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 featuresBehaviorConceptualizationCommentaryStudyFindingsSubstancesData 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 ResearchMeSH KeywordsBrainCognitionConnectomeFemaleHumansInfantInfant, NewbornPregnancyPremature BirthWhite MatterCross 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 connectivityCravingAltered 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 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 ResearchImproving 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 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
2021
Resample aggregating improves the generalizability of connectome predictive modeling
O’Connor D, Lake EMR, Scheinost D, Constable RT. Resample aggregating improves the generalizability of connectome predictive modeling. NeuroImage 2021, 236: 118044. PMID: 33848621, PMCID: PMC8282199, DOI: 10.1016/j.neuroimage.2021.118044.Peer-Reviewed Original Research
2020
Transdiagnostic, Connectome-Based Prediction of Memory Constructs Across Psychiatric Disorders
Barron DS, Gao S, Dadashkarimi J, Greene AS, Spann MN, Noble S, Lake EMR, Krystal JH, Constable RT, Scheinost D. Transdiagnostic, Connectome-Based Prediction of Memory Constructs Across Psychiatric Disorders. Cerebral Cortex 2020, 31: 2523-2533. PMID: 33345271, PMCID: PMC8023861, DOI: 10.1093/cercor/bhaa371.Peer-Reviewed Original ResearchConceptsMacroscale brain networksIndividual differencesBrain networksMemory deficitsFunctional connectivityAttention deficit hyper-activity disorderTask-based functional MRI dataLong-term memoryWhole-brain functional connectivityDiagnostic groupsWhole-brain patternsDefault mode networkFunctional MRI dataHuman Connectome ProjectPsychiatric disordersMemory constructsMemory performanceTransdiagnostic sampleBrain correlatesMode networkFunctional connectomeConnectome ProjectLimbic networkHealthy participantsMemoryDe novo damaging variants associated with congenital heart diseases contribute to the connectome
Ji W, Ferdman D, Copel J, Scheinost D, Shabanova V, Brueckner M, Khokha MK, Ment LR. De novo damaging variants associated with congenital heart diseases contribute to the connectome. Scientific Reports 2020, 10: 7046. PMID: 32341405, PMCID: PMC7184603, DOI: 10.1038/s41598-020-63928-2.Peer-Reviewed Original ResearchMeSH KeywordsConnectomeDNA HelicasesDNA-Binding ProteinsExomeFemaleHeart Defects, CongenitalHistone-Lysine N-MethyltransferaseHomeodomain ProteinsHumansMaleMi-2 Nucleosome Remodeling and Deacetylase ComplexMutationMutation, MissenseMyeloid-Lymphoid Leukemia ProteinNerve Tissue ProteinsProtein Tyrosine Phosphatase, Non-Receptor Type 11Receptor, Notch1ConceptsDe novo variantsNDD genesCardiac patterningDe novo damaging variantsDamaging de novo variantsCHD genesDamaging variantsGenesProtein truncatingGenetic originNovo variantsGene mutationsPatterningRecent studiesDendritic developmentVariantsMutationsNeurogenesisSynaptogenesisBonferroni correctionConnectome-based neurofeedback: A pilot study to improve sustained attention
Scheinost D, Hsu TW, Avery EW, Hampson M, Constable RT, Chun MM, Rosenberg MD. Connectome-based neurofeedback: A pilot study to improve sustained attention. NeuroImage 2020, 212: 116684. PMID: 32114151, PMCID: PMC7165055, DOI: 10.1016/j.neuroimage.2020.116684.Peer-Reviewed Original ResearchMeSH KeywordsAdultAttentionBrainConnectomeFemaleHumansMagnetic Resonance ImagingMaleNeurofeedbackPilot ProjectsConceptsFunctional connectivityRt-fMRIReal-time functional magnetic resonance imaging (rt-fMRI) neurofeedbackWhole-brain functional connectivityClinical trial designFunctional magnetic resonance imaging (fMRI) neurofeedbackDistinct brain areasConnectome-based modelsClinical symptomsTrial designBrain areasBrain regionsSustained attentionTherapeutic toolPilot studyBrain activityFunctional connectionsSymptomsNeurofeedbackFunctional networksTraining durationAttention taskComplex functional networksPilot sampleFunctional connectivity predicts changes in attention observed across minutes, days, and months
Rosenberg MD, Scheinost D, Greene AS, Avery EW, Kwon YH, Finn ES, Ramani R, Qiu M, Constable RT, Chun MM. Functional connectivity predicts changes in attention observed across minutes, days, and months. Proceedings Of The National Academy Of Sciences Of The United States Of America 2020, 117: 3797-3807. PMID: 32019892, PMCID: PMC7035597, DOI: 10.1073/pnas.1912226117.Peer-Reviewed Original ResearchConceptsConnectome-based predictive modelsAttentional stateSustained attentionIndividual differencesSustained attention functionFunctional connectivity signaturesFunctional brain connectivityFunctional connectivity patternsAttention functionConnectivity signaturesFunctional connectivityBrain connectivityConnectivity patternsAttentionSingle personSame patternIndividualsConnectivityIndependent studiesRecent workState changesPersonsPeopleDifferencesAbilityDistributed 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