Development of a computerized adaptive screening tool for overall psychopathology ("p")

The General Psychopathology 'p' Factor in Adolescence: Multi-Informant Assessment and Computerized Adaptive Testing

Accumulating evidence supports the presence of a general psychopathology dimension, the p factor ('p'). Despite growing interest in the p factor, questions remain about how p is assessed. Although multi-informant assessment of psychopathology is commonplace in clinical research and practice with children and adolescents, almost no research has taken a multi-informant approach to studying youth p or has examined the degree of concordance between parent and youth reports. Further, estimating p requires assessment of a large number of symptoms, resulting in high reporter burden that may not be feasible in many clinical and research settings. In the present study, we used bifactor multidimensional item response theory models to estimate parent- and adolescent-reported p in a large community sample of youth (11-17 years) and parents (N = 5,060 dyads). We examined agreement between parent and youth p scores and associations with assessor-rated youth global functioning. We also applied computerized adaptive testing (CAT) simulations to parent and youth reports to determine whether adaptive testing substantially alters agreement on p or associations with youth global functioning. Parent-youth agreement on p was moderate (r =.44) and both reports were negatively associated with youth global functioning. Notably, 7 out of 10 of the highest loading items were common across reporters. CAT reduced the average number of items administered by 57%. Agreement between CAT-derived p scores was similar to the full form (r =.40) and CAT scores were negatively correlated with youth functioning. These novel results highlight the promise and potential clinical utility of a multi-informant p factor approach.

Hoarding behavior and its association with mental health and functioning in a large youth sample

Hoarding behavior is prevalent in children and adolescents, yet clinicians do not routinely inquire about it and youth may not spontaneously report it due to stigma. It is unknown whether hoarding behavior, over and above obsessive-compulsive symptoms (OCS), is associated with major clinical factors in a general youth population. This observational study included N = 7054 youth who were not seeking help for mental health problems (ages 11-21, 54% female) and completed a structured interview that included evaluation of hoarding behavior and OCS, as a part of the Philadelphia Neurodevelopmental Cohort between November 2009 and December 2011. We employed regression models with hoarding behavior and OCS (any/none) as independent variables, and continuous (linear regression) or binary (logistic regression) mental health measures as dependent variables. All models covaried for age, sex, race, and socioeconomic status. A total of 374 participants endorsed HB (5.3%), most of which reported additional OCS (n = 317). When accounting for OCS presence, hoarding behavior was associated with greater dimensional psychopathology burden (i.e., higher P-factor) (β = 0.19, p < .001), and with poorer functioning (i.e., lower score on the child global assessment scale) (β = - 0.07, p < .001). The results were consistent when modeling psychopathology using binary variables. The results remained significant in sensitivity analyses accounting for count of endorsed OCS and excluding participants who met criteria for obsessive-compulsive disorder (n = 210). These results suggest that hoarding behavior among youth is associated with poorer mental health and functioning, independent of OCS. Brief hoarding-behavior assessments in clinical settings may prove useful given hoarding behavior's stigma and detrimental health associations.

Validation of the structured interview section of the penn computerized adaptive test for neurocognitive and clinical psychopathology assessment (CAT GOASSESS)

Large-scale studies and burdened clinical settings require precise, efficient measures that assess multiple domains of psychopathology. Computerized adaptive tests (CATs) can reduce administration time without compromising data quality. We examined feasibility and validity of an adaptive psychopathology measure, GOASSESS, in a clinical community-based sample (N = 315; ages 18-35) comprising three groups: healthy controls, psychosis, mood/anxiety disorders. Assessment duration was compared between the Full and CAT GOASSESS. External validity was tested by comparing how the CAT and Full versions related to demographic variables, study group, and socioeconomic status. The relationships between scale scores and criteria were statistically compared within a mixed-model framework to account for dependency between relationships. Convergent validity was assessed by comparing scores of the CAT and the Full GOASSESS using Pearson correlations. The CAT GOASSESS reduced interview duration by more than 90 % across study groups and preserved relationships to external criteria and demographic variables as the Full GOASSESS. All CAT GOASSESS scales could replace those of the Full instrument. Overall, the CAT GOASSESS showed acceptable psychometric properties and demonstrated feasibility by markedly reducing assessment time compared to the Full GOASSESS. The adaptive version could be used in large-scale studies or clinical settings for intake screening.

A Developmentally Informed Systematic Review and Meta-Analysis of the Strength of General Psychopathology in Childhood and Adolescence

Considerable support exists for higher-order dimensional conceptualizations of psychopathology in adults. A growing body of work has focused on understanding the structure of general and specific psychopathology in children and adolescents. No prior meta-analysis has examined whether the strength of the general psychopathology factor (p factor)-measured by explained common variance (ECV)-changes from childhood to adolescence. The primary objective of this multilevel meta-analysis was to determine whether general psychopathology strength changes across development (i.e. across ages) in childhood and adolescence. Several databases were searched in November 2021; 65 studies, with 110 effect sizes (ECV), nested within shared data sources, were identified. Included empirical studies used a factor analytic modeling approach that estimated latent factors for child/adolescent internalizing, externalizing, and optionally thought-disordered psychopathology, and a general factor. Studies spanned ages 2-17 years. Across ages, general psychopathology explained over half (~ 56%) of the reliable variance in symptoms of psychopathology. Age-moderation analyses revealed that general factor strength remained stable across ages, suggesting that general psychopathology strength does not significantly change across childhood to adolescence. Even if the structure of psychopathology changes with development, the prominence of general psychopathology across development has important implications for future research and intervention.

