Journal of psychopathology and clinical science最新文献

{"title":"Genetic risk for schizophrenia and brain activation during the Penn Conditional Exclusion Test: A multiplex extended pedigree study.","authors":"Petra E Rupert, David R Roalf, Konasale M Prasad, Susan S Kuo, Christie W Musket, Joel Wood, Ruben C Gur, Laura Almasy, Raquel E Gur, Vishwajit L Nimgaonkar, Michael F Pogue-Geile","doi":"10.1037/abn0000973","DOIUrl":"10.1037/abn0000973","url":null,"abstract":"<p><p>Individuals with schizophrenia have poorer performance and often differing patterns of brain activation compared to controls on a variety of cognitive tasks, including those that require inhibition of responses and shifting to new responses. This study sought to examine the degree to which performance on a task developed to measure cognitive flexibility, the Penn Conditional Exclusion Test (PCET), and its related brain activation, as assessed on functional magnetic resonance imaging, may reflect schizophrenia genetic risk using an extended pedigree design. A total of 455 participants (27 schizophrenia probands, 170 of their first- to fourth-degree relatives, and 258 unrelated controls) completed similar versions of the PCET, both outside and inside a magnetic resonance imaging scanner. To examine brain activation that may underlie performance, ten regions of interest were identified where activation was significantly correlated with performance. To examine diagnostic specificity, we also investigated genetic correlations between diagnosed major depression and PCET performance and brain activation. Performance was significantly genetically correlated with schizophrenia both out of (<i>R</i>_g = -0.49, <i>p</i> < .001) and in the scanner (<i>R</i>_g = -0.59, <i>p</i> < .001) after false discovery rate correction. In contrast, none of the genetic correlations between schizophrenia and brain activation in the identified regions of interest were significant after false discovery rate correction. Neither behavioral performance nor brain activation measures were significantly genetically correlated with depression. These results suggest that behavioral performance on the PCET is more sensitive (and also specific compared with depression) to schizophrenia genetic risk than is functional magnetic resonance imaging activation that is correlated with performance. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":73914,"journal":{"name":"Journal of psychopathology and clinical science","volume":" ","pages":"272-283"},"PeriodicalIF":3.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11949699/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational modeling of reversal learning impairments in schizophrenia and bipolar disorder reveals shared failure to exploit rewards.","authors":"Angus W MacDonald, Edward Patzelt, Zeb Kurth-Nelson, Deanna M Barch, Cameron S Carter, James M Gold, J Daniel Ragland, Steven M Silverstein","doi":"10.1037/abn0000944","DOIUrl":"10.1037/abn0000944","url":null,"abstract":"<p><p>The distinction between the concepts of schizophrenia and bipolar disorder is fundamental to the Kraepelinian tradition in psychiatry. One mechanism undergirding this distinction, a difference in reward sensitivity, has been championed by a number of scholars. As part of the Cognitive Neuroscience Test Reliability and Clinical applications for Serious mental illnesses consortium, 225 participants including people with schizophrenia (<i>n</i> = 69), schizoaffective disorder (<i>n</i> = 55), and bipolar affective disorder (<i>n</i> = 53) performed a probabilistic reversal learning task. This task switches the rewarded stimulus at various times throughout the task. Our analyses leveraged a Hidden Markov Model to examine trial-by-trial decisions of participants to reveal the differences between patient groups in their response to reward feedback. Whereas no patient group showed difficulty reversing their preferred categories after a switch in the task's contingencies and bipolar patient performance was spared in some other ways, all patient groups made more errors throughout the task because of a greater tendency to shift away from rewarded categories (i.e., win-switching). Furthermore, patients' cognitive ability is specifically related to this aspect of the task. Rather than validating a Kraepelinian dichotomy, these findings suggest that a failure to exploit rewards may reflect a mechanistic deficit common across both affective and nonaffective psychoses related to cognitive impairments in patients. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":73914,"journal":{"name":"Journal of psychopathology and clinical science","volume":" ","pages":"262-271"},"PeriodicalIF":3.