The P-factor and its genomic and neural equivalents: an integrated perspective

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.

Common Cause Versus Dynamic Mutualism: An Empirical Comparison of Two Theories of Psychopathology in Two Large Longitudinal Cohorts

Mental disorders are among the leading causes of global disease burden. To respond effectively, a strong understanding of the structure of psychopathology is critical. We empirically compared two competing frameworks, dynamic-mutualism theory and common-cause theory, that vie to explain the development of psychopathology. We formalized these theories in statistical models and applied them to explain change in the general factor of psychopathology (p factor) from early to late adolescence (N = 1,482) and major depression in middle adulthood and old age (N = 6,443). Change in the p factor was better explained by mutualism according to model-fit indices. However, a core prediction of mutualism was not supported (i.e., predominantly positive causal interactions among distinct domains). The evidence for change in depression was more ambiguous. Our results support a multicausal approach to understanding psychopathology and showcase the value of translating theories into testable statistical models for understanding developmental processes in clinical sciences.

Running in the FAMILY: understanding and predicting the intergenerational transmission of mental illness

Over 50% of children with a parent with severe mental illness will develop mental illness by early adulthood. However, intergenerational transmission of risk for mental illness in one's children is insufficiently considered in clinical practice, nor is it sufficiently utilised into diagnostics and care for children of ill parents. This leads to delays in diagnosing young offspring and missed opportunities for protective actions and resilience strengthening. Prior twin, family, and adoption studies suggest that the aetiology of mental illness is governed by a complex interplay of genetic and environmental factors, potentially mediated by changes in epigenetic programming and brain development. However, how these factors ultimately materialise into mental disorders remains unclear. Here, we present the FAMILY consortium, an interdisciplinary, multimodal (e.g., (epi)genetics, neuroimaging, environment, behaviour), multilevel (e.g., individual-level, family-level), and multisite study funded by a European Union Horizon-Staying-Healthy-2021 grant. FAMILY focuses on understanding and prediction of intergenerational transmission of mental illness, using genetically informed causal inference, multimodal normative prediction, and animal modelling. Moreover, FAMILY applies methods from social sciences to map social and ethical consequences of risk prediction to prepare clinical practice for future implementation. FAMILY aims to deliver: (i) new discoveries clarifying the aetiology of mental illness and the process of resilience, thereby providing new targets for prevention and intervention studies; (ii) a risk prediction model within a normative modelling framework to predict who is at risk for developing mental illness; and (iii) insight into social and ethical issues related to risk prediction to inform clinical guidelines.

Perinatal and neonatal factors and mental disorders in children and adolescents: looking for the contributions of the early environment to common and dissociable aspects of psychopathology

High rates of co-occurrence of mental disorders have been hypothesized to represent a result of common susceptibility to overall psychopathology. The purpose of this study is to test the hypothesis that commonalities among psychiatric disorders might be partially driven by sharable perinatal and neonatal environmental factors for mental disorders. Participants were 6-14 years of age children and their parents. Primary caregivers provided data on perinatal and neonatal information assessed retrospectively (n = 2231). Psychiatric disorders diagnoses were assessed using the Development and Well Being Behavior Assessment (DAWBA). We used bifactor models to disentangle common from dissociable aspects of psychopathology. These models allow modeling psychiatric disorders as the result of a common domain of psychopathology (p-factor) and three dissociable domains (fear, distress, and externalizing symptoms). Associations were tested using linear and tobit regression models. The p-factor was associated with male sex, low socioeconomic status, gestational smoking, gestational drinking, low levels of maternal education and presence of mental disorder in the mother. Associations with specific factors also emerged suggesting some risk factors might also have some role for fear, distress and externalizing factors. Our study supports the hypothesis that overall susceptibility to psychopathology might be partially driven by sharable perinatal and neonatal factors.

Distal-to-proximal etiologically relevant variables associated with the general (p) and specific factors of psychopathology

BACKGROUND

The general factor of psychopathology, often denoted as p, captures the common variance among a broad range of psychiatric symptoms. Specific factors are co-modeled based on subsets of closely related symptoms. This paper investigated the extent to which wide-ranging genetic, personal, and environmental etiologically relevant variables are associated with p and specific psychopathology factors.