Development of white matter fiber covariance networks supports executive function in youth

During adolescence, the brain undergoes extensive changes in white matter structure that support cognition. Data-driven approaches applied to cortical surface properties have led the field to understand brain development as a spatially and temporally coordinated mechanism that follows hierarchically organized gradients of change. Although white matter development also appears asynchronous, previous studies have relied largely on anatomical tract-based atlases, precluding a direct assessment of how white matter structure is spatially and temporally coordinated. Harnessing advances in diffusion modeling and machine learning, we identified 14 data-driven patterns of covarying white matter structure in a large sample of youth. Fiber covariance networks aligned with known major tracts, while also capturing distinct patterns of spatial covariance across distributed white matter locations. Most networks showed age-related increases in fiber network properties, which were also related to developmental changes in executive function. This study delineates data-driven patterns of white matter development that support cognition.

Multivariate analytical approaches for investigating brain-behavior relationships

Background

Many studies of brain-behavior relationships rely on univariate approaches where each variable of interest is tested independently, which does not allow for the simultaneous investigation of multiple correlated variables. Alternatively, multivariate approaches allow for examining relationships between psychopathology and neural substrates simultaneously. There are multiple multivariate methods to choose from that each have assumptions which can affect the results; however, many studies employ one method without a clear justification for its selection. Additionally, there are few studies illustrating how differences between methods manifest in examining brain-behavior relationships. The purpose of this study was to exemplify how the choice of multivariate approach can change brain-behavior interpretations.

Method

We used data from 9,027 9- to 10-year-old children from the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®) to examine brain-behavior relationships with three commonly used multivariate approaches: canonical correlation analysis (CCA), partial least squares correlation (PLSC), and partial least squares regression (PLSR). We examined the associations between psychopathology dimensions including general psychopathology, attention-deficit/hyperactivity symptoms, conduct problems, and internalizing symptoms with regional brain volumes.

Results

The results of CCA, PLSC, and PLSR showed both consistencies and differences in the relationship between psychopathology symptoms and brain structure. The leading significant component yielded by each method demonstrated similar patterns of associations between regional brain volumes and psychopathology symptoms. However, the additional significant components yielded by each method demonstrated differential brain-behavior patterns that were not consistent across methods.

Conclusion

Here we show that CCA, PLSC, and PLSR yield slightly different interpretations regarding the relationship between child psychopathology and brain volume. In demonstrating the divergence between these approaches, we exemplify the importance of carefully considering the method's underlying assumptions when choosing a multivariate approach to delineate brain-behavior relationships.

A transdiagnostic network for psychiatric illness derived from atrophy and lesions

Psychiatric disorders share neurobiology and frequently co-occur. This neurobiological and clinical overlap highlights opportunities for transdiagnostic treatments. In this study, we used coordinate and lesion network mapping to test for a shared brain network across psychiatric disorders. In our meta-analysis of 193 studies, atrophy coordinates across six psychiatric disorders mapped to a common brain network defined by positive connectivity to anterior cingulate and insula, and by negative connectivity to posterior parietal and lateral occipital cortex. This network was robust to leave-one-diagnosis-out cross-validation and specific to atrophy coordinates from psychiatric versus neurodegenerative disorders (72 studies). In 194 patients with penetrating head trauma, lesion damage to this network correlated with the number of post-lesion psychiatric diagnoses. Neurosurgical ablation targets for psychiatric illness (four targets) also aligned with the network. This convergent brain network for psychiatric illness may partially explain high rates of psychiatric comorbidity and could highlight neuromodulation targets for patients with more than one psychiatric disorder.

Construction of a computerized adaptive test (CAT-CCNB) for efficient neurocognitive and clinical psychopathology assessment

BACKGROUND

Traditional paper-and-pencil neurocognitive evaluations and semi-structured mental health interviews can take hours to administer and score. Computerized assessment has decreased that burden substantially, and contemporary psychometric tools such as item response theory and computerized adaptive testing (CAT) allow even further abbreviation.

NEW METHOD

The goal of this paper was to describe the application of CAT and related methods to the Penn Computerized Neurocognitive Battery (CNB) and a well-validated clinical assessment in order to increase efficiency in assessment and relevant domain coverage. To calibrate item banks for CAT, N = 5053 participants (63% female; mean age 45 years, range 18-80) were collected from across the United States via crowdsourcing, providing item parameters that were then linked to larger item banks and used in individual test construction. Tests not amenable to CAT were abbreviated using complementary short-form methods.

RESULTS

The final "CAT-CCNB" battery comprised 21 cognitive tests (compared to 14 in the original) and five adaptive clinical scales (compared to 16 in the original).

COMPARISON WITH EXISTING METHODS

This new battery, derived with contemporary psychometric approaches, provides further improvements over existing assessments that use collections of fixed-length tests developed for stand-alone administration. The CAT-CCNB provides an improved version of the CNB that shows promise as a maximally efficient tool for neuropsychiatric assessment.

CONCLUSIONS

We anticipate CAT-CCNB will help satisfy the clear need for broad yet efficient measurement of cognitive and clinical domains, facilitating implementation of large-scale, "big science" approaches to data collection, and potential widespread clinical implementation.

Development of empirically derived brief program evaluation measures in Pennsylvania first-episode psychosis coordinated specialty care programs

AIM

The Pennsylvania first episode psychosis program evaluation (PA-FEP-PE) core assessment battery was developed as a standard and comprehensive clinical assessment and data collection tool in Pennsylvania coordinated specialty care programs (CSC). To reduce administrative time and maximize clinical utility by maintaining acceptable levels of precision, we aimed to generate a short form using item response theory (IRT)-based computer-adaptive test (CAT) simulation and analyse the implementation and acceptability of the short form among providers from PA-CSC.

