Precision behavioral phenotyping as a strategy for uncovering the biological correlates of psychopathology

Beyond Increasing Sample Sizes: Optimizing Effect Sizes in Neuroimaging Research on Individual Differences

Linking neurobiology to relatively stable individual differences in cognition, emotion, motivation, and behavior can require large sample sizes to yield replicable results. Given the nature of between-person research, sample sizes at least in the hundreds are likely to be necessary in most neuroimaging studies of individual differences, regardless of whether they are investigating the whole brain or more focal hypotheses. However, the appropriate sample size depends on the expected effect size. Therefore, we propose four strategies to increase effect sizes in neuroimaging research, which may help to enable the detection of replicable between-person effects in samples in the hundreds rather than the thousands: (1) theoretical matching between neuroimaging tasks and behavioral constructs of interest; (2) increasing the reliability of both neural and psychological measurement; (3) individualization of measures for each participant; and (4) using multivariate approaches with cross-validation instead of univariate approaches. We discuss challenges associated with these methods and highlight strategies for improvements that will help the field to move toward a more robust and accessible neuroscience of individual differences.

A computational cognitive neuroscience approach for characterizing individual differences in autism: Introduction to Special Issue

Traditional psychological research has often treated inter-subject variability as statistical noise (even, nuisance variance), focusing instead on averages rather than individual differences. This approach has limited our understanding of the substantial heterogeneity observed in neuropsychiatric disorders, particularly autism spectrum disorder (ASD). In this introduction to a special issue on this theme, we discuss recent advances in cognitive computational neuroscience that can lead to a more systematic notion of core symptom dimensions that differentiate between ASD subtypes. These advances include large participant databases and data-sharing initiatives to increase sample sizes of autistic individuals across a wider range of cultural and socioeconomic backgrounds. Our perspective helps to build bridges between autism symptomatology and individual differences in autistic traits in the non-autistic population and introduces finer-grained dynamic methods to capture behavioral dynamics at the individual level. We specifically focus on how cognitive computational models have emerged as powerful tools to better characterize autistic traits in the general population and autistic population, particularly with respect to social decision-making. We finally outline how we can combine and harness these recent advances, on the one hand, big data initiatives, and on the other hand, cognitive computational models, to achieve a more systematic and nuanced understanding of autism that can lead to improved diagnostic accuracy and personalized interventions.

Multiscale heterogeneity of white matter morphometry in psychiatric disorders

BACKGROUND

Inter-individual variability in the neurobiological and clinical characteristics of mental illnesses are often overlooked by classical group-mean case-control studies. Studies using normative modelling to infer person-specific deviations of grey matter volume have indicated that group means are not representative of most individuals. The extent to which this variability is present in white matter morphometry, which is integral to brain function, remains unclear.

METHODS

We applied Warped Bayesian Linear Regression normative models to T1-weighted magnetic resonance imaging data and mapped inter-individual variability in person-specific white matter volume deviations in 1,294 cases (58% male) diagnosed with one of six disorders (attention-deficit/hyperactivity, autism, bipolar, major depressive, obsessive-compulsive and schizophrenia) and 1,465 matched controls (54% male) recruited across 25 scan sites. We developed a framework to characterize deviation heterogeneity at multiple spatial scales, from individual voxels, through inter-regional connections, specific brain regions, and spatially extended brain networks.

RESULTS

The specific locations of white matter volume deviations were highly heterogeneous across participants, affecting the same voxel in fewer than 8% of individuals with the same diagnosis. For autism and schizophrenia, negative deviations (i.e., areas where volume is lower than normative expectations) aggregated into common tracts, regions, and large-scale networks in up to 69% of individuals.

CONCLUSIONS

The prevalence of white matter volume deviations was lower than previously observed in grey matter, and the specific location of these deviations was highly heterogeneous when considering voxel-wise spatial resolution. Evidence of aggregation within common pathways and networks was apparent in schizophrenia and autism, but not other disorders.

A Transdiagnostic, Hierarchical Taxonomy of Psychopathology Following Traumatic Brain Injury (HiTOP-TBI)

Psychopathology, including depression, anxiety, and post-traumatic stress, is a significant yet inadequately addressed feature of moderate-severe traumatic brain injury (TBI). Progress in understanding and treating post-TBI psychopathology may be hindered by limitations associated with conventional diagnostic approaches, specifically the Diagnostic and Statistical Manual of Mental Disorders (DSM) and International Classification of Diseases (ICD). The Hierarchical Taxonomy of Psychopathology (HiTOP) offers a promising, transdiagnostic alternative to psychiatric classification that may more effectively capture the experiences of individuals with TBI. However, HiTOP lacks validation in the TBI population. To address this gap, we administered a comprehensive questionnaire battery, including 56 scales assessing homogeneous symptom components and maladaptive traits within HiTOP, to 410 individuals with moderate-severe TBI. We evaluated the reliability and unidimensionality of each scale and revised those with psychometric problems. Using a top-down, exploratory latent variable approach (bass-ackwards modeling), we subsequently constructed a hierarchical model of psychopathological dimensions tailored to TBI. The results showed that, relative to norms, participants with moderate-severe TBI experienced greater problems in the established HiTOP internalizing and detachment spectra, but fewer problems with thought disorder and antagonism. Fourteen of the 56 scales demonstrated psychometric problems, which often appeared reflective of the TBI experience and associated disability. The Hierarchical Taxonomy of Psychopathology Following Traumatic Brain Injury (HiTOP-TBI) model encompassed broad internalizing and externalizing spectra, splitting into seven narrower dimensions: Detachment, Dysregulated Negative Emotionality, Somatic Symptoms, Compensatory and Phobic Reactions, Self-Harm and Psychoticism, Rigid Constraint, and Harmful Substance Use. This study presents the most comprehensive empirical classification of psychopathology after TBI to date. It introduces a novel, TBI-specific transdiagnostic questionnaire battery and model, which addresses the limitations of conventional DSM and ICD diagnoses. The empirical structure of psychopathology after TBI largely aligned with the established HiTOP model (e.g., a detachment spectrum). However, these constructs need to be interpreted in relation to the unique experiences associated with TBI (e.g., considering the injury's impact on the person's social functioning). By overcoming the limitations of conventional diagnostic approaches, the HiTOP-TBI model has the potential to accelerate our understanding of the causes, correlates, consequences, and treatment of psychopathology after TBI.