METHODS

Using data from four waves (ages 11-19) of TRAILS, we modeled a bifactor model of p and four specific factors [internalizing, externalizing, ADHD, Autism Spectrum Disorder (ASD)]. Next, we examined the associations of 19 etiologically relevant variables with these psychology factors using path models that organized the variables according to the distal-to-proximal risk principle.

RESULTS

Collectively, the etiologically relevant factors, including temperament traits, accounted for 55% of p's variance, 46% in ADHD, 35% in externalizing, 19% in internalizing, and 7% in ASD. The low 7% is due to insufficient unique variance in ASD indicators that load more strongly on p. Excluding temperament, variables accounted for 29% variance in p, 9% ADHD, 14% EXT, 7% INT, and 4% ASD. Most etiologically relevant factors were generic, predicting p. In addition, we identified effects on specific factors in addition to effects on p (e.g., parental SES, executive functioning); only effects on specific factors (e.g., parental rejection); opposite effects on different factors [e.g., diurnal cortisol (high INT but low EXT, p); developmental delay (high ASD and p but low EXT)]. Frustration, family functioning, parental psychopathology, executive functioning, and fearfulness had strong effects on p.

CONCLUSIONS

(1) Strong generic effects on p suggest that etiologically relevant factors and psychopathology tend to cluster in persons. (2) While many factors predict p, additional as well as opposite effects on specific factors indicate the relevance of specific psychopathology factors in understanding mental disorder. (3) High frustration, neurodevelopmental problems, and a disadvantaged family environment primarily characterize p.

How early environment influences the developing brain and long-term mental health

The March 2024 issue of JCPP Advances features two neuroimaging studies that investigate links between early environmental risk factors for mental health problems, brain development and psychopathology in children and young adults. The papers provide new insights into how adverse environments and negative experiences in childhood increase risk for depression and mental health problems, and how this may or may not be mediated, or moderated, by individual differences in the brain.

Taking stock to move forward: Where the field of developmental psychopathology might be heading

In this paper, dedicated to Dante Cicchetti's contributions and enduring influence, we explore the prospective directions of developmental psychopathology. Our focus centers on key domains where Cicchetti's significant achievements have continually shaped our evolving thinking about psychological development. These domains include (a) the concepts of equifinality and multifinality, along with the challenges in predicting developmental trajectories, (b) the imperative to integrate wider sociocultural viewpoints into developmental psychopathology frameworks, (c) the interplay of genetic and environmental influences in developmental courses, (d) the significance of mental state language, and (e) the progress, or its absence, in the development of prevention and intervention tactics for children, adolescents, and their caregivers. While many of our forecasts regarding the future of developmental psychopathology may not materialize, we maintain optimistic that the essential ideas presented will influence the research agenda in this field and contribute to its growth over the next fifty years.

Testing the sensitivity of diagnosis-derived patterns in functional brain networks to symptom burden in a Norwegian youth sample

Aberrant brain network development represents a putative aetiological component in mental disorders, which typically emerge during childhood and adolescence. Previous studies have identified resting-state functional connectivity (RSFC) patterns reflecting psychopathology, but the generalisability to other samples and politico-cultural contexts has not been established. We investigated whether a previously identified cross-diagnostic case-control and autism spectrum disorder (ASD)-specific pattern of RSFC (discovery sample; aged 5-21 from New York City, USA; n = 1666) could be validated in a Norwegian convenience-based youth sample (validation sample; aged 9-25 from Oslo, Norway; n = 531). As a test of generalisability, we investigated if these diagnosis-derived RSFC patterns were sensitive to levels of symptom burden in both samples, based on an independent measure of symptom burden. Both the cross-diagnostic and ASD-specific RSFC pattern were validated across samples. Connectivity patterns were significantly associated with thematically appropriate symptom dimensions in the discovery sample. In the validation sample, the ASD-specific RSFC pattern showed a weak, inverse relationship with symptoms of conduct problems, hyperactivity and prosociality, while the cross-diagnostic pattern was not significantly linked to symptoms. Diagnosis-derived connectivity patterns in a developmental clinical US sample were validated in a convenience sample of Norwegian youth, however, they were not associated with mental health symptoms.