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11955188/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143598438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Delineating empirically plausible causal pathways to suicidality among people at clinical high risk for psychosis.","authors":"Michael V Bronstein, Erich Kummerfeld, Carrie E Bearden, Barbara A Cornblatt, Elaine F Walker, Scott W Woods, Daniel Mathalon, Diana Perkins, Kristen S Cadenhead, Jean Addington, Tyrone D Cannon, Sophia Vinogradov","doi":"10.1037/abn0000969","DOIUrl":"10.1037/abn0000969","url":null,"abstract":"<p><p>Suicidality is common among people at clinical high risk (CHR) for psychosis. Delineating causal pathways to suicidality and identifying its determinants would inform tailored intervention efforts for these individuals. To this end, we analyzed data on CHR samples from the second and third North American Prodrome Longitudinal Studies (NAPLS-2, <i>n</i> = 355; NAPLS-3, <i>n</i> = 266). Data on correlates of suicidality-including depression and attenuated psychosis symptoms, sleep, and childhood trauma-from two initial study timepoints were submitted to the greedy relaxations of the sparsest permutation algorithm. Intervention calculus was used to estimate the (lower bound) total empirically plausible causal effects of each variable on suicidality. Across both samples, greedy relaxations of the sparsest permutation suggested that symptoms of depression-particularly hopelessness, self-deprecation, and depressed mood-were likely direct causes of suicidality among people at CHR for psychosis. Across samples and measurement time points, intervention calculus indicated that depressed mood exerted the greatest influence over suicidality of all measured variables. This study provides data-driven, testable hypotheses about the causal pathways leading to suicidality among people at CHR for psychosis and suggests promising targets for interventions on suicidality tailored to these individuals. Future experimental research should test these hypotheses by, for example, comparing the suicide risk reduction afforded by interventions aimed at each aforementioned target. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":73914,"journal":{"name":"Journal of psychopathology and clinical science","volume":" ","pages":"239-250"},"PeriodicalIF":3.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143366956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trajectories of mothers' perinatal depressive symptoms during COVID-19 pandemic lockdowns: The protective role of romantic relationship quality.","authors":"Gabriel A León, Yael H Waizman, Sofia I Cardenas, Elizabeth C Aviv, Phil Newsome, Anthony G Vaccaro, Alyssa R Morris, Darby E Saxbe","doi":"10.1037/abn0000994","DOIUrl":"https://doi.org/10.1037/abn0000994","url":null,"abstract":"<p><p>This study tracked depressive symptoms across the first year of parenthood in two cohorts of mothers recruited during pregnancy: one (<i>n</i> = 99) recruited before spring 2020, and one (<i>n</i> = 615) recruited during the first wave of pandemic lockdowns in spring 2020. We fit a series of multigroup covariance pattern models to our data. Within the pandemic cohort, symptoms were highest during pregnancy and decreased curvilinearly from pregnancy to 6 months postpartum, before leveling off by 12 months postpartum. Nonetheless, depressive symptoms were significantly higher in the pandemic cohort at all time points from pregnancy to 12 months compared to the prepandemic cohort. This effect was weaker among mothers who endorsed greater romantic relationship quality during pregnancy. Namely, pandemic-exposed mothers reporting high relationship quality showed trajectories of depressive symptoms that resembled the prepandemic sample. This evidence of sustained depression risk in pandemic-exposed mothers is of high public health concern given the consequences of perinatal mood disorders. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":73914,"journal":{"name":"Journal of psychopathology and clinical science","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143733145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A matter of timing? Effects of parent-adolescent conflict on adolescent negative affect and depressive symptoms on six timescales.","authors":"Anne Bülow, Savannah Boele, Jessica P Lougheed, Jaap J A Denissen, Eeske van Roekel, Loes Keijsers","doi":"10.1037/abn0000987","DOIUrl":"https://doi.org/10.1037/abn0000987","url":null,"abstract":"<p><p>Development is an iterative dynamic process that unfolds over time. Few theories, however, discuss the speed of developmental processes. Therefore, decisions about measurement timing often rely on arbitrary or practical choices, disregarding the timescale dependency of the results. As an exemplary case, this preregistered study assessed reciprocal associations between parent-adolescent conflict and ill-being (i.e., negative affect and depressive symptoms) with daily, weekly, biweekly, monthly, bimonthly, and three-monthly intervals. A 100-day diary study (<i>N</i> = 159, <i>M</i> = 13.31 years, 62% girls, 89% Dutch, data collection: 2020-2021) and a 26-wave biweekly study (<i>N</i> = 253, <i>M</i> = 14.37 years, 72% girls, 96% Dutch, data collection: 2019-2020) were used. By aggregating measurements, multiple timescales could be assessed within the same data set. Multilevel structural equation modeling revealed that conflict predicted depressive symptoms 1 month (β = .09) and 3 months later (β = .13). Reversely, negative affect predicted conflict 1 week later (β = .07) and depressive symptoms predicted conflict 2 weeks later (β = .08). Thus, transactional processes may function differently at different timescales, which has implications for expanding developmental theories about the timescales of relevant processes. