2025
Connectome-Based Predictive Modeling of PTSD Development Among Recent Trauma Survivors
Ben-Zion Z, Simon A, Rosenblatt M, Korem N, Duek O, Liberzon I, Shalev A, Hendler T, Levy I, Harpaz-Rotem I, Scheinost D. Connectome-Based Predictive Modeling of PTSD Development Among Recent Trauma Survivors. JAMA Network Open 2025, 8: e250331. PMID: 40063028, PMCID: PMC11894499, DOI: 10.1001/jamanetworkopen.2025.0331.Peer-Reviewed Original ResearchConceptsConnectome-based predictive modelingPosttraumatic stress disorderClinician-Administered PTSD Scale for DSM-5Months post-traumaFunctional magnetic resonance imagingTrauma survivorsDSM-5Post-traumaPosttraumatic stress disorder symptom clustersPosttraumatic stress disorder symptom severityPosttraumatic stress disorder symptom trajectoriesPosttraumatic stress disorder severityTask-based fMRI dataAnterior default modePredictive of symptomsFollow-up assessmentDevelopment of effective personalized treatmentsComprehensive clinical assessmentClinician-AdministeredNeurobiological indicesPosttraumatic psychopathologyHyperarousal symptomsCentral executiveTrauma exposureSalience networkConnectome-based predictive modeling of early and chronic psychosis symptoms
Foster M, Ye J, Powers A, Dvornek N, Scheinost D. Connectome-based predictive modeling of early and chronic psychosis symptoms. Neuropsychopharmacology 2025, 1-9. PMID: 40016363, DOI: 10.1038/s41386-025-02064-9.Peer-Reviewed Original ResearchConnectome-based predictive modelingPositive and Negative Syndrome ScalePsychosis symptomsSymptom networksSymptom severityBrain networksNeural correlates of CPResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingNegative Syndrome ScaleIdentified group differencesPredicted effect sizeCorrelates of CPGeneral psychopathologyNegative symptomsPositive symptomsSyndrome ScaleFrontoparietal networkNeural correlatesVirtual lesion analysisGroup differencesConnectivity changesEffect sizeLesion analysisLongitudinal studyPREDICTING DEPRESSED AND ELEVATED MOOD SYMPTOMATOLOGY IN BIPOLAR DISORDER USING BRAIN FUNCTIONAL CONNECTOMES
Sankar *, 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. The International Journal Of Neuropsychopharmacology 2025, 28: i15-i15. PMCID: PMC11814904, DOI: 10.1093/ijnp/pyae059.027.Peer-Reviewed Original ResearchConnectome-based predictive modelingBipolar disorderMood symptomatologyMood symptomsFunctional connectomeYoung Mania Rating ScaleFunctional magnetic resonance imaging dataPredictors of functional impairmentEmotion processing taskMania Rating ScaleMood symptom severityMood symptom scoresBrain functional connectomeMagnetic resonance imaging dataBrain function disturbancesHamilton DepressionPredicting depressionSymptom severityRating ScaleFunctional impairmentDisordersMoodSymptomatologyProcessing tasksDepressionNeurobiological fingerprints of negative symptoms in schizophrenia identified by connectome‐based modeling
Gao Z, Xiao Y, Zhu F, Tao B, Zhao Q, Yu W, Bishop J, Gong Q, Lui S. Neurobiological fingerprints of negative symptoms in schizophrenia identified by connectome‐based modeling. Psychiatry And Clinical Neurosciences 2025, 79: 108-116. PMID: 39815736, DOI: 10.1111/pcn.13782.Peer-Reviewed Original ResearchConceptsConnectome-based predictive modelingNegative symptomsResting-state functional connectivity dataSeverity of negative symptomsDevelopment of novel treatment interventionsPrediction of negative symptomsDrug-naive schizophrenia patientsFirst-episode drug-naive schizophrenia patientsUnique neural substratesNovel treatment interventionsFunctional connectivity dataConnectome-based modelsSchizophrenia psychopathologySchizophrenia patientsNeurobiological mechanismsNeural substratesSymptom-specificSchizophreniaIndependent validation sampleError processNeural fingerprintsTreatment interventionsConnectivity patternsFunctional networksConnectivity data
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 dataConnectomeBrain-phenotype predictions of language and executive function can survive across diverse real-world data: Dataset shifts in developmental populations
