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 featuresBehaviorConceptualizationCommentaryStudyFindingsSubstances
2023
Maternal psychological risk and the neural correlates of infant face processing: A latent profile analysis
Wall K, Penner F, Dell J, Lowell A, Potenza M, Mayes L, Rutherford H. Maternal psychological risk and the neural correlates of infant face processing: A latent profile analysis. Developmental Psychobiology 2023, 66: e22445. PMID: 38131237, PMCID: PMC10783861, DOI: 10.1002/dev.22445.Peer-Reviewed Original ResearchAnxietyElectroencephalographyEvoked PotentialsFacial RecognitionFemaleHumansInfantMothersSubstance-Related DisordersWhat Are the Problems With Substance Use?
Potenza M. What Are the Problems With Substance Use? Biological Psychiatry 2023, 94: 839-841. PMID: 37914505, DOI: 10.1016/j.biopsych.2023.09.008.Commentaries, Editorials and LettersHumansSubstance-Related DisordersNeural correlates of negative life events and their relationships with alcohol and cannabis use initiation
Zhao Y, Potenza M, Tapert S, Paulus M. Neural correlates of negative life events and their relationships with alcohol and cannabis use initiation. Dialogues In Clinical Neuroscience 2023, 25: 112-121. PMID: 37916739, PMCID: PMC10623894, DOI: 10.1080/19585969.2023.2252437.Peer-Reviewed Original ResearchConceptsNegative life eventsCannabis initiationGreater cortical thicknessCaudate gray matter volumeGray matter volumeDose-response relationshipOdds of alcoholLife eventsInitiation of alcoholSubstance Use InitiationCortical thicknessSubstance-naïveSuperior temporal sulcusNeural pathwaysCannabis useBrain structuresAlcohol useUse initiationAlcohol initiationSubstance use trajectoriesPoor academic performanceTemporal sulcusNeural mechanismsCannabisLinear regression modelsDiagnostic group differences and exploratory sex differences in intrinsic connectivity during fMRI Stroop in individuals with and without cocaine use disorder
Zakiniaeiz Y, Lacadie C, Macdonald-Gagnon G, DeVito E, Potenza M. Diagnostic group differences and exploratory sex differences in intrinsic connectivity during fMRI Stroop in individuals with and without cocaine use disorder. Drug And Alcohol Dependence 2023, 251: 110962. PMID: 37716288, PMCID: PMC10557108, DOI: 10.1016/j.drugalcdep.2023.110962.Peer-Reviewed Original ResearchMeSH KeywordsBrainBrain MappingCocaineFemaleHumansMagnetic Resonance ImagingMaleSex CharacteristicsStroop TestSubstance-Related DisordersConceptsCocaine use disorderCognitive controlStroop performanceIntrinsic connectivityStroop task performanceFMRI Stroop taskDiagnostic groupsUse disordersDiagnostic group differencesMain effectHealthy comparison subjectsStroop effectStroop taskBehavioral resultsTask performanceFrontal gyrusIntrinsic connectivity distributionPrefrontal cortexAuditory areasGroup differencesBrain connectivityCingulate gyrusStroopBehavioral differencesHC groupProblematic smartphone use and two types of problematic use of the internet and self-stigma among people with substance use disorders
Chang C, Chen J, Huang S, Potenza M, Su J, Chang K, Pakpour A, Lin C. Problematic smartphone use and two types of problematic use of the internet and self-stigma among people with substance use disorders. Addictive Behaviors 2023, 147: 107807. PMID: 37542974, DOI: 10.1016/j.addbeh.2023.107807.Peer-Reviewed Original ResearchMeSH KeywordsBehavior, AddictiveCross-Sectional StudiesHumansInternetSmartphoneSubstance-Related DisordersConceptsProblematic smartphone useSubstance use disordersCognition-Execution (I-PACE) modelI-PACE modelSmartphone useProblematic useUse disordersPROCESS macro Model 4Person-AffectMediation modelCross-sectional designMediation analysisLongitudinal studyPeopleDisordersRelationshipPsychiatric centerFindingsParticipantsHayesModel 4ContextThe eleven-item Alcohol, Smoking and Substance Involvement Screening Test (ASSIST-11): Cross-cultural psychometric evaluation across 42 countries
