Evidence for Dissociable Linkage of Dimensions of Psychopathology to Brain Structure in Youths

Investigating cross-sectional and longitudinal relationships between brain structure and distinct dimensions of externalizing psychopathology in the ABCD sample

Externalizing psychopathology in childhood is a predictor of poor outcomes across the lifespan. Children exhibiting elevated externalizing symptoms also commonly show emotion dysregulation and callous-unemotional (CU) traits. Examining cross-sectional and longitudinal neural correlates across dimensions linked to externalizing psychopathology during childhood may clarify shared or distinct neurobiological vulnerability for psychopathological impairment later in life. We used tabulated brain structure and behavioural data from baseline, year 1, and year 2 timepoints of the Adolescent Brain Cognitive Development Study (ABCD; baseline n = 10,534). We fit separate linear mixed effect models to examine whether baseline brain structures in frontolimbic and striatal regions (cortical thickness or subcortical volume) were associated with externalizing symptoms, emotion dysregulation, and/or CU traits at baseline and over a two-year period. The most robust relationships found at the cross-sectional level was between cortical thickness in the right rostral middle frontal gyrus and bilateral pars orbitalis was positively associated with CU traits (β = |0.027-0.033|, pcorrected = 0.009-0.03). Over the two-year follow-up period, higher baseline cortical thickness in the left pars triangularis and rostral middle frontal gyrus predicted greater decreases in externalizing symptoms ((F = 6.33-6.94, pcorrected = 0.014). The results of the current study suggest that unique regions within frontolimbic and striatal networks may be more strongly associated with different dimensions of externalizing psychopathology. The longitudinal findings indicate that brain structure in early childhood may provide insight into structural features that influence behaviour over time.

Structural brain differences in patients with schizophrenia spectrum disorders with and without auditory verbal hallucinations

Schizophrenia spectrum disorders (SSD) are debilitating, with auditory verbal hallucinations (AVHs) being a core characteristic. While gray matter volume (GMV) reductions are commonly replicated in SSD populations, the neural basis of AVHs remains unclear. Using previously published data, this study comprises two main analyses, one of GMV dissimilarities between SSD and healthy controls (HC), and one of GMV differences specifically associated with AVHs. Structural brain images from 71 adults with (n = 46) and without (n = 25) SSD were employed. Group differences in GMVs of the cortex, anterior cingulate (ACC), superior temporal gyrus (STG), hippocampi, and thalami were assessed. Additionally, volumes of left Heschl's gyrus (HG) in a subgroup experiencing AVHs (AVH+, n = 23) were compared with those of patients who did not (AVH-, n = 23). SSD patients displayed reduced GMVs of the cortex, ACC, STG, hippocampi, and thalami compared to HC. AVH+ had significantly reduced left HG volume when compared to AVH-. Finally, a right-lateralized ventral prefrontal cluster was found to be uniquely associated with AVH severity. This study corroborates previous findings of GMV reductions in SSD cohorts. Chiefly, our secondary analysis suggests that AVHs are associated with language areas and their contralateral homologues.

Voxelwise Multivariate Analysis of Brain-Psychosocial Associations in Adolescents Reveals 6 Latent Dimensions of Cognition and Psychopathology

BACKGROUND

Adolescence heralds the onset of considerable psychopathology, which may be conceptualized as an emergence of altered covariation between symptoms and brain measures. Multivariate methods can detect such modes of covariation or latent dimensions, but none specifically relating to psychopathology have yet been found using population-level structural brain data. Using voxelwise (instead of parcellated) brain data may strengthen latent dimensions' brain-psychosocial relationships, but this creates computational challenges.

METHODS

We obtained voxelwise gray matter density and psychosocial variables from the baseline (ages 9-10 years) Adolescent Brain Cognitive Development (ABCD) Study cohort (N = 11,288) and employed a state-of-the-art segmentation method, sparse partial least squares, and a rigorous machine learning framework to prevent overfitting.

RESULTS

We found 6 latent dimensions, 4 of which pertain specifically to mental health. The mental health dimensions were related to overeating, anorexia/internalizing, oppositional symptoms (all ps < .002) and attention-deficit/hyperactivity disorder symptoms (p = .03). Attention-deficit/hyperactivity disorder was related to increased and internalizing symptoms related to decreased gray matter density in dopaminergic and serotonergic midbrain areas, whereas oppositional symptoms were related to increased gray matter in a noradrenergic nucleus. Internalizing symptoms were related to increased and oppositional symptoms to reduced gray matter density in the insular, cingulate, and auditory cortices. Striatal regions featured strongly, with reduced caudate nucleus gray matter in attention-deficit/hyperactivity disorder and reduced putamen gray matter in oppositional/conduct problems. Voxelwise gray matter density generated stronger brain-psychosocial correlations than brain parcellations.

