Jutta Joormann
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Sadness and Depression
Gadassi-Polack R, Siemer M, Joormann J. Sadness and Depression. 2024, 341-351. DOI: 10.4324/9781003469018-21.Peer-Reviewed Original ResearchBack to Normal? Harnessing Long Short-term Memory Network to Examine the Associations Between Adolescent Social Interactions and Depressive Symptoms During Different Stages of COVID-19
Polack R, Zhang A, Kober H, Joormann J, Benisty H. Back to Normal? Harnessing Long Short-term Memory Network to Examine the Associations Between Adolescent Social Interactions and Depressive Symptoms During Different Stages of COVID-19. Research On Child And Adolescent Psychopathology 2024, 1-13. PMID: 38922462, DOI: 10.1007/s10802-024-01208-7.Peer-Reviewed Original ResearchDepressive symptomsSocial interactionMental healthPredicting depressive symptomsDaily social interactionsAdolescent social interactionsAdolescents' social environmentAge differencesDevelopmental periodAdolescentsSymptomsSocial environmentPre-pandemic patternsSocial influenceMemory networkStages of COVID-19Seventy-nineChronic stageLong-short term memory recurrent networkYouthDepressionDiaryCOVID-19AssociationIt's a balancing act: The ratio of maladaptive (vs. All) emotion regulation strategies is associated with depression
Gadassi-Polack R, Paganini G, Zhang A, Dworschak C, Silk J, Kober H, Joormann J. It's a balancing act: The ratio of maladaptive (vs. All) emotion regulation strategies is associated with depression. Behaviour Research And Therapy 2024, 180: 104600. PMID: 38950508, DOI: 10.1016/j.brat.2024.104600.Peer-Reviewed Original ResearchEmotion regulationER repertoireER flexibilityER strategiesAssociated with psychopathologyMaladaptive emotion regulationEmotion regulation strategiesAssociated with depressive symptomsAssociated with depressionYears of adolescenceDaily-diaryAtypical developmentDepressive symptomsRegulation strategiesDevelopmental periodDepressionPsychopathologyEmotionsAdolescentsYouthYear levelSymptomsResearchFamilial risk for depression moderates neural circuitry in healthy preadolescents to predict adolescent depression symptoms in the Adolescent Brain Cognitive Development (ABCD) Study
Holt-Gosselin B, Keding T, Rodrigues K, Rueter A, Hendrickson T, Perrone A, Byington N, Houghton A, Miranda-Dominguez O, Feczko E, Fair D, Joormann J, Gee D. Familial risk for depression moderates neural circuitry in healthy preadolescents to predict adolescent depression symptoms in the Adolescent Brain Cognitive Development (ABCD) Study. Developmental Cognitive Neuroscience 2024, 68: 101400. PMID: 38870601, PMCID: PMC11225685, DOI: 10.1016/j.dcn.2024.101400.Peer-Reviewed Original ResearchAdolescent Brain Cognitive DevelopmentAdolescent depressive symptomsDepressive symptomsFamilial riskFunctional connectivityResting-state FC patternsCognitive developmentFamily history of depressionAdolescent-onset depressionLow familial riskHistory of depressionElevated familial riskMixed-effects regression analysisNucleus accumbensStriatal FCLR youthHR youthSensory/somatomotor networkFC patternsNeural circuitryNeural markersDepression riskDepressionPreadolescentsLongitudinal studyThe Development of a Novel Scale to Assess Intra- and Interpersonal Emotion Regulation Strategies: The Emotion Regulation Strategy Scale (ERSS)
Kneeland E, Hay A, Curtiss J, Hennessey A, Vanderlind W, Joormann J, Clark M. The Development of a Novel Scale to Assess Intra- and Interpersonal Emotion Regulation Strategies: The Emotion Regulation Strategy Scale (ERSS). Emotion 2024 PMID: 38829352, DOI: 10.1037/emo0001375.Peer-Reviewed Original ResearchEmotion Regulation Strategies ScaleEmotion regulation strategiesInterpersonal emotion regulation strategiesRegulation strategiesConfirmatory factor analysisEmotion regulationStudy 3Specific emotion regulation strategiesLevels of clinical symptomsEmotion regulation researchEmotion Regulation ScaleFactor analysisIntra-personalNine-factor solutionInterpersonal scalesCognitive reappraisalRegulation ScaleExpressive suppressionSituation selectionStudy 1Study 2Scale itemsRegulation researchEmotionsClinical samplesEmpathy is Associated With Interpersonal Emotion Regulation Goals in Everyday Life
