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
2023
Elevated 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 ResearchMeSH KeywordsAdultAgedAged, 80 and overBrainC-Reactive ProteinCross-Sectional StudiesHumansInflammationLiver CirrhosisMagnetic Resonance ImagingMiddle AgedUnited StatesConceptsRegional 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 ResearchMeSH KeywordsBrainCocaineConnectomeCravingGamblingHumansMagnetic Resonance ImagingSubstance-Related DisordersConceptsCocaine 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 connectivityCravingFunctional 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 ResearchAltered 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 ResearchMeSH KeywordsAgedBipolar DisorderBrainConnectomeHumansLearningMagnetic Resonance ImagingSchizophreniaConceptsAberrant 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 ResearchMeSH KeywordsBehavior, AddictiveBrainConnectomeCravingCuesHumansMagnetic Resonance ImagingSubstance-Related DisordersConceptsConnectome-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 ResearchMeSH KeywordsAdultBrainConnectomeDefault Mode NetworkFemaleHealthy AgingHumansMagnetic Resonance ImagingMaleNerve NetNeuropsychological TestsSex CharacteristicsConceptsDefault 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 ResearchThe Art, Science, and Secrets of Scanning Young Children
Spann M, Wisnowski J, Group H, Ahtam B, Gao W, Huang H, Nebel M, Norton E, Ouyang M, Rajagopalan V, Riggins T, Saygin Z, Scott L, Smyser C, Thomason M, Wakschlag L, Smyser C, Fetal I, Ahmad S, Aydin E, Barkovich A, Berger-Jenkins E, Brick J, Bowman L, Camacho M, Lugo-Candelas C, Cusack R, DuBois J, Dufford A, Elison J, Ellis C, Ferradal S, Filippi C, Ford A, Fouladivanda M, Gaab N, Gano D, Ganz-Benjaminsen M, Ghetti S, Glenn O, Gomez M, Graham A, Hendrix C, Holland C, Humphreys K, Korom M, Kosakowski H, Li G, Manessis A, Nolvi S, Pineda R, Pollatou A, Rae C, Rasmussen J, Scheinost D, Shultz S, Simon-Martinez C, Madsen K, Sung S, Sylvester C, Turesky T, Vaughn K, Wagner L, Wang L, Warton F, Wilson S, Wintermark P, Wu Y, Yap P, Yates T, Yen E, Yu X, Zhu H, Zöllei L, Howell B, Dean D. The Art, Science, and Secrets of Scanning Young Children. Biological Psychiatry 2022, 93: 858-860. PMID: 36336497, PMCID: PMC10050222, DOI: 10.1016/j.biopsych.2022.09.025.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 ResearchMeSH KeywordsAmygdalaBrainBrain MappingFemaleHumansInfantInfant, NewbornMagnetic Resonance ImagingNeural PathwaysPregnancySocial ChangeConceptsFirst 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 ResearchMeSH KeywordsAdolescentBipolar DisorderBrainDepressive Disorder, MajorHumansMagnetic Resonance ImagingNeuroimagingYoung AdultConceptsAdolescents/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 treatmentImproving power in functional magnetic resonance imaging by moving beyond cluster-level inference
Noble S, Mejia AF, Zalesky A, Scheinost D. Improving power in functional magnetic resonance imaging by moving beyond cluster-level inference. Proceedings Of The National Academy Of Sciences Of The United States Of America 2022, 119: e2203020119. PMID: 35925887, PMCID: PMC9371642, DOI: 10.1073/pnas.2203020119.Peer-Reviewed Original ResearchA functional connectome signature of blood pressure in >30 000 participants from the UK biobank.