The general factor of psychopathology (p): Choosing among competing models and interpreting p

Over the past 10 years, the general factor of psychopathology, p, has attracted interest and scrutiny. We review the history of the idea that all mental disorders share something in common, p; how we arrived at this idea; and how it became conflated with a statistical representation, the Bi-Factor Model. We then leverage the Environmental Risk (E-Risk) longitudinal twin study to examine the properties and nomological network of different statistical representations of p. We find that p performed similarly regardless of how it was modelled, suggesting that if the sample and content are the same the resulting p factor will be similar. We suggest that the meaning of p is not to be found by dueling over statistical models but by conducting well-specified criterion-validation studies and developing new measurement approaches. We outline new directions to refresh research efforts to uncover what all mental disorders have in common.

An augmented Mendelian randomization approach provides causality of brain imaging features on complex traits in a single biobank-scale dataset

Mendelian randomization (MR) is an effective approach for revealing causal risk factors that underpin complex traits and diseases. While MR has been more widely applied under two-sample settings, it is more promising to be used in one single large cohort given the rise of biobank-scale datasets that simultaneously contain genotype data, brain imaging data, and matched complex traits from the same individual. However, most existing multivariable MR methods have been developed for two-sample setting or a small number of exposures. In this study, we introduce a one-sample multivariable MR method based on partial least squares and Lasso regression (MR-PL). MR-PL is capable of considering the correlation among exposures (e.g., brain imaging features) when the number of exposures is extremely upscaled, while also correcting for winner's curse bias. We performed extensive and systematic simulations, and demonstrated the robustness and reliability of our method. Comprehensive simulations confirmed that MR-PL can generate more precise causal estimates with lower false positive rates than alternative approaches. Finally, we applied MR-PL to the datasets from UK Biobank to reveal the causal effects of 36 white matter tracts on 180 complex traits, and showed putative white matter tracts that are implicated in smoking, blood vascular function-related traits, and eating behaviors.

Associations of broad eating disorder symptoms with later alcohol problems in Finnish adult twins: A nationwide 10-year follow-up

OBJECTIVE

Eating disorders are associated with subsequent alcohol problems, but it is not known whether this association also extends to broader eating disorder symptoms not captured by clinical diagnoses. We assessed the longitudinal association of broad eating disorder symptoms with alcohol problems in a nationwide twin sample (FinnTwin16).

METHODS

Finnish women (N = 1905) and men (N = 1449) self-reported their eating disorder symptoms using the Eating Disorder Inventory-2 Bulimia, Drive for Thinness, and Body Dissatisfaction subscales at the mean age of 24.4 years in 2000-2003. A subsample of participants also completed items on drive for muscularity, height dissatisfaction, and muscle-enhancing supplement use. Alcohol problems were assessed 10 years later at the age of 34.1 in 2010-2012 with the Rutgers Alcohol Problem Index.

RESULTS

Eating disorder symptoms were associated with later alcohol problems (odds ratio per point increase 1.02-1.18). Bulimia showed stronger associations among men than women (p for interaction .012). Drive for muscularity and height dissatisfaction were also associated with later alcohol problems, but supplement use was not. When accounting for baseline alcohol problems, only Bulimia (among women and men) and Drive for Thinness (among men) were significantly associated with later alcohol problems. Bulimia was also significantly associated with later alcohol problems in within-twin-pair analyses among dizygotic twins, but not among monozygotic twins.

DISCUSSION

In a longitudinal setting, eating disorder symptoms were associated with later alcohol problems. Bulimic symptoms were a stronger risk factor for men than women. These associations may be attributable to baseline alcohol problems, childhood environment and genetic liability.

PUBLIC SIGNIFICANCE

This study found that both young adult women and men with broad eating disorder symptoms are at a higher risk of alcohol-related problems than those without such symptoms. Men with bulimic symptoms were at a particularly high risk. These findings emphasize the need for better prevention and treatment of disordered eating, body image concerns and alcohol problems for both young adult women and men.