CONCLUSIONS

Voxelwise brain data strengthen latent dimensions of brain-psychosocial covariation, and sparse multivariate methods increase their psychopathological specificity. Internalizing and externalizing symptoms are associated with opposite gray matter changes in similar cortical and subcortical areas.

MULTIMODAL NEURAL CORRELATES OF CHILDHOOD PSYCHOPATHOLOGY

Complex structural and functional changes occurring in typical and atypical development necessitate multidimensional approaches to better understand the risk of developing psychopathology. Here, we simultaneously examined structural and functional brain network patterns in relation to dimensions of psychopathology in the Adolescent Brain Cognitive Development dataset. Several components were identified, recapitulating the psychopathology hierarchy, with the general psychopathology (p) factor explaining most covariance with multimodal imaging features, while the internalizing, externalizing, and neurodevelopmental dimensions were each associated with distinct morphological and functional connectivity signatures. Connectivity signatures associated with the p factor and neurodevelopmental dimensions followed the sensory-to-transmodal axis of cortical organization, which is related to the emergence of complex cognition and risk for psychopathology. Results were consistent in two separate data subsamples, supporting generalizability, and robust to variations in analytical parameters. Our findings help in better understanding biological mechanisms underpinning dimensions of psychopathology, and could provide brain-based vulnerability markers.

Bifactor models of psychopathology using multi-informant and multi-instrument dimensional measures in the ABCD study

Background

Due to limitations of categorical definitions of mental illness, there is a need for quantitative empirical investigations of the dimensional structure of psychopathology. Using exploratory bifactor methods, this study investigated a comprehensive and representative structure of psychopathology in children to better understand how psychotic-like experiences (PLEs), autism spectrum disorder (ASD) symptoms, impulsivity, and sensitivity to reward and punishment, may be integrated into extant general factor models of psychopathology.

Methods

We used seven child-report and three parent-report instruments capturing diverse mental health symptoms in 11,185 children aged 9-10 from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study. We built on previous modeling frameworks by conducting both split sample and full sample factor analytic approaches that harnessed recent methodological advances in bifactor exploratory structural equation modeling (B-ESEM) to examine a wide range of psychopathology measures not previously integrated into a single analysis. Validity of psychopathology dimensions was examined by investigating associations with sex, age, cognition, imaging measures, and medical service usage.

Results

All four factor analytic models showed excellent fit and similar structure within informant. PLEs loaded most highly onto a general psychopathology factor, suggesting that they may reflect non-specific risk for mental illness. ASD symptoms loaded separately from attention/hyperactivity symptoms. Symptoms of impulsivity and sensitivity to reward and punishment loaded onto specific factors, distinct from externalizing and internalizing factors. All identified factors were associated with clinically relevant risk factors, providing preliminary evidence for their construct validity.

Conclusion

By integrating diverse child-report and parent-report psychopathology measures for children in the ABCD sample, we deliver data on the quantitative structure of psychopathology for an exceptionally large set of measurements and discuss implications for the field.

Psychosis superspectrum II: neurobiology, treatment, and implications

Alternatives to traditional categorical diagnoses have been proposed to improve the validity and utility of psychiatric nosology. This paper continues the companion review of an alternative model, the psychosis superspectrum of the Hierarchical Taxonomy of Psychopathology (HiTOP). The superspectrum model aims to describe psychosis-related psychopathology according to data on distributions and associations among signs and symptoms. The superspectrum includes psychoticism and detachment spectra as well as narrow subdimensions within them. Auxiliary domains of cognitive deficit and functional impairment complete the psychopathology profile. The current paper reviews evidence on this model from neurobiology, treatment response, clinical utility, and measure development. Neurobiology research suggests that psychopathology included in the superspectrum shows similar patterns of neural alterations. Treatment response often mirrors the hierarchy of the superspectrum with some treatments being efficacious for psychoticism, others for detachment, and others for a specific subdimension. Compared to traditional diagnostic systems, the quantitative nosology shows an approximately 2-fold increase in reliability, explanatory power, and prognostic accuracy. Clinicians consistently report that the quantitative nosology has more utility than traditional diagnoses, but studies of patients with frank psychosis are currently lacking. Validated measures are available to implement the superspectrum model in practice. The dimensional conceptualization of psychosis-related psychopathology has implications for research, clinical practice, and public health programs. For example, it encourages use of the cohort study design (rather than case-control), transdiagnostic treatment strategies, and selective prevention based on subclinical symptoms. These approaches are already used in the field, and the superspectrum provides further impetus and guidance for their implementation. Existing knowledge on this model is substantial, but significant gaps remain. We identify outstanding questions and propose testable hypotheses to guide further research. Overall, we predict that the more informative, reliable, and valid characterization of psychopathology offered by the superspectrum model will facilitate progress in research and clinical care.

