2023
Association of Body Mass Index and Waist Circumference With Imaging Metrics of Brain Integrity and Functional Connectivity in Children Aged 9 to 10 Years in the US, 2016-2018
Kaltenhauser S, Weber C, Lin H, Mozayan A, Malhotra A, Constable R, Acosta J, Falcone G, Taylor S, Ment L, Sheth K, Payabvash S. Association of Body Mass Index and Waist Circumference With Imaging Metrics of Brain Integrity and Functional Connectivity in Children Aged 9 to 10 Years in the US, 2016-2018. JAMA Network Open 2023, 6: e2314193. PMID: 37200030, PMCID: PMC10196880, DOI: 10.1001/jamanetworkopen.2023.14193.Peer-Reviewed Original ResearchConceptsBody mass indexHigher body mass indexWaist circumferenceBMI z-scoreCross-sectional studyBrain healthCross-sectional analysisFunctional connectivityMass indexMean ageInterval developmentCorpus callosumAssociation of BMIHigher baseline body mass indexBaseline cross-sectional analysisBaseline body mass indexBrain integrityResting-state functional connectivityLongitudinal analysisFuture treatment trialsABCD studyLower microstructural integrityCognitive Development StudyAdolescent Brain Cognitive Development (ABCD) studyCardiovascular implications
2022
Coordinated anatomical and functional variability in the human brain during adolescence
Bero J, Li Y, Kumar A, Humphries C, Nag S, Lee H, Ahn W, Hahn S, Constable R, Kim H, Lee D. Coordinated anatomical and functional variability in the human brain during adolescence. Human Brain Mapping 2022, 44: 1767-1778. PMID: 36479851, PMCID: PMC9921246, DOI: 10.1002/hbm.26173.Peer-Reviewed Original ResearchConceptsCortical thicknessCortical areasFunctional connectivityResting-state functional connectivityAssociation cortical areasMultiple cortical areasCortical information processingAge-related changesCortical myelinationAdult brainCytoarchitectural featuresBrain developmentInformation processingBrainDevelopmental changesHuman brainAdolescenceMyelinationPatterns of coordinationMeasuresRegional variationMyelin232-OR: Alterations in Decision-Making Neurocircuits during Hypoglycemia in Patients with T1DM
DEAJON-JACKSON J, RANGEL E, LACADIE C, GREENE A, BELFORT-DEAGUIAR R, CONSTABLE T, ROTHMAN D, HWANG J. 232-OR: Alterations in Decision-Making Neurocircuits during Hypoglycemia in Patients with T1DM. Diabetes 2022, 71 DOI: 10.2337/db22-232-or.Peer-Reviewed Original ResearchDorsal anterior cingulate cortexGroup x Session interactionFunctional connectivityOrbitofrontal cortexControl subjectsNon-diabetic control subjectsHealthy control subjectsState functional connectivityDirect neuronal connectionsRegional brain activityAnterior cingulate cortexSeed-based analysisBOLD-fMRI scanningHyperinsulinemic euglycemicHypoglycemic clampT1DM patientsSession interactionT1DM subjectsGlucose levelsT1DMHypoglycemiaNeurocognitive changesNeuronal connectionsCingulate cortexCognitive function
2021
Sex differences in connectivity in the default mode network in healthy aging adults
Ficek B, Horien C, Lacadie C, Shen X, Scheinost D, Constable T, Fredericks C. Sex differences in connectivity in the default mode network in healthy aging adults. Alzheimer's & Dementia 2021, 17 DOI: 10.1002/alz.056050.Peer-Reviewed Original ResearchIntrinsic connectivity distributionDefault mode networkAlzheimer's diseaseHealthy aging adultsElevated riskAging AdultsLarge cross-sectional studyPosterior default mode networkSex differencesMode networkCross-sectional cohortCross-sectional studyPreclinical Alzheimer's diseaseSymptomatic Alzheimer's diseaseResting-state scansSex-based differencesAnterior nodeAD showDMN connectivityHealthy adultsFunctional MRI dataNormal individualsResults FemalesZ-scoreFunctional connectivityFunctional Connectivity during Encoding Predicts Individual Differences in Long-Term Memory
Lin Q, Yoo K, Shen X, Constable TR, Chun MM. Functional Connectivity during Encoding Predicts Individual Differences in Long-Term Memory. Journal Of Cognitive Neuroscience 2021, 33: 2279-2296. PMID: 34272957, PMCID: PMC8497062, DOI: 10.1162/jocn_a_01759.Peer-Reviewed Original ResearchConceptsLong-term memoryN-back taskIndividual differencesFunctional connectivityMemory modelWhole-brain functional connectivity networksSubsequent memory effectsRecognition memory modelsMedial temporal lobeWhole-brain functional networksRetention of informationRecollection memoryRecognition memoryMemory performanceNeural basisFunctional connectivity networksLTM formationFMRI dataTemporal lobeMemoryBrain regionsFunctional networksConnectivity networksTaskLittle predictive powerUsing functional connectivity models to characterize relationships between working and episodic memory