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":73914,"journal":{"name":"Journal of psychopathology and clinical science","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143733122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding types of transitions in clinical change: An introduction from the complex dynamic systems perspective.","authors":"Jingmeng Cui, Fred Hasselman, Merlijn Olthof, Anna Lichtwarck-Aschoff","doi":"10.1037/abn0000991","DOIUrl":"https://doi.org/10.1037/abn0000991","url":null,"abstract":"<p><p>Sudden changes are common in clinical trajectories. While theoretical work in complex dynamic systems has provided mathematical theories for various types and mechanisms of change, a concrete application for the field of psychopathology is still lacking. We aim to bridge this gap by outlining an applied theoretical framework using theoretical concepts of the natural sciences for the field of clinical psychopathology, also devoting attention to issues and providing recommendations that are specific to the psychopathology domain. First, the mechanisms and features of four distinct types of transitions are introduced: bifurcation-induced tipping (B-tipping), noise-induced tipping (N-tipping), rate-induced tipping (R-tipping), and noise-induced diffusion (N-diffusion). Those types of transitions differ in the main cause of the change and data characteristics. To illustrate their application to clinical phenomena, we present two real-life scenarios using simulated time series. These examples demonstrate how theoretical types of change may connect to clinical phenomena and highlight how different types of transitions can co-occur in various subsystems. In the first example, we show that the mood system and the momentary affect system of a patient with sudden loss may show B-tipping and N-diffusion at the same time; in the second example, we show that increasing the stimulus strengthening speed in exposure therapy may lead to R-tipping, while the therapeutic decision in this context may be caused by N-tipping. Finally, we lay out possible pathways for determining the appropriate type of transition for future empirical research, highlighting methods both from dynamic system research and special opportunities for research in clinical psychology. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":73914,"journal":{"name":"Journal of psychopathology and clinical science","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143671980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data.","authors":"Nicholas A Livingston, Amar D Mandavia, Anne N Banducci, Rebecca Sistad Hall, Lauren B Loeffel, Michael Davenport, Brittany Mathes-Winnicki, Maria Ting, Clara E Roth, Alexis Sarpong, Noam Newberger, Zig Hinds, Jennifer R Fonda, Daniel Chen, Frank Meng","doi":"10.1037/abn0000984","DOIUrl":"https://doi.org/10.1037/abn0000984","url":null,"abstract":"<p><p>Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (<i>n</i> = 53,803; January 2019-March 2021) to a matched prepandemic cohort (<i>n</i> = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP methods to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":73914,"journal":{"name":"Journal of psychopathology and clinical science","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143671975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward quantitative cognitive-behavioral modeling of psychopathology: An active inference account of social anxiety disorder.","authors":"Yinghao Zhang, Friederike Elisabeth Hedley, Ru-Yuan Zhang, Jingwen Jin","doi":"10.1037/abn0000972","DOIUrl":"https://doi.org/10.1037/abn0000972","url":null,"abstract":"<p><p>Understanding psychopathological mechanisms is a central goal in clinical science. While existing theories have demonstrated high research and clinical utility, they have provided limited quantitative explanations of mechanisms. Previous computational modeling studies have primarily focused on isolated factors, posing challenges for advancing clinical theories holistically. To address this gap and leverage the strengths of clinical theories and computational modeling in a synergetic manner, it is crucial to develop quantitative models that integrate major factors proposed by comprehensive theoretical models. In this study, using social anxiety disorder (SAD) as an example, we present a novel approach to formalize conceptual models by combining cognitive-behavioral theory (CBT) with active