Characterizing the phenotypic and genetic structure of psychopathology in UK Biobank

Mental conditions exhibit a higher-order transdiagnostic factor structure which helps to explain the widespread comorbidity observed in psychopathology. However, the phenotypic and genetic structures of psychopathology may differ, raising questions about the validity and utility of these factors. Here, we study the phenotypic and genetic factor structures of ten psychiatric conditions using UK Biobank and public genomic data. Although the factor structure of psychopathology was generally genetically and phenotypically consistent, conditions related to externalizing (e.g., alcohol use disorder) and compulsivity (e.g., eating disorders) exhibited cross-level disparities in their relationships with other conditions, plausibly due to environmental influences. Domain-level factors, especially thought disorder and internalizing factors, were more informative than a general psychopathology factor in genome-wide association and polygenic index analyses. Collectively, our findings enhance the understanding of comorbidity and shared etiology, highlight the intricate interplay between genes and environment, and offer guidance for psychiatric research using polygenic indices.

Neurobiological Clusters Are Associated With Trajectories of Overall Psychopathology in Youth

BACKGROUND

Integrating multiple neuroimaging modalities to identify clusters of individuals and then associating these clusters with psychopathology is a promising approach for understanding neurobiological mechanisms that underlie psychopathology and the extent to which these features are associated with clinical symptoms.

METHODS

We leveraged neuroimaging data from T1-weighted, diffusion-weighted, and resting-state functional magnetic resonance images from the Adolescent Brain Cognitive Development (ABCD) Study (N = 8035) and used similarity network fusion and spectral clustering to identify subgroups of participants. We examined neuroimaging measures as a function of clustering profiles using 1, 2, or 3 imaging modalities (i.e., data combinations), calculated the stability of the clustering assignment in each respective data combination, and compared the consistency of clusters across different data combinations. We then compared the extent to which clusters were associated with overall psychopathology at the baseline assessment and at 2 yearly follow-up visits.

RESULTS

Each data combination resulted in optimal clusters ranging from 2 to 4 subgroups for each data combination. Clusters were stable across subsampling of the ABCD Study cohort. Widespread structural measures (surface area, fractional anisotropy, and mean diffusivity) were important features contributing to clustering across different data combinations. Five of the seven data combinations were associated with overall psychopathology, both at baseline and over time (d = 0.08-0.41). Generally, lower global cortical volume and surface area, widespread reduced fractional anisotropy, and increased radial diffusivity were associated with increased overall psychopathology.

CONCLUSIONS

Profiles constructed from neuroimaging data combinations are associated with concurrent and future psychopathology trajectories.

Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes

There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population-wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size.

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.

Cortical profiles of numerous psychiatric disorders and normal development share a common pattern

The neurobiological bases of the association between development and psychopathology remain poorly understood. Here, we identify a shared spatial pattern of cortical thickness (CT) in normative development and several psychiatric and neurological disorders. Principal component analysis (PCA) was applied to CT of 68 regions in the Desikan-Killiany atlas derived from three large-scale datasets comprising a total of 41,075 neurotypical participants. PCA produced a spatially broad first principal component (PC1) that was reproducible across datasets. Then PC1 derived from healthy adult participants was compared to the pattern of CT differences associated with psychiatric and neurological disorders comprising a total of 14,886 cases and 20,962 controls from seven ENIGMA disease-related working groups, normative maturation and aging comprising a total of 17,697 scans from the ABCD Study® and the IMAGEN developmental study, and 17,075 participants from the ENIGMA Lifespan working group, as well as gene expression maps from the Allen Human Brain Atlas. Results revealed substantial spatial correspondences between PC1 and widespread lower CT observed in numerous psychiatric disorders. Moreover, the PC1 pattern was also correlated with the spatial pattern of normative maturation and aging. The transcriptional analysis identified a set of genes including KCNA2, KCNS1 and KCNS2 with expression patterns closely related to the spatial pattern of PC1. The gene category enrichment analysis indicated that the transcriptional correlations of PC1 were enriched to multiple gene ontology categories and were specifically over-represented starting at late childhood, coinciding with the onset of significant cortical maturation and emergence of psychopathology during the prepubertal-to-pubertal transition. Collectively, the present study reports a reproducible latent pattern of CT that captures interregional profiles of cortical changes in both normative brain maturation and a spectrum of psychiatric disorders. The pubertal timing of the expression of PC1-related genes implicates disrupted neurodevelopment in the pathogenesis of the spectrum of psychiatric diseases emerging during adolescence.

