Journal of psychopathology and clinical science最新文献

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) 2024 APA, all rights reserved).

Social anxiety-which typically emerges in adolescence-lies on a continuum and, when extreme, can be devastating. Socially anxious individuals are prone to heightened fear, anxiety, and the avoidance of contexts associated with potential social scrutiny. Yet most neuroimaging research has focused on acute social threat. Much less attention has been devoted to understanding the neural systems recruited during the uncertain anticipation of potential encounters with social threat. Here we used a novel functional magnetic resonance imaging paradigm to probe the neural circuitry engaged during the anticipation and acute presentation of threatening faces and voices in a racially diverse sample of 66 adolescents selectively recruited to encompass a range of social anxiety and enriched for clinically significant levels of distress and impairment. Results demonstrated that adolescents with more severe social anxiety symptoms experience heightened distress when anticipating encounters with social threat, and reduced discrimination of uncertain social threat and safety in the bed nucleus of the stria terminalis, a key division of the central extended amygdala (EAc). Although the EAc-including the bed nucleus of the stria terminalis and central nucleus of the amygdala-was robustly engaged by the acute presentation of threatening faces and voices, the degree of EAc engagement was unrelated to the severity of social anxiety. Together, these observations provide a neurobiologically grounded framework for conceptualizing adolescent social anxiety and set the stage for the kinds of prospective-longitudinal and mechanistic research that will be necessary to determine causation and, ultimately, to develop improved interventions for this often-debilitating illness. (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).

Brain structure correlates of obsessive-compulsive personality disorder (OCPD) remain poorly understood as limited OCPD assessment has precluded well-powered studies. Here, we tested whether machine learning (ML; elastic net regression, gradient boosting machines, support vector regression with linear and radial kernels) could estimate OCPD scores from personality data and whether ML-predicted scores are associated with indices of brain structure (cortical thickness and surface area and subcortical volumes). Among older adults (ns = 898-1,606) who completed multiple OCPD assessments, ML elastic net regression with Revised NEO Personality Inventory personality items as features best predicted Five-Factor Obsessive-Compulsive Inventory-Short Form (FFOCI-SF) scores, root-mean-squared error (RMSE)/SD = 0.66; performance generalized to a sample of college students (n = 175; RMSE/SD = 0.51). Items from all five-factor model personality traits contributed to predicted FFOCI-SF (p-FFOCI-SF) scores; conscientiousness and openness items were the most influential. In college students (n = 1,253), univariate analyses of cortical thickness, surface area, and subcortical volumes revealed only a positive association between p-FFOCI-SF and right superior frontal gyrus cortical thickness after adjusting for multiple testing (b = 2.21, p = .0014; all other |b|s < 1.04; all other ps > .009). Multivariate ML models of brain features predicting FFOCI, conscientiousness, and neuroticism performed poorly (RMSE/SDs > 1.00). These data reveal that all five-factor model traits contribute to maladaptive OCPD traits and identify greater right superior frontal gyrus cortical thickness as a promising correlate of OCPD for future study. Broadly, this study highlights the utility of ML to estimate unmeasured psychopathology phenotypes in neuroimaging data sets but that our application of ML to neuroimaging may not resolve unreliable associations and small effects characteristic of univariate psychiatric neuroimaging research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Individual differences in reward functioning have been associated with numerous disorders in adolescence. Given relations with multiple forms of psychopathology, it is unclear whether these associations are disorder specific or reflective of shared variance across multiple disorders. In a sample of adolescents (N = 418), we examined associations between neural and self-reported indices of early reward functioning (age 12) with different levels of a hierarchical psychopathology model assessed later in adolescence (age 18). We examined whether prospective relationships between reward functioning are specific to individual disorders or better explained by transdiagnostic dimensions. We found modest results for prospective associations between reward indices and different dimensions of psychopathology, with most significant associations not surviving correction for multiple comparisons. We discuss the benefits and limitations of the modeling approach used to examine dimension-specific associations that future work can build on. Overall, more work is needed to better understand how reward functioning is specifically associated with different forms of and hierarchical levels of psychopathology. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A primary goal of clinical neuroscience is to identify associations between individual differences in psychopathology and the brain. However, despite a significant amount of resources invested in this endeavor, few reliable neural correlates of psychopathology have been identified. A common suspect for this lack of success is the significant heterogeneity in symptoms observed in psychiatric disorders. However, this is not the only potential source of heterogeneity, as substantial heterogeneity is also observed in brain structure and function. Thus, for clinical neuroscience to identify reliable neural correlates of psychopathology, it will be necessary to better understand heterogeneity in both psychopathology and the brain. In this commentary, we suggest four shared principles that can help disentangle heterogeneity in both of these domains: (a) the brain and behavior should both be treated as complex measures, (b) a priori assumptions should be viewed with caution unless they can be replicated robustly in individuals, (c) complex models of individual differences require appropriate data to estimate them, and (d) the field would benefit from an increased focus on extensively measuring individuals, such as through the use of personalized models of psychopathology and neuroimaging data. Together, these shared principles can aid in better characterizing-and separating relevant and irrelevant-heterogeneity in measures of psychopathology and neuroimaging. (PsycInfo Database Record (c) 2024 APA, all rights reserved).