METHODS

FEP participants (n = 759; age 14-36) from nine coordinated specialty care programs completed 156 items drawn from the PA-FEP-PE battery. Multidimensional IRT-based CAT simulations were used to select the best PA-FEP-PE items for abbreviated forms.

RESULTS

A 67-item PA-FEP-PE short form was developed to capture six factors: (1) positive affect and surgency (with negative loadings on Anxious-Misery items); (2) psychiatric services satisfaction; (3) antipsychotic side effect severity; (4) family turmoil and associated traumas; (5) trauma load; and (6) psychosis. The total number of items was reduced more than 50% in the PA-FEP-PE shortened forms. The short form demonstrated good psychometric properties, and it was well accepted by our providers in the implementation.

CONCLUSIONS

The empirical derivation and implementation of abbreviated measures of key domains and constructs in FEP care have streamlined and facilitated PA-FEP program evaluation. Our work supports potential application of IRT based methods to empirically reduce core assessment battery measures in large-scale data collection efforts such as in the Early Psychosis Intervention Network.

Linking Individual Differences in Personalized Functional Network Topography to Psychopathology in Youth

BACKGROUND

The spatial layout of large-scale functional brain networks differs between individuals and is particularly variable in the association cortex, implicated in a broad range of psychiatric disorders. However, it remains unknown whether this variation in functional topography is related to major dimensions of psychopathology in youth.

METHODS

The authors studied 790 youths ages 8 to 23 years who had 27 minutes of high-quality functional magnetic resonance imaging data as part of the Philadelphia Neurodevelopmental Cohort. Four correlated dimensions were estimated using a confirmatory correlated traits factor analysis on 112 item-level clinical symptoms, and one overall psychopathology factor with 4 orthogonal dimensions were extracted using a confirmatory factor analysis. Spatially regularized nonnegative matrix factorization was used to identify 17 individual-specific functional networks for each participant. Partial least square regression with split-half cross-validation was conducted to evaluate to what extent the topography of personalized functional networks encodes major dimensions of psychopathology.

RESULTS

Personalized functional network topography significantly predicted unseen individuals' major dimensions of psychopathology, including fear, psychosis, externalizing, and anxious-misery. Reduced representation of association networks was among the most important features for the prediction of all 4 dimensions. Further analysis revealed that personalized functional network topography predicted overall psychopathology (r = 0.16, permutation testing p < .001), which drove prediction of the 4 correlated dimensions.

CONCLUSIONS

These results suggest that individual differences in functional network topography in association networks is related to overall psychopathology in youth. Such results underscore the importance of considering functional neuroanatomy for personalized diagnostics and therapeutics in psychiatry.

Connectome-wide Functional Connectivity Abnormalities in Youth With Obsessive-Compulsive Symptoms

BACKGROUND

Obsessive-compulsive symptomatology (OCS) is common in adolescence but usually does not meet the diagnostic threshold for obsessive-compulsive disorder. Nevertheless, both obsessive-compulsive disorder and subthreshold OCS are associated with increased likelihood of experiencing other serious psychiatric conditions, including depression and suicidal ideation. Unfortunately, there is limited information on the neurobiology of OCS.

METHODS

Here, we undertook one of the first brain imaging studies of OCS in a large adolescent sample (analyzed n = 832) from the Philadelphia Neurodevelopmental Cohort. We investigated resting-state functional magnetic resonance imaging functional connectivity using complementary analytic approaches that focus on different neuroanatomical scales, from known functional systems to connectome-wide tests.

RESULTS

We found a robust pattern of connectome-wide, OCS-related differences, as well as evidence of specific abnormalities involving known functional systems, including dorsal and ventral attention, frontoparietal, and default mode systems. Analysis of cerebral perfusion imaging and high-resolution structural imaging did not show OCS-related differences, consistent with domain specificity to functional connectivity.

CONCLUSIONS

The brain connectomic associations with OCS reported here, together with early studies of its clinical relevance, support the potential for OCS as an early marker of psychiatric risk that may enhance our understanding of mechanisms underlying the onset of adolescent psychopathology.

Development of an Adaptive Computer-Aided Soft Sensor Diagnosis System for Assessment of Executive Functions

The main objective of the present study is to highlight the role of technological (soft sensor) methodologies in the assessment of the neurocognitive dysfunctions specific to neurodevelopmental disorders (for example, autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), and specific learning disorder). In many cases neurocognitive dysfunctions can be detected in neurodevelopmental disorders, some of them having a well-defined syndrome-specific clinical pattern. A number of evidence-based neuropsychological batteries are available for identifying these domain-specific functions. Atypical patterns of cognitive functions such as executive functions are present in almost all developmental disorders. In this paper, we present a novel adaptation of the Tower of London Test, a widely used neuropsychological test for assessing executive functions (in particular planning and problem-solving). Our version, the Tower of London Adaptive Test, is based on computer adaptive test theory (CAT). Adaptive testing using novel algorithms and parameterized task banks allows the immediate evaluation of the participant's response which in turn determines the next task's difficulty level. In this manner, the subsequent item is adjusted to the participant's estimated capability. The adaptive procedure enhances the original test's diagnostic power and sensitivity. By measuring the targeted cognitive capacity and its limitations more precisely, it leads to more accurate diagnoses. In some developmental disorders (e.g., ADHD, ASD) it could be very useful in improving the diagnosis, planning the right interventions, and choosing the most suitable assistive digital technological service.

