2022
Associations among Household and Neighborhood Socioeconomic Disadvantages, Resting-state Frontoamygdala Connectivity, and Internalizing Symptoms in Youth
Ip KI, Sisk LM, Horien C, Conley MI, Rapuano KM, Rosenberg MD, Greene AS, Scheinost D, Constable RT, Casey BJ, Baskin-Sommers A, Gee DG. Associations among Household and Neighborhood Socioeconomic Disadvantages, Resting-state Frontoamygdala Connectivity, and Internalizing Symptoms in Youth. Journal Of Cognitive Neuroscience 2022, 34: 1810-1841. PMID: 35104356, DOI: 10.1162/jocn_a_01826.Peer-Reviewed Original ResearchConceptsSocioeconomic disadvantageNeighbourhood deprivationResting-state functional connectivityInternalizing symptomsSymptoms 1 yearHigher neighborhood deprivationNeighborhood socioeconomic disadvantageCognitive Development StudyAdolescent Brain Cognitive Development (ABCD) studyEarly interventionBilateral amygdalaElevated symptomsNegative connectivitySymptomsFunctional connectivityMental healthPositive connectivityBaselineHigher internalizing symptomsFrontoparietal networkOFC regionsFunctional couplingDeleterious effectsHigh disadvantageNeeds ratioBrain–phenotype models fail for individuals who defy sample stereotypes
Greene AS, Shen X, Noble S, Horien C, Hahn CA, Arora J, Tokoglu F, Spann MN, Carrión CI, Barron DS, Sanacora G, Srihari VH, Woods SW, Scheinost D, Constable RT. Brain–phenotype models fail for individuals who defy sample stereotypes. Nature 2022, 609: 109-118. PMID: 36002572, PMCID: PMC9433326, DOI: 10.1038/s41586-022-05118-w.Peer-Reviewed Original ResearchConceptsBrain-phenotype relationshipsBrain functional organizationCognitive constructsIndividual differencesNeurocognitive measuresBrain activityNeurocognitive scoresStereotypical profileNeural targetsClinical interventionsNeural circuitsFunctional organizationIndividualsSuch relationshipsData-driven approachRelationshipStereotypesFunctional 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 StatementsLarge-scale differences in functional organization of left- and right-handed individuals using whole-brain, data-driven analysis of connectivity
Tejavibulya L, Peterson H, Greene A, Gao S, Rolison M, Noble S, Scheinost D. Large-scale differences in functional organization of left- and right-handed individuals using whole-brain, data-driven analysis of connectivity. NeuroImage 2022, 252: 119040. PMID: 35272202, PMCID: PMC9013515, DOI: 10.1016/j.neuroimage.2022.119040.Peer-Reviewed Original ResearchConceptsHanded individualsFunctional connectivityLanguage areasWhole-brain functional connectivityRight-handed individualsFunctional organizationWhole-brain levelIndividual differencesHandedness differencesHandedness effectsFunctional connectomeBrain levelsSomatosensory cortexNetworks of interestWhole brainSex differencesBrainConnectomeIndividualsData-driven analysisConnectivityDistinct patternsLateralizationDifferencesSimilar amounts
2021
Genetic variation in endocannabinoid signaling is associated with differential network‐level functional connectivity in youth
Sisk LM, Rapuano KM, Conley MI, Greene AS, Horien C, Rosenberg MD, Scheinost D, Constable RT, Glatt CE, Casey BJ, Gee DG. Genetic variation in endocannabinoid signaling is associated with differential network‐level functional connectivity in youth. Journal Of Neuroscience Research 2021, 100: 731-743. PMID: 34496065, PMCID: PMC8866205, DOI: 10.1002/jnr.24946.Peer-Reviewed Original ResearchConceptsEndocannabinoid signalingAllele carriersLower anxiety symptomsC385A polymorphismNetwork-level functional connectivityEnhanced endocannabinoid signalingLarge-scale resting-state brain networksAnxiety symptomsResting-state brain networksGenotype-associated differencesBrain networksFronto-amygdala connectivityFunctional connectionsCognitive Development StudyNetwork-level changesPotential protective factorsAdolescent Brain Cognitive Development (ABCD) studyEndocannabinoid systemNetwork-level differencesYounger ageFunctional connectivityProtective factorsNeural phenotypesAnxiety disordersNeural connectivityWithin node connectivity changes, not simply edge changes, influence graph theory measures in functional connectivity studies of the brain
Luo W, Greene AS, Constable RT. Within node connectivity changes, not simply edge changes, influence graph theory measures in functional connectivity studies of the brain. NeuroImage 2021, 240: 118332. PMID: 34224851, PMCID: PMC8493952, DOI: 10.1016/j.neuroimage.2021.118332.Peer-Reviewed Original ResearchUsing 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
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 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 motionProtocolA hitchhiker’s guide to working with large, open-source neuroimaging datasets
Horien C, Noble S, Greene AS, Lee K, Barron DS, Gao S, O’Connor D, Salehi M, Dadashkarimi J, Shen X, Lake EMR, Constable RT, Scheinost D. A hitchhiker’s guide to working with large, open-source neuroimaging datasets. Nature Human Behaviour 2020, 5: 185-193. PMID: 33288916, PMCID: PMC7992920, DOI: 10.1038/s41562-020-01005-4.Peer-Reviewed Original ResearchBehavioral 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 imagingHow 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 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
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 ResearchCombining 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 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 ResearchTen simple rules for predictive modeling of individual differences in neuroimaging
Scheinost D, Noble S, Horien C, Greene AS, Lake EM, Salehi M, Gao S, Shen X, O’Connor D, Barron DS, Yip SW, Rosenberg MD, Constable RT. Ten simple rules for predictive modeling of individual differences in neuroimaging. NeuroImage 2019, 193: 35-45. PMID: 30831310, PMCID: PMC6521850, DOI: 10.1016/j.neuroimage.2019.02.057.Peer-Reviewed Original Research
2018
Task-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 manipulationIntelligenceVariance