2024
Brain age prediction and deviations from normative trajectories in the neonatal connectome
Sun H, Mehta S, Khaitova M, Cheng B, Hao X, Spann M, Scheinost D. Brain age prediction and deviations from normative trajectories in the neonatal connectome. Nature Communications 2024, 15: 10251. PMID: 39592647, PMCID: PMC11599754, DOI: 10.1038/s41467-024-54657-5.Peer-Reviewed Original ResearchConceptsPostmenstrual agePerinatal periodBrain age predictionFunctional connectomeMonths of postnatal lifeMonths of lifePreterm infantsNormative trajectoryConnectome-based predictive modelingThird trimesterPerinatal exposureBrain age gapPostnatal lifeResting-state fMRIInfantsHuman Connectome ProjectNeonatal connectomeDevelopmental trajectoriesBrainBehavioral outcomesNormative dataMonthsConnectome ProjectDTI dataConnectomeBrain-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 ResearchPrediction 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 datasetPsychopathologyFMRIEngagementApplying fetal, infant, and toddler (FIT) neuroimaging to understand mental health
Spann M, Scheinost D. Applying fetal, infant, and toddler (FIT) neuroimaging to understand mental health. Neuropsychopharmacology 2024, 50: 310-311. PMID: 39117902, PMCID: PMC11525938, DOI: 10.1038/s41386-024-01957-5.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 samplesDevelopmental trajectories of the default mode, frontoparietal, and salience networks from the third trimester through the newborn period
Scheinost D, Chang J, Brennan-Wydra E, Lacadie C, Constable R, Chawarska K, Ment L. Developmental trajectories of the default mode, frontoparietal, and salience networks from the third trimester through the newborn period. Imaging Neuroscience 2024, 2: 1-16. DOI: 10.1162/imag_a_00201.Peer-Reviewed Original ResearchFronto-parietalDevelopmental trajectoriesImpact of maternal mental healthResting-state functional MRIWeeks postmenstrual ageInter-network connectivityIntra-network connectivityMaternal stress levelsMaternal mental healthPostmenstrual ageSalience networkFunctional MRINeurobehavioral disordersNeurodevelopmental disordersDMNMental healthThird trimesterConnectivity measuresSalienceConnectivity valuesEdge-centric network control on the human brain structural network
Sun H, Rosenblatt M, Dadashkarimi J, Rodriguez R, Tejavibulya L, Scheinost D. Edge-centric network control on the human brain structural network. Imaging Neuroscience 2024, 2: 1-15. DOI: 10.1162/imag_a_00191.Peer-Reviewed Original ResearchHuman brain structural networksNetwork control theoryEdge controlWhole-brain networksHuman Connectome ProjectDiffusion MRI dataWhite matter connectivityConnectome ProjectBrain dynamicsExecutive functionBrain structural networksBrain network connectivityBrain connectivityFunctional connectomeState transitionsTransitionEnergy patternsTheory modelBrain energy consumptionDynamic processStructural networkStateNetwork control mechanismsCognitive statesNetwork pairsThe brain structure, inflammatory, and genetic mechanisms mediate the association between physical frailty and depression
Jiang R, Noble S, Rosenblatt M, Dai W, Ye J, Liu S, Qi S, Calhoun V, Sui J, Scheinost D. The brain structure, inflammatory, and genetic mechanisms mediate the association between physical frailty and depression. Nature Communications 2024, 15: 4411. PMID: 38782943, PMCID: PMC11116547, DOI: 10.1038/s41467-024-48827-8.Peer-Reviewed Original ResearchConceptsIncident depressionPre-frailPhysical frailtyFrail individualsPopulation attributable fraction analysisRisk factors of depressionMendelian randomization analysisFactors of depressionPotential causal effectModifiable risk factorsNon-frail individualsCross-sectional studyEffect of frailtyHigher disease burdenUK BiobankRandomization analysisBrain volumeDepression casesDisease burdenFrailtyRegional brain volumesIncreased riskDepressionHigh riskFollow-upInvestigating Brain State Engagement Variability in Individuals With Opioid Use Disorder Using Naturalistic and Task-Based fMRI Data
Ye J, Mehta S, Peterson H, Ibrahim A, Saeed G, Linsky S, Kreinin I, Tsang S, Nwanaji-Enwerem U, Raso A, Yip S, Tokoglu F, Arora J, Constable R, Barry D, Redeker N, Yaggi H, Scheinost D. Investigating Brain State Engagement Variability in Individuals With Opioid Use Disorder Using Naturalistic and Task-Based fMRI Data. Biological Psychiatry 2024, 95: s6. DOI: 10.1016/j.biopsych.2024.02.022.Peer-Reviewed Original Research1. Multimodal Neuroimaging Findings in Individuals With Opioid Use Disorder