Copy Number Variant Risk Scores Associated With Cognition, Psychopathology, and Brain Structure in Youths in the Philadelphia Neurodevelopmental Cohort

Importance

Psychiatric and cognitive phenotypes have been associated with a range of specific, rare copy number variants (CNVs). Moreover, IQ is strongly associated with CNV risk scores that model the predicted risk of CNVs across the genome. But the utility of CNV risk scores for psychiatric phenotypes has been sparsely examined.

Objective

To determine how CNV risk scores, common genetic variation indexed by polygenic scores (PGSs), and environmental factors combine to associate with cognition and psychopathology in a community sample.

Design, Setting, and Participants

The Philadelphia Neurodevelopmental Cohort is a community-based study examining genetics, psychopathology, neurocognition, and neuroimaging. Participants were recruited through the Children's Hospital of Philadelphia pediatric network. Participants with stable health and fluency in English underwent genotypic and phenotypic characterization from November 5, 2009, through December 30, 2011. Data were analyzed from January 1 through July 30, 2021.

Exposures

The study examined (1) CNV risk scores derived from models of burden, predicted intolerance, and gene dosage sensitivity; (2) PGSs from genomewide association studies related to developmental outcomes; and (3) environmental factors, including trauma exposure and neighborhood socioeconomic status.

Main Outcomes and Measures

The study examined (1) neurocognition, with the Penn Computerized Neurocognitive Battery; (2) psychopathology, with structured interviews based on the Schedule for Affective Disorders and Schizophrenia for School-Age Children; and (3) brain volume, with magnetic resonance imaging.

Results

Participants included 9498 youths aged 8 to 21 years; 4906 (51.7%) were female, and the mean (SD) age was 14.2 (3.7) years. After quality control, 18 185 total CNVs greater than 50 kilobases (10 517 deletions and 7668 duplications) were identified in 7101 unrelated participants genotyped on Illumina arrays. In these participants, elevated CNV risk scores were associated with lower overall accuracy on cognitive tests (standardized β = 0.12; 95% CI, 0.10-0.14; P = 7.41 × 10-26); lower accuracy across a range of cognitive subdomains; increased overall psychopathology; increased psychosis-spectrum symptoms; and higher deviation from a normative developmental model of brain volume. Statistical models of developmental outcomes were significantly improved when CNV risk scores were combined with PGSs and environmental factors.

Conclusions and Relevance

In this study, elevated CNV risk scores were associated with lower cognitive ability, higher psychopathology including psychosis-spectrum symptoms, and greater deviations from normative magnetic resonance imaging models of brain development. Together, these results represent a step toward synthesizing rare genetic, common genetic, and environmental factors to understand clinically relevant outcomes in youth.

Issues in Estimating Interpretable Lower Order Factors in Second-Order Hierarchical Models: Commentary on Clark et al. (2021)

Clark and colleagues asserted that lower-order factors in second-order models are comparable to specific factors in bifactor models when residualized on the general factor (Clark et al., 2021). Modeling simulated data demonstrated that residualized lower-order factors are correlated with bifactor specific factors only to the extent that factor loadings are proportional. Modeling actual data with violations of proportionality showed that specific and residualized lower-order factors are not always highly correlated and have differential correlations with criterion variables, even when both models fit acceptably. Because proportionality constraints limit only second-order models, bifactor models should be the first option for hierarchical modeling.

Risk and Resilience Measures Related to Psychopathology in Youth

Childhood adversity places youth at risk for multiple negative outcomes. The current study aimed to understand how a constellation of risk and resilience factors influenced mental health outcomes as a function of adversities: socioeconomic status (SES) and traumatic stressful events (TSEs). Specifically, we examined outcomes related to psychosis and mood disorders, as well as global clinical functioning. The current study is a longitudinal follow up of 140 participants from the Philadelphia Neurodevelopmental Cohort (PNC) assessed for adversities at Time 1 (Mean age: 14.11 years) and risk, resilience, and clinical outcomes at Time 2 (mean age: 21.54 years). In the context of TSE, a limited set of predictors emerged as important; a more diverse set of moderators emerged in the context of SES. Across adversities, social support was a unique predictor of psychosis spectrum diagnoses and global functioning; emotion dysregulation was an important predictor for mood diagnoses. The current findings underscore the importance of understanding effects of childhood adversity on maladaptive outcomes within a resilience framework.

Modeling environment through a general exposome factor in two independent adolescent cohorts

Exposures to perinatal, familial, social, and physical environmental stimuli can have substantial effects on human development. We aimed to generate a single measure that capture's the complex network structure of the environment (ie, exposome) using multi-level data (participant's report, parent report, and geocoded measures) of environmental exposures (primarily from the psychosocial environment) in two independent adolescent cohorts: The Adolescent Brain Cognitive Development Study (ABCD Study, N = 11 235; mean age, 10.9 years; 47.7% females) and an age- and sex-matched sample from the Philadelphia Neurodevelopmental Cohort (PNC, N = 4993). We conducted a series of data-driven iterative factor analyses and bifactor modeling in the ABCD Study, reducing dimensionality from 348 variables tapping to environment to six orthogonal exposome subfactors and a general (adverse) exposome factor. The general exposome factor was associated with overall psychopathology (B = 0.28, 95% CI, 0.26-0.3) and key health-related outcomes: obesity (odds ratio [OR] , 1.4; 95% CI, 1.3-1.5) and advanced pubertal development (OR, 1.3; 95% CI, 1.2-1.5). A similar approach in PNC reduced dimensionality of environment from 29 variables to 4 exposome subfactors and a general exposome factor. PNC analyses yielded consistent associations of the general exposome factor with psychopathology (B = 0.15; 95% CI, 0.13-0.17), obesity (OR, 1.4; 95% CI, 1.3-1.6), and advanced pubertal development (OR, 1.3; 95% CI, 1-1.6). In both cohorts, inclusion of exposome factors greatly increased variance explained in overall psychopathology compared with models relying solely on demographics and parental education (from <4% to >38% in ABCD; from <4% to >18.5% in PNC). Findings suggest that a general exposome factor capturing multi-level environmental exposures can be derived and can consistently explain variance in youth's mental and general health.