Exploring the Genetic Link Between Thyroid Dysfunction and Common Psychiatric Disorders: A Specific Hormonal or a General Autoimmune Comorbidity

Background: The hypothalamus-pituitary-thyroid axis coordinates brain development and postdevelopmental function. Thyroid hormone (TH) variations, even within the normal range, have been associated with the risk of developing common psychiatric disorders, although the underlying mechanisms remain poorly understood. Methods: To get new insight into the potentially shared mechanisms underlying thyroid dysfunction and psychiatric disorders, we performed a comprehensive analysis of multiple phenotypic and genotypic databases. We investigated the relationship of thyroid disorders with depression, bipolar disorder (BIP), and anxiety disorders (ANXs) in 497,726 subjects from U.K. Biobank. We subsequently investigated genetic correlations between thyroid disorders, thyrotropin (TSH), and free thyroxine (fT4) levels, with the genome-wide factors that predispose to psychiatric disorders. Finally, the observed global genetic correlations were furthermore pinpointed to specific local genomic regions. Results: Hypothyroidism was positively associated with an increased risk of major depressive disorder (MDD; OR = 1.31, p = 5.29 × 10-89), BIP (OR = 1.55, p = 0.0038), and ANX (OR = 1.16, p = 6.22 × 10-8). Hyperthyroidism was associated with MDD (OR = 1.11, p = 0.0034) and ANX (OR = 1.34, p = 5.99 × 10-⁶). Genetically, strong coheritability was observed between thyroid disease and both major depressive (rg = 0.17, p = 2.7 × 10-⁴) and ANXs (rg = 0.17, p = 6.7 × 10-⁶). This genetic correlation was particularly strong at the major histocompatibility complex locus on chromosome 6 (p < 10-⁵), but further analysis showed that other parts of the genome also contributed to this global effect. Importantly, neither TSH nor fT4 levels were genetically correlated with mood disorders. Conclusions: Our findings highlight an underlying association between autoimmune hypothyroidism and mood disorders, which is not mediated through THs and in which autoimmunity plays a prominent role. While these findings could shed new light on the potential ineffectiveness of treating (minor) variations in thyroid function in psychiatric disorders, further research is needed to identify the exact underlying molecular mechanisms.

Categorical and Dimensional Deficits in Hippocampal Subfields Among Schizophrenia, Obsessive-Compulsive Disorder, Bipolar Disorder, and Major Depressive Disorder

BACKGROUND

The hippocampus is a core region of interest for all major mental disorders, and its subfields implement distinctive functions. It is unclear whether the mental disorders exhibit common patterns of hippocampal impairments, and we lack knowledge on whether and how hippocampal subfields represent deficit spectra across mental disorders.

METHODS

Using brain images of 1123 individuals scanned on a single magnetic resonance imaging scanner, we examined the commonality, specificity, and symptom associations of the volume of hippocampal subfields across patients with schizophrenia, patients with obsessive-compulsive disorder, patients with bipolar disorder, patients with major depressive disorder, and healthy control subjects. We further performed a transdiagnostic analysis of the individual variability of the volume of hippocampal subfields to reflect cross-disease gradients in the hippocampus.

RESULTS

We found common and disease-specific abnormalities in a few hippocampal fields and identified 2 reliable transdiagnostic factors in the hippocampal subfields, each reflecting a spectrum of mental disorders. The plane spanned by the 2 most reliable factors provided a clearer view of hippocampal volume abnormality spectra among the major mental disorders. In addition, functional and genetic enrichment analyses supported the different roles of the 2 hippocampal factors in mental disorders.

