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
2022
Improving 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
2019
Cluster failure or power failure? Evaluating sensitivity in cluster-level inference
Noble S, Scheinost D, Constable RT. Cluster failure or power failure? Evaluating sensitivity in cluster-level inference. NeuroImage 2019, 209: 116468. PMID: 31852625, PMCID: PMC8061745, DOI: 10.1016/j.neuroimage.2019.116468.Peer-Reviewed Original ResearchDissociable neural substrates of opioid and cocaine use identified via connectome-based modelling
Lichenstein SD, Scheinost D, Potenza MN, Carroll KM, Yip SW. Dissociable neural substrates of opioid and cocaine use identified via connectome-based modelling. Molecular Psychiatry 2019, 26: 4383-4393. PMID: 31719641, PMCID: PMC7214212, DOI: 10.1038/s41380-019-0586-y.Peer-Reviewed Original ResearchConceptsBrain statesDissociable neural substratesMultiple brain statesSubstance use outcomesHealthy comparison subjectsWhole-brain approachFMRI scanningFrontoparietal networkNeural substratesSubstance use treatmentNeural mechanismsDifferent brain statesFurther clinical relevanceDefault modeFMRI dataSubject replicationTreatment approachesReduced connectivityUse outcomesComparison subjectsNetwork strengthUse disordersSensory networksTreatment respondersSensory connectivityThe 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 postpartumIndividualized functional networks reconfigure with cognitive state
Salehi M, Karbasi A, Barron DS, Scheinost D, Constable RT. Individualized functional networks reconfigure with cognitive state. NeuroImage 2019, 206: 116233. PMID: 31574322, PMCID: PMC7216521, DOI: 10.1016/j.neuroimage.2019.116233.Peer-Reviewed Original ResearchConceptsCognitive stateFunctional networksMultiple cognitive statesFunctional network organizationFunctional organizationBrain functional networksTask demandsFMRI dataSimilar tasksParcellation approachHuman brainNetwork organizationExtensive evidenceMultiple subjectsBrainNetwork membershipTaskOrganizationSubjectsParcellationSuch reconfigurationMeasuresMembershipFindingsSuch definitionsDifferential Resting State Connectivity Responses to Glycemic State in Type 1 Diabetes
Parikh L, Seo D, Lacadie C, Belfort-Deaguiar R, Groskreutz D, Hamza M, Dai F, Scheinost D, Sinha R, Constable R, Sherwin R, Hwang JJ. Differential Resting State Connectivity Responses to Glycemic State in Type 1 Diabetes. The Journal Of Clinical Endocrinology & Metabolism 2019, 105: dgz004. PMID: 31511876, PMCID: PMC6936965, DOI: 10.1210/clinem/dgz004.Peer-Reviewed Original ResearchConceptsState functional connectivityHealthy controlsDefault mode networkType 1 diabetes mellitusFunctional connectivityImpact of T1DMAcademic medical centerAngular gyrus connectivityBlood oxygenation levelState connectivity patternsFunctional connectivity analysisHyperinsulinemic euglycemicHypoglycemic unawarenessHypoglycemia unawarenessDiabetes mellitusHypoglycemic clampHypoglycemia awarenessFunctional outcomeMild hypoglycemiaGlycemic stateObservational studyMedical CenterT1DMHC volunteersType 1Combining 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 ResearchMeSH KeywordsAdultAlgorithmsConnectomeFemaleForecastingHumansMaleModels, NeurologicalPhenotypeYoung AdultConceptsMultiple 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 ProjectRegions and Connections: Complementary Approaches to Characterize Brain Organization and Function
Horien C, Greene AS, Constable RT, Scheinost D. Regions and Connections: Complementary Approaches to Characterize Brain Organization and Function. The Neuroscientist 2019, 26: 117-133. PMID: 31304866, PMCID: PMC7079335, DOI: 10.1177/1073858419860115.Peer-Reviewed Original ResearchAn information network flow approach for measuring functional connectivity and predicting behavior
Kumar S, Yoo K, Rosenberg MD, Scheinost D, Constable RT, Zhang S, Li C, Chun MM. An information network flow approach for measuring functional connectivity and predicting behavior. Brain And Behavior 2019, 9: e01346. PMID: 31286688, PMCID: PMC6710195, DOI: 10.1002/brb3.1346.Peer-Reviewed Original ResearchConceptsFunctional brain connectivityFunctional magnetic resonance imagingFMRI time coursesIndividual differencesTask performanceMeasures of attentionSustained attention taskAttention task performanceResting-state fMRI dataSample of individualsAttention taskFMRI dataFunctional connectivityFC patternsBrain connectivityPearson correlationInformation theory statisticsInformation flowMachine-learning modelsMeasuresMagnetic resonance imagingAttentionNetwork flow approachTime courseDifferent datasetsMultivariate approaches improve the reliability and validity of functional connectivity and prediction of individual behaviors
Yoo K, Rosenberg MD, Noble S, Scheinost D, Constable RT, Chun MM. Multivariate approaches improve the reliability and validity of functional connectivity and prediction of individual behaviors. NeuroImage 2019, 197: 212-223. PMID: 31039408, PMCID: PMC6591084, DOI: 10.1016/j.neuroimage.2019.04.060.Peer-Reviewed Original ResearchConceptsFunctional brain organizationFunctional connectivityFunctional connectivity featuresTest-retest sampleMultivariate functional connectivityCognitive skillsMental representationsIndividual differencesFMRI measuresBrain organizationBrain statesStrong predictionSpatial activity patternsFMRI datasetsConnectivity featuresIndividual behaviorProject samplesConnectivity estimatesTimecoursesActivity patternsCognitionPearson correlationIndividualsConnectivityUnivariate approach