Network Controllability in Transmodal Cortex Predicts Positive Psychosis Spectrum Symptoms

BACKGROUND

The psychosis spectrum (PS) is associated with structural dysconnectivity concentrated in transmodal cortex. However, understanding of this pathophysiology has been limited by an overreliance on examining direct interregional connectivity. Using network control theory, we measured variation in both direct and indirect connectivity to a region to gain new insights into the pathophysiology of the PS.

METHODS

We used psychosis symptom data and structural connectivity in 1068 individuals from the Philadelphia Neurodevelopmental Cohort. Applying a network control theory metric called average controllability, we estimated each brain region's capacity to leverage its direct and indirect structural connections to control linear brain dynamics. Using nonlinear regression, we determined the accuracy with which average controllability could predict PS symptoms in out-of-sample testing. We also examined the predictive performance of regional strength, which indexes only direct connections to a region, as well as several graph-theoretic measures of centrality that index indirect connectivity. Finally, we assessed how the prediction performance for PS symptoms varied over the functional hierarchy spanning unimodal to transmodal cortex.

RESULTS

Average controllability outperformed all other connectivity features at predicting positive PS symptoms and was the only feature to yield above-chance predictive performance. Improved prediction for average controllability was concentrated in transmodal cortex, whereas prediction performance for strength was uniform across the cortex, suggesting that indexing indirect connections through average controllability is crucial in association cortex.

CONCLUSIONS

Examining interindividual variation in direct and indirect structural connections to transmodal cortex is crucial for accurate prediction of positive PS symptoms.

Multimorbidity networks of mental disorder symptom domains across psychopathology severity levels in community youth

Our aim was to scrutinize multimorbidity in a community sample of youths (Philadelphia Neurodevelopmental Cohort) in form of co-occurrences of DSM-IV disorder symptom domains, elucidating if and when specific symptom domain interrelations emerge as mental disorder severity levels increase. We estimated four multimorbidity networks based on four severity cut-offs ('at least symptomatic', 'at least subthreshold', 'at least threshold', and 'impaired') and compared them pairwise on two measures: global network strength and network structure. We further computed community clusters for each network to detect symptom domain interrelations. Pairwise comparisons of the multimorbidity networks based on data from 9410 probands showed significant differences in global strength of the networks with the two highest severity cut-offs ('impaired' and 'at least threshold') with the at least symptomatic networks (p < .05). The networks with the three highest severity cut-offs ('impaired', 'at least threshold', and 'at least subthreshold') differed significantly (p < .001) from the at least symptomatic network regarding global network structure but did not significantly differ from each other (p > .05). We identified four common clusters in the impaired, at least threshold, and at least subthreshold networks consisting of i) domains associated with behavioral disorders; ii) domains associated with anxiety disorders (agoraphobia, social anxiety and specific phobia); iii) domains associated with anxiety/mood/eating and; iv) domains associated with mood/eating disorders. We found that major mental disorder symptom domain interrelations become consistent from a subthreshold level onwards. Findings suggest that specific multimorbidity patterns emerge as psychopathology severity levels increase.

Association of gray matter volumes with general and specific dimensions of psychopathology in children

Childhood is an important time for the manifestation of psychopathology. Psychopathology is characterized by considerable comorbidity which is mirrored in the underlying neural correlates of psychopathology. Both common and dissociable variations in brain volume have been found across multiple mental disorders in adult and youth samples. However, the majority of these studies used samples with broad age ranges which may obscure developmental differences. The current study examines associations between regional gray matter volumes (GMV) and psychopathology in a large sample of children with a narrowly defined age range. We used data from 9607 children 9-10 years of age collected as part of the Adolescent Brain Cognitive Development^SM Study (ABCD Study^®). A bifactor model identified a general psychopathology factor that reflects common variance across disorders and specific factors representing internalizing symptoms, ADHD symptoms, and conduct problems. Brain volume was acquired using 3T MRI. After correction for multiple testing, structural equation modeling revealed nearly global inverse associations between regional GMVs and general psychopathology and conduct problems, with associations also found for ADHD symptoms (p_fdr-values ≤ 0.048). Age, sex, and race were included as covariates. Sensitivity analyses including total GMV or intracranial volume (ICV) as covariates support this global association, as a large majority of region-specific results became nonsignificant. Sensitivity analyses including income, parental education, and medication use as additional covariates demonstrate largely convergent results. These findings suggest that globally smaller GMVs are a nonspecific risk factor for general psychopathology, and possibly for conduct problems and ADHD as well.

