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 representationTaskPrediction of craving across studies: A commentary on conceptual and methodological considerations when using data-driven methods
Antons S, Yip S, Lacadie C, Dadashkarimi J, Scheinost D, Brand M, Potenza M. Prediction of craving across studies: A commentary on conceptual and methodological considerations when using data-driven methods. Journal Of Behavioral Addictions 2024, 13: 695-701. PMID: 39356557, PMCID: PMC11457034, DOI: 10.1556/2006.2024.00050.Peer-Reviewed Original ResearchConceptsAddictive behaviorsDisorders due to addictive behaviorsConnectome-based predictive modelingPrediction of cravingInvestigate neural mechanismsSubstance use disordersNeural mechanismsCravingSubstance useMethodological considerationsDisordersMethodological featuresBehaviorConceptualizationCommentaryStudyFindingsSubstancesNetwork state dynamics underpin basal craving in a transdiagnostic population
Ye J, Garrison K, Lacadie C, Potenza M, Sinha R, Goldfarb E, Scheinost D. Network state dynamics underpin basal craving in a transdiagnostic population. Molecular Psychiatry 2024, 1-10. PMID: 39183336, DOI: 10.1038/s41380-024-02708-0.Peer-Reviewed Original ResearchConnectome-based predictive modelingBrain responsesRegulation of affective statesSample of healthy controlsTransdiagnostic populationTransdiagnostic sampleHigher cravingMotivational stateCravingFMRI methodsAffective statesScan runsExperimental stimuliNetwork engagementBrain dynamicsClinical implicationsHealthy controlsBrainIndividual variationState dynamicsCharacterize individualsReplication datasetPsychopathologyFMRIEngagement
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
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 settings
2020
Behavioral and brain signatures of substance use vulnerability in childhood
Rapuano KM, Rosenberg MD, Maza MT, Dennis NJ, Dorji M, Greene AS, Horien C, Scheinost D, Constable R, Casey BJ. Behavioral and brain signatures of substance use vulnerability in childhood. Developmental Cognitive Neuroscience 2020, 46: 100878. PMID: 33181393, PMCID: PMC7662869, DOI: 10.1016/j.dcn.2020.100878.Peer-Reviewed Original ResearchConceptsConnectome-based predictive modelingSubstance useFunctional connectivityCognitive Development StudyFuture substance useSubstance use vulnerabilityAdolescent substance useSubstance use increasesSubstance use outcomesIndividual differencesAdolescent brainBrain signaturesTask conditionsBehavioral measuresFamilial risk factorsUse outcomesRisky behaviorsLatent dimensionsFamilial factorsBrain modelCurrent studyWeak predictorDevelopment studiesEarly susceptibilityFunctional imagingDistributed Patterns of Functional Connectivity Predict Working Memory Performance in Novel Healthy and Memory-impaired Individuals
Avery EW, Yoo K, Rosenberg MD, Greene AS, Gao S, Na DL, Scheinost D, Constable TR, Chun MM. Distributed Patterns of Functional Connectivity Predict Working Memory Performance in Novel Healthy and Memory-impaired Individuals. Journal Of Cognitive Neuroscience 2020, 32: 241-255. PMID: 31659926, PMCID: PMC8004893, DOI: 10.1162/jocn_a_01487.Peer-Reviewed Original ResearchConceptsFunctional connectivity patternsFluid intelligenceMemory performanceIndividual differencesAttention modelConnectome-based predictive modelingConnectome-based predictive modelsWhole-brain functional connectivity patternsGeneral cognitive abilitySuch individual differencesConnectivity patternsAdult life spanHuman Connectome ProjectHuman Connectome Project dataMemory relateCognitive abilitiesNeural basisSustained attentionMemory scoresParietal regionsFunctional connectivityConnectome ProjectMemory modelOlder adultsMemory
2019
The Application of Connectome-Based Predictive Modeling to the Maternal Brain: Implications for Mother–Infant Bonding