Geiger E, Pruessner L, Barnow S, Joormann J. Empathy is Associated With Interpersonal Emotion Regulation Goals in Everyday Life. Emotion 2024, 24: 1092-1108. PMID: 38127535, DOI: 10.1037/emo0001332.Peer-Reviewed Original ResearchInterpersonal emotion regulationPersonal distressEmpathic concernStudy 2Emotion regulation goalsEmotion regulation processesLower personal distressFacets of empathyHigher empathic concernEcological momentary assessment studyFemale student sampleMomentary assessment studyU.S. community sampleEveryday lifeEmotion regulationRegulation goalsCommunity sampleStudy 1Student sampleMentalizingEmpathyDifferential associationsExperience sharingRegulation processesDistressTowards implementation of cognitive bias modification in mental health care: State of the science, best practices, and ways forward
Vrijsen J, Grafton B, Koster E, Lau J, Wittekind C, Bar-Haim Y, Becker E, Brotman M, Joormann J, Lazarov A, MacLeod C, Manning V, Pettit J, Rinck M, Salemink E, Woud M, Hallion L, Wiers R. Towards implementation of cognitive bias modification in mental health care: State of the science, best practices, and ways forward. Behaviour Research And Therapy 2024, 179: 104557. PMID: 38797055, DOI: 10.1016/j.brat.2024.104557.Peer-Reviewed Original ResearchMental health careCognitive bias modificationHealth careBias modificationDigital mental health careMental health toolsCBM interventionsStand-alone interventionCognitive bias modification approachesHealth toolsMechanisms of psychopathologyInterpretation bias modificationAlcohol use disorderAdjunctive interventionTarget populationClinically relevant effectsInterventionAnxiety disordersUse disorderSymptom changeCareCognitive mechanismsImplementation frameworkExpert opinionClinical implementationIntensive longitudinal assessment following index trauma to predict development of PTSD using machine learning
Horwitz A, McCarthy K, House S, Beaudoin F, An X, Neylan T, Clifford G, Linnstaedt S, Germine L, Rauch S, Haran J, Storrow A, Lewandowski C, Musey P, Hendry P, Sheikh S, Jones C, Punches B, Swor R, Hudak L, Pascual J, Seamon M, Harris E, Pearson C, Peak D, Domeier R, Rathlev N, Sergot P, Sanchez L, Bruce S, Joormann J, Harte S, Koenen K, McLean S, Sen S. Intensive longitudinal assessment following index trauma to predict development of PTSD using machine learning. Journal Of Anxiety Disorders 2024, 104: 102876. PMID: 38723405, PMCID: PMC11215748, DOI: 10.1016/j.janxdis.2024.102876.Peer-Reviewed Original ResearchTraumatic exposureIntensive longitudinal assessmentDevelopment of PTSDTrauma-related symptomsExposure to traumaFollow-up careTrauma exposureIndex traumaEmergency carePTSDAssess symptomsLongitudinal assessmentAssessment protocolCareParticipantsSymptomsFollow-upInterventionMachine learning analysisTraumaRiskIndividualsWeeksLearning analysisNervousnessAuthor Correction: Defining the r factor for post-trauma resilience and its neural predictors
van Rooij S, Santos J, Hinojosa C, Ely T, Harnett N, Murty V, Lebois L, Jovanovic T, House S, Bruce S, Beaudoin F, An X, Neylan T, Clifford G, Linnstaedt S, Germine L, Bollen K, Rauch S, Haran J, Storrow A, Lewandowski C, Musey P, Hendry P, Sheikh S, Jones C, Punches B, Swor R, Pascual J, Seamon M, Harris E, Pearson C, Peak D, Merchant R, Domeier R, Rathlev N, O’Neil B, Sanchez L, Joormann J, Pizzagalli D, Sheridan J, Harte S, Kessler R, Koenen K, McLean S, Ressler K, Stevens J. Author Correction: Defining the r factor for post-trauma resilience and its neural predictors. Nature Mental Health 2024, 2: 627-627. DOI: 10.1038/s44220-024-00258-6.Peer-Reviewed Original ResearchDefining the r factor for post-trauma resilience and its neural predictors
van Rooij S, Santos J, Hinojosa C, Ely T, Harnett N, Murty V, Lebois L, Jovanovic T, House S, Bruce S, Beaudoin F, An X, Neylan T, Clifford G, Linnstaedt S, Germine L, Bollen K, Rauch S, Haran J, Storrow A, Lewandowski C, Musey P, Hendry P, Sheikh S, Jones C, Punches B, Swor R, Pascual J, Seamon M, Harris E, Pearson C, Peak D, Merchant R, Domeier R, Rathlev N, O’Neil B, Sanchez L, Joormann J, Pizzagalli D, Sheridan J, Harte S, Kessler R, Koenen K, McLean S, Ressler K, Stevens J. Defining the r factor for post-trauma resilience and its neural predictors. Nature Mental Health 2024, 2: 680-693. DOI: 10.1038/s44220-024-00242-0.Peer-Reviewed Original ResearchDefault mode network activityFunctional magnetic resonance imagingResponse to rewardMode network activityPost-trauma resilienceWeeks post-traumaNeurobiological profilesReward valuationReward processingSpecific psychopathologyTransdiagnostic approachBrain mechanismsCognitive functionResilience factorsWhole-brainComponents of resilienceNeural predictorsAURORA studyRewardPost-traumaMagnetic resonance imagingProcess of recoveryNetwork activityResonance imagingPsychopathology
Clinical Trials
Current Trials
Using Neuroimaging to track symptom change in PTSD treatment
HIC ID2000025892RoleSub InvestigatorPrimary Completion Date07/31/2029Recruiting Participants