Transdiagnostic dimensions of psychopathology explain individuals' unique deviations from normative neurodevelopment in brain structure

Psychopathology is rooted in neurodevelopment. However, clinical and biological heterogeneity, together with a focus on case-control approaches, have made it difficult to link dimensions of psychopathology to abnormalities of neurodevelopment. Here, using the Philadelphia Neurodevelopmental Cohort, we built normative models of cortical volume and tested whether deviations from these models better predicted psychiatric symptoms compared to raw cortical volume. Specifically, drawing on the p-factor hypothesis, we distilled 117 clinical symptom measures into six orthogonal psychopathology dimensions: overall psychopathology, anxious-misery, externalizing disorders, fear, positive psychosis symptoms, and negative psychosis symptoms. We found that multivariate patterns of deviations yielded improved out-of-sample prediction of psychopathology dimensions compared to multivariate patterns of raw cortical volume. We also found that correlations between overall psychopathology and deviations in ventromedial prefrontal, inferior temporal, and dorsal anterior cingulate cortices were stronger than those observed for specific dimensions of psychopathology (e.g., anxious-misery). Notably, these same regions are consistently implicated in a range of putatively distinct disorders. Finally, we performed conventional case-control comparisons of deviations in a group of individuals with depression and a group with attention-deficit hyperactivity disorder (ADHD). We observed spatially overlapping effects between these groups that diminished when controlling for overall psychopathology. Together, our results suggest that modeling cortical brain features as deviations from normative neurodevelopment improves prediction of psychiatric symptoms in out-of-sample testing, and that p-factor models of psychopathology may assist in separating biomarkers that are disorder-general from those that are disorder-specific.

Genetic influences on externalizing psychopathology overlap with cognitive functioning and show developmental variation

BACKGROUND

Questions remain regarding whether genetic influences on early life psychopathology overlap with cognition and show developmental variation.

METHODS

Using data from 9,421 individuals aged 8-21 from the Philadelphia Neurodevelopmental Cohort, factors of psychopathology were generated using a bifactor model of item-level data from a psychiatric interview. Five orthogonal factors were generated: anxious-misery (mood and anxiety), externalizing (attention deficit hyperactivity and conduct disorder), fear (phobias), psychosis-spectrum, and a general factor. Genetic analyses were conducted on a subsample of 4,662 individuals of European American ancestry. A genetic relatedness matrix was used to estimate heritability of these factors, and genetic correlations with executive function, episodic memory, complex reasoning, social cognition, motor speed, and general cognitive ability. Gene × Age analyses determined whether genetic influences on these factors show developmental variation.

RESULTS

Externalizing was heritable (h2 = 0.46, p = 1 × 10-6), but not anxious-misery (h2 = 0.09, p = 0.183), fear (h2 = 0.04, p = 0.337), psychosis-spectrum (h2 = 0.00, p = 0.494), or general psychopathology (h2 = 0.21, p = 0.040). Externalizing showed genetic overlap with face memory (ρg = -0.412, p = 0.004), verbal reasoning (ρg = -0.485, p = 0.001), spatial reasoning (ρg = -0.426, p = 0.010), motor speed (ρg = 0.659, p = 1x10-4), verbal knowledge (ρg = -0.314, p = 0.002), and general cognitive ability (g)(ρg = -0.394, p = 0.002). Gene × Age analyses revealed decreasing genetic variance (γg = -0.146, p = 0.004) and increasing environmental variance (γe = 0.059, p = 0.009) on externalizing.

CONCLUSIONS

Cognitive impairment may be a useful endophenotype of externalizing psychopathology and, therefore, help elucidate its pathophysiological underpinnings. Decreasing genetic variance suggests that gene discovery efforts may be more fruitful in children than adolescents or young adults.

Deconstructing the role of the exposome in youth suicidal ideation: Trauma, neighborhood environment, developmental and gender effects

Environment (E) is pivotal in explaining variability in brain and behavior development, including suicidal ideation (SI) and behavior. It is therefore critical to systematically study relationships among environmental exposures (i.e., exposome) and suicidal phenotypes. Here, we evaluated the role of individual-level adversity and neighborhood environment and their interaction (E x E) in association with youth SI. Sample included youth (N = 7,054, ages 11–21) from the Philadelphia Neurodevelopmental Cohort, which investigated clinical phenotypes in a diverse US community population. We examined cross-sectional associations of environmental exposures with lifetime history of SI (n = 671), focusing on interactions between individual-level exposures to assaultive trauma (n = 917) and neighborhood-level socioeconomic status (SES) quantified using geocoded Census data. Models included potential confounds and overall psychopathology. Results showed that assaultive trauma was strongly associated with SI (OR = 3.3, 95%CI 2.7–4, p < .001), while low SES was not (p = .395). Both assault and low SES showed stronger association with SI in females, and in early adolescence (all E X gender/age interactions, p < .05). In traumatized youths, lower SES was associated with less SI, with no SES effects on SI in non-traumatized youths (Assault X SES interaction, Wald = 8.19, p = .004). Associations remained significant controlling for overall psychopathology. No single SES variable emerged above others to explain the moderating effect of SES. These findings may suggest a stress inoculation effect in low SES, where youths from higher SES are more impacted by the deleterious trauma-SI association. Determining which environmental factors contribute to resilience may inform population specific suicide prevention interventions. The cross-sectional study design limits causal inferences.

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Environment (E) is key in shaping development of suicidal ideation (SI).

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We integrated individual-level trauma exposure and neighborhood-level data on socioeconomic status (SES) in N=7,054 youths.

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Trauma was robustly associated with youth SI in our cohort, while SES had no association with SI.

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Only in youth with history of assaultive trauma, low SES was associated with lower SI rates (trauma by SES interaction).