Sex Differences and Behavioral Associations with Typically Developing Pediatric Regional Cerebellar Gray Matter Volume

The cerebellum contributes to motor and higher-order control throughout neurodevelopment, with marked growth during childhood. Few studies have investigated differential associations of cerebellar morphometry with function in males and females. The present study examines sex differences in regional cerebellar gray matter volume (GMV) and the moderating effect of sex on the relationship between GMV and motor, cognitive, and emotional functions in a large cohort of typically developing (TD) children. Participants included 371 TD children (123 females, age 8-12 years). A convolutional neural network-based approach was employed for cerebellar parcellation. Volumes were harmonized using ComBat to adjust for hardware-induced variations. Regression analyses examined the effect of sex on GMV and whether sex moderated the relationship between GMV and motor, cognitive, and emotional functions. Males showed larger GMV in right lobules I-V, bilateral lobules VI, crus II/VIIb, and VIII, left lobule X, and vermis regions I-V and VIII-X. Greater motor function correlated with less vermis VI-VII GMV in females. Greater cognitive function correlated with greater left lobule VI GMV in females and less left lobule VI GMV in males. Finally, greater internalizing symptoms correlated with greater bilateral lobule IX GMV in females but less in males. These findings reveal sexually dimorphic patterns of cerebellar structure and associations with motor, cognitive, and emotional functions. Males generally show larger GMV than females. Larger GMV was associated with better cognitive functioning for females and better motor/emotional functioning for males.

A Shared Multivariate Brain-Behavior Relationship in a Transdiagnostic Sample of Adolescents

BACKGROUND

Internalizing and externalizing psychopathology typically present in early childhood and can have negative implications on general functioning and quality of life. Prior work has linked increased psychopathology symptoms with altered brain structure. Multivariate analysis such as partial least squares correlation can help identify patterns of covariation between brain regions and psychopathology symptoms. This study examined the relationship between gray matter volume (GMV) and psychopathology symptoms in adolescents with various psychiatric diagnoses.

METHODS

Structural magnetic resonance imaging data were collected from 490 participants with various internalizing and externalizing diagnoses (197 female/293 male; age = 14.68 ± 2.35 years; IQ = 104.05 ± 13.11). Cortical and subcortical volumes were parcellated using the Desikan-Killiany atlas. Partial least squares correlation was used to identify multivariate linear relationships between GMV and the Strength and Difficulties Questionnaire difficulties domains (emotional, peer, conduct, and hyperactivity issues). Resampling approaches were used to determine significance (permutation test), stability (bootstrap resampling), and reproducibility (split-half resampling) of identified relationships.

RESULTS

We found a significant, stable, and largely reproducible dimension that linked lower Strength and Difficulties Questionnaire scores (less impairment) across all difficulties domains with greater widespread GMV (singular value = 1.17, accounts for 87.1% of the covariance; p < .001). This dimension emphasized the relationship between lower conduct problems and greater GMV in frontotemporal regions.

CONCLUSIONS

Our results indicate that the most significant and stable brain-behavior relationship in a transdiagnostic sample is a domain-general relationship, linking lower psychopathology symptom scores to greater global GMV. This finding suggests that a shared brain-behavior relationship may be present across adolescents with and without clinically significant psychopathology symptoms.

Modeling Shared and Specific Variances of Irritability, Inattention, and Hyperactivity Yields Novel Insights Into White Matter Perturbations

OBJECTIVE

Irritability, inattention, and hyperactivity, which are common presentations of childhood psychopathology, have been associated with perturbed white matter microstructure. However, similar tracts have been implicated across these phenotypes; such non-specificity could be rooted in their high co-occurrence. To address this problem, we use a bifactor approach parsing unique and shared components of irritability, inattention, and hyperactivity, which we then relate to white matter microstructure.