Lee C, Lin C, Koós M, Nagy L, Kraus S, Demetrovics Z, Potenza M, Ballester-Arnal R, Batthyány D, Bergeron S, Billieux J, Burkauskas J, Cárdenas-López G, Carvalho J, Castro-Calvo J, Chen L, Ciocca G, Corazza O, Csako R, Fernandez D, Fernandez E, Fujiwara H, Fuss J, Gabrhelík R, Gewirtz-Meydan A, Gjoneska B, Gola M, Grubbs J, Hashim H, Islam S, Ismail M, Jiménez-Martínez M, Jurin T, Kalina O, Klein V, Költő A, Lee S, Lewczuk K, Lochner C, López-Alvarado S, Lukavská K, Mayta-Tristán P, Milea I, Miller D, Orosová O, Orosz G, Team S, Ponce F, Quintana G, Garzola G, Ramos-Diaz J, Rigaud K, Rousseau A, De Tubino Scanavino M, Schulmeyer M, Sharan P, Shibata M, Shoib, Sigre-Leirós V, Sniewski L, Spasovski O, Steibliene V, Stein D, Strizek J, Ünsal B, Vaillancourt-Morel M, Van Hout M, Bőthe B. The eleven-item Alcohol, Smoking and Substance Involvement Screening Test (ASSIST-11): Cross-cultural psychometric evaluation across 42 countries. Journal Of Psychiatric Research 2023, 165: 16-27. PMID: 37453212, DOI: 10.1016/j.jpsychires.2023.06.033.Peer-Reviewed Original ResearchMeSH KeywordsAdultCross-Cultural ComparisonFemaleGender IdentityHumansMalePsychometricsReproducibility of ResultsSmokingSubstance-Related DisordersSurveys and QuestionnairesConceptsGender identitySexual orientationSurvey dataBroader conceptCountriesIdentityConfirmatory factor analysisDifferent countriesMultigroup confirmatory factor analysisQuestionsHealthcare providersFactor analysisSubstance useMeasurement invarianceInstrumentOrientationDifferent languagesDetailed questionsUnidimensional factor structureLanguageGambling participation among Connecticut adolescents from 2007 to 2019: Potential risk and protective factors
Stefanovics E, Gueorguieva R, Zhai Z, Potenza M. Gambling participation among Connecticut adolescents from 2007 to 2019: Potential risk and protective factors. Journal Of Behavioral Addictions 2023, 12: 490-499. PMID: 37335777, PMCID: PMC10316163, DOI: 10.1556/2006.2023.00027.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdolescent BehaviorConnecticutCross-Sectional StudiesFemaleGamblingHumansMaleProtective FactorsRisk FactorsSubstance-Related DisordersConceptsPrevalence of gamblingGambling participationPatterns of gamblingSocial supportSocial support programsGambling advertisementsTraumatic experiencesMedia coverageState of ConnecticutSocio-demographic characteristicsAffective concernsSubstance useConnecticut high school studentsGamblingAdolescent gamblingAnonymous self-completed questionnairePotential risk factorsParticipationSupport programsPublic health concernSelf-completed questionnaireCurrent substance useSocio-demographic dataCross-sectional surveyWarrants further studyDistinct 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 ResearchMeSH KeywordsCannabisCocaineCocaine-Related DisordersCognitive Behavioral TherapyHumansMaleOpioid-Related DisordersSubstance-Related DisordersTreatment OutcomeConceptsConnectome-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 respondersChronically homeless veterans with gambling disorder: Epidemiology, clinical correlates, and traumatic experiences
Stefanovics E, Potenza M, Szymkowiak D, Tsai J. Chronically homeless veterans with gambling disorder: Epidemiology, clinical correlates, and traumatic experiences. Journal Of Psychiatric Research 2023, 164: 118-124. PMID: 37339548, DOI: 10.1016/j.jpsychires.2023.05.062.Peer-Reviewed Original ResearchMeSH KeywordsGamblingHumansIll-Housed PersonsMaleMilitary PersonnelSubstance-Related DisordersUnited StatesUnited States Department of Veterans AffairsVeteransConceptsPsychiatric treatmentGambling disorderChi-square testChronic homelessnessClinical correlatesDescriptive epidemiologyLow educational attainmentElevated oddsSuicidal thoughtsLogistic regressionTraumatic experiencesMental healthSubstance useHomeless veteransHomeless ProgramVeteransTreatmentLower ratesEpidemiologyBehavioral concernsDisordersStudy of factorsCorrelatesAnalysis of varianceEducational attainmentConnectome-based prediction of craving in gambling disorder and cocaine use disorder
Antons S, Yip S, Lacadie C, Dadashkarimi J, Scheinost D, Brand M, Potenza M. Connectome-based prediction of craving in gambling disorder and cocaine use disorder. Dialogues In Clinical Neuroscience 2023, 25: 33-42. PMID: 37190759, PMCID: PMC10190201, DOI: 10.1080/19585969.2023.2208586.Peer-Reviewed Original ResearchMeSH KeywordsBrainCocaineConnectomeCravingGamblingHumansMagnetic Resonance ImagingSubstance-Related DisordersConceptsCocaine use disorderGambling disorderBehavioral addictionsCue-reactivity taskComponents of memoryGeneral neural mechanismCommon neural networkFunctional magnetic resonanceMedial frontal regionsDefault mode networkFeatures of addictionAutobiographical memoryValence ratingsMeta-analytic dataPrefrontal regionsNeural mechanismsPrefrontal cortexFronto-parietalFrontal regionsMotor imageryMotor/Diverse sampleLimbic networkNeural connectivityCravingTransdiagnostic 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 ResearchMeSH KeywordsBehavior, AddictiveBrainConnectomeCravingCuesHumansMagnetic Resonance ImagingSubstance-Related DisordersConceptsConnectome-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
2022
Temporal associations between depressive features and self-stigma in people with substance use disorders related to heroin, amphetamine, and alcohol use: a cross-lagged analysis