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Results suggest a stress inoculation effect that was shown in animal models, but has not been shown in human suicide research.

Hierarchical models of psychopathology: empirical support, implications, and remaining issues

There is an ongoing revolution in psychology and psychiatry that will likely change how we conceptualize, study and treat psychological problems.- Many theorists now support viewing psychopathology as consisting of continuous dimensions rather than discrete diagnostic categories. Indeed, recent papers have proposed comprehensive taxonomies of psychopathology dimensions to replace the DSM and ICD taxonomies of categories. The proposed dimensional taxonomies, which portray psychopathology as hierarchically organized correlated dimensions, are now well supported at phenotypic levels. Multiple studies show that both a general factor of psychopathology at the top of the hierarchy and specific factors at lower levels predict different functional outcomes. Our analyses of data on a large representative sample of child and adolescent twins suggested the causal hypothesis that phenotypic correlations among dimensions of psychopathology are the result of many familial influences being pleiotropic. That is, most genetic variants and shared environmental factors are hypothesized to non-specifically influence risk for multiple rather than individual dimensions of psychopathology. In contrast, person-specific experiences tend to be related to individual dimensions. This hierarchical causal hypothesis has been supported by both large-scale family and molecular genetic studies. Current research focuses on three issues. First, the field has not settled on a preferred statistical model for studying the hierarchy of causes and phenotypes. Second, in spite of encouraging progress, the neurobiological correlates of the hierarchy of dimensions of psychopathology are only partially described. Third, although there are potentially important clinical implications of the hierarchical model, insufficient research has been conducted to date to rec-ommend evidence-based clinical practices.

Association between family history of suicide attempt and neurocognitive functioning in community youth

BACKGROUND

Suicidal behavior is highly familial. Neurocognitive deficits have been proposed as an endophenotype for suicide risk that may contribute to the familial transmission of suicide. Yet, there is a lack of research on the neurocognitive functioning of first-degree biological relatives of suicide attempters. The aim of the present study is to conduct the largest investigation to date of neurocognitive functioning in community youth with a family history of a fatal or nonfatal suicide attempt (FH).

METHODS

Participants aged 8-21 years from the Philadelphia Neurodevelopmental Cohort completed detailed clinical and neurocognitive evaluations. A subsample of 501 participants with a FH was matched to a comparison group of 3,006 participants without a family history of suicide attempt (no-FH) on age, sex, race, and lifetime depression.

RESULTS

After adjusting for multiple comparisons and including relevant clinical and demographic covariates, youth with a FH had significantly lower executive function factor scores (F[1,3432] = 6.63, p = .010) and performed worse on individual tests of attention (F[1,3382] = 7.08, p = .008) and language reasoning (F[1,3387] = 5.12, p = .024) than no-FH youth.

CONCLUSIONS

Youth with a FH show small differences in executive function, attention, and language reasoning compared to youth without a FH. Further research is warranted to investigate neurocognitive functioning as an endophenotype for suicide risk. Implications for the prevention and treatment of suicidal behaviors are discussed.

When CAT is not an option: complementary methods of test abbreviation for neurocognitive batteries

INTRODUCTION

There is an obvious need for efficient measurement of neuropsychiatric phenomena. A proven method-computerized adaptive testing (CAT)-is not feasible for all tests, necessitating alternatives for increasing test efficiency.

METHODS

We combined/compared two methods for abbreviating rapid tests using two tests unamenable to CAT (a Continuous Performance Test [CPT] and n-back test [NBACK]). N=9,498 (mean age 14.2 years; 52% female) were administered the tests, and abbreviation was accomplished using methods answering two questions: what happens to measurement error as items are removed, and what happens to correlations with validity criteria as items are removed. The first was investigated using quasi-CAT simulation, while the second was investigated using bootstrapped confidence intervals around full-form-short-form comparisons.

RESULTS

Results for the two methods overlapped, suggesting that the CPT could be abbreviated to 57% of original and NBACK could be abbreviated to 87% of original with the max-acceptable loss of precision and min-acceptable relationships with validity criteria.

CONCLUSIONS

This method combination shows promise for use in other test types, and the divergent results for the CPT/NBACK demonstrate the methods' abilities to detect when a test should not be shortened. The methods should be used in combination because they emphasize complementary measurement qualities: precision/validity..

Towards precise resting-state fMRI biomarkers in psychiatry: synthesizing developments in transdiagnostic research, dimensional models of psychopathology, and normative neurodevelopment

Searching for biomarkers has been a chief pursuit of the field of psychiatry. Toward this end, studies have catalogued candidate resting-state biomarkers in nearly all forms of mental disorder. However, it is becoming increasingly clear that these biomarkers lack specificity, limiting their capacity to yield clinical impact. We discuss three avenues of research that are overcoming this limitation: (i) the adoption of transdiagnostic research designs, which involve studying and explicitly comparing multiple disorders from distinct diagnostic axes of psychiatry; (ii) dimensional models of psychopathology that map the full spectrum of symptomatology and that cut across traditional disorder boundaries; and (iii) modeling individuals' unique functional connectomes throughout development. We provide a framework for tying these subfields together that draws on tools from machine learning and network science.