inference modeling, an innovative computational approach that elucidates human cognition and action. This CBT-informed active inference model integrates multiple mechanistic factors of SAD in a quantitative manner. Through a series of simulations, we systematically examined the effects of these factors on the belief about social threat and tendency of engaging in safety behaviors. The resultant model inherits the conceptual comprehensiveness of CBT and the quantitative rigor of active inference modeling, delineating previously elusive pathogenetic pathways and enabling the formulation of concrete model predictions for future research. Overall, this research presents a novel quantitative model of SAD that unifies major mechanistic factors proposed by CBT and active inference modeling. It highlights the feasibility and potential of integrating clinical theory and computational modeling to advance our understanding of psychopathology. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":73914,"journal":{"name":"Journal of psychopathology and clinical science","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143568896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the overlap of personality traits and internalizing psychopathology using multi-informant data: Two sides of the same coin?","authors":"Helo Liis Soodla, Kelli Lehto, Kadri Kõiv, Uku Vainik, Kirsti Akkermann, René Mõttus","doi":"10.1037/abn0000967","DOIUrl":"https://doi.org/10.1037/abn0000967","url":null,"abstract":"<p><p>Personality and psychopathology share a hierarchical dimensional structure, developmental trajectories and correlations with varied outcomes. However, quantifying the extent and details of their direct empirical overlap has been hindered by overreliance on self-reports and broad construct domains. Using multimethod data, we estimated the Big Five personality domains' and nuances' (items') \"true\" correlations (rtrue) with, and true predictive accuracy (rtruePRED) for, various psychopathology state domains, free of single-method and occasion-specific biases, random error, and direct content overlap. Our sample included Estonian Biobank participants (<i>N</i> = 16,226) who completed psychopathology and comprehensive personality questionnaires, and whose personality traits were also rated by close informants. Personality nuances out-predicted the Big Five domains for psychopathology, with items' <i>r</i>_truePRED = .31 … .58 for specific psychopathology domains of distress, fear, inattention, hyperactivity, insomnia, and fatigue, and <i>r</i>_truePRED = .52 for the general p factor. Individual items had various meaningful rtrues with the psychopathology domains. Among the Big Five, neuroticism was the strongest correlate of distress (<i>r</i>_true = .29) and fear (<i>r</i>_true = .13), while inattention was most correlated with conscientiousness (<i>r</i>_true = -.56), hyperactivity with extraversion (rtrue = .25), fatigue with openness (r_true = .12), and insomnia with conscientiousness (<i>r</i>_true = .12). Associations based on self-reports alone were weaker. We argue for multirater and finer grained assessments of both personality and psychopathology to fully reveal the extent and details of their overlap. This association is likely stronger than typical self-report data suggest, yet psychopathology is not empirically redundant with personality traits. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":73914,"journal":{"name":"Journal of psychopathology and clinical science","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emotion reactivity research: Methodological differences make a difference.","authors":"David A Cole, George Abitante, Sophia B Mueller","doi":"10.1037/abn0000982","DOIUrl":"https://doi.org/10.1037/abn0000982","url":null,"abstract":"<p><p>Operationalizations of emotion reactivity (ER) have changed rather dramatically over the past decade. Comparing the results across studies that use these diverse methods is difficult. The current article reviews and critiques these approaches to studying ER. Three desirable characteristics are identified: (a) using multiple diverse stimuli to assess emotions will enable researchers to characterize ER more completely, (b) incorporating measures of mood-triggering stimuli will enable researchers to avoid key confounds in ER-depression research, and (c) using multilevel statistical approaches will enable researchers to differentiate the within- versus between-person aspects of ER. Studies that use measures that lack one or more of these characteristics may generate incomplete if not systematically biased results. An idiothetic ER approach is described that incorporates these strengths and may help to resolve contradictions that pervade ER-depression research. Implications emerge for clinical research and practice. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":73914,"journal":{"name":"Journal of psychopathology and clinical science","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}