2025
Identification and external validation of a problem cannabis risk network
Lichenstein S, Kiluk B, Potenza M, Garavan H, Chaarani B, Banaschewski T, Bokde A, Desrivières S, Flor H, Grigis A, Gowland P, Heinz A, Brühl R, Martinot J, Paillère Martinot M, Artiges E, Nees F, Orfanos D, Poustka L, Hohmann S, Holz N, Baeuchl C, Smolka M, Vaidya N, Walter H, Whelan R, Schumann G, Pearlson G, Yip S. Identification and external validation of a problem cannabis risk network. Biological Psychiatry 2025 PMID: 39909136, DOI: 10.1016/j.biopsych.2025.01.022.Peer-Reviewed Original ResearchAlcohol use outcomesCannabis useNeural mechanismsSample of treatment-seeking adultsNeural mechanisms of riskTreatment-seeking adultsCannabis use disorderNon-clinical sampleMechanisms of riskFunctional connectivity dataSample of adolescentsTreatment outcomesAssociated with harmful outcomesPoor treatment outcomesAddiction severityUse disorderEmerging adulthoodWhole-brainCannabisCollege studentsBrain developmentConnectivity dataIdentified networksTreatment approachesAdultsNeurobiological 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
Tensor dictionary-based heterogeneous transfer learning to study emotion-related gender differences in brain
Yang L, Qiao C, Kanamori T, Calhoun V, Stephen J, Wilson T, Wang Y. Tensor dictionary-based heterogeneous transfer learning to study emotion-related gender differences in brain. Neural Networks 2024, 183: 106974. PMID: 39657530, PMCID: PMC12202986, DOI: 10.1016/j.neunet.2024.106974.Peer-Reviewed Original ResearchFeature spaceClassification performanceHeterogeneous transfer learningTensor dictionary learningHeterogeneous knowledge sharingTransfer learning frameworkReduce training costsDictionary learningKnowledge sharing strategyHeterogeneous transferGender classificationTransfer learningLearning frameworkConnectivity dataHeterogeneous dataHeterogeneous knowledgeBrain activity dataPriori knowledgeTraining costsSharing strategyProblem of insufficient sample sizeKnowledge sharingEEG dataExperimental resultsDictionaryPower and reproducibility in the external validation of brain-phenotype predictions
Rosenblatt M, Tejavibulya L, Sun H, Camp C, Khaitova M, Adkinson B, Jiang R, Westwater M, Noble S, Scheinost D. Power and reproducibility in the external validation of brain-phenotype predictions. Nature Human Behaviour 2024, 8: 2018-2033. PMID: 39085406, DOI: 10.1038/s41562-024-01931-7.Peer-Reviewed Original ResearchHuman Connectome ProjectAdolescent Brain Cognitive Development StudyConnectome ProjectCognitive Development StudyPhiladelphia Neurodevelopmental CohortHealthy Brain NetworkStructural connectivity dataMatrix reasoningWorking memoryAnxiety/depression symptomsAttention problemsNeurodevelopmental CohortBrain networksBrain-phenotype associationsEffect sizeConnectivity dataExternal validationRelated processesValidation studySample sizeBrain ProjectDevelopment studiesTraining sample sizeGeneralizability of modelsExternal samples
2023
Transdiagnostic 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 disordersConnectomeIndividuals
2021
Role of the cerebellum in the phenotype of neurodegenerative diseases: Mitigate or exacerbate?
