2024
Brain age prediction and deviations from normative trajectories in the neonatal connectome
Sun H, Mehta S, Khaitova M, Cheng B, Hao X, Spann M, Scheinost D. Brain age prediction and deviations from normative trajectories in the neonatal connectome. Nature Communications 2024, 15: 10251. PMID: 39592647, PMCID: PMC11599754, DOI: 10.1038/s41467-024-54657-5.Peer-Reviewed Original ResearchConceptsPostmenstrual agePerinatal periodBrain age predictionFunctional connectomeMonths of postnatal lifeMonths of lifePreterm infantsNormative trajectoryConnectome-based predictive modelingThird trimesterPerinatal exposureBrain age gapPostnatal lifeResting-state fMRIInfantsHuman Connectome ProjectNeonatal connectomeDevelopmental trajectoriesBrainBehavioral outcomesNormative dataMonthsConnectome ProjectDTI dataConnectomeEdge-centric network control on the human brain structural network
Sun H, Rosenblatt M, Dadashkarimi J, Rodriguez R, Tejavibulya L, Scheinost D. Edge-centric network control on the human brain structural network. Imaging Neuroscience 2024, 2: 1-15. DOI: 10.1162/imag_a_00191.Peer-Reviewed Original ResearchHuman brain structural networksNetwork control theoryEdge controlWhole-brain networksHuman Connectome ProjectDiffusion MRI dataWhite matter connectivityConnectome ProjectBrain dynamicsExecutive functionBrain structural networksBrain network connectivityBrain connectivityFunctional connectomeState transitionsTransitionEnergy patternsTheory modelBrain energy consumptionDynamic processStructural networkStateNetwork control mechanismsCognitive statesNetwork pairs
2023
Connectome-based machine learning models are vulnerable to subtle data manipulations
Rosenblatt M, Rodriguez R, Westwater M, Dai W, Horien C, Greene A, Constable R, Noble S, Scheinost D. Connectome-based machine learning models are vulnerable to subtle data manipulations. Patterns 2023, 4: 100756. PMID: 37521052, PMCID: PMC10382940, DOI: 10.1016/j.patter.2023.100756.Peer-Reviewed Original ResearchData manipulationNoise attacksPrediction performanceMachine learning modelsManipulated dataLearning modelHigh trustworthinessConnectome dataTrustworthinessAttacksModel performancePredictive modelDownstream analysisPerformanceAcademic researchMachineRobustnessModelConnectomeConnectome-based modelsFunctional connectomeManipulationTransdiagnostic 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 disordersConnectomeIndividualsPredicting depressed and elevated mood symptomatology in bipolar disorder using brain functional connectomes
Sankar A, Shen X, Colic L, Goldman D, Villa L, Kim J, Pittman B, Scheinost D, Constable R, Blumberg H. Predicting depressed and elevated mood symptomatology in bipolar disorder using brain functional connectomes. Psychological Medicine 2023, 53: 6656-6665. PMID: 36891769, PMCID: PMC10491744, DOI: 10.1017/s003329172300003x.Peer-Reviewed Original ResearchBipolar disorderYoung Mania Rating ScaleMania Rating ScaleFunctional connectomeBrain functional connectomeSymptom scoresHamilton DepressionMagnetic resonance imaging dataEmotion processing taskMood symptomatologyRating ScaleFunctional magnetic resonance imaging (fMRI) dataConnectomeAdultsImaging dataIndependent samplesPredictive abilitySymptomatology
2022
A 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 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
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 participantsMemory
2019
The 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