Sample size matters when estimating test-retest reliability of behaviour

Intraclass correlation coefficients (ICCs) are a commonly used metric in test-retest reliability research to assess a measure's ability to quantify systematic between-subject differences. However, estimates of between-subject differences are also influenced by factors including within-subject variability, random errors, and measurement bias. Here, we use data collected from a large online sample (N = 150) to (1) quantify test-retest reliability of behavioural and computational measures of reversal learning using ICCs, and (2) use our dataset as the basis for a simulation study investigating the effects of sample size on variance component estimation and the association between estimates of variance components and ICC measures. In line with previously published work, we find reliable behavioural and computational measures of reversal learning, a commonly used assay of behavioural flexibility. Reliable estimates of between-subject, within-subject (across-session), and error variance components for behavioural and computational measures (with ± .05 precision and 80% confidence) required sample sizes ranging from 10 to over 300 (behavioural median N: between-subject = 167, within-subject = 34, error = 103; computational median N: between-subject = 68, within-subject = 20, error = 45). These sample sizes exceed those often used in reliability studies, suggesting that sample sizes larger than are commonly used for reliability studies (circa 30) are required to robustly estimate reliability of task performance measures. Additionally, we found that ICC estimates showed highly positive and highly negative correlations with between-subject and error variance components, respectively, as might be expected, which remained relatively stable across sample sizes. However, ICC estimates were weakly or not correlated with within-subject variance, providing evidence for the importance of variance decomposition for reliability studies.

Schizotypy, Psychosis Proneness, and the Polygenic Risk for Schizophrenia and Resilience

BACKGROUND AND HYPOTHESIS

Schizotypy is a well-established phenotype for psychosis proneness and risk. Yet, its genetic underpinnings and relations to genetic bases of the schizophrenia spectrum are not well understood owing to conflicting findings. In a deep phenotyping approach, we hypothesized that genetic markers of risk for and to schizophrenia are differentially associated with (trait-level) dimensions of schizotypy and (state-level) prodromal symptoms.

STUDY DESIGN

In 367 (130 male, 237 female) psychiatrically healthy young adults, we assessed multiple schizotypy instruments (OLIFE, SPQ-B, Multidimensional Schizotypy Scales), aggregated into composite scores, and a measure of prodromal symptoms (PQ-16). Those were tested for direct and interactive associations with the polygenic risk score (PRS) for schizophrenia and a novel PRS for resilience to schizophrenia.

STUDY RESULTS

Both prodromal symptom number (rho = 0.16, pcorr = .018) and distress (rho = 0.14, pcorr = .027) were positively related to the schizophrenia PRS. Positive schizotypy showed a similar association but did not remain significant after correction (rho = 0.11, pcorr = .082). Schizophrenia PRS and disorganized schizotypy had a negative interactive effect on prodromal symptom distress (b = -0.10, pcorr = .048). The resilience score did not show any significant associations with any of the measures.

CONCLUSIONS

These results further support the idea of a (partially) shared genetic basis of schizophrenia and nonclinical, predominantly positive expressions of the psychosis spectrum but also indicate relevant distinctions between the 2, possibly related to other modulating factors or general (transdiagnostic) psychopathological risk. In line with previous findings, effects seem to be more robust for state- than trait-level markers, but these may also be influencing each other.

The impact of self-report inaccuracy in the UK Biobank and its interplay with selective participation

Although the use of short self-report measures is common practice in biobank initiatives, such a phenotyping strategy is inherently prone to reporting errors. To explore challenges related to self-report errors, we first derived a reporting error score in the UK Biobank (UKBB; n = 73,127), capturing inconsistent self-reporting in time-invariant phenotypes across multiple measurement occasions. We then performed genome-wide scans on the reporting error score, applied downstream analyses (linkage disequilibrium score regression and Mendelian randomization) and compared its properties to the UKBB participation propensity. Finally, we improved phenotype resolution for 24 measures and inspected the changes in genomic findings. We found that reporting error was present across all 33 assessed self-report measures, with repeatability levels as low as 47% (childhood body size). Reporting error was not independent from UKBB participation, evidenced by the negative genetic correlation between the two outcomes (rg = -0.77), their shared causes (for example, education) and the loss in self-report accuracy following participation bias correction. Across all analyses, the impact of reporting error ranged from reduced power (for example, for gene discovery) to biased estimates (for example, if present in the exposure variable) and attenuation of genome-wide quantities (for example, 21% relative attenuation in SNP heritability for childhood height). Our findings highlight that both self-report accuracy and selective participation are competing biases and sources of poor reproducibility for biobank-scale research.

