Journal of psychopathology and clinical science最新文献

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).

Understanding the neurobiological mechanisms involved in psychopathology has been hindered by the limitations of categorical nosologies. The Hierarchical Taxonomy of Psychopathology (HiTOP) is an alternative dimensional system for characterizing psychopathology, derived from quantitative studies of covariation among diagnoses and symptoms. HiTOP provides more promising targets for clinical neuroscience than traditional psychiatric diagnoses and can facilitate cumulative integration of existing research. We systematically reviewed 164 human neuroimaging studies with sample sizes of 194 or greater that have investigated dimensions of psychopathology classified within HiTOP. Replicated results were identified for constructs at five different levels of the hierarchy, including the overarching p-factor, the externalizing superspectrum, the thought disorder and internalizing spectra, the distress subfactor, and the depression symptom dimension. Our review highlights the potential of dimensional clinical neuroscience research and the usefulness of HiTOP while also suggesting limitations of existing work in this relatively young field. We discuss how HiTOP can be integrated synergistically with neuroscience-oriented, transdiagnostic frameworks developed by the National Institutes of Health, including the Research Domain Criteria, Addictions Neuroclinical Assessment, and the National Institute on Drug Abuse's Phenotyping Assessment Battery, and how researchers can use HiTOP to accelerate clinical neuroscience research in humans and other species. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Traditional mental health diagnoses rely on symptom-based classifications. Yet this approach can oversimplify clinical presentations as diagnoses often do not adequately map onto neurobiological features. Alternatively, our study used structural imaging data and a semisupervised machine learning technique, heterogeneity through discriminative analysis, to identify neurobiological subtypes in 9- to 10-year-olds with high psychopathology endorsements (n = 9,027). Our model revealed two stable neurobiological subtypes (adjusted Rand index = 0.38). Subtype 1 showed smaller structural properties, elevated conduct problems and attention-deficit/hyperactivity disorder symptoms, and impaired cognitive performance compared to Subtype 2 and typically developing youth. Subtype 2 had larger structural properties, cognitive abilities comparable to typically developing youth, and elevated internalizing symptoms relative to Subtype 1 and typically developing youth. These subtypes remained stable in their neurobiological characteristics, cognitive ability, and associated psychopathology traits over time. Taken together, our data-driven approach uncovered evidence of neural heterogeneity as demonstrated by structural patterns that map onto divergent profiles of psychopathology symptoms and cognitive performance in youth. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Theoretical and methodological research on threat conditioning provides important neuroscience-informed approaches to studying fear and anxiety. The threat conditioning framework is at the vanguard of physiological and neurobiological research into core mechanistic symptoms of anxiety-related psychopathology, providing detailed models of neural circuitry underlying variability in clinically relevant behaviors (e.g., decreased extinction, heightened generalization) and heterogeneity in clinical anxiety presentations. Despite the strengths of this approach in explaining symptom and syndromal heterogeneity, the vast majority of psychopathology-oriented threat conditioning work has been conducted using Diagnostic and Statistical Manual of Mental Disorders (DSM) diagnostic categories, which fail to capture the symptom-level resolution that is afforded by threat conditioning indices. Furthermore, relations between fine-grained neurobehavioral measures of threat conditioning and anxiety traits and symptoms are substantially attenuated by within-category heterogeneity, arbitrary boundaries, and inherent comorbidity in the DSM approach. Conversely, the Hierarchical Taxonomy of Psychopathology (HiTOP) is a promising approach for modeling anxiety symptoms relevant to threat conditioning work and for relating threat conditioning to broader anxiety-related constructs. To date, HiTOP has had a minimal impact on the threat conditioning field. Here, we propose that combining the HiTOP and neurobehavioral threat conditioning approaches is an important next step in studying anxiety-related pathology. We provide a brief review of prominent DSM critiques and how they affect threat conditioning studies and review relevant research and suggest solutions and recommendations that flow from the HiTOP perspective. Our hope is that this effort serves as both an inflection point and practical primer for HiTOP-aligned threat conditioning research that benefits both fields. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Within-disorder heterogeneity complicates mapping the neurobiological features of psychopathology to Diagnostic and Statistical Manual of Mental Disorders conceptualizations. The present study explored the patterns of diagnostic classification errors among disorders with commonly co-occurring features to examine this heterogeneity. Classification analyses were conducted with the University of California, Los Angeles Phenomics Study database using a support-vector classifier to differentiate disorders via whole brain task-based functional connectivity, predicting that model misclassifications would provide insight about brain connectivity characteristics shared across disorders. Whether symptoms and specific brain networks could account for misclassification rates was also explored. The classification model performed better than chance (44% accuracy, p = .01) and revealed that misclassification of schizophrenia (SCZ) as bipolar disorder (BD; 38%) and BD as SCZ (36%) was symmetrical. Attention-deficit/hyperactivity disorder (ADHD) was misclassified as BD at the highest rate (46%) and higher than the converse (17%). SCZ and ADHD were misclassified least (15% SCZ as ADHD and 22% ADHD as SCZ). Considerable variance in misclassification of SCZ as BD (R2 = .83) and BD as SCZ (R2 = .71) could be accounted for by symptoms of both SCZ and BD. Permutation testing revealed disorder- and network-specific effects, with certain networks improving classification accuracy and others hindering it for specific disorders. An approach focused on classification errors replicated known disorder overlap, producing errors in the expected configuration. Further, it identified clinical and neural features within and across diagnostic categories that contribute to disorder misclassification and within-disorder heterogeneity. This approach may facilitate neurobiologically informed phenotypic differentiation within diagnostic groups. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Clinical heterogeneity is a significant factor to contend with when seeking to organize, understand, and treat psychopathology. In recent years, the field has prioritized efforts to minimize nonmeaningful heterogeneity and leverage meaningful heterogeneity to improve assessment and diagnostics, inform mechanistic understanding, and facilitate the development of novel treatments. Indeed, exciting developments such as the National Institute for Mental Health Research Domain Criteria and the Hierarchical Taxonomy of Psychopathology have provided powerful frameworks for facing clinical complexity. While these developments have spurred many advancements, the movement has yet to effectively harness the tremendous potential provided by the brain. Initial work incorporating brain data has focused on validating clinical observations with a biomarker rather than leveraging the brain to provide unique insight into meaningful clinical heterogeneity. To provide future guidance and examples of innovation in the area, we solicited articles from teams seeking to utilize brain research to manage clinical heterogeneity. The search resulted in a diverse illustration of how best to leverage brain data to greater mechanistic understanding and clinical utility. In this introduction, we consider this work and discuss strategies through which brain data can best be used to provide unique insight into clinical heterogeneity. As the science of psychopathology continues to grapple with the promise and costs inherent in utilizing this powerful and complex array of methodologies, it will be important to leverage unique insights from brain science. This special issue provides a useful guide for new and upcoming work and a catalyst for moving the field forward. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Anhedonia emerges during adolescence and is characteristic of severe mental illness (SMI). To understand how anhedonia emerges, changes with time, and relates with other symptoms, there is a need to understand patterns of this symptom's course reflecting change or stability-and associations with clinical symptoms and neural reward circuitry in adolescents at risk of SMI. In total, 113 adolescents at low or high familial risk of developing SMI completed clinical measures at up to five time points across 2 years and functional magnetic resonance imaging scanning during a guessing reward task at baseline. Growth curve analysis was used to determine the trajectory of anhedonia across 2 years, including different phases (consummatory and anticipatory) and their association with clinical features (risk status, average suicidal ideation, and average depression across time) and neural activation in response to rewards (ventral striatum and dorsal medial prefrontal cortex). The findings revealed anhedonia decreased across 2 years. Furthermore, lower depression severity was associated with decreases in anhedonia across 2 years. There were no interactions between neural reward activation and anhedonia slopes in predicting clinical features. Exploratory analyses examining latent classes revealed three trajectory classes of anhedonia across phases. While preliminary, in the low and decreasing consummatory anhedonia trajectory class, there was a positive association between neural activation of the right ventral striatum in response to rewards and depression. Certain patterns of anhedonia development could confer risk or resilience for specific types of psychopathologies. The results are preliminary but do highlight the complexity and heterogeneity in anhedonia development. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

This is an invited commentary article for the special issue. The main thesis is that an effective strategy for computational psychiatry to handle the (possibly intrinsic) heterogeneity of psychiatric disorders is to focus on developing clinical principles rather than solely precision medicine. (PsycInfo Database Record (c) 2024 APA, all rights reserved).