performance in connectome-based machine learning models. Nature Communications 2024, 15: 1829. PMID: 38418819, PMCID: PMC10901797, DOI: 10.1038/s41467-024-46150-w.Peer-Reviewed Original Research
2022
Functional Connectivity of the Chemosenses: A Review
Farruggia MC, Pellegrino R, Scheinost D. Functional Connectivity of the Chemosenses: A Review. Frontiers In Systems Neuroscience 2022, 16: 865929. PMID: 35813269, PMCID: PMC9257046, DOI: 10.3389/fnsys.2022.865929.Peer-Reviewed Original ResearchFunctional connectivityConnectivity approachBrain-behavior relationshipsDynamic causal modelingFunctional connectivity approachSeed-based functional connectivityEffective connectivity approachStructural equation modelingTask-based paradigmsCognitive neuroscienceNeural correlatesPsychophysiological interactionStimulus qualityCausal modelingEquation modelingOlfactory paradigmBrain regionsChemosensory perceptionRelative dearthPathways of communication
2018
Task-induced brain state manipulation improves prediction of individual traits
Greene AS, Gao S, Scheinost D, Constable RT. Task-induced brain state manipulation improves prediction of individual traits. Nature Communications 2018, 9: 2807. PMID: 30022026, PMCID: PMC6052101, DOI: 10.1038/s41467-018-04920-3.Peer-Reviewed Original ResearchConceptsBrain statesIndividual differencesBrain-behavior relationshipsFluid intelligence scoresTask-based functional connectivity analysisResting-state fMRI dataBrain functional organizationFunctional connectivity analysisCognitive tasksFluid intelligenceIntelligence scoresFunctional connectivityFMRI dataConnectivity analysisHuman behaviorIndividual traitsTaskCertain tasksFunctional organizationOutperform modelsSuch relationshipsCognitionState manipulationIntelligenceVariance
2014
Optimizing real time fMRI neurofeedback for therapeutic discovery and development
Stoeckel LE, Garrison KA, Ghosh S, Wighton P, Hanlon CA, Gilman JM, Greer S, Turk-Browne NB, deBettencourt MT, Scheinost D, Craddock C, Thompson T, Calderon V, Bauer CC, George M, Breiter HC, Whitfield-Gabrieli S, Gabrieli JD, LaConte SM, Hirshberg L, Brewer JA, Hampson M, Van Der Kouwe A, Mackey S, Evins AE. Optimizing real time fMRI neurofeedback for therapeutic discovery and development. NeuroImage Clinical 2014, 5: 245-255. PMID: 25161891, PMCID: PMC4141981, DOI: 10.1016/j.nicl.2014.07.002.Peer-Reviewed Original ResearchConceptsBrain disordersBrain functionReal-time functional magnetic resonance imaging (rt-fMRI) neurofeedbackRtfMRI neurofeedbackFunctional magnetic resonance imaging (fMRI) neurofeedbackWorld Health OrganizationEffective treatmentClinical problemBrain-behavior relationshipsTherapeutic toolEffective interventionsHealth OrganizationDisordersNational InstituteIntervention approachesTherapeutic discoveryNeurofeedbackPersonalized assessmentRtfMRIMaladaptive patternsMorbidityNeurotherapeutics