Examining differential item functioning in self-reported health survey data: via multilevel modeling

Few health-related constructs or measures have received a critical evaluation in terms of measurement equivalence, such as self-reported health survey data. Differential item functioning (DIF) analysis is crucial for evaluating measurement equivalence in self-reported health surveys, which are often hierarchical in structure. Traditional single-level DIF methods in this case fall short, making multilevel models a better alternative. We highlight the benefits of multilevel modeling for DIF analysis, when applying a health survey data set to multilevel binary logistic regression (for analyzing binary response data) and multilevel multinominal logistic regression (for analyzing polytomous response data), and comparing them with their single-level counterparts. Our findings show that multilevel models fit better and explain more variance than single-level models. This article is expected to raise awareness of multilevel modeling and help healthcare researchers and practitioners understand the use of multilevel modeling for DIF analysis.

Representing Brain-Behavior Associations by Retaining High-Motion Minoritized Youth

BACKGROUND

Population neuroscience datasets provide an opportunity for researchers to estimate reproducible effect sizes for brain-behavior associations because of their large sample sizes. However, these datasets undergo strict quality control to mitigate sources of noise, such as head motion. This practice often excludes a disproportionate number of minoritized individuals.

METHODS

We used motion-ordering and motion-ordering+resampling (bagging) to test whether these methods preserve functional magnetic resonance imaging (fMRI) data in the Adolescent Brain Cognitive Development (ABCD) Study (N = 5733). For the 2 methods, brain-behavior associations were computed as the partial Spearman's rank correlations (Rs) between functional connectivity and cognitive performance (NIH Cognition Toolbox) as well as externalizing and internalizing psychopathology (Child Behavior Checklist) while adjusting for participant sex assigned at birth and head motion.

RESULTS

Black and Hispanic youth exhibited excess head motion relative to data collected from White youth and were discarded disproportionately when conventional approaches were used. Motion-ordering and bagging methods retained more than 99% of Black and Hispanic youth. Both methods produced reproducible brain-behavior associations across low-/high-motion racial/ethnic groups based on motion-limited fMRI data.

CONCLUSIONS

The motion-ordering and bagging methods are 2 feasible approaches that can enhance sample representation for testing brain-behavior associations and that result in reproducible effect sizes in diverse populations.

Adolescent social anxiety is associated with diminished discrimination of anticipated threat and safety in the bed nucleus of the stria terminalis

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) 2025 APA, all rights reserved).

The Transition From Homogeneous to Heterogeneous Machine Learning in Neuropsychiatric Research

Despite the advantage of neuroimaging-based machine learning (ML) models as pivotal tools for investigating brain-behavior relationships in neuropsychiatric studies, these data-driven predictive approaches have yet to yield substantial, clinically actionable insights for mental health care. A notable impediment lies in the inadequate accommodation of most ML research to the natural heterogeneity within large samples. Although commonly thought of as individual-level analyses, many ML algorithms are unimodal and homogeneous and thus incapable of capturing the potentially heterogeneous relationships between biology and psychopathology. We review the current landscape of computational research targeting population heterogeneity and argue that there is a need to expand from brain subtyping and behavioral phenotyping to analyses that focus on heterogeneity at the relational level. To this end, we review and suggest several existing ML models with the capacity to discern how external environmental and sociodemographic factors moderate the brain-behavior mapping function in a data-driven fashion. These heterogeneous ML models hold promise for enhancing the discovery of individualized brain-behavior associations and advancing precision psychiatry.

Embracing variability in the search for biological mechanisms of psychiatric illness

Despite decades of research, we lack objective diagnostic or prognostic biomarkers of mental health problems. A key reason for this limited progress is a reliance on the traditional case-control paradigm, which assumes that each disorder has a single cause that can be uncovered by comparing average phenotypic values of patient and control samples. Here, we discuss the problematic assumptions on which this paradigm is based and highlight recent efforts that seek to characterize, rather than minimize, the inherent clinical and biological variability that underpins psychiatric populations. Embracing such variability is necessary to understand pathophysiological mechanisms and develop more targeted and effective treatments.

Update on the Neurobiology of Borderline Personality Disorder: A Review of Structural, Resting-State and Task-Based Brain Imaging Studies

PURPOSE OF REVIEW

This review summarizes recent advances in research on the neurobiology of borderline personality disorder (BPD) according to structural brain imaging investigations and resting-state and task-based functional brain activation studies.

RECENT FINDINGS

Extending established findings on differences in regional brain volumes and cortical thickness between BPD and healthy controls, recent research illuminates shared and distinct brain structural characteristics compared to other psychiatric diagnoses, and uncovers relations of these brain structures with transdiagnostic symptoms and clinical features. Resting-state functional brain imaging studies reveal disruptions among adolescents and adults with BPD in frontolimbic and default-mode networks, which primarily underlie affect regulation and self-referential processes, respectively. Recent task-based functional brain imaging research builds on existing neurobiological understanding of emotion and cognition in BPD by revealing novel intersections with interpersonal- and stress-related processes. Studies of psychological and pharmacological interventions suggest possible effects on neural regions underlying emotion processing and behavioral control. Recent advances in neurobiological research on BPD underscore the pathophysiology of affective, behavioral and self-interpersonal symptoms, with growing interest in adolescents with BPD and the impacts of psychological and biological interventions. Corresponding with the increased prominence of alternative dimensional models of personality disorder in recent years, there is a gradual rise in studies examining the relationships of brain structures and functional brain activation with BPD-relevant symptom dimensions, including within transdiagnostic samples.

AI for Analyzing Mental Health Disorders Among Social Media Users: Quarter-Century Narrative Review of Progress and Challenges

BACKGROUND

Mental health disorders are currently the main contributor to poor quality of life and years lived with disability. Symptoms common to many mental health disorders lead to impairments or changes in the use of language, which are observable in the routine use of social media. Detection of these linguistic cues has been explored throughout the last quarter century, but interest and methodological development have burgeoned following the COVID-19 pandemic. The next decade may see the development of reliable methods for predicting mental health status using social media data. This might have implications for clinical practice and public health policy, particularly in the context of early intervention in mental health care.