Saffari M, Chang K, Chen J, Chang C, Chen I, Huang S, Liu C, Lin C, Potenza M. Temporal associations between depressive features and self-stigma in people with substance use disorders related to heroin, amphetamine, and alcohol use: a cross-lagged analysis. BMC Psychiatry 2022, 22: 815. PMID: 36544132, PMCID: PMC9768939, DOI: 10.1186/s12888-022-04468-z.Peer-Reviewed Original ResearchMeSH KeywordsAdultAmphetamineCross-Sectional StudiesDepressionHeroinHumansMaleMiddle AgedSocial StigmaSubstance-Related DisordersConceptsSubstance use disordersFeatures of depressionUse disordersDepression Anxiety Stress ScaleCross-lagged analysisCross-lagged associationsCross-lagged modelsAnxiety Stress ScaleGood fit indicesSubstance use concernsStructural equation modelingSelf-Stigma ScaleDepressive featuresMental health problemsLongitudinal relationshipStress ScaleEquation modelingSubstance useDirection of associationAlcohol useFit indicesUse concernsSocial relationshipsTemporal associationUnacceptable behaviorPrevalence and Clinical Characteristics of Recreational and At-Risk/Problematic Gambling in a National Sample of U.S. Military Veterans
Stefanovics E, Potenza M, Tsai J, Pietrzak R. Prevalence and Clinical Characteristics of Recreational and At-Risk/Problematic Gambling in a National Sample of U.S. Military Veterans. Journal Of Gambling Studies 2022, 39: 1077-1097. PMID: 36378356, DOI: 10.1007/s10899-022-10165-z.Peer-Reviewed Original ResearchMeSH KeywordsGamblingHumansMalePrevalenceStress Disorders, Post-TraumaticSubstance-Related DisordersSuicidal IdeationVeteransConceptsCurrent psychiatric diagnosisU.S. military veteransUse disordersPsychiatric diagnosisMilitary veteransU.S. veteransDrug use disordersMental health treatmentSubstance use disordersChi-square testClinical characteristicsTrauma burdenNational HealthAdjusted associationsRoutine screeningClinical measuresRecreational gamblingCurrent prevalenceMale veteransHealth treatmentHigher household incomeSuicidal ideationVeterans StudyLogistic regressionExtensive involvementTemporal associations between problematic use of the internet and self-stigma among people with substance use disorders: A cross-lagged model across one year
Chen I, Chang K, Chang C, Huang S, Potenza M, Pakpour A, Lin C. Temporal associations between problematic use of the internet and self-stigma among people with substance use disorders: A cross-lagged model across one year. Journal Of Psychiatric Research 2022, 156: 339-348. PMID: 36323137, DOI: 10.1016/j.jpsychires.2022.10.044.Peer-Reviewed Original Research
2020
Neurobiology of cue-reactivity, craving, and inhibitory control in non-substance addictive behaviors
Antons S, Brand M, Potenza MN. Neurobiology of cue-reactivity, craving, and inhibitory control in non-substance addictive behaviors. Journal Of The Neurological Sciences 2020, 415: 116952. PMID: 32534370, DOI: 10.1016/j.jns.2020.116952.Peer-Reviewed Original ResearchConceptsNon-substance addictive behaviorsAddictive behaviorsInhibitory controlPotential neurobiological mechanismsExecutive functioningSalience attributionNeurobiological foundationsSubstance use disordersNeurobiological factorsNeurobiological mechanismsBrain networksLife domainsRewarding behaviorNegative consequencesHabit formationCravingSpecific disordersNeurobiologyNeurochemical pathwaysAddictionFunctional impairmentFuture directionsSexual activityDisordersCues
2018
Correlates of frequent gambling and gambling-related chasing behaviors in individuals with schizophrenia-spectrum disorders
Yakovenko I, Fortgang R, Prentice J, Hoff RA, Potenza MN. Correlates of frequent gambling and gambling-related chasing behaviors in individuals with schizophrenia-spectrum disorders. Journal Of Behavioral Addictions 2018, 7: 375-383. PMID: 29788756, PMCID: PMC6174591, DOI: 10.1556/2006.7.2018.31.Peer-Reviewed Original ResearchMeSH KeywordsAge of OnsetAmbulatory CareCross-Sectional StudiesFemaleGamblingGenetic Predisposition to DiseaseHumansMaleMiddle AgedMotivationPsychiatric Status Rating ScalesPsychotic DisordersRisk FactorsSchizophreniaSchizophrenic PsychologySelf ReportSeverity of Illness IndexSubstance-Related DisordersConceptsSchizophrenia/schizoaffective disorderSchizoaffective disorderCo-occurring disordersDSM-IV criteriaSymptoms of schizophreniaSchizophrenia spectrum disordersPsychotic spectrum disordersHigh prevalenceFamily historyGreater gambling involvementSchizophreniaDisordersLower functioningContinuum of severityHallmark featureMethods DataSample of individualsGambling disorderIndividualsCorrelates