Rutherford HJV, Potenza MN, Mayes LC, Scheinost D. The Application of Connectome-Based Predictive Modeling to the Maternal Brain: Implications for Mother–Infant Bonding. Cerebral Cortex 2019, 30: 1538-1547. PMID: 31690936, PMCID: PMC7132918, DOI: 10.1093/cercor/bhz185.Peer-Reviewed Original ResearchConceptsConnectome-based predictive modelingAuditory networkMaternal anxietyMaternal bondingContext of anxietyMaternal Brain NetworkMother-infant bondBrain functional connectivityChild developmentMother-infant bondingBrain networksFunctional connectivityAnxietyBehavioral qualitiesBonding relationshipsBonding impairmentBrain structuresMaternal brainMother's mindGreater segregationNetwork connectivityMindGreater integrationConnectivityMonths postpartumCombining 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 ResearchConceptsMultiple 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 ProjectThe Functional Brain Organization of an Individual Allows Prediction of Measures of Social Abilities Transdiagnostically in Autism and Attention-Deficit/Hyperactivity Disorder
Lake EMR, Finn ES, Noble SM, Vanderwal T, Shen X, Rosenberg MD, Spann MN, Chun MM, Scheinost D, Constable RT. The Functional Brain Organization of an Individual Allows Prediction of Measures of Social Abilities Transdiagnostically in Autism and Attention-Deficit/Hyperactivity Disorder. Biological Psychiatry 2019, 86: 315-326. PMID: 31010580, PMCID: PMC7311928, DOI: 10.1016/j.biopsych.2019.02.019.Peer-Reviewed Original ResearchConceptsAttention-deficit/hyperactivity disorderAutism spectrum disorderSpectrum disorderFunctional connectivity profilesHyperactivity disorderBrain organizationAutism Brain Imaging Data ExchangeConnectome-based predictive modelingAutism Diagnostic Observation ScheduleAutism spectrum disorder traitsAutism spectrum disorder severitySocial Responsiveness Scale scoresADHD Rating Scale scoresFunctional magnetic resonance imagingBrain-behavior associationsSocial Responsiveness ScaleResting-state functional magnetic resonanceFunctional brain organizationFunctional magnetic resonanceADHD traitsNeurofunctional basisADHD symptomsSplit-half analysisResponsiveness ScaleSocial abilitiesConnectome-Based Prediction of Cocaine Abstinence
Yip SW, Scheinost D, Potenza MN, Carroll KM. Connectome-Based Prediction of Cocaine Abstinence. American Journal Of Psychiatry 2019, 176: 156-164. PMID: 30606049, PMCID: PMC6481181, DOI: 10.1176/appi.ajp.2018.17101147.Peer-Reviewed Original ResearchMeSH KeywordsAdultBehavior TherapyBrainCholinesterase InhibitorsCocaine-Related DisordersCognitionConnectomeExecutive FunctionFemaleFunctional NeuroimagingGalantamineHumansIndividualityMachine LearningMagnetic Resonance ImagingMaleMiddle AgedNeural PathwaysOpiate Substitution TreatmentOpioid-Related DisordersPrognosisRewardTreatment OutcomeConceptsConnectome-based predictive modelingCocaine use disorderUse disordersBrain-based predictorsLarge-scale neural networksFunctional MRI dataCocaine abstinenceExecutive controlReward responsivenessIndividual differencesBaseline cocaine usePosttreatment assessmentConnectivity strengthHeterogeneous sampleAbstinenceIndependent samplesNovel interventionsCanonical networksSpecific behaviorsCocaine useSignificant correspondenceDisordersTreatment outcomesNetwork strengthMRI data
2018
Resting-state functional connectivity predicts neuroticism and extraversion in novel individuals
Hsu WT, Rosenberg MD, Scheinost D, Constable RT, Chun MM. Resting-state functional connectivity predicts neuroticism and extraversion in novel individuals. Social Cognitive And Affective Neuroscience 2018, 13: 224-232. PMID: 29373729, PMCID: PMC5827338, DOI: 10.1093/scan/nsy002.Peer-Reviewed Original ResearchConceptsConnectome-based predictive modeling
2017
Connectome-based Models Predict Separable Components of Attention in Novel Individuals
Rosenberg MD, Hsu WT, Scheinost D, Constable R, Chun MM. Connectome-based Models Predict Separable Components of Attention in Novel Individuals. Journal Of Cognitive Neuroscience 2017, 30: 160-173. PMID: 29040013, DOI: 10.1162/jocn_a_01197.Peer-Reviewed Original ResearchConceptsConnectome-based predictive modelingAttention Network TaskExecutive controlIntrinsic functional organizationRT variabilityANT performanceInfluential modelFunctional connectivityBrain's intrinsic functional organizationComponents of attentionExecutive control scoresResting-state functional connectivityResting-state dataFunctional brain networksFunctional organizationTask-based dataAttentional abilitiesUpcoming stimulusExplicit taskSustained attentionFMRI scanningAttention factorNovel individualsAdditional independent componentNetwork tasksUsing 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 ResearchConceptsConnectome-based predictive modelingBrain connectivity dataBrain connectivityConnectivity dataPredictive features