OBJECTIVE

This study aims to examine the state of the art in methods for predicting mental health statuses of social media users. Our focus is the development of artificial intelligence-driven methods, particularly natural language processing, for analyzing large volumes of written text. This study details constraints affecting research in this area. These include the dearth of high-quality public datasets for methodological benchmarking and the need to adopt ethical and privacy frameworks acknowledging the stigma experienced by those with a mental illness.

METHODS

A Google Scholar search yielded peer-reviewed articles dated between 1999 and 2024. We manually grouped the articles by 4 primary areas of interest: datasets on social media and mental health, methods for predicting mental health status, longitudinal analyses of mental health, and ethical aspects of the data and analysis of mental health. Selected articles from these groups formed our narrative review.

RESULTS

Larger datasets with precise dates of participants' diagnoses are needed to support the development of methods for predicting mental health status, particularly in severe disorders such as schizophrenia. Inviting users to donate their social media data for research purposes could help overcome widespread ethical and privacy concerns. In any event, multimodal methods for predicting mental health status appear likely to provide advancements that may not be achievable using natural language processing alone.

CONCLUSIONS

Multimodal methods for predicting mental health status from voice, image, and video-based social media data need to be further developed before they may be considered for adoption in health care, medical support, or as consumer-facing products. Such methods are likely to garner greater public confidence in their efficacy than those that rely on text alone. To achieve this, more high-quality social media datasets need to be made available and privacy concerns regarding the use of these data must be formally addressed. A social media platform feature that invites users to share their data upon publication is a possible solution. Finally, a review of literature studying the effects of social media use on a user's depression and anxiety is merited.

Out with the bad, in with the good: A review on augmented extinction learning in humans

Several leading therapies for anxiety-related disorders rely on the principles of extinction learning. However, despite decades of development and research, many of these treatments remain only moderately effective. Developing techniques to improve extinction learning is an important step towards developing improved and mechanistically-informed exposure-based therapies. In this review, we highlight human research on strategies that might augment extinction learning through reward neurocircuitry and dopaminergic pathways, with an emphasis on counterconditioning and other behaviorally-augmented forms of extinction learning (e.g., novelty-facilitated extinction, positive affect training). We also highlight emerging pharmacological and non-pharmacological methods of augmenting extinction, including L-DOPA and aerobic exercise. Finally, we discuss future directions for augmented extinction learning and memory research, including the need for more work examining the influence of individual differences and psychopathology.

Replicability and generalizability in population psychiatric neuroimaging

Studies linking mental health with brain function in cross-sectional population-based association studies have historically relied on small, underpowered samples. Given the small effect sizes typical of such brain-wide associations, studies require samples into the thousands to achieve the statistical power necessary for replicability. Here, we detail how small sample sizes have hampered replicability and provide sample size targets given established association strength benchmarks. Critically, while replicability will improve with larger samples, it is not guaranteed that observed effects will meaningfully apply to target populations of interest (i.e., be generalizable). We discuss important considerations related to generalizability in psychiatric neuroimaging and provide an example of generalizability failure due to "shortcut learning" in brain-based predictions of mental health phenotypes. Shortcut learning is a phenomenon whereby machine learning models learn an association between the brain and an unmeasured construct (the shortcut), rather than the intended target of mental health. Given the complex nature of brain-behavior interactions, the future of epidemiological approaches to brain-based studies of mental health will require large, diverse samples with comprehensive assessment.

Quality over quantity: powering neuroimaging samples in psychiatry

Neuroimaging has been widely adopted in psychiatric research, with hopes that these non-invasive methods will provide important clues to the underpinnings and prediction of various mental health symptoms and outcomes. However, the translational impact of neuroimaging has not yet reached its promise, despite the plethora of computational methods, tools, and datasets at our disposal. Some have lamented that too many psychiatric neuroimaging studies have been underpowered with respect to sample size. In this review, we encourage this discourse to shift from a focus on sheer increases in sample size to more thoughtful choices surrounding experimental study designs. We propose considerations at multiple decision points throughout the study design, data modeling and analysis process that may help researchers working in psychiatric neuroimaging boost power for their research questions of interest without necessarily increasing sample size. We also provide suggestions for leveraging multiple datasets to inform each other and strengthen our confidence in the generalization of findings to both population-level and clinical samples. Through a greater emphasis on improving the quality of brain-based and clinical measures rather than merely quantity, meaningful and potentially translational clinical associations with neuroimaging measures can be achieved with more modest sample sizes in psychiatry.

The hierarchical taxonomy of psychopathology and the search for neurobiological substrates of mental illness: A systematic review and roadmap for future research

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).

Pathological personality dimensions and neurobiological emotional reactivity

BACKGROUND

The Hierarchical Taxonomy of Psychopathology (HiTOP) offers a promising framework to identify the neurobiological mechanisms of psychopathology. Many forms of psychopathology are characterized by dysfunctional emotional reactivity. The late positive potential (LPP) is an event-related potential component that provides an index of neurobiological emotional reactivity. Several categorical disorders have demonstrated a similar association with the emotion-modulated LPP. It is possible that higher-order dimensional representations of psychopathology might explain the comparable results. The present study examined the association between HiTOP-consistent pathological personality dimensions across multiple levels of the hierarchy and neurobiological emotional reactivity.

METHODS

The sample included 215 18-35-year-old adults (86% female) who were oversampled for psychopathology. Participants completed the emotional interrupt task while electroencephalography was recorded to examine the LPP. Participants also completed the Comprehensive Assessment of Traits relevant to Personality Disorders to assess pathological personality.