Jiang R, Calhoun VD, Noble S, Sui J, Liang Q, Qi S, Scheinost D. A functional connectome signature of blood pressure in >30 000 participants from the UK biobank. Cardiovascular Research 2022, 119: 1427-1440. PMID: 35875865, PMCID: PMC10262183, DOI: 10.1093/cvr/cvac116.Peer-Reviewed Original ResearchMeSH KeywordsBiological Specimen BanksBlood PressureBrainConnectomeHumansMagnetic Resonance ImagingUnited KingdomConceptsBlood pressureBP levelsSystolic/diastolic blood pressurePrevalent modifiable risk factorFunctional connectivityMeaningful blood pressureDiastolic blood pressureElevated blood pressureModifiable risk factorsBody mass indexWhole-brain functional connectivityCentral autonomic networkAnterior cingulate cortexAntihypertensive medicationsMass indexMultiple confoundersPulse pressureRisk factorsCardiovascular diseaseIrreversible structural damageMedicated participantsMedication statusCingulate cortexCognitive declineAlzheimer's diseaseA Neuroimaging Signature of Cognitive Aging from Whole‐Brain Functional Connectivity
Jiang R, Scheinost D, Zuo N, Wu J, Qi S, Liang Q, Zhi D, Luo N, Chung Y, Liu S, Xu Y, Sui J, Calhoun V. A Neuroimaging Signature of Cognitive Aging from Whole‐Brain Functional Connectivity. Advanced Science 2022, 9: 2201621. PMID: 35811304, PMCID: PMC9403648, DOI: 10.1002/advs.202201621.Peer-Reviewed Original ResearchConceptsCognitive declineNormal agingFunctional connectivitySimilar neural correlatesWhole-brain functional connectivityDorsal attention networkBrain network organizationNeural dedifferentiationFluid intelligenceCognitive agingCognitive abilitiesNeural correlatesAttention networkCognitive functionNetwork organizationHuman ageNeuroimaging signaturesCognitionUnique patternAgingConnectivityIntelligenceCorrelatesConstructsHealthy cohortArousal impacts distributed hubs modulating the integration of brain functional connectivity
Lee K, Horien C, O’Connor D, Garand-Sheridan B, Tokoglu F, Scheinost D, Lake EMR, Constable RT. Arousal impacts distributed hubs modulating the integration of brain functional connectivity. NeuroImage 2022, 258: 119364. PMID: 35690257, PMCID: PMC9341222, DOI: 10.1016/j.neuroimage.2022.119364.Peer-Reviewed Original ResearchAge‐related topographic map of magnetic resonance diffusion metrics in neonatal brains
Bobba PS, Weber CF, Mak A, Mozayan A, Malhotra A, Sheth KN, Taylor SN, Vossough A, Grant PE, Scheinost D, Constable RT, Ment LR, Payabvash S. Age‐related topographic map of magnetic resonance diffusion metrics in neonatal brains. Human Brain Mapping 2022, 43: 4326-4334. PMID: 35599634, PMCID: PMC9435001, DOI: 10.1002/hbm.25956.Peer-Reviewed Original ResearchConceptsGestational ageCerebellar white matterWhite matterNeonatal brainDiffusion-weighted imagingCorpus callosumMD valuesCorpus callosum myelinationLower gestational ageTerm-equivalent ageSubcortical white matterFA/MDApparent diffusion coefficient (ADC) valuesMonths of ageDiffusion tensor imaging (DTI) metricsDiffusion metricsAge-related evolutionAge-related changesAge-specific normative valuesNeonatal ageBrain parenchymaCorticospinal tractNeonate brainDiffusivity metricsADC valuesA cognitive state transformation model for task-general and task-specific subsystems of the brain connectome
Yoo K, Rosenberg MD, Kwon YH, Scheinost D, Constable RT, Chun MM. A cognitive state transformation model for task-general and task-specific subsystems of the brain connectome. NeuroImage 2022, 257: 119279. PMID: 35577026, PMCID: PMC9307138, DOI: 10.1016/j.neuroimage.2022.119279.Peer-Reviewed Original ResearchConceptsDifferent cognitive statesCognitive stateWhole-brain functional connectomeRelevant individual differencesFunctional reorganizationFunctional magnetic resonanceResting-state dataSpecific task goalsTask-induced modulationHuman Connectome ProjectContext-dependent changesIndividual differencesTask goalsContextual demandsBehavioral predictionsCognitive behaviorFunctional connectomeConnectome ProjectBrain connectomeHuman brainBrain functional reorganizationC2C modelConnectomeBrainMemoryFunctional Connectome–Based Predictive Modeling in Autism
Horien C, Floris DL, Greene AS, Noble S, Rolison M, Tejavibulya L, O'Connor D, McPartland JC, Scheinost D, Chawarska K, Lake EMR, Constable RT. Functional Connectome–Based Predictive Modeling in Autism. Biological Psychiatry 2022, 92: 626-642. PMID: 35690495, PMCID: PMC10948028, DOI: 10.1016/j.biopsych.2022.04.008.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsMeSH KeywordsAutism Spectrum DisorderAutistic DisorderBrainConnectomeForecastingHumansMagnetic Resonance Imaging