METHOD

We developed a bifactor model based on the Conners Comprehensive Behavioral Rating Scale in a sample of youth with no psychiatric diagnosis or a primary diagnosis of attention-deficit/hyperactivity disorder or disruptive mood dysregulation disorder (n = 521). We applied the model to an independent yet sociodemographically and clinically comparable sample (n = 152), in which we tested associations between latent variables and fractional anisotropy (FA).

RESULTS

The bifactor model fit well (comparative fit index = 0.99; root mean square error of approximation = 0.07). The shared factor was positively associated with an independent measure of impulsivity (ρS = 0.88, pFDR < .001) and negatively related to whole-brain FA (r = -0.20), as well as FA of the corticospinal tract (all pFWE < .05). FA increased with age and deviation from this curve, indicating that altered white matter maturation was associated with the hyperactivity-specific factor (r = -0.16, pFWE < .05). Inattention-specific and irritability-specific factors were not linked to FA.

CONCLUSION

Perturbed white matter microstructure may represent a shared neurobiological mechanism of irritability, inattention, and hyperactivity related to heightened impulsivity. Furthermore, hyperactivity might be uniquely associated with a delay in white matter maturation.

Examining the association between posttraumatic stress disorder and disruptions in cortical networks identified using data-driven methods

Posttraumatic stress disorder (PTSD) is associated with lower cortical thickness (CT) in prefrontal, cingulate, and insular cortices in diverse trauma-affected samples. However, some studies have failed to detect differences between PTSD patients and healthy controls or reported that PTSD is associated with greater CT. Using data-driven dimensionality reduction, we sought to conduct a well-powered study to identify vulnerable networks without regard to neuroanatomic boundaries. Moreover, this approach enabled us to avoid the excessive burden of multiple comparison correction that plagues vertex-wise methods. We derived structural covariance networks (SCNs) by applying non-negative matrix factorization (NMF) to CT data from 961 PTSD patients and 1124 trauma-exposed controls without PTSD. We used regression analyses to investigate associations between CT within SCNs and PTSD diagnosis (with and without accounting for the potential confounding effect of trauma type) and symptom severity in the full sample. We performed additional regression analyses in subsets of the data to examine associations between SCNs and comorbid depression, childhood trauma severity, and alcohol abuse. NMF identified 20 unbiased SCNs, which aligned closely with functionally defined brain networks. PTSD diagnosis was most strongly associated with diminished CT in SCNs that encompassed the bilateral superior frontal cortex, motor cortex, insular cortex, orbitofrontal cortex, medial occipital cortex, anterior cingulate cortex, and posterior cingulate cortex. CT in these networks was significantly negatively correlated with PTSD symptom severity. Collectively, these findings suggest that PTSD diagnosis is associated with widespread reductions in CT, particularly within prefrontal regulatory regions and broader emotion and sensory processing cortical regions.

A role for the serotonin 2A receptor in the expansion and functioning of human transmodal cortex

Integrating independent but converging lines of research on brain function and neurodevelopment across scales, this article proposes that serotonin 2A receptor (5-HT2AR) signalling is an evolutionary and developmental driver and potent modulator of the macroscale functional organization of the human cerebral cortex. A wealth of evidence indicates that the anatomical and functional organization of the cortex follows a unimodal-to-transmodal gradient. Situated at the apex of this processing hierarchy-where it plays a central role in the integrative processes underpinning complex, human-defining cognition-the transmodal cortex has disproportionately expanded across human development and evolution. Notably, the adult human transmodal cortex is especially rich in 5-HT2AR expression and recent evidence suggests that, during early brain development, 5-HT2AR signalling on neural progenitor cells stimulates their proliferation-a critical process for evolutionarily-relevant cortical expansion. Drawing on multimodal neuroimaging and cross-species investigations, we argue that, by contributing to the expansion of the human cortex and being prevalent at the apex of its hierarchy in the adult brain, 5-HT2AR signalling plays a major role in both human cortical expansion and functioning. Owing to its unique excitatory and downstream cellular effects, neuronal 5-HT2AR agonism promotes neuroplasticity, learning and cognitive and psychological flexibility in a context-(hyper)sensitive manner with therapeutic potential. Overall, we delineate a dual role of 5-HT2ARs in enabling both the expansion and modulation of the human transmodal cortex.

