2024
The 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-up
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
Big data approaches to identifying sex differences in long-term memory
Tejavibulya L, Scheinost D. Big data approaches to identifying sex differences in long-term memory. Cognitive Neuroscience 2020, 12: 185-186. PMID: 33356847, PMCID: PMC8222419, DOI: 10.1080/17588928.2020.1866520.Peer-Reviewed Original ResearchTransdiagnostic, 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 participantsMemoryLow-motion fMRI data can be obtained in pediatric participants undergoing a 60-minute scan protocol
Horien C, Fontenelle S, Joseph K, Powell N, Nutor C, Fortes D, Butler M, Powell K, Macris D, Lee K, Greene AS, McPartland JC, Volkmar FR, Scheinost D, Chawarska K, Constable RT. Low-motion fMRI data can be obtained in pediatric participants undergoing a 60-minute scan protocol. Scientific Reports 2020, 10: 21855. PMID: 33318557, PMCID: PMC7736342, DOI: 10.1038/s41598-020-78885-z.Peer-Reviewed Original ResearchConceptsPediatric participantsMRI protocolMagnetic resonance imaging (MRI) scansFunctional magnetic resonance imaging (fMRI) scansShorter MRI protocolsScan protocolResonance imaging scansImaging scansMRI sessionsFMRI connectivity analysisFMRI dataFMRI findingsSignificant confoundScansReplication groupConnectivity analysisAutism spectrum disorderMock scanSpectrum disorderParticipantsHead motionProtocolBehavioral and brain signatures of substance use vulnerability in childhood
Rapuano KM, Rosenberg MD, Maza MT, Dennis NJ, Dorji M, Greene AS, Horien C, Scheinost D, Constable R, Casey BJ. Behavioral and brain signatures of substance use vulnerability in childhood. Developmental Cognitive Neuroscience 2020, 46: 100878. PMID: 33181393, PMCID: PMC7662869, DOI: 10.1016/j.dcn.2020.100878.Peer-Reviewed Original ResearchConceptsConnectome-based predictive modelingSubstance useFunctional connectivityCognitive Development StudyFuture substance useSubstance use vulnerabilityAdolescent substance useSubstance use increasesSubstance use outcomesIndividual differencesAdolescent brainBrain signaturesTask conditionsBehavioral measuresFamilial risk factorsUse outcomesRisky behaviorsLatent dimensionsFamilial factorsBrain modelCurrent studyWeak predictorDevelopment studiesEarly susceptibilityFunctional imagingAssociation of Prefrontal-Striatal Functional Pathology With Alcohol Abstinence Days at Treatment Initiation and Heavy Drinking After Treatment Initiation
Blaine SK, Wemm S, Fogelman N, Lacadie C, Seo D, Scheinost D, Sinha R. Association of Prefrontal-Striatal Functional Pathology With Alcohol Abstinence Days at Treatment Initiation and Heavy Drinking After Treatment Initiation. American Journal Of Psychiatry 2020, 177: 1048-1059. PMID: 32854534, PMCID: PMC7606814, DOI: 10.1176/appi.ajp.2020.19070703.Peer-Reviewed Original ResearchConceptsAlcohol use disorderTreatment initiationAlcohol abstinenceHeavy drinkingFunctional MRIEarly treatment outcomesEarly treatment phaseHealthy control subjectsEarly outpatient treatmentNumber of daysStress imagesDrinking outcomesChronic alcohol abuseHeavy drinking daysSignificant hyperreactivityControl subjectsOutpatient treatmentStriatal dysfunctionEarly treatmentTreatment outcomesBrain stressFunctional pathologyProspective assessmentTreatment phaseAlcohol abuseHow Tasks Change Whole-Brain Functional Organization to Reveal Brain-Phenotype Relationships
Greene AS, Gao S, Noble S, Scheinost D, Constable RT. How Tasks Change Whole-Brain Functional Organization to Reveal Brain-Phenotype Relationships. Cell Reports 2020, 32: 108066. PMID: 32846124, PMCID: PMC7469925, DOI: 10.1016/j.celrep.2020.108066.Peer-Reviewed Original ResearchFunctional brain connectivity in ex utero premature infants compared to in utero fetuses