Adkinson B, Rosenblatt M, Dadashkarimi J, Tejavibulya L, Jiang R, Noble S, Scheinost D. Brain-phenotype predictions of language and executive function can survive across diverse real-world data: Dataset shifts in developmental populations. Developmental Cognitive Neuroscience 2024, 70: 101464. PMID: 39447452, PMCID: PMC11538622, DOI: 10.1016/j.dcn.2024.101464.Peer-Reviewed Original ResearchBrain-phenotype associationsConnectome-based predictive modelingBrain-behavior associationsPrediction of languagePhiladelphia Neurodevelopmental CohortHealthy Brain NetworkClinical symptom burdenFMRI taskHuman Connectome ProjectExecutive functionBehavioral measuresDevelopmental populationsNeurodevelopmental CohortBrain networksDevelopmental sampleConnectome ProjectResearch settingsGeneralizabilitySymptom burdenExternal validationFMRIClinical settingAssociationEthnic minority representationTaskPrediction of craving across studies: A commentary on conceptual and methodological considerations when using data-driven methods
Antons S, Yip S, Lacadie C, Dadashkarimi J, Scheinost D, Brand M, Potenza M. Prediction of craving across studies: A commentary on conceptual and methodological considerations when using data-driven methods. Journal Of Behavioral Addictions 2024, 13: 695-701. PMID: 39356557, PMCID: PMC11457034, DOI: 10.1556/2006.2024.00050.Peer-Reviewed Original ResearchConceptsAddictive behaviorsDisorders due to addictive behaviorsConnectome-based predictive modelingPrediction of cravingInvestigate neural mechanismsSubstance use disordersNeural mechanismsCravingSubstance useMethodological considerationsDisordersMethodological featuresBehaviorConceptualizationCommentaryStudyFindingsSubstancesNetwork state dynamics underpin basal craving in a transdiagnostic population
Ye J, Garrison K, Lacadie C, Potenza M, Sinha R, Goldfarb E, Scheinost D. Network state dynamics underpin basal craving in a transdiagnostic population. Molecular Psychiatry 2024, 30: 619-628. PMID: 39183336, DOI: 10.1038/s41380-024-02708-0.Peer-Reviewed Original ResearchConnectome-based predictive modelingBrain responsesRegulation of affective statesSample of healthy controlsTransdiagnostic populationTransdiagnostic sampleHigher cravingMotivational stateCravingFMRI methodsAffective statesScan runsExperimental stimuliNetwork engagementBrain dynamicsClinical implicationsHealthy controlsBrainIndividual variationState dynamicsCharacterize individualsReplication datasetPsychopathologyFMRIEngagementFunctional Networks of Reward and Punishment Processing and Their Molecular Profiles Predicting the Severity of Young Adult Drinking
Li Y, Yang L, Hao D, Chen Y, Ye-Lin Y, Li C, Li G. Functional Networks of Reward and Punishment Processing and Their Molecular Profiles Predicting the Severity of Young Adult Drinking. Brain Sciences 2024, 14: 610. PMID: 38928610, PMCID: PMC11201596, DOI: 10.3390/brainsci14060610.Peer-Reviewed Original ResearchConnectome-based predictive modelingAlcohol use severityReward processingPunishment processingFunctional connectivityAssociated with alcohol use severityAlcohol misusePatterns of network connectivitySemi-Structured AssessmentYoung adult drinkersYoung adultsGenetics of AlcoholismYoung adult drinkingHuman Connectome Project dataNetwork connectivity featuresFronto-parietalAdult drinkersJuSpace toolboxAdult drinkingNeural fingerprintsMolecular profilingGABAA signalingRewardFunctional networksConnectivity features
2023
ICA-based Individualized Differential Structure Similarity Networks for Predicting Symptom Scores in Adolescents with Major Depressive Disorder
Li X, Xu M, Jiang R, Li X, Calhoun V, Zhou X, Sui J. ICA-based Individualized Differential Structure Similarity Networks for Predicting Symptom Scores in Adolescents with Major Depressive Disorder. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-5. PMID: 38082692, DOI: 10.1109/embc40787.2023.10340456.Peer-Reviewed Original ResearchConceptsMajor depressive disorderGray matter volumeDepressive disorderWhole-brain structural covariance networksConnectome-based predictive modelingAdolescent MDD patientsComplex mood disorderMeasure individual differencesDefault-mode networkStructural brain alterationsStructural covariance networksHamilton Depression ScaleHamilton Anxiety ScaleSpatially constrained ICAMDD patientsMood disordersBrain alterationsMatter volumeIndividual differencesBrain structuresCovariance networksAnxiety ScaleVisual networkDepression ScaleStructure similarity networkDistinct neural networks predict cocaine versus cannabis treatment outcomes