Associations between a metal mixture and infant negative affectivity: Effect modification by prenatal cortisol and infant sex

In-utero exposures interact in complex ways that influence neurodevelopment. Animal research demonstrates that fetal sex moderates the impact of joint exposure to metals and prenatal stress measures, including cortisol, on offspring socioemotional outcomes. Further research is needed in humans. We evaluated the joint association of prenatal exposures to a metal mixture and cortisol with infant negative affectivity, considering sex differences. Analyses included 226 (29% White, Non-Hispanic) mother-infant pairs with data on exposures and negative affectivity assessed using the Infant Behavior Questionnaire-Revised in 6-month-olds. Results showed that girls whose mothers had higher cortisol had significantly higher scores of Fear and Sadness with greater exposure to the mixture. Examining higher-order interactions may better elucidate the effects of prenatal exposure to metals and cortisol on socioemotional functioning.

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.

Transdiagnostic biomarkers of mental illness across the lifespan: A systematic review examining the genetic and neural correlates of latent transdiagnostic dimensions of psychopathology in the general population

This systematic review synthesizes evidence from research investigating the biological correlates of latent transdiagnostic dimensions of psychopathology (e.g., the p-factor, internalizing, externalizing) across the lifespan. Eligibility criteria captured genomic and neuroimaging studies investigating general and/or specific dimensions in general population samples across all age groups. MEDLINE, Embase, and PsycINFO were searched for relevant studies published up to March 2023 and 46 studies were selected for inclusion. The results revealed several biological correlates consistently associated with transdiagnostic dimensions of psychopathology, including polygenic scores for ADHD and neuroticism, global surface area and global gray matter volume. Shared and unique associations between symptom dimensions are highlighted, as are potential age-specific differences in biological associations. Findings are interpreted with reference to key methodological differences across studies. The included studies provide compelling evidence that the general dimension of psychopathology reflects common underlying genetic and neurobiological vulnerabilities that are shared across diverse manifestations of mental illness. Substantive interpretations of general psychopathology in the context of genetic and neurobiological evidence are discussed.

Reduced volume of the left cerebellar lobule VIIb and its increased connectivity within the cerebellum predict more general psychopathology one year later via worse cognitive flexibility in children

Predicting the risk for general psychopathology (the p factor) requires the examination of multiple factors ranging from brain to cognitive skills. While an increasing number of findings have reported the roles of the cerebral cortex and executive functions, it is much less clear whether and how the cerebellum and cognitive flexibility (a core component of executive function) may be associated with the risk for general psychopathology. Based on the data from more than 400 children aged 6-12 in the Children School Functions and Brain Development (CBD) Project, this study examined whether the left cerebellar lobule VIIb and its connectivity within the cerebellum may prospectively predict the risk for general psychopathology one year later and whether cognitive flexibility may mediate such predictions in school-age children. The reduced gray matter volume in the left cerebellar lobule VIIb and the increased connectivity of this region to the left cerebellar lobule VI prospectively predicted the risk for general psychopathology and was partially mediated by worse cognitive flexibility. Deficits in cognitive flexibility may play an important role in linking cerebellar structure and function to the risk for general psychopathology.

Translating the hierarchical taxonomy of psychopathology (HiTOP) from potential to practice: Ten research questions

The Hierarchical Taxonomy of Psychopathology (HiTOP) is a novel diagnostic system grounded in empirical research into the architecture of mental illness. Its basic units are continuous dimensions-as opposed to categories-that are organized into a hierarchy according to patterns of symptom co-occurrence observed in quantitative studies. Previous HiTOP discussions have focused on existing evidence regarding the model's structure and ability to account for neurobiological, social, cultural, and clinical variation. The present article looks ahead to the next decade of applied research and clinical practice using the HiTOP rubric. We highlight 10 topics where HiTOP has the potential to make significant breakthroughs. Research areas include genetic influences, environmental contributions, neural mechanisms, real-time dynamics, and lifespan development of psychopathology. We also discuss development of novel assessments, forecasting methods, and treatments. Finally, we consider implications for clinicians and educators. For each of these domains, we propose directions for future research and venture hypotheses as to what HiTOP will reveal about psychopathology. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Harmonizing bifactor models of psychopathology between distinct assessment instruments: Reliability, measurement invariance, and authenticity

OBJECTIVES

Model configuration is important for mental health data harmonization. We provide a method to investigate the performance of different bifactor model configurations to harmonize different instruments.