Stark GF, Avery EW, Rosenberg MD, Greene AS, Gao S, Scheinost D, Constable R, Chun MM, Yoo K. Using functional connectivity models to characterize relationships between working and episodic memory. Brain And Behavior 2021, 11: e02105. PMID: 34142458, PMCID: PMC8413720, DOI: 10.1002/brb3.2105.Peer-Reviewed Original ResearchConceptsConnectome-based predictive modelsN-back task performanceEpisodic memoryMemory test scoresFunctional connectivityTask performanceN-back memory taskWord scoresTest scoresCritical cognitive abilityPicture-sequencing taskHuman Connectome Project participantsN-back taskFunctional magnetic resonance imaging (fMRI) dataWhole-brain functional connectivityTask functional connectivityFunctional brain connectionsFunctional connectivity modelsFunctional connectionsMemory taskCognitive processesMemory testCognitive abilitiesSequence taskList Sorting
2020
Behavioral 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 imaging1994-P: Task-Oriented Connectivity Analysis of Disease-Dependent Effects of Diet
WATT C, STANLEY T, LACADIE C, LAM K, SAVOYE M, SINHA R, CONSTABLE T, SEO D, HWANG J, BELFORT-DEAGUIAR R. 1994-P: Task-Oriented Connectivity Analysis of Disease-Dependent Effects of Diet. Diabetes 2020, 69 DOI: 10.2337/db20-1994-p.Peer-Reviewed Original ResearchMedial hippocampusFunctional MRIFunctional connectivityFood cue taskLow-calorie dietNational InstituteBrain functional connectivityLevel-dependent responsesModulation of emotionSignificant diet effectAnterior amygdalaInferior ponsObese subjectsKidney diseaseGlucose metabolismNeuronal controlMetabolic diseasesNeuronal adaptionBrain regionsBrain connectivityWeight lossDisease statesDiet effectsDietHippocampusLoss of nucleus accumbens low-frequency fluctuations is a signature of chronic pain
Makary MM, Polosecki P, Cecchi GA, DeAraujo IE, Barron DS, Constable TR, Whang PG, Thomas DA, Mowafi H, Small DM, Geha P. Loss of nucleus accumbens low-frequency fluctuations is a signature of chronic pain. Proceedings Of The National Academy Of Sciences Of The United States Of America 2020, 117: 10015-10023. PMID: 32312809, PMCID: PMC7211984, DOI: 10.1073/pnas.1918682117.Peer-Reviewed Original ResearchConceptsChronic low back pain patientsLow back pain patientsChronic painPain patientsChronic phaseChronic back pain patientsBack pain patientsRostral anterior cingulate cortexAnterior cingulate cortexAdditional independent datasetsRisk of transitionResting-state activityPersistent painBack painAccumbens volumeHealthy controlsNucleus accumbensPainSeparate cohortPatientsCingulate cortexPrevalent diseaseFunctional connectivityLoss of nucleiSubcortical signaturesDistributed 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
Differential 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 1Differential 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 J. Differential resting state connectivity responses to glycemic state in type 1 diabetes. The Journal Of Clinical Endocrinology & Metabolism 2019 DOI: 10.1210/jcem/dgz004.Peer-Reviewed Original ResearchType 1 diabetesState functional connectivityHealthy controlsDefault mode networkFunctional connectivityImpact of T1DMHealthy control volunteersAcademic medical centerAngular gyrus connectivityState connectivity patternsFunctional connectivity analysisHyperinsulinemic euglycemicHypoglycemic unawarenessHypoglycemia unawarenessHypoglycemic clampHypoglycemia awarenessFunctional outcomeControl volunteersMild hypoglycemiaGlycemic stateObservational studyMedical CenterT1DMHypoglycemiaNeurocognitive consequencesS179. Transdiagnostic Prediction of Memory and Executive Function From Whole-Brain Functional Connectivity
Barron D, Gao S, Greene A, Constable R, Krystal J, Scheinost D. S179. Transdiagnostic Prediction of Memory and Executive Function From Whole-Brain Functional Connectivity. Biological Psychiatry 2019, 85: s366-s367. DOI: 10.1016/j.biopsych.2019.03.930.Peer-Reviewed Original ResearchThe individual functional connectome is unique and stable over months to years
Horien C, Shen X, Scheinost D, Constable RT. The individual functional connectome is unique and stable over months to years. NeuroImage 2019, 189: 676-687. PMID: 30721751, PMCID: PMC6422733, DOI: 10.1016/j.neuroimage.2019.02.002.Peer-Reviewed Original ResearchConceptsHigh ID ratesIndividual differencesFunctional connectomeIndividual functional connectomesStable individual differencesID rateResting-state fMRI datasetsFrontoparietal networkFunctional connectivityParietal cortexFMRI datasetsIdiosyncratic aspectsConnectomeHead motionEntire brainFMRIBrainCortexSpecific datasetDifferencesConnectivity
2018
Combining Multiple Connectomes via Canonical Correlation Analysis Improves Predictive Models