Approaches to Defining Common and Dissociable Neurobiological Deficits Associated With Psychopathology in Youth

Psychiatric disorders show high rates of comorbidity and nonspecificity of presenting clinical symptoms, while demonstrating substantial heterogeneity within diagnostic categories. Notably, many of these psychiatric disorders first manifest in youth. We review progress and next steps in efforts to parse heterogeneity in psychiatric symptoms in youths by identifying abnormalities within neural circuits. To address this fundamental challenge in psychiatry, a number of methods have been proposed. We provide an overview of these methods, broadly organized into dimensional versus categorical approaches and single-view versus multiview approaches. Dimensional approaches including factor analysis and canonical correlation analysis aim to capture dimensional associations between psychopathology and brain measures across a continuous spectrum from health to disease. In contrast, categorical approaches, such as clustering and community detection, aim to identify subtypes of individuals within a class of symptoms or brain features. We highlight several studies that apply these methods to samples of youths and discuss issues to consider when using these approaches. Finally, we end by highlighting avenues for future research.

Methods and Challenges for Assessing Heterogeneity

The widely acknowledged homogeneity assumption limits progress in refining clinical diagnosis, understanding mechanisms, and developing new treatments for mental health disorders. This homogeneity assumption drives both a comorbidity and a heterogeneity problem, where two different approaches tackle the problems. One, a unifying approach, tackles the comorbidity problem by assuming that a single general psychopathology factor underlies multiple disorders. Another, a multifactorial approach, tackles the heterogeneity problem by assuming that disorders comprise multiple subtypes driven by multiple discrete factors. We show how each of these approaches can make useful contributions to mental health-related research and clinical practice. For example, the unifying approach can develop a rapid assessment tool that may be clinically valuable for triaging cases. The multifactorial approach can reveal subtypes that are differentially responsive to treatments and highlight distinct mechanisms leading to similar phenotypes. Because both approaches tackle different problems, both have different limitations. We describe the statistical frameworks that incorporate and adjudicate between both approaches (e.g., the bifactor model, normative modeling, and the functional random forest). Such frameworks can identify whether sets of disorders are more affected by heterogeneity or comorbidity. Therefore, future studies that incorporate such frameworks can provide further insight into the nature of psychopathology.

Distinct and shared contributions of diagnosis and symptom domains to cognitive performance in severe mental illness in the Paisa population: a case-control study

BACKGROUND

Severe mental illness diagnoses have overlapping symptomatology and shared genetic risk, motivating cross-diagnostic investigations of disease-relevant quantitative measures. We analysed relationships between neurocognitive performance, symptom domains, and diagnoses in a large sample of people with severe mental illness not ascertained for a specific diagnosis (cases), and people without mental illness (controls) from a single, homogeneous population.

METHODS

In this case-control study, cases with severe mental illness were ascertained through electronic medical records at Clínica San Juan de Dios de Manizales (Manizales, Caldas, Colombia) and the Hospital Universitario San Vicente Fundación (Medellín, Antioquía, Colombia). Participants were assessed for speed and accuracy using the Penn Computerized Neurocognitive Battery (CNB). Cases had structured interview-based diagnoses of schizophrenia, bipolar 1, bipolar 2, or major depressive disorder. Linear mixed models, using CNB tests as repeated measures, modelled neurocognition as a function of diagnosis, sex, and all interactions. Follow-up analyses in cases included symptom factor scores obtained from exploratory factor analysis of symptom data as main effects.

FINDINGS

Between Oct 1, 2017, and Nov 1, 2019, 2406 participants (1689 cases [schizophrenia n=160; bipolar 1 disorder n=519; bipolar 2 disorder n=204; and major depressive disorder n=806] and 717 controls; mean age 39 years (SD 14); and 1533 female) were assessed. Participants with bipolar 1 disorder and schizophrenia had similar impairments in accuracy and speed across cognitive domains. Participants with bipolar 2 disorder and major depressive disorder performed similarly to controls, with subtle deficits in executive and social cognition. A three-factor model (psychosis, mania, and depression) best represented symptom data. Controlling for diagnosis, premorbid IQ, and disease severity, high lifetime psychosis scores were associated with reduced accuracy and speed across cognitive domains, whereas high depression scores were associated with increased social cognition accuracy.

INTERPRETATION

Cross-diagnostic investigations showed that neurocognitive function in severe mental illness is characterised by two distinct profiles (bipolar 1 disorder and schizophrenia, and bipolar 2 disorder and major depressive disorder), and is associated with specific symptom domains. These results suggest the utility of this design for elucidating severe mental illness causes and trajectories.

FUNDING

US National Institute of Mental Health.

Effects of Skip-Logic on the Validity of Dimensional Clinical Scores: A Simulation Study

Structured assessment of clinical phenotypes is a burdensome procedure, largely due to the time required. One method to alleviate this is "skip-logic," which allows for portions of an interview to be skipped if initial ("screen") items are not endorsed. The bias that skip-logic introduces to resultant continuous scores is unknown and can be explored using Item Response Theory. Interview response data were simulated while varying 5 characteristics of the measures: number of screen items, difficulty (clinical severity) of the screens, difficulty of non-screen items, shape of the trait distribution, and range of discrimination parameters. The number of simulations and examinees were held constant at 2,000 and 10,000, respectively. A criterion variable correlating 0.80 with the measured trait was also simulated, and the outcome of interest was the difference between the correlations of the criterion variable and the two estimated scores (with and without skip-logic). Effects of the simulation conditions on this outcome were explored using ANOVA. All main effects and interactions were significant. The largest 2-way interaction was between number of screen items and average item discrimination, such that the number of screen items had a large effect on bias only when discrimination parameters were low. This, among other interactions explored here, suggests that skip-logic can bias results using continuous scores; however, the effects of this bias are usually inconsequential. Skip-logic in clinical assessments can introduce bias in continuous sum scores, but this bias can usually be ignored.