Azizi S. Role of the cerebellum in the phenotype of neurodegenerative diseases: Mitigate or exacerbate? Neuroscience Letters 2021, 760: 136105. PMID: 34246702, DOI: 10.1016/j.neulet.2021.136105.Peer-Reviewed Original ResearchConceptsBehavioural component of emotionsFunctional connectivity dataComponents of emotionVisuospatial disordersFunctional connectivityBrain activityFMRI mapsBehavioral componentsCerebellar anatomyCerebellar vermisCerebellumConnectivity dataParkinson's patientsCerebral cortexCognitionIncreased activityBrainMotor-related inputsFMRIDegenerative diseasesSensory systemsCortexEmotionsNeuroscienceClinical symptomsConnectome-Based Predictive Modelling With Missing Connectivity Data Using Robust Matrix Completion
Liang Q, Negahban S, Chang J, Zhou H, Scheinost D. Connectome-Based Predictive Modelling With Missing Connectivity Data Using Robust Matrix Completion. 2021, 00: 738-742. DOI: 10.1109/isbi48211.2021.9434138.Peer-Reviewed Original ResearchRobust Matrix CompletionMatrix completionMachine learning modelsPortion of dataFeature selection stepConnectivity dataDataset showPredictive modellingLearning modelMultiple tasksHuge amountTask increasesModelling pipelineSelection stepExperimental resultsComplementary informationPredictive performanceUseful informationComparison methodDownstream analysisSmall subsetInformationComplete casesModellingTask
2019
Chapter 4 The uniqueness of the individual functional connectome
Horien C, Scheinost D, Constable R. Chapter 4 The uniqueness of the individual functional connectome. 2019, 63-81. DOI: 10.1016/b978-0-12-813838-0.00004-2.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingIndividual differencesIndividual functional connectomesBrain functionConnectivity dataGroup-level differencesFunctional connectivity dataHuman neuroimagingBehavioral measuresFunctional connectomeMagnetic resonance imagingResonance imagingInterindividual heterogeneityNext turnConnectomeCognitionBest predictive modelNeuroimagingDifferencesParticipantsDisease
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 analysis79. Transdiagnostic Prediction of Memory and Cognitive Abilities From Functional Connectivity Data: A Multidimensional Connectome-Based Predictive Modeling Study
Scheinost D, Gao S, Greene A, Constable R. 79. Transdiagnostic Prediction of Memory and Cognitive Abilities From Functional Connectivity Data: A Multidimensional Connectome-Based Predictive Modeling Study. Biological Psychiatry 2018, 83: s33. DOI: 10.1016/j.biopsych.2018.02.096.Peer-Reviewed Original ResearchTask 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
An exemplar-based approach to individualized parcellation reveals the need for sex specific functional networks
Salehi M, Karbasi A, Shen X, Scheinost D, Constable RT. An exemplar-based approach to individualized parcellation reveals the need for sex specific functional networks. NeuroImage 2017, 170: 54-67. PMID: 28882628, PMCID: PMC5905726, DOI: 10.1016/j.neuroimage.2017.08.068.Peer-Reviewed Original ResearchConceptsIndividualized parcellationParcellation techniqueFunctional networksCross-validated predictive modelSpecific functional networksCerebral cortexPatient subgroupsFunctional connectivity dataFunctional organizationBrainParcellation schemesClinical applicationParcellation approachParcellationSexSubgroupsConnectivity dataIndividualized studyNetwork organizationIndividualsAmple evidencePatientsCortexUsing connectome-based predictive modeling to predict individual behavior from brain connectivity
Shen X, Finn ES, Scheinost D, Rosenberg MD, Chun MM, Papademetris X, Constable RT. Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nature Protocols 2017, 12: 506-518. PMID: 28182017, PMCID: PMC5526681, DOI: 10.1038/nprot.2016.178.Peer-Reviewed Original Research
2014
Coupled Intrinsic Connectivity Distribution Analysis: A Method for Exploratory Connectivity Analysis of Paired fMRI Data
Scheinost D, Shen X, Finn E, Sinha R, Constable RT, Papademetris X. Coupled Intrinsic Connectivity Distribution Analysis: A Method for Exploratory Connectivity Analysis of Paired fMRI Data. PLOS ONE 2014, 9: e93544. PMID: 24676034, PMCID: PMC3968179, DOI: 10.1371/journal.pone.0093544.Peer-Reviewed Original ResearchConceptsHealthy controlsIntrinsic connectivity distributionCocaine-dependent subjectsResting-state scansSeed-based analysisConnectivity analysisSimilar brain regionsBasic neuroscience researchMagnetic resonance imaging dataPsychiatric diseasesVoxel-based resultsConnectivity differencesBrain regionsClinical toolFunctional changesFunctional magnetic resonance imaging (fMRI) dataScansVoxel-based methodConnectivity approachNeuroscience researchConnectivity data
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