Lichenstein S, Kohler R, Ye F, Potenza M, Kiluk B, Yip S. Distinct neural networks predict cocaine versus cannabis treatment outcomes. Molecular Psychiatry 2023, 28: 3365-3372. PMID: 37308679, PMCID: PMC10713861, DOI: 10.1038/s41380-023-02120-0.Peer-Reviewed Original ResearchConceptsConnectome-based predictive modelingCognitive behavior therapyCognitive behavioral therapySubstance use disordersCannabis abstinenceNeural mechanismsBehavior therapyDistinct neural networksComputer-based trainingCannabis use disorderFMRI scanningNeural predictorsStudy 1Study 2Treatment outcomesContingency managementPrior workComparison subjectsNetwork strengthUse disordersNovel treatment targetsAbstinenceIndependent samplesCocaine abstinenceTreatment respondersTransdiagnostic 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 disordersConnectomeIndividualsConnectome-based predictive modeling shows sex differences in brain-based predictors of memory performance
Ju S, Horien C, Shen X, Abuwarda H, Trainer A, Constable R, Fredericks C. Connectome-based predictive modeling shows sex differences in brain-based predictors of memory performance. Frontiers In Dementia 2023, 2: 1126016. PMID: 39082002, PMCID: PMC11285565, DOI: 10.3389/frdem.2023.1126016.Peer-Reviewed Original ResearchDefault mode networkConnectome-based predictive modelingMemory performanceMemory taskMemory scoresShort-term memory performanceBrain-based predictorsShort-term memoryPosterior default mode networkAmnestic Alzheimer's diseaseRecollective memoryDMN activityMode networkVisual networkPast researchSex-specific modelsVisual circuitryAlzheimer's diseaseSex differencesMemoryDifferent circuitryTaskElevated riskMachine learning approachesNetwork activity
2022
A generalizable connectome-based marker of in-scan sustained attention in neurodiverse youth
Horien C, Greene A, Shen X, Fortes D, Brennan-Wydra E, Banarjee C, Foster R, Donthireddy V, Butler M, Powell K, Vernetti A, Mandino F, O'Connor D, Lake E, McPartland J, Volkmar F, Chun M, Chawarska K, Rosenberg M, Scheinost D, Constable R. A generalizable connectome-based marker of in-scan sustained attention in neurodiverse youth. Cerebral Cortex 2022, 33: 6320-6334. PMID: 36573438, PMCID: PMC10183743, DOI: 10.1093/cercor/bhac506.Peer-Reviewed Original ResearchConceptsAttention taskAttentional stateConnectome-based predictive modelingNeurodiverse conditionsSustained attention taskAttention network modelSample of youthNeurotypical participantsSustained attentionBrain correlatesNeurobiological correlatesAttention networkIndividual participantsSeparate samplesYouthParticipantsHead motionTaskCorrelatesAttentionAutismConfoundsNetwork modelGeneralizesHealthcare settingsRobust prediction of memory and neuroticism in men and women using connectome‐based predictive modeling
Ju S, Horien C, Constable T, Fredericks C. Robust prediction of memory and neuroticism in men and women using connectome‐based predictive modeling. Alzheimer's & Dementia 2022, 18 DOI: 10.1002/alz.063015.Peer-Reviewed Original ResearchConnectome-based predictive modelingAlzheimer's diseaseRAVLT measuresFunctional MRI scansBackground Alzheimer's diseaseHealthy womenNeurobehavioral scoresHealthy subjectsHigh riskMemory performanceMRI scansBrain circuitryBrain connectivityWomenMenSexBrain connectomeDiseasePearson correlationScoresPredictorsConnectivity matrixBrain-based predictorsBehavioral measuresSubjects
2021
Large-scale functional brain networks of maladaptive childhood aggression identified by connectome-based predictive modeling
Ibrahim K, Noble S, He G, Lacadie C, Crowley MJ, McCarthy G, Scheinost D, Sukhodolsky DG. Large-scale functional brain networks of maladaptive childhood aggression identified by connectome-based predictive modeling. Molecular Psychiatry 2021, 27: 985-999. PMID: 34690348, PMCID: PMC9035467, DOI: 10.1038/s41380-021-01317-5.Peer-Reviewed Original ResearchConceptsLarge-scale functional networksChildhood aggressionSeverity of aggressionIndividual differencesBrain networksAdolescent Brain Cognitive Development (ABCD) studyConnectome-based predictive modelingReactive-Proactive Aggression QuestionnaireEmotion perception taskLarge-scale functional brain networksAssociation of connectivityFunctional networksCognitive Development StudyAggressive behaviorFunctional brain networksIndependent samplesCognitive controlEmotion regulationEmotion processingPerception taskCalm facesMaladaptive aggressionPsychiatric disordersTransdiagnostic sampleFrontoparietal network
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 imagingDistributed 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
The 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 postpartumCombining 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 Project
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