RESULTS

At the spectra level, higher negative emotionality was associated with a larger emotion-modulated LPP, while higher detachment was associated with a smaller emotion-modulated LPP. There were no associations between higher-order psychopathology levels and the emotion-modulated LPP. Compared to categorical diagnoses, spectra-level personality pathology dimensions significantly improved the prediction of the emotion-modulated LPP.

CONCLUSIONS

The present study indicates that HiTOP spectra levels of negative emotionality and detachment demonstrate unique associations with neurobiological emotional reactivity. The study highlights the utility of examining dimensional and hierarchical, rather than categorical, representations of psychopathology in the attempt to identify the neurobiological origins of psychopathology.

Polygenic Scores and Networks of Psychopathology Symptoms

Importance

Studies on polygenic risk for psychiatric traits commonly use a disorder-level approach to phenotyping, implicitly considering disorders as homogeneous constructs; however, symptom heterogeneity is ubiquitous, with many possible combinations of symptoms falling under the same disorder umbrella. Focusing on individual symptoms may shed light on the role of polygenic risk in psychopathology.

Objective

To determine whether polygenic scores are associated with all symptoms of psychiatric disorders or with a subset of indicators and whether polygenic scores are associated with comorbid phenotypes via specific sets of relevant symptoms.

Design, Setting, and Participants

Data from 2 population-based cohort studies were used in this cross-sectional study. Data from children in the Avon Longitudinal Study of Parents and Children (ALSPAC) were included in the primary analysis, and data from children in the Twins Early Development Study (TEDS) were included in confirmatory analyses. Data analysis was conducted from October 2021 to January 2024. Pregnant women based in the Southwest of England due to deliver in 1991 to 1992 were recruited in ALSPAC. Twins born in 1994 to 1996 were recruited in TEDS from population-based records. Participants with available genetic data and whose mothers completed the Short Mood and Feelings Questionnaire and the Strength and Difficulties Questionnaire when children were 11 years of age were included.

Main Outcomes and Measures

Psychopathology relevant symptoms, such as hyperactivity, prosociality, depression, anxiety, and peer and conduct problems at age 11 years. Psychological networks were constructed including individual symptoms and polygenic scores for depression, anxiety, attention-deficit/hyperactivity disorder (ADHD), body mass index (BMI), and educational attainment in ALSPAC. Following a preregistered confirmatory analysis, network models were cross-validated in TEDS.

Results

Included were 5521 participants from ALSPAC (mean [SD] age, 11.8 [0.14] years; 2777 [50.3%] female) and 4625 participants from TEDS (mean [SD] age, 11.27 [0.69] years; 2460 [53.2%] female). Polygenic scores were preferentially associated with restricted subsets of core symptoms and indirectly associated with other, more distal symptoms of psychopathology (network edges ranged between r = -0.074 and r = 0.073). Psychiatric polygenic scores were associated with specific cross-disorder symptoms, and nonpsychiatric polygenic scores were associated with a variety of indicators across disorders, suggesting a potential contribution of nonpsychiatric traits to comorbidity. For example, the polygenic score for ADHD was associated with a core ADHD symptom, being easily distracted (r = 0.07), and the polygenic score for BMI was associated with symptoms across disorders, including being bullied (r = 0.053) and not thinking things out (r = 0.041).

Conclusions and Relevance

Genetic associations observed at the disorder level may hide symptom-level heterogeneity. A symptom-level approach may enable a better understanding of the role of polygenic risk in shaping psychopathology and comorbidity.

Pathological personality domains and punishment-enhanced error-related negativity

The error-related negativity (ERN) is an event-related potential that is observed after the commission of an error and is hypothesized to index threat sensitivity. The ERN is associated with multiple psychiatric disorders, but it is unclear if similar results are due to higher-order dimensions of psychopathology. When errors are punished, the ERN is further enhanced, which might better isolate threat sensitivity. However, few studies have examined whether psychopathology is associated with punishment enhancement of the ERN. In a clinical sample of 170 adults, the present study examined the association between pathological personality domains and predictable vs. unpredictable punishment-enhanced ERN. Results indicated that the ERN was enhanced when errors were punished compared to not punished. Greater negative emotionality was associated with a greater predictable punishment-enhanced ERN, while greater disinhibition was associated with smaller predictable punishment-enhanced ERN. The study suggests that higher-order pathological personality domains demonstrate discriminate relationships with punishment-enhanced error-related brain activity.

Neuroticism Is Prospectively Associated With 30-Month Changes in Broadband Internalizing Symptoms, but Not Narrowband Positive Affect or Anxious Arousal, in Emerging Adulthood

Elevated levels of Neuroticism/Negative Emotionality (N/NE) and, less consistently, lower levels of Extraversion/Positive Emotionality (E/PE) confer risk for pathological depression and anxiety. To date, most prospective-longitudinal research has narrowly focused on traditional diagnostic categories, creating uncertainty about the precise nature of these prospective associations. Adopting an explicitly hierarchical-dimensional approach, we examined the association between baseline variation in personality and longitudinal changes in broad and narrow internalizing-symptom dimensions in 234 emerging adults followed for 2.5 years, during the transition from older adolescence to early adulthood. N/NE was uniquely associated with increases in broadband internalizing-the core cognitive and affective symptoms that cut across the emotional disorders-and unrelated to the narrower dimensions of positive affect and anxious arousal that differentiate specific internalizing presentations. Variation in E/PE and several other Big Five traits was cross-sectionally, but not prospectively, related to longitudinal changes in specific internalizing symptoms. Exploratory personality-facet-level analyses provided preliminary evidence of more granular associations between personality and longitudinal changes in internalizing symptoms. These observations enhance the precision of models linking personality to internalizing illness; highlight the centrality of N/NE to increases in transdiagnostic internalizing symptoms during a key developmental chapter; and set the stage for developing more effective prevention and treatment strategies.