Mehta S, Peterson H, Ibrahim A, Saeed G, Linsky S, Kreinin I, Tsang S, Nwanaji-Enwerem U, Raso A, Arora J, Tokoglu F, Hahn C, Lacadie C, Greene A, Constable T, Barry D, Redeker N, Yaggi H, Scheinost D. 1. Multimodal Neuroimaging Findings in Individuals With Opioid Use Disorder. Biological Psychiatry 2024, 95: s99. DOI: 10.1016/j.biopsych.2024.02.236.Peer-Reviewed Original ResearchThe tip of the iceberg: A call to embrace anti-localizationism in human neuroscience research
Noble S, Curtiss J, Pessoa L, Scheinost D. The tip of the iceberg: A call to embrace anti-localizationism in human neuroscience research. Imaging Neuroscience 2024, 2: 1-10. DOI: 10.1162/imag_a_00138.Peer-Reviewed Original ResearchMapping Early Brain–Body Interactions: Associations of Fetal Heart Rate Variation with Newborn Brainstem, Hypothalamic, and Dorsal Anterior Cingulate Cortex Functional Connectivity
Pollatou A, Holland C, Stockton T, Peterson B, Scheinost D, Monk C, Spann M. Mapping Early Brain–Body Interactions: Associations of Fetal Heart Rate Variation with Newborn Brainstem, Hypothalamic, and Dorsal Anterior Cingulate Cortex Functional Connectivity. Journal Of Neuroscience 2024, 44: e2363232024. PMID: 38604780, PMCID: PMC11140686, DOI: 10.1523/jneurosci.2363-23.2024.Peer-Reviewed Original ResearchFetal heart rate indicesDorsal anterior cingulate cortexAutonomic nervous systemDorsal anterior cingulate cortex functional connectivityFetal HRHeart rate variabilityBrain regionsFetal heart rate variationWeeks of postmenstrual ageAutonomic nervous system developmentFetal autonomic nervous systemFetal heart rate variabilityAutonomic regulationHeart rate indicesFetal heart rateNervous systemFunctional connectivityHeart rateAnterior cingulate cortex functional connectivityAnterior cingulate cortexChildren's language abilitiesFetal actocardiographInfant brainstemPostmenstrual ageWeeks gestationBrain states as wave-like motifs
Foster M, Scheinost D. Brain states as wave-like motifs. Trends In Cognitive Sciences 2024, 28: 492-503. PMID: 38582654, DOI: 10.1016/j.tics.2024.03.004.Peer-Reviewed Original ResearchData 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 ResearchRescuing 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 ResearchExperiences of Stigma and Discrimination Compounded by Intersecting Identities among Individuals Receiving Medication for Opioid Use Disorder.
Nwanaji-Enwerem U, Redeker N, O'Connell M, Barry D, Iheanacho T, Knobf T, Scheinost D, Wang K, Yaggi K, Sadler L. Experiences of Stigma and Discrimination Compounded by Intersecting Identities among Individuals Receiving Medication for Opioid Use Disorder. Journal Of Health Care For The Poor And Underserved 2024, 35: 94-115. PMID: 38661862, DOI: 10.1353/hpu.2024.a919810.Peer-Reviewed Original ResearchOpioid use disorderExperiences of stigmaIndividuals experience stigmaMarginalized social positionsIdentity of peopleUse disorderIntersectional identitiesMarginalized identitiesSupport interventionsSocial positionQualitative findingsStigmaNegative experiencesNortheast United StatesUnited StatesImprove outcomesIdentityTreatment centersMOUDIndividual vulnerabilityMedicationPeopleDiscriminationIndividualsCorrection: The effects of experience of discrimination and acculturation during pregnancy on the developing offspring brain
Spann M, Alleyne K, Holland C, Davids A, Pierre-Louis A, Bang C, Oyeneye V, Kiflom R, Shea E, Cheng B, Peterson B, Monk C, Scheinost D. Correction: The effects of experience of discrimination and acculturation during pregnancy on the developing offspring brain. Neuropsychopharmacology 2024, 49: 765-765. PMID: 38212444, PMCID: PMC10876701, DOI: 10.1038/s41386-024-01798-2.Peer-Reviewed Original Research
2023
Autism spectrum disorder-specific changes in white matter connectome edge density based on functionally defined nodes
Weber C, Lake E, Haider S, Mozayan A, Bobba P, Mukherjee P, Scheinost D, Constable R, Ment L, Payabvash S. Autism spectrum disorder-specific changes in white matter connectome edge density based on functionally defined nodes. Frontiers In Neuroscience 2023, 17: 1285396. PMID: 38075286, PMCID: PMC10702224, DOI: 10.3389/fnins.2023.1285396.Peer-Reviewed Original ResearchWhite matterPositive symptom correlationAutism spectrum disorderSymptom severity scoresBetween-group differencesAbnormal connectivity patternsCortical nodesVoxel-wise analysisDiffusion tensor imagingSeverity scoreWM fiber tractsSymptom correlationClinical dataConnectome disruptionConnectome changesEarly changesCortical regionsInfantsED reductionWM disruptionDisorder-specific changesTensor imagingFunctional imagingFiber tractsProbabilistic tractography