2024
Prediction 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 featuresBehaviorConceptualizationCommentaryStudyFindingsSubstancesA shared spatial topography links the functional connectome correlates of cocaine use disorder and dopamine D2/3 receptor densities
Ricard J, Labache L, Segal A, Dhamala E, Cocuzza C, Jones G, Yip S, Chopra S, Holmes A. A shared spatial topography links the functional connectome correlates of cocaine use disorder and dopamine D2/3 receptor densities. Communications Biology 2024, 7: 1178. PMID: 39300138, PMCID: PMC11413242, DOI: 10.1038/s42003-024-06836-9.Peer-Reviewed Original ResearchConceptsCocaine use disorderPatterns of functional connectivityUse disorderFunctional connectivityReceptor densityLarge-scale functional brain networksMaintenance of substance useDopamine D2/3 receptorsDopamine receptor densityCortico-striatal circuitsProfile of functional connectivitySubstance use disordersFunctional brain networksNeurotransmitter receptor densityD2/3 receptorsDopamine systemTransporter bindingSubcortical systemsBrain networksSubstance useCocaineSpatial topographyDisordersDopamineBiological mechanisms
2022
Bayesian network mediation analysis with application to the brain functional connectome
Zhao Y, Chen T, Cai J, Lichenstein S, Potenza M, Yip S. Bayesian network mediation analysis with application to the brain functional connectome. Statistics In Medicine 2022, 41: 3991-4005. PMID: 35795965, PMCID: PMC10131252, DOI: 10.1002/sim.9488.Peer-Reviewed Original ResearchMeSH KeywordsBayes TheoremBrainConnectomeHumansMagnetic Resonance ImagingMediation AnalysisNerve NetConceptsStochastic block modelBayesian paradigmBrain functional connectomeBlock modelConnectivity weightsFunctional connectomeNetwork measurementsEffect componentApproach applicationBlock allocationOpioid abstinenceAnalytic approachNetwork neurosciencePractical illustrationTherapeutic interventionsMediation analysisNeural circuitsNetwork structureBrain functioningMediatorsFunctional networksFeature selectionApplicationsModelNetwork
2019
Dissociable neural substrates of opioid and cocaine use identified via connectome-based modelling
Lichenstein SD, Scheinost D, Potenza MN, Carroll KM, Yip SW. Dissociable neural substrates of opioid and cocaine use identified via connectome-based modelling. Molecular Psychiatry 2019, 26: 4383-4393. PMID: 31719641, PMCID: PMC7214212, DOI: 10.1038/s41380-019-0586-y.Peer-Reviewed Original ResearchConceptsBrain statesDissociable neural substratesMultiple brain statesSubstance use outcomesHealthy comparison subjectsWhole-brain approachFMRI scanningFrontoparietal networkNeural substratesSubstance use treatmentNeural mechanismsDifferent brain statesFurther clinical relevanceDefault modeFMRI dataSubject replicationTreatment approachesReduced connectivityUse outcomesComparison subjectsNetwork strengthUse disordersSensory networksTreatment respondersSensory connectivityTen 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 ResearchMeSH KeywordsBrainConnectomeHumansMachine LearningMagnetic Resonance ImagingModels, NeurologicalNeuroimagingConceptsBrain-behavior associationsConnectome-Based Prediction of Cocaine Abstinence
Yip SW, Scheinost D, Potenza MN, Carroll KM. Connectome-Based Prediction of Cocaine Abstinence. American Journal Of Psychiatry 2019, 176: 156-164. PMID: 30606049, PMCID: PMC6481181, DOI: 10.1176/appi.ajp.2018.17101147.Peer-Reviewed Original ResearchMeSH KeywordsAdultBehavior TherapyBrainCholinesterase InhibitorsCocaine-Related DisordersCognitionConnectomeExecutive FunctionFemaleFunctional NeuroimagingGalantamineHumansIndividualityMachine LearningMagnetic Resonance ImagingMaleMiddle AgedNeural PathwaysOpiate Substitution TreatmentOpioid-Related DisordersPrognosisRewardTreatment OutcomeConceptsConnectome-based predictive modelingCocaine use disorderUse disordersBrain-based predictorsLarge-scale neural networksFunctional MRI dataCocaine abstinenceExecutive controlReward responsivenessIndividual differencesBaseline cocaine usePosttreatment assessmentConnectivity strengthHeterogeneous sampleAbstinenceIndependent samplesNovel interventionsCanonical networksSpecific behaviorsCocaine useSignificant correspondenceDisordersTreatment outcomesNetwork strengthMRI data