2024
Prediction 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 featuresBehaviorConceptualizationCommentaryStudyFindingsSubstancesThe 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-upData 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
2023
Network controllability of structural connectomes in the neonatal brain
Sun H, Jiang R, Dai W, Dufford A, Noble S, Spann M, Gu S, Scheinost D. Network controllability of structural connectomes in the neonatal brain. Nature Communications 2023, 14: 5820. PMID: 37726267, PMCID: PMC10509217, DOI: 10.1038/s41467-023-41499-w.Peer-Reviewed Original ResearchTest-Retest Reliability of Functional Connectivity in Adolescents With Depression
Camp C, Noble S, Scheinost D, Stringaris A, Nielson D. Test-Retest Reliability of Functional Connectivity in Adolescents With Depression. Biological Psychiatry Cognitive Neuroscience And Neuroimaging 2023, 9: 21-29. PMID: 37734478, PMCID: PMC10843837, DOI: 10.1016/j.bpsc.2023.09.002.Peer-Reviewed Original ResearchConceptsMajor depressive disorderIntraclass correlation coefficientTest-retest reliabilityPsychiatric illnessFunctional connectivityMean intraclass correlation coefficientFunctional magnetic resonance imagingMagnetic resonance imagingAverage intraclass correlation coefficientEffect sizeDepressive disorderLongitudinal cohortHealthy individualsMultivariate analysisResonance imagingSymptom severityReproducible biomarkersBrain-behavior associationsGroup differencesDepressionHealthy samplesCorrelation coefficientIllnessAdolescentsBiomarker identificationThe challenges and prospects of brain-based prediction of behaviour
Wu J, Li J, Eickhoff S, Scheinost D, Genon S. The challenges and prospects of brain-based prediction of behaviour. Nature Human Behaviour 2023, 7: 1255-1264. PMID: 37524932, DOI: 10.1038/s41562-023-01670-1.Peer-Reviewed Original ResearchConceptsInterindividual differencesIndividual brain patternsNeural correlatesBehavioral measuresBrain patternsSystems neuroscienceConceptual limitationsLarge open datasetData-driven fashionNeuroscienceBrain levelsComputational resourcesOpen datasetsPredictive modelling approachCorrelatesMindBehaviorMeasuresPredictive modellingModelling approachDifferencesPotential solutionsImpact of postnatal weight gain on brain white matter maturation in very preterm infants
Bobba P, Weber C, Higaki A, Mukherjee P, Scheinost D, Constable R, Ment L, Taylor S, Payabvash S. Impact of postnatal weight gain on brain white matter maturation in very preterm infants. Journal Of Neuroimaging 2023, 33: 991-1002. PMID: 37483073, PMCID: PMC10800683, DOI: 10.1111/jon.13145.Peer-Reviewed Original ResearchConceptsBirth weight z-scoreMagnetic resonance imagingVery preterm infantsPostnatal weight gainWeight z-scoreWhite matter maturationBirth weightDiffusion tensor imagingNeurological outcomePreterm infantsGestational ageWeight gainCorpus callosumHigher birth weight z-scoresBrain white matter maturationLong-term neurological deficitsZ-scoreBrain developmentWeight z-score changeWM tractsZ-score changeWM maturationWeeks of lifeNeurological deficitsNutritional interventionElevated C-reactive protein mediates the liver-brain axis: a preliminary study
Jiang R, Wu J, Rosenblatt M, Dai W, Rodriguez R, Sui J, Qi S, Liang Q, Xu B, Meng Q, Calhoun V, Scheinost D. Elevated C-reactive protein mediates the liver-brain axis: a preliminary study. EBioMedicine 2023, 93: 104679. PMID: 37356206, PMCID: PMC10320521, DOI: 10.1016/j.ebiom.2023.104679.Peer-Reviewed Original ResearchConceptsRegional gray matter volumeGray matter volumeCognitive functioningMost cognitive measuresUnderlying neurobiological factorsEffect sizeLarge effect sizesProspective memoryVisual memoryCognitive measuresExecutive functionTrail MakingCognitive performanceNeurobiological factorsSmall effect sizesProcessing speedVentral striatumParahippocampal gyrusCognitive declineCognitive impairmentMatter volumeMemoryFunctioningCross-sectional associationsLimited researchCross 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 seriesDatasetConnectomeConnectome-based prediction of craving in gambling disorder and cocaine use disorder
