Journal of psychopathology and clinical science最新文献

This article provides an overview of Stress Generation Methodology. Stress generation is a phenomenon in which individuals with depression or vulnerability to depression experience greater dependent stressful life events (SLEs), defined as stressors in which individuals at least partially contributed to occurrence. The stress generation process demonstrates how depressed individuals shape their environments, contributing to depression maintenance and exacerbation. Subsequent extensions have shown that other forms of psychopathology and a variety of cognitive and personality risk factors also predict stress generation. The focus on stress generation in women is accompanied by an emphasis on interpersonal stress. In addition to emphasizing communal SLEs, stress generation studies have also focused on communal vulnerability factors. However, men do not typically exhibit communal vulnerabilities to the degree that women do. Thus, it is also important to broaden the scope of vulnerability factors examined to include vulnerabilities associated with stress generation in men. These could include impulsivity, anger and aggression, and the need for autonomy and self-definition, all of which tend to be more common in males. Lastly, studies often employ self-report measures of SLEs which could artificially accentuate gender differences in stress generation findings. As existing studies may be more sensitive to detecting stress generation in women, future research should examine this phenomenon with the following methodological refinements: (a) use male-only or adequately sized samples with equal gender representation to test gender moderation effects, (b) expand the range of SLEs to include agentic and achievement-oriented stressors and use wider assessment windows, and (c) examine vulnerability factors that may be relevant to men such as impulsivity, anger, aggression, and the need for autonomy and self-definition. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Substance use relapse is difficult to define, and previous work has used one-size-fits-all ad hoc definitions. Researchers have called for a dynamic and personalized understanding of relapse as a concept and model, necessitating novel statistical tools. We aimed to develop and validate a novel statistical model of latent relapse processes: the double-well potential model (DWPM). This model describes posttreatment substance use in terms of a dynamical system with stable equilibria of abstinence and relapse, person-specific dominant equilibria (tilt), the ease of changing between equilibria (steepness), and an overall relapse risk (RR). Using timeline follow-back data from N = 139 adults with a substance use disorder transitioning back to the community after residential treatment, we examined individual differences and the criterion-related validity of DWPM parameters to determine the clinical utility of the double-well model. While nonuse was the predominant stable state across participants, we found significant between-subjects variability steepness and RR. These individual differences were predictable via demographics, baseline psychopathology, treatment history, and treatment condition. Steepness and RR also predicted long-term outcomes, including life satisfaction and criminal behavior, above and beyond traditional metrics of relapse (proportion of days used and time to first use). Thus, the DWPM is a strong theoretical and statistical representation of the underlying relapse processes. Moreover, the parameters show criterion-related validity and may be useful in precision medicine. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Most studies aimed at understanding suicidal behavior have focused on quantifying the associations between putative risk factors and suicidal behavior in comparative studies of cases and controls. The current study, in comparison, exclusively focused on cases-89 Army soldiers presenting for hospital care following a suicide attempt-and attempted to reveal the antecedents of, reasons for, and consequences of suicide attempts. This mixed-methods study using qualitative interviews and self-report surveys/interviews revealed that in most cases, the most recent onset of suicidal thoughts began shortly before the suicide attempt and were not disclosed to others, limiting opportunities for intervention via traditional approaches. The primary reason given for attempting suicide was to escape from psychologically aversive conditions after concluding that no other effective strategies or options were available. Participants reported both negative (e.g., self-view, guilt) and positive (e.g., learning new skills, receiving support) consequences of their suicide attempt-and described things they believe would have prevented them from making the attempt. These findings provide new insights into the motivational and contextual factors for suicidal behavior and highlight several novel directions for prevention and intervention efforts. