Journal of psychopathology and clinical science最新文献

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).

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).

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).

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).

Psychophysiology can help elucidate the structure and developmental mechanisms of psychopathology, consistent with the Research Domain Criteria initiative. Cross-sectional research using categorical diagnoses indicates that P300 is an electrocortical endophenotype indexing genetic vulnerability to externalizing problems. However, current diagnostic systems' limitations impede a precise understanding of risk. The Hierarchical Taxonomy of Psychopathology (HiTOP) overcomes these limitations by delineating reliable dimensions ranging in specificity from broad spectra to narrow syndromes. The current study used a HiTOP-aligned approach to clarify P300's associations with a higher-order externalizing factor versus syndrome-specific manifestations within externalizing and internalizing spectra during middle and late adolescence. Participants from the Minnesota Twin Family Study's Enrichment Sample contributed psychophysiological and clinical data at age 14 (N = 930) and follow-up clinical data at age 17 (N = 913). Blunted target P300 at age 14 was selectively associated with externalizing as manifested at age 17 at the superspectrum level (rather than specific externalizing syndromes). Unlike in prior work, target P300 amplitude was positively associated with age 17 depressive symptoms (once controlling for standard stimuli). No association was observed with lifetime symptoms of childhood externalizing or depression evident by age 14. The results indicate that blunted target P300 elucidates specific risk for the development of late-adolescent/young-adult expressions of general externalizing, over and above symptoms evident by middle adolescence. Additionally, the findings speak to the synergistic utility of studying HiTOP-aligned dimensions using multiple measurement modalities to build a more comprehensive understanding of the development of psychopathology. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

As clinical psychological science and biological psychiatry push to assess, model, and integrate heterogeneity and individual differences, approaches leveraging computational modeling, translational methods, and dimensional approaches to psychopathology are increasingly useful in establishing brain-behavior relationships. The field is ultimately interested in complex human behavior, and disruptions in such behaviors can arise through many different pathways, leading to heterogeneity in etiology for seemingly similar presentations. Parsing this complexity may be enhanced using "simple" tasks-which we define as those assaying elemental processes that are the building blocks to complexity. Using eyeblink conditioning as one illustrative example, we propose that simple tasks assessing elemental processes can be leveraged by and enhance computational psychiatry and dimensional approaches in service of understanding heterogeneity in psychiatry, especially when these tasks meet three principles: (a) an extensively mapped circuit, (b) clear brain-behavior relationships, and (c) relevance to understanding etiological processes and/or treatment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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) 2024 APA, all rights reserved).