CONCLUSIONS

The volume of hippocampal subfields reflected some commonality and specificity among the 3 major mental disorders. We propose a new pathophysiological dimensional view of the hippocampus, reflecting at least 2 spectra of mental disorders, suggesting multivariate links among the diseases. This work highlights the value of the complementary categorical and dimensional views of the hippocampal deficits in mental disorders.

The harmonium model and its unified system view of psychopathology: a validation study by means of a convolutional neural network

The harmonium model (HM) is a recent conceptualization of the unifying view of psychopathology, namely the idea of a general mechanism underpinning all mental disorders (the p factor). According to HM, psychopathology consists of a low dimensional Phase Space of Meaning (PSM), where each dimension of meaning maps a component of the environmental variability. Accordingly, the lower thenumber of independent dimensions in the PSM, and hence its intrinsic complexity, the more limited the way of interpreting the environment. The current simulation study, based on a Convolutional Neural Network (CNN) framework, aims at validating the HM low-dimensionality hypothesis. CNN-based classifiers were employed to simulate normotypical and pathological cognitive processes. Results revealed that normotypical and pathological CNNs were different in terms of both classification performance and layer activation patterns. Using Principal Component Analysis to characterize the PSM associated with the two algorithms, we found that the performance of the normotypical CNN relies on a larger and more evenly distributed number of components, compared with the pathological one. This finding might be indicative of the fact that psychopathology can be modelled as a low-dimensional, poorly modulable PSM, which means the environment is detected through few components of meaning, preventing complex information patterns from being taken into account.

Branched-chain amino acids linked to depression in young adults

Depression is a heterogeneous mental health problem affecting millions worldwide, but a majority of individuals with depression do not experience relief from initial treatments. Therefore, we need to improve our understanding of the biology of depression. Metabolomic approaches, especially untargeted ones, can suggest new hypotheses for further exploring biological mechanisms. Using the FinnTwin12 cohort, a longitudinal Finnish population-based twin cohort, with data collected in adolescence and young adulthood including 725 blood plasma samples, we investigated associations between depression and 11 low–molecular weight metabolites (amino acids and ketone bodies). In linear regression models with the metabolite (measured at age 22) as the dependent variable and depression ratings (measured at age 12, 14, 17, or 22 from multiple raters) as independent variables [adjusted first for age, sex, body mass index (BMI), and additional covariates (later)], we initially identified a significant negative association of valine with depression. Upon further analyses, valine remained significantly negatively associated with depression cross-sectionally and over time [meta-analysis beta = −13.86, 95% CI (−18.48 to −9.25)]. Analyses of the other branched-chain amino acids showed a significant negative association of leucine with depression [meta-analysis beta = −9.24, 95% CI (−14.53 to −3.95)], while no association was observed between isoleucine and depression [meta-analysis beta = −0.95, 95% CI (−6.00 to 4.11)]. These exploratory epidemiologic findings support further investigations into the role of branched-chain amino acids in depression.

A Systematic Review of Longitudinal Studies Investigating the Association Between Early Life Maternal Depression and Offspring ADHD

OBJECTIVE

The systematic review sought to understand the relationship between maternal depression and later ADHD in children.

METHOD

Three databases were used to identify the studies (Medline, Web of Science and PsychInfo) resulting in 1,223 studies being screened and 14 articles being included in the review.

RESULTS

The majority of studies (N = 11) reported a significant relationship between maternal depression (across both prenatal and postnatal periods) and ADHD symptoms in children. This relationship remained significant when temperament, or past ADHD symptoms were controlled for. Several methodological issues were identified including; overreliance on maternal report and parental ADHD not being accounted for in most studies.

CONCLUSION

The review adds to the literature regarding the temporal relationship between maternal depression and the development of ADHD in children, and thus supports the case for improving access to mental health services for mothers as a preventative strategy in the development of child psychopathology.