Antons S, Yip S, Lacadie C, Dadashkarimi J, Scheinost D, Brand M, Potenza M. Connectome-based prediction of craving in gambling disorder and cocaine use disorder. Dialogues In Clinical Neuroscience 2023, 25: 33-42. PMID: 37190759, PMCID: PMC10190201, DOI: 10.1080/19585969.2023.2208586.Peer-Reviewed Original ResearchConceptsCocaine use disorderGambling disorderBehavioral addictionsCue-reactivity taskComponents of memoryGeneral neural mechanismCommon neural networkFunctional magnetic resonanceMedial frontal regionsDefault mode networkFeatures of addictionAutobiographical memoryValence ratingsMeta-analytic dataPrefrontal regionsNeural mechanismsPrefrontal cortexFronto-parietalFrontal regionsMotor imageryMotor/Diverse sampleLimbic networkNeural connectivityCravingWhy is everyone talking about brain state?
Greene A, Horien C, Barson D, Scheinost D, Constable R. Why is everyone talking about brain state? Trends In Neurosciences 2023, 46: 508-524. PMID: 37164869, PMCID: PMC10330476, DOI: 10.1016/j.tins.2023.04.001.Peer-Reviewed Original ResearchFunctional brain networks reflect spatial and temporal autocorrelation
Shinn M, Hu A, Turner L, Noble S, Preller K, Ji J, Moujaes F, Achard S, Scheinost D, Constable R, Krystal J, Vollenweider F, Lee D, Anticevic A, Bullmore E, Murray J. Functional brain networks reflect spatial and temporal autocorrelation. Nature Neuroscience 2023, 26: 867-878. PMID: 37095399, DOI: 10.1038/s41593-023-01299-3.Peer-Reviewed Original ResearchAssociations of physical frailty with health outcomes and brain structure in 483 033 middle-aged and older adults: a population-based study from the UK Biobank
Jiang R, Noble S, Sui J, Yoo K, Rosenblatt M, Horien C, Qi S, Liang Q, Sun H, Calhoun V, Scheinost D. Associations of physical frailty with health outcomes and brain structure in 483 033 middle-aged and older adults: a population-based study from the UK Biobank. The Lancet Digital Health 2023, 5: e350-e359. PMID: 37061351, PMCID: PMC10257912, DOI: 10.1016/s2589-7500(23)00043-2.Peer-Reviewed Original ResearchConceptsPopulation-based studyPhysical frailtyHealth-related outcomesBrain structuresMental healthHealth outcomesHealth measuresTotal white matter hyperintensitiesIndicators of frailtySeverity of frailtyLower gray matter volumePoor physical fitnessWhite matter hyperintensitiesGray matter volumeUK BiobankHealth-related measuresPoor mental healthMental health measuresDirection of associationMatter hyperintensitiesUnhealthy lifestyleEarly-life risksPsychiatric disordersNumerous confoundersPreventative strategiesAltered Brain Dynamics Across Bipolar Disorder and Schizophrenia During Rest and Task Switching Revealed by Overlapping Brain States
Ye J, Sun H, Gao S, Dadashkarimi J, Rosenblatt M, Rodriguez R, Mehta S, Jiang R, Noble S, Westwater M, Scheinost D. Altered Brain Dynamics Across Bipolar Disorder and Schizophrenia During Rest and Task Switching Revealed by Overlapping Brain States. Biological Psychiatry 2023, 94: 580-590. PMID: 37031780, PMCID: PMC10524212, DOI: 10.1016/j.biopsych.2023.03.024.Peer-Reviewed Original ResearchConceptsAberrant brain dynamicsMultiple brain statesBipolar disorderTask-based functional magnetic resonanceFunctional magnetic resonanceAltered brain dynamicsBrain statesTask-based functional magnetic resonance imagingFunctional magnetic resonance imagingMagnetic resonance imagingHealthy control participantsBrain dynamicsSignificant group differencesMagnetic resonanceMultivariate analysisResonance imagingSchizophreniaTime pointsControl participantsGroup differencesNeural mechanismsOlder participantsPreliminary evidenceDynamic alterationsDisordersTransdiagnostic 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 settingsSex differences in default mode network connectivity in healthy aging adults