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders defines anorexia nervosa (AN) severity based on body mass index (BMI). However, BMI categories do not reliably differentiate the intensity of AN and comorbid symptoms. Shape/weight overvaluation has been proposed as an alternative severity specifier. The present study used structural equation model (SEM) Trees to empirically determine specific levels of BMI and/or shape/weight overvaluation that differentiate AN severity. We also compared whether the SEM Tree-derived severity groups outperformed existing AN severity definitions. Participants were 1,666 adolescents and adults with AN who were receiving eating disorder treatment at one of the three levels of care (outpatient, partial hospital program, or residential). Participants completed self-reported questionnaires assessing eating pathology and comorbid symptoms. SEM Tree analyses first specified an outcome model of AN severity, and then recursively partitioned the outcome model into subgroups based on all values of BMI and shape/weight overvaluation. One-way analyses of variance and t tests determined which severity definition explained the most variance in clinical characteristics. SEM Tree analyses yielded five severity groups, all of which were defined based on increasing intensities of shape/weight overvaluation: < 2.25, 2.25-3.24, 3.25-4.24, 4.25-5.24, and ≥ 5.25. No groups were defined based on BMI. SEM Tree-derived groupings explained more variance in clinical characteristics than existing severity definitions. Taken together, shape/weight overvaluation appears to be a more accurate marker of AN severity than BMI. The empirically determined AN severity scheme accounted for the most variance in clinical characteristics. Future research should assess the predictive value of these empirically defined AN severity indicators. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Modern psychopathology classification systems position attention-deficit/hyperactivity disorder (ADHD) with different groups of conditions, either with externalizing or neurodevelopmental. As such, the optimal placement of ADHD in modern classification systems remains unclear. We advanced the literature by mapping ADHD symptoms onto three transdiagnostic psychopathology spectra-externalizing, neurodevelopmental, and internalizing-and their symptoms. ADHD symptoms had varied associations with different spectra, with subsets of symptoms relating most strongly to externalizing, others to neurodevelopmental, and still others to internalizing. Impulsivity, poor schoolwork, and low perseverance were most closely tied to externalizing, cognitive disengagement symptoms (e.g., confused, stared blankly, daydreamed) and immaturity were most closely tied to neurodevelopment, and cognitive disengagement symptoms were also tied to internalizing. Our findings advise against conceptualizing and treating ADHD as a unitary construct and against placing ADHD exclusively under either externalizing or neurodevelopmental spectra. Symptom-focused approaches will better inform modern psychopathology classification systems. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Summarizing specific psychopathology symptoms into higher order factors has a long tradition in mental health science. More recently, the general psychopathology factor (p factor) has gained much interest and currently reflects the highest level of the psychopathology hierarchy. The p factor is modeled from covariance of transdiagnostic psychopathology symptoms. Because such covariance is robust (persons who score higher on symptom X compared to others also tend to score higher on symptom Y), there have been many factor-analytic studies that claim the discovery of-and/or empirical support for-a general psychopathology factor. The reification of the p factor has put person-internal common causes of psychopathology high on the research agenda, while person-external common causes are overlooked. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

We examined prevalence, incidence, impairment, course, and diagnostic transitions for Diagnostic and Statistical Manual of Mental Disorders, fifth edition, eating disorders, overweight, and obesity in a high-risk sample of 1,952 young women (M_age = 19.7 years) who completed diagnostic interviews over a 3-year period. The baseline prevalence of any eating disorder was 13.3% and 25.4% showed onset (incidence) over 3-year follow-up. Baseline prevalence of overweight and obesity were 17.2% and 11.9%, respectively, with respective 3-year incidence rates of 18.3% and 6.8%. The average duration of eating disorders ranged from 2.2 to 5.0 months. Episode duration for overweight and obesity were 14.9 and 20.0 months, respectively. Most eating disorders (82%-96%) showed remission within 1 year; recurrence rates varied from 12% (atypical anorexia nervosa [AN]) to 44% (subthreshold bulimia nervosa). Three-year remission rates for overweight (53%) and obesity (34%) were lower, as was recurrence (15% and 9%, respectively). All eating disorders were characterized by a mixture of binge eating and compensatory weight control behaviors. Functional impairment was elevated for half the examined eating disorders and obesity. Diagnostic progression varied from 3% of those with atypical AN progressing to AN to 29% of those with subthreshold binge eating disorder progressing to binge eating disorder. Regarding diagnostic crossover, the most frequent pattern was shifting from a threshold to a subthreshold eating disorder, followed by shifting from a binge-related eating disorder to overweight. Results extend knowledge of the natural history of eating disorders and provide novel evidence of the relation between eating disorders and overweight/obesity. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Internalizing psychopathology is associated with abnormalities in heart rate variability (HRV). Lower HRV that reflects reduced parasympathetic nervous system activity has been observed in depressive and anxiety disorders. Existing studies predominantly used categorical rather than dimensional approaches, the latter of which better addresses clinical comorbidity and heterogeneity. Moreover, there is little evidence on the role of HRV in longitudinal symptom trajectory in adolescents and young adults. The current study examined the association between HRV and internalizing symptom trajectory using a dimensional approach-the tri-level model of depression and anxiety. Adolescents and young adults (N = 362) were recruited in a 3-year longitudinal study, where they completed electrocardiogram recordings and self-report symptom questionnaires. Multilevel modeling was conducted with high-frequency power bands (HF power) of interbeat intervals at baseline as the predictor, and tri-level symptom factors over 3 years as the outcome. HF power significantly predicted the trajectory of the broad General Distress symptom factor, but not the intermediate Fears or Anhedonia-Apprehension symptom factors. Higher HF power was associated with a decline in General Distress over time. This association was held when neuroticism, other tri-level symptom factors, and demographic variables were covaried. That is, greater parasympathetic nervous system activity at baseline was significantly associated with a greater decline in the broad internalizing symptom factor, but not symptom factors that are more specific to depressive or anxiety disorders. Parasympathetic activity, therefore, may be a transdiagnostic biomarker for internalizing symptoms in adolescents and young adults. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A "natural kind" is a specific classification that identifies some structure of truth and reality, a delimited entity. Psychiatric disorders are not natural kinds. As one moves from physics and chemistry to biology and medicine, natural kinds degrade, and the boundaries of differentiating phenomena become less clear. Within psychiatry, the categorization of psychopathology has further ontological challenges, especially across development. We suggest that to identify and isolate clinical subgroups, it is critical to integrate external validators in an iterative process, with the goal of linking classification to treatments with maximal clinical benefit. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Autism spectrum disorder ("autism") is a neurodevelopmental condition characterized by substantial behavioral and neuroanatomical heterogeneity. This poses significant challenges to understanding its neurobiological mechanisms and developing effective interventions. Identifying phenotypically more homogeneous subgroups and shifting the focus from average group differences to individuals is a promising approach to addressing heterogeneity. In the present study, we aimed to parse clinical and neuroanatomical heterogeneity in autism by combining clustering of clinical features with normative modeling based on neuroanatomical measures (cortical thickness [CT] and subcortical volume) within the Autism Brain Imaging Data Exchange data sets (N autism = 861, N nonautistic individuals [N NAI] = 886, age range = 5-64). First, model-based clustering was applied to autistic symptoms as measured by the Autism Diagnostic Observation Schedule to identify clinical subgroups of autism (N autism = 499). Next, we ran normative modeling on CT and subcortical parcellations (N autism = 690, N NAI = 744) and examined whether clinical subgrouping resulted in increased neurobiological homogeneity within the subgroups compared to the entire autism group by comparing their spatial overlap of neuroanatomical deviations. We further investigated whether the identified subgroups improved the accuracy of autism classification based on the neuroanatomical deviations using supervised machine learning with cross-validation. Results yielded two clinical subgroups primarily differing in restrictive and repetitive behaviors (RRBs). Both subgroups showed increased homogeneity in localized deviations with the high-RRB subgroup showing increased volume deviations in the cerebellum and the low-RRB subgroup showing decreased CT deviations predominantly in the postcentral gyrus and fusiform cortex. Nevertheless, substantial within-group heterogeneity remained highlighted by the lack of improvement of the classifier's performance when distinguishing between the subgroups and NAI. Future research should aim to further reduce heterogeneity incorporating additional neuroanatomical clustering in even larger samples. This will eventually pave the way for more tailored behavioral interventions and improving clinical outcomes. (PsycInfo Database Record (c) 2024 APA, all rights reserved).