Identifying internalizing transdiagnostic profiles through motivational and cognitive control systems: Relations with symptoms, functionality, and quality of life

BACKGROUND

The diversity of patients' symptomatology among people seeking treatment on community-based mental health services poses significant challenges to traditional models of care. Recent approaches favor identifying transdiagnostic factors that allow a better understanding of patient heterogeneity and designing more effective and quality interventions. This study examines the heterogeneity of patients with internalizing symptoms based on profiles identified with cognitive and motivational control variables. Differences between these profiles on dimensional measures of psychopathology and quality of life are examined.

METHODS

263 patients were selected by non-probabilistic sampling procedures on mental health services in the province of Huelva (Spain). A latent class analysis on the standardized scale scores of The Behavioral Inhibition/Behavioral Activation System Scales and the Effortful Control Scale of the Adult Temperament Questionnaire Short-Form was conducted. Profiles were compared on the scores of the Inventory of Depression and Anxiety Symptoms-II, the WHO Disability Assessment Schedule II, and the Health Assessment Questionnaire SF-36.

RESULTS

The four latent profile solution is the one that showed the best fit indicators and substantive interpretability, with a kappa of 0.94 in the cross-validation procedure with 75% of the sample. No sex differences were found between the profiles (χ32 5.17, p = .160). Profiles #1 and #3, both characterized by an imbalance between low activation and high inhibition, had lower well-being, lower functionality, and quality of life. When comparing profile #2 (featuring the highest inhibitory control) lower scores on most internalizing scales are observed, specially claustrophobia, social anxiety, panic mania. Profile #4 (low control, high activation, and high inhibition) showed greater scores on both mania and euphoria and lower scores on emotional role.

CONCLUSIONS

We identified four distinctive profiles that had overly increased behavioral inhibition (as expected in internalizing disorders) and differed in the degree of imbalance between inhibition and activation systems, and between motivational systems and top-down cognitive control. The profile characterized by high activation and reduced cognitive (inhibitory) control was the one showing greater mood-related symptoms and lower levels of quality of life. These profiles could be generated by treatment providers to guide clinical management in an evidence-based manner.

An externally validated resting-state brain connectivity signature of pain-related learning

Pain can be conceptualized as a precision signal for reinforcement learning in the brain and alterations in these processes are a hallmark of chronic pain conditions. Investigating individual differences in pain-related learning therefore holds important clinical and translational relevance. Here, we developed and externally validated a novel resting-state brain connectivity-based predictive model of pain-related learning. The pre-registered external validation indicates that the proposed model explains 8-12% of the inter-individual variance in pain-related learning. Model predictions are driven by connections of the amygdala, posterior insula, sensorimotor, frontoparietal, and cerebellar regions, outlining a network commonly described in aversive learning and pain. We propose the resulting model as a robust and highly accessible biomarker candidate for clinical and translational pain research, with promising implications for personalized treatment approaches and with a high potential to advance our understanding of the neural mechanisms of pain-related learning.

Blunted ventral striatal reactivity to social reward is associated with more severe motivation and pleasure deficits in psychosis

Among individuals living with psychotic disorders, social impairment is common, debilitating, and challenging to treat. While the roots of this impairment are undoubtedly complex, converging lines of evidence suggest that social motivation and pleasure (MAP) deficits play a key role. Yet most neuroimaging studies have focused on monetary rewards, precluding decisive inferences. Here we leveraged parallel social and monetary incentive delay fMRI paradigms to test whether blunted reactivity to social incentives in the ventral striatum-a key component of the distributed neural circuit mediating appetitive motivation and hedonic pleasure-is associated with more severe MAP symptoms in a transdiagnostic sample enriched for psychosis. To maximize ecological validity and translational relevance, we capitalized on naturalistic audiovisual clips of an established social partner expressing positive feedback. Although both paradigms robustly engaged the ventral striatum, only reactivity to social incentives was associated with clinician-rated MAP deficits. This association remained significant when controlling for other symptoms, binary diagnostic status, or ventral striatum reactivity to monetary incentives. Follow-up analyses suggested that this association predominantly reflects diminished striatal activation during the receipt of social reward. These observations provide a neurobiologically grounded framework for conceptualizing the social-anhedonia symptoms and social impairments that characterize many individuals living with psychotic disorders and underscore the need to establish targeted intervention strategies.

Leveraging the adolescent brain cognitive development study to improve behavioral prediction from neuroimaging in smaller replication samples

Neuroimaging is a popular method to map brain structural and functional patterns to complex human traits. Recently published observations cast doubt upon these prospects, particularly for prediction of cognitive traits from structural and resting state functional magnetic resonance imaging (MRI). We leverage baseline data from thousands of children in the Adolescent Brain Cognitive DevelopmentSM Study to inform the replication sample size required with univariate and multivariate methods across different imaging modalities to detect reproducible brain-behavior associations. We demonstrate that by applying multivariate methods to high-dimensional brain imaging data, we can capture lower dimensional patterns of structural and functional brain architecture that correlate robustly with cognitive phenotypes and are reproducible with only 41 individuals in the replication sample for working memory-related functional MRI, and ~ 100 subjects for structural and resting state MRI. Even with 100 random re-samplings of 100 subjects in discovery, prediction can be adequately powered with 66 subjects in replication for multivariate prediction of cognition with working memory task functional MRI. These results point to an important role for neuroimaging in translational neurodevelopmental research and showcase how findings in large samples can inform reproducible brain-behavior associations in small sample sizes that are at the heart of many research programs and grants.