Gao S, Greene A, Todd Constable R, Scheinost D. Combining Multiple Connectomes via Canonical Correlation Analysis Improves Predictive Models. Lecture Notes In Computer Science 2018, 11072: 349-356. DOI: 10.1007/978-3-030-00931-1_40.Peer-Reviewed Original ResearchTask conditionsDifferent cognitive tasksMultiple task conditionsDifferent task conditionsConnectivity dataDifferent cognitive conditionsFunctional connectivity dataComputational modelHuman Connectome ProjectPrediction of behaviorCognitive tasksIndividual differencesBehavioral measuresBehavioral predictionsCognitive conditionsMultiple connectomesSingle taskFunctional connectivityConnectome ProjectDifferent tasksComplementary informationMultiple tasksTaskPrincipled methodCanonical correlation analysisTask-induced brain state manipulation improves prediction of individual traits
Greene AS, Gao S, Scheinost D, Constable RT. Task-induced brain state manipulation improves prediction of individual traits. Nature Communications 2018, 9: 2807. PMID: 30022026, PMCID: PMC6052101, DOI: 10.1038/s41467-018-04920-3.Peer-Reviewed Original ResearchConceptsBrain statesIndividual differencesBrain-behavior relationshipsFluid intelligence scoresTask-based functional connectivity analysisResting-state fMRI dataBrain functional organizationFunctional connectivity analysisCognitive tasksFluid intelligenceIntelligence scoresFunctional connectivityFMRI dataConnectivity analysisHuman behaviorIndividual traitsTaskCertain tasksFunctional organizationOutperform modelsSuch relationshipsCognitionState manipulationIntelligenceVarianceTask Integration for Connectome-Based Prediction Via Canonical Correlation Analysis
Gao S, Greene A, Constable R, Scheinost D. Task Integration for Connectome-Based Prediction Via Canonical Correlation Analysis. 2018, 87-91. DOI: 10.1109/isbi.2018.8363529.Peer-Reviewed Original ResearchTask conditionsDifferent tasksDifferent cognitive tasksMultiple task conditionsDifferent task conditionsConnectivity dataDifferent cognitive conditionsFunctional connectivity dataHuman Connectome ProjectComputational modelPrediction of behaviorCognitive tasksFluid intelligenceIndividual differencesBehavioral measuresBehavioral predictionsCognitive conditionsSingle taskFunctional connectivityConnectome ProjectComplementary informationTask integrationTaskProof of conceptCanonical correlation analysis
2017
Connectome-based Models Predict Separable Components of Attention in Novel Individuals
Rosenberg MD, Hsu WT, Scheinost D, Constable R, Chun MM. Connectome-based Models Predict Separable Components of Attention in Novel Individuals. Journal Of Cognitive Neuroscience 2017, 30: 160-173. PMID: 29040013, DOI: 10.1162/jocn_a_01197.Peer-Reviewed Original ResearchConceptsConnectome-based predictive modelingAttention Network TaskExecutive controlIntrinsic functional organizationRT variabilityANT performanceInfluential modelFunctional connectivityBrain's intrinsic functional organizationComponents of attentionExecutive control scoresResting-state functional connectivityResting-state dataFunctional brain networksFunctional organizationTask-based dataAttentional abilitiesUpcoming stimulusExplicit taskSustained attentionFMRI scanningAttention factorNovel individualsAdditional independent componentNetwork tasksCan brain state be manipulated to emphasize individual differences in functional connectivity?
Finn ES, Scheinost D, Finn DM, Shen X, Papademetris X, Constable RT. Can brain state be manipulated to emphasize individual differences in functional connectivity? NeuroImage 2017, 160: 140-151. PMID: 28373122, PMCID: PMC8808247, DOI: 10.1016/j.neuroimage.2017.03.064.Peer-Reviewed Original ResearchConceptsIndividual differencesFunctional connectivityBrain statesIndividual differences researchBrain functional organizationHuman Connectome ProjectDifferences researchBrain activityConnectome ProjectSubject variabilityNetworks of interestBehavioral phenotypesCertain tasksFunctional organizationDefault stateNeutral backdropOutline questionsFuture studiesConnectivityTask
2016
Individual variation in functional brain connectivity: implications for personalized approaches to psychiatric disease
Finn E, Constable R. Individual variation in functional brain connectivity: implications for personalized approaches to psychiatric disease. Dialogues In Clinical Neuroscience 2016, 18: 277-287. PMID: 27757062, PMCID: PMC5067145, DOI: 10.31887/dcns.2016.18.3/efinn.Peer-Reviewed Original ResearchConceptsFunctional brain connectivityFunctional magnetic resonance imagingFunctional brain connectionsBrain connectivityMagnetic resonance imagingFunctional connectivity profilesPsychiatric illnessHealthy subjectsFuture illnessPsychiatric diagnosisPsychiatric diseasesMental illnessResonance imagingSingle-subject levelInterindividual variabilityPersonalized approachFunctional connectivityBrain connectionsIllnessConnectivity profilesRecent evidenceReliable correlateBehavioral phenotypesNeural organizationSubjects