Ficek-Tani B, Horien C, Ju S, Xu W, Li N, Lacadie C, Shen X, Scheinost D, Constable T, Fredericks C. Sex differences in default mode network connectivity in healthy aging adults. Cerebral Cortex 2022, 33: 6139-6151. PMID: 36563018, PMCID: PMC10183749, DOI: 10.1093/cercor/bhac491.Peer-Reviewed Original ResearchConceptsDefault mode networkPreclinical Alzheimer's diseaseAlzheimer's diseaseSex differencesBrain connectivity changesDefault mode network connectivityIntrinsic connectivity distributionSeed-based analysisMode network connectivityMedial prefrontal cortexPosterior DMN nodesHealthy aging adultsImpact of sexLifetime riskDMN connectivityWhole brainPosterior cingulateDMN nodesSignificant sex differencesPrefrontal cortexConnectivity changesAging AdultsHealthy participantsDMN functionMode networkLeveraging edge-centric networks complements existing network-level inference for functional connectomes
Rodriguez R, Noble S, Tejavibulya L, Scheinost D. Leveraging edge-centric networks complements existing network-level inference for functional connectomes. NeuroImage 2022, 264: 119742. PMID: 36368501, PMCID: PMC9838718, DOI: 10.1016/j.neuroimage.2022.119742.Peer-Reviewed Original ResearchHypoconnectivity between anterior insula and amygdala associates with future vulnerabilities in social development in a neurodiverse sample of neonates
Scheinost D, Chang J, Lacadie C, Brennan-Wydra E, Foster R, Boxberger A, Macari S, Vernetti A, Constable RT, Ment LR, Chawarska K. Hypoconnectivity between anterior insula and amygdala associates with future vulnerabilities in social development in a neurodiverse sample of neonates. Scientific Reports 2022, 12: 16230. PMID: 36171268, PMCID: PMC9517994, DOI: 10.1038/s41598-022-20617-6.Peer-Reviewed Original ResearchConceptsFirst Year InventoryAnterior insulaFunctional connectivityMaternal mental health factorsLeft anterior insulaState functional connectivityMental health factorsSubsample of participantsPostmenstrual ageSalience networkFamily historySocial domainsNeural circuitryAutismSocial behaviorBrain imagingHigher likelihoodInsulaSocial developmentHypoconnectivityExploratory analysisThird trimesterFuture onsetHealth factorsRisk scoreA graph theory neuroimaging approach to distinguish the depression of bipolar disorder from major depressive disorder in adolescents and young adults
Goldman DA, Sankar A, Rich A, Kim JA, Pittman B, Constable RT, Scheinost D, Blumberg HP. A graph theory neuroimaging approach to distinguish the depression of bipolar disorder from major depressive disorder in adolescents and young adults. Journal Of Affective Disorders 2022, 319: 15-26. PMID: 36103935, PMCID: PMC9669784, DOI: 10.1016/j.jad.2022.09.016.Peer-Reviewed Original ResearchConceptsAdolescents/young adultsMajor depressive disorderDepressive disorderYoung adultsICD increasesBipolar disorderInterhemispheric functional connectivityFunctional connectivity differencesSeed-based analysisFunctional connectivity patternsSeed-based connectivityFunctional magnetic resonanceFunctional connectivity measuresBasal gangliaFunctional dysconnectivityIllness progressionTreatment strategiesClinical measuresEarly diagnosisHC groupTargeted treatmentConnectivity differencesSuicide thoughtsFunctional connectivityDeleterious treatment