Generalized genetic liability to substance use disorders

Lifetime and temporal co-occurrence of substance use disorders (SUDs) is common and compared with individual SUDs is characterized by greater severity, additional psychiatric comorbidities, and worse outcomes. Here, we review evidence for the role of generalized genetic liability to various SUDs. Coaggregation of SUDs has familial contributions, with twin studies suggesting a strong contribution of additive genetic influences undergirding use disorders for a variety of substances (including alcohol, nicotine, cannabis, and others). GWAS have documented similarly large genetic correlations between alcohol, cannabis, and opioid use disorders. Extending these findings, recent studies have identified multiple genomic loci that contribute to common risk for these SUDs and problematic tobacco use, implicating dopaminergic regulatory and neuronal development mechanisms in the pathophysiology of generalized SUD genetic liability, with certain signals demonstrating cross-species and translational validity. Overlap with genetic signals for other externalizing behaviors, while substantial, does not explain the entirety of the generalized genetic signal for SUD. Polygenic scores (PGS) derived from the generalized genetic liability to SUDs outperform PGS for individual SUDs in prediction of serious mental health and medical comorbidities. Going forward, it will be important to further elucidate the etiology of generalized SUD genetic liability by incorporating additional SUDs, evaluating clinical presentation across the lifespan, and increasing the granularity of investigation (e.g., specific transdiagnostic criteria) to ultimately improve the nosology, prevention, and treatment of SUDs.

Multivariate brain-behaviour associations in psychiatric disorders

Mapping brain-behaviour associations is paramount to understand and treat psychiatric disorders. Standard approaches involve investigating the association between one brain and one behavioural variable (univariate) or multiple variables against one brain/behaviour feature ('single' multivariate). Recently, large multimodal datasets have propelled a new wave of studies that leverage on 'doubly' multivariate approaches capable of parsing the multifaceted nature of both brain and behaviour simultaneously. Within this movement, canonical correlation analysis (CCA) and partial least squares (PLS) emerge as the most popular techniques. Both seek to capture shared information between brain and behaviour in the form of latent variables. We provide an overview of these methods, review the literature in psychiatric disorders, and discuss the main challenges from a predictive modelling perspective. We identified 39 studies across four diagnostic groups: attention deficit and hyperactive disorder (ADHD, k = 4, N = 569), autism spectrum disorders (ASD, k = 6, N = 1731), major depressive disorder (MDD, k = 5, N = 938), psychosis spectrum disorders (PSD, k = 13, N = 1150) and one transdiagnostic group (TD, k = 11, N = 5731). Most studies (67%) used CCA and focused on the association between either brain morphology, resting-state functional connectivity or fractional anisotropy against symptoms and/or cognition. There were three main findings. First, most diagnoses shared a link between clinical/cognitive symptoms and two brain measures, namely frontal morphology/brain activity and white matter association fibres (tracts between cortical areas in the same hemisphere). Second, typically less investigated behavioural variables in multivariate models such as physical health (e.g., BMI, drug use) and clinical history (e.g., childhood trauma) were identified as important features. Finally, most studies were at risk of bias due to low sample size/feature ratio and/or in-sample testing only. We highlight the importance of carefully mitigating these sources of bias with an exemplar application of CCA.

Respiratory modulation of the heart rate: A potential biomarker of cardiorespiratory function in human

In recent decade, wearable digital devices have shown potentials for the discovery of novel biomarkers of humans' physiology and behavior. Heart rate (HR) and respiration rate (RR) are most crucial bio-signals in humans' digital phenotyping research. HR is a continuous and non-invasive proxy to autonomic nervous system and ample evidence pinpoints the critical role of respiratory modulation of cardiac function. In the present study, we recorded longitudinal (7 days, 4.63 ± 1.52) HR and RR of 89 freely behaving human subjects (Female: 39, age 57.28 ± 5.67, Male: 50, age 58.48 ± 6.32) and analyzed their dynamics using linear models and information theoretic measures. While HR's linear and nonlinear characteristics were expressed within the plane of the HR-RR directed flow of information (HR→RR - RR→HR), their dynamics were determined by its RR→HR axis. More importantly, RR→HR quantified the effect of alcohol consumption on individuals' cardiorespiratory function independent of their consumed amount of alcohol, thereby signifying the presence of this habit in their daily life activities. The present findings provided evidence for the critical role of the respiratory modulation of HR, which was previously only studied in non-human animals. These results can contribute to humans' phenotyping research by presenting RR→HR as a digital diagnosis/prognosis marker of humans' cardiorespiratory pathology.

Do We Need a Novel Framework for Classifying Psychopathology? A Discussion Paper

Introduction

The ICD-11 and DSM-5 are the leading systems for the classification of mental disorders, and their relevance for clinical work and research, as well as their impact for policy making and legal questions, has increased considerably. In recent years, other frameworks have been proposed to supplement or even replace the ICD and the DSM, raising many questions regarding clinical utility, scientific relevance, and, at the core, how best to conceptualize mental disorders.

Method

As examples of the new approaches that have emerged, here we introduce the Hierarchical Taxonomy of Psychopathology (HiTOP), the Research Domain Criteria (RDoC), systems and network approaches, process-based approaches, as well as a new approach to the classification of personality disorders.

Results and Discussion

We highlight main distinctions between these classification frameworks, largely related to different priorities and goals, and discuss areas of overlap and potential compatibility. Synergies among these systems may provide promising new avenues for research and clinical practice.