Reliability and validity of bifactor models of dimensional psychopathology in youth

Bifactor models are a promising strategy to parse general from specific aspects of psychopathology in youth. Currently, there are multiple configurations of bifactor models originating from different theoretical and empirical perspectives. We aimed to test the reliability, validity, measurement invariance, and the correlation of different bifactor models of psychopathology using the Child Behavior Checklist (CBCL). We used data from the Reproducible Brain Charts (RBC) initiative (N = 7,011, ages 5 to 22 years, 40.2% females). Factor models were tested using the baseline data. To address our aim, we (a) searched for the published item-level bifactor models using the CBCL; (b) tested their global model fit; (c) calculated model-based reliability indices; (d) tested associations with symptoms' impact in everyday life; (e) tested measurement invariance across many characteristics, and (f) analyzed the observed factor correlation across the models. We found 11 bifactor models ranging from 39 to 116 items. Their global model fit was broadly similar. Factor determinacy and H index were acceptable for the p-factors, internalizing, externalizing, and somatic specific factors in most models. However, only the p- and attention factors predicted daily life symptoms' impact in all models. Models were broadly invariant across different characteristics. P-factors were highly correlated across models (r = .88 to .99) and homotypic specific factors were highly correlated. These results suggest that regardless of item selection and strategy to compose CBCL bifactor models, they assess very similar constructs. Taken together, our results support the robustness of the p-factor across distinct bifactor models and studies of distinct characteristics. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Integrative omics of schizophrenia: from genetic determinants to clinical classification and risk prediction

Schizophrenia (SCZ) is a debilitating neuropsychiatric disorder with high heritability and complex inheritance. In the past decade, successful identification of numerous susceptibility loci has provided useful insights into the molecular etiology of SCZ. However, applications of these findings to clinical classification and diagnosis, risk prediction, or intervention for SCZ have been limited, and elucidating the underlying genomic and molecular mechanisms of SCZ is still challenging. More recently, multiple Omics technologies - genomics, transcriptomics, epigenomics, proteomics, metabolomics, connectomics, and gut microbiomics - have all been applied to examine different aspects of SCZ pathogenesis. Integration of multi-Omics data has thus emerged as an approach to provide a more comprehensive view of biological complexity, which is vital to enable translation into assessments and interventions of clinical benefit to individuals with SCZ. In this review, we provide a broad survey of the single-omics studies of SCZ, summarize the advantages and challenges of different Omics technologies, and then focus on studies in which multiple omics data are integrated to unravel the complex pathophysiology of SCZ. We believe that integration of multi-Omics technologies would provide a roadmap to create a more comprehensive picture of interactions involved in the complex pathogenesis of SCZ, constitute a rich resource for elucidating the potential molecular mechanisms of the illness, and eventually improve clinical assessments and interventions of SCZ to address clinical translational questions from bench to bedside.

Widespread attenuating changes in brain connectivity associated with the general factor of psychopathology in 9- and 10-year olds

Convergent research identifies a general factor ("P factor") that confers transdiagnostic risk for psychopathology. Large-scale networks are key organizational units of the human brain. However, studies of altered network connectivity patterns associated with the P factor are limited, especially in early adolescence when most mental disorders are first emerging. We studied 11,875 9- and 10-year olds from the Adolescent Brain and Cognitive Development (ABCD) study, of whom 6593 had high-quality resting-state scans. Network contingency analysis was used to identify altered interconnections associated with the P factor among 16 large-scale networks. These connectivity changes were then further characterized with quadrant analysis that quantified the directionality of P factor effects in relation to neurotypical patterns of positive versus negative connectivity across connections. The results showed that the P factor was associated with altered connectivity across 28 network cells (i.e., sets of connections linking pairs of networks); pPERMUTATION values < 0.05 FDR-corrected for multiple comparisons. Higher P factor scores were associated with hypoconnectivity within default network and hyperconnectivity between default network and multiple control networks. Among connections within these 28 significant cells, the P factor was predominantly associated with "attenuating" effects (67%; pPERMUTATION < 0.0002), i.e., reduced connectivity at neurotypically positive connections and increased connectivity at neurotypically negative connections. These results demonstrate that the general factor of psychopathology produces attenuating changes across multiple networks including default network, involved in spontaneous responses, and control networks involved in cognitive control. Moreover, they clarify mechanisms of transdiagnostic risk for psychopathology and invite further research into developmental causes of distributed attenuated connectivity.