De Asis-Cruz J, Kapse K, Basu SK, Said M, Scheinost D, Murnick J, Chang T, du Plessis A, Limperopoulos C. Functional brain connectivity in ex utero premature infants compared to in utero fetuses. NeuroImage 2020, 219: 117043. PMID: 32534962, PMCID: PMC7493786, DOI: 10.1016/j.neuroimage.2020.117043.Peer-Reviewed Original ResearchConceptsPremature infantsHealthy fetusesBrain connectivityStructural brain injuryBlood oxygenation level-dependent (BOLD) signalBrain structural changesStress-related areasNormal brain MRIState functional MRIFunctional connectivity strengthFunctional brain connectivityLevel-dependent signalMedial temporal lobeNetwork-based statisticsSeed-based correlation analysisUtero fetusesBasal gangliaBrain injuryTerm ageBrain MRITemporal lobeExposure altersInfantsRegion of interestFunctional MRIDe 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 ResearchConceptsFunctional 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 changesPersonsPeopleDifferencesAbility
2019
There is no single functional atlas even for a single individual: Functional parcel definitions change with task
Salehi M, Greene AS, Karbasi A, Shen X, Scheinost D, Constable RT. There is no single functional atlas even for a single individual: Functional parcel definitions change with task. NeuroImage 2019, 208: 116366. PMID: 31740342, DOI: 10.1016/j.neuroimage.2019.116366.Peer-Reviewed Original ResearchDifferential 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 ResearchConceptsMultiple 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 ProjectAn 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 approachDynamic‐flip‐angle ECG‐gating with nuisance signal regression improves resting‐state BOLD functional connectivity mapping by reducing cardiogenic noise
Hu C, Tokoglu F, Scheinost D, Qiu M, Shen X, Peters DC, Galiana G, Constable RT. Dynamic‐flip‐angle ECG‐gating with nuisance signal regression improves resting‐state BOLD functional connectivity mapping by reducing cardiogenic noise. Magnetic Resonance In Medicine 2019, 82: 911-923. PMID: 31016782, DOI: 10.1002/mrm.27775.Peer-Reviewed Original ResearchLower synaptic density is associated with depression severity and network alterations
Holmes SE, Scheinost D, Finnema SJ, Naganawa M, Davis MT, DellaGioia N, Nabulsi N, Matuskey D, Angarita GA, Pietrzak RH, Duman RS, Sanacora G, Krystal JH, Carson RE, Esterlis I. Lower synaptic density is associated with depression severity and network alterations. Nature Communications 2019, 10: 1529. PMID: 30948709, PMCID: PMC6449365, DOI: 10.1038/s41467-019-09562-7.Peer-Reviewed Original ResearchConceptsMajor depressive disorderPost-traumatic stress disorderLower synaptic densitySynaptic densityPositron emission tomographyFunctional connectivityNetwork alterationsSynaptic vesicle glycoprotein 2ASymptoms of depressionSynaptic lossDepressive disorderHealthy controlsNerve terminalsDepressive symptomsDepression severityUnmedicated individualsSynaptic connectionsEmission tomographyStress disorderVivo evidenceSymptomsDepressionSeverityDisordersAlterationsPrefrontal Cortical and Behavioral Adaptations to Surgical Delivery Mediated by Metabolic Principles
Taylor-Giorlando M, Scheinost D, Ment L, Rothman D, Horvath TL. Prefrontal Cortical and Behavioral Adaptations to Surgical Delivery Mediated by Metabolic Principles. Cerebral Cortex 2019, 29: 5061-5071. PMID: 30877804, PMCID: PMC6918927, DOI: 10.1093/cercor/bhz046.Peer-Reviewed Original ResearchConceptsMode of deliverySurgical deliveryLayer 3 pyramidal neuronsAlters mitochondrial dynamicsValues of miceMurine findingsCerebral cortexPyramidal neuronsAdult behaviorHuman neonatesMaze testPrepulse inhibitionSpine synapsesPsychiatric illnessAdult miceNeuronal circuitryAnimal modelsClinical relevanceHuman clinical relevanceUCP-2Prefrontal cortexMitochondrial adaptationsImpaired performanceMitochondrial mechanismsBehavioral phenotypes