Mindfulness Intervention Improves Coping and Perceptions of Children's Behavior among Families with Elevated Risk

Mindfulness-informed interventions (MIIs) are increasingly common but have not been extensively studied among families with elevated levels of risk (e.g., those involved in child protective services and/or receiving financial assistance). These families often experience high rates of stressors that can impact coping strategies, interpersonal dynamics, and relationships. Given that mindfulness has been shown to promote health and wellbeing, this study used a sample from two pilot randomized controlled trials to test the extent to which a mindfulness-informed intervention improved coping strategies and perceptions of children's behavior among 53 families with elevated risk. A principal components analysis with a direct oblimin rotation revealed that cognitive-emotion coping strategies could be characterized by three factors: positive adaptation, negative adaptation, and positive refocusing. Intention-to-treat analysis indicated significant group by time differences, with intervention participants demonstrating improvements in positive refocusing coping, positive adaptation coping, and perceptions of children's behavior problems compared to participants in the waitlist control group. No significant differences were found for negative adaptation coping strategies. Findings provide preliminary support for the benefits of mindfulness training in a sample generally underrepresented in the mindfulness intervention literature.

Leveraging the Adolescent Brain Cognitive Development Study to improve behavioral prediction from neuroimaging in smaller replication samples

Magnetic resonance imaging (MRI) is a popular and useful non-invasive method to map patterns of brain structure and function to complex human traits. Recently published observations in multiple large scale studies cast doubt upon these prospects, particularly for prediction of cognitive traits from structural and resting state functional MRI, which seems to account for little behavioral variability. We leverage baseline data from thousands of children in the Adolescent Brain Cognitive DevelopmentSM (ABCD®) Study to inform the replication sample size required with both univariate and multivariate methods across different imaging modalities to detect reproducible brain-behavior associations. We demonstrate that by applying multivariate methods to high-dimensional brain imaging data, we can capture lower dimensional patterns of structural and functional brain architecture that correlate robustly with cognitive phenotypes and are reproducible with only 41 individuals in the replication sample for working memory-related functional MRI, and ~100 subjects for structural MRI. Even with 100 random re-samplings of 50 subjects in the discovery sample, prediction can be adequately powered with 98 subjects in the replication sample for multivariate prediction of cognition with working memory task functional MRI. These results point to an important role for neuroimaging in translational neurodevelopmental research and showcase how findings in large samples can inform reproducible brain-behavior associations in small sample sizes that are at the heart of many investigators' research programs and grants.

Regional, circuit and network heterogeneity of brain abnormalities in psychiatric disorders

The substantial individual heterogeneity that characterizes people with mental illness is often ignored by classical case-control research, which relies on group mean comparisons. Here we present a comprehensive, multiscale characterization of the heterogeneity of gray matter volume (GMV) differences in 1,294 cases diagnosed with one of six conditions (attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, depression, obsessive-compulsive disorder and schizophrenia) and 1,465 matched controls. Normative models indicated that person-specific deviations from population expectations for regional GMV were highly heterogeneous, affecting the same area in <7% of people with the same diagnosis. However, these deviations were embedded within common functional circuits and networks in up to 56% of cases. The salience-ventral attention system was implicated transdiagnostically, with other systems selectively involved in depression, bipolar disorder, schizophrenia and attention-deficit/hyperactivity disorder. Phenotypic differences between cases assigned the same diagnosis may thus arise from the heterogeneous localization of specific regional deviations, whereas phenotypic similarities may be attributable to the dysfunction of common functional circuits and networks.

Understanding anxiety symptoms as aberrant defensive responding along the threat imminence continuum

Threat-anticipatory defensive responses have evolved to promote survival in a dynamic world. While inherently adaptive, aberrant expression of defensive responses to potential threat could manifest as pathological anxiety, which is prevalent, impairing, and associated with adverse outcomes. Extensive translational neuroscience research indicates that normative defensive responses are organized by threat imminence, such that distinct response patterns are observed in each phase of threat encounter and orchestrated by partially conserved neural circuitry. Anxiety symptoms, such as excessive and pervasive worry, physiological arousal, and avoidance behavior, may reflect aberrant expression of otherwise normative defensive responses, and therefore follow the same imminence-based organization. Here, empirical evidence linking aberrant expression of specific, imminence-dependent defensive responding to distinct anxiety symptoms is reviewed, and plausible contributing neural circuitry is highlighted. Drawing from translational and clinical research, the proposed framework informs our understanding of pathological anxiety by grounding anxiety symptoms in conserved psychobiological mechanisms. Potential implications for research and treatment are discussed.

Modeling vulnerability and intervention targets in the Borderline Personality Disorder system: A network analysis of in silico and in vivo interventions

Modeling psychopathology as a complex dynamic system represents Borderline Personality Disorder (BPD) as a constellation of symptoms (e.g., nodes) that feedback and self-sustain each other shaping a network structure. Through in silico interventions, we simulated the evolution of the BPD system by manipulating: 1) the connectivity strength between nodes (i.e., vulnerability), 2) the external disturbances (i.e., stress) and 3) the predisposition of symptoms to manifest. Similarly, using network analysis we evaluated the effect of an in vivo group psychotherapy to detect the symptoms modified by the intervention. We found that a network with greater connectivity strength between nodes (more vulnerable) showed a higher number of activated symptoms than networks with less strength connectivity. We also found that increases in stress affected more vulnerable networks compared to less vulnerable ones, while decreases in stress revealed a hysteresis effect in the most strongly connected networks. The in silico intervention to symptom alleviation revealed the relevance of nodes related to difficulty in anger regulation, nodes which were also detected as impacted by the in vivo intervention. The complex systems methodology is an alternative to the common cause model with which research has approached the BPD phenomenon.