METHODS

We used data from six samples from the Reproducible Brain Charts initiative (N = 8,606, ages 5-22 years, 41.0% females). We harmonized items from two psychopathology instruments, Child Behavior Checklist (CBCL) and GOASSESS, based on semantic content. We estimated bifactor models using confirmatory factor analysis, and calculated their model fit, factor reliability, between-instrument invariance, and authenticity (i.e., the correlation and factor score difference between the harmonized and original models).

RESULTS

Five out of 12 model configurations presented acceptable fit and were instrument-invariant. Correlations between the harmonized factor scores and the original full-item models were high for the p-factor (>0.89) and small to moderate (0.12-0.81) for the specific factors. 6.3%-50.9% of participants presented factor score differences between harmonized and original models higher than 0.5 z-score.

CONCLUSIONS

The CBCL-GOASSESS harmonization indicates that few models provide reliable specific factors and are instrument-invariant. Moreover, authenticity was high for the p-factor and moderate for specific factors. Future studies can use this framework to examine the impact of harmonizing instruments in psychiatric research.

Evaluation of the default mode network using nonnegative matrix factorization in patients with cognitive impairment induced by occupational aluminum exposure

Aluminum (Al) is an important environmental pathogenic factor for neurodegenerative diseases, especially mild cognitive impairment (MCI). The aim of this study was to evaluate the gray matter volume of structural covariance network alterations in patients with Al-induced MCI. Male subjects who had been exposed to Al for >10 years were included in the present study. The plasma Al concentration, Montreal cognitive assessment (MoCA) score, and verbal memory assessed by the Rey auditory verbal learning test (AVLT) score were collected from each participant. Nonnegative matrix factorization was used to identify the structural covariance network. The neural structural basis for patients with Al-induced MCI was investigated using correlation analysis and group comparison. Plasma Al concentration was inversely related to MoCA scores, particularly AVLT scores. In patients with Al-induced MCI, the gray matter volume of the default mode network (DMN) was considerably lower than that in controls. Positive correlations were discovered between the DMN and MoCA scores as well as between the DMN and AVLT scores. In sum, long-term occupational Al exposure has a negative impact on cognition, primarily by affecting delayed recognition. The reduced gray matter volume of the DMN may be the neural mechanism of Al-induced MCI.

Brain Structure Relations With Psychopathology Trajectories in the ABCD Study

OBJECTIVE

A general psychopathology (p) factor captures shared variation across mental disorders. Structural neural alterations have been associated with the p factor concurrently, but less is known about whether these alterations relate to within-person change in the p factor over time, especially during preadolescence, a period of neurodevelopmental changes.

METHOD

This study examined whether baseline brain structure was prospectively related to the trajectory of the p factor and specific forms of psychopathology over 2 years in 9,220 preadolescents (aged 9-10 at baseline) from the Adolescent Brain Cognitive Development Study (ABCD). Longitudinal multilevel models were conducted to determine whether baseline brain structure (volume, surface area, thickness) was associated with between-person differences and within-person change in the p factor (from a higher-order confirmatory factor model) and internalizing, externalizing, neurodevelopmental, somatization, and detachment factor scores (from a correlated factors model) over 3 study waves.

RESULTS

Smaller global volume and surface area, but not thickness, were associated with higher between-person levels of the p factor scores, which persisted over time. None of the brain structure measures were related to within-person change in the p factor scores. Lower baseline cortical thickness was associated with steeper decreases in internalizing psychopathology, which was driven by lower thickness within sensorimotor and temporal regions.

CONCLUSION

These novel results identify specific brain structure features that might contribute to transdiagnostic psychopathology development in preadolescence. Children with smaller total brain volume and surface area may be vulnerable to persistent general psychopathology during preadolescence. Cortical thinning reflective of pruning and myelination in sensorimotor and temporal brain regions specifically may protect against increases in internalizing, but not general psychopathology, during preadolescence.

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.

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.

Atypical Functional Network Properties and Associated Dimensions of Child Psychopathology During Rest and Task Performance

Background

When brain networks deviate from typical development, this is thought to contribute to varying forms of psychopathology. However, research has been limited by the reliance on discrete diagnostic categories that overlook the potential for psychological comorbidity and the dimensional nature of symptoms.

Methods

This study examined the topology of functional networks in association with 4 bifactor-defined psychopathology dimensions-general psychopathology, internalizing symptoms, conduct problems, and attention-deficit/hyperactivity disorder symptoms-via the Child Behavior Checklist in a sample of 3568 children from the ABCD (Adolescent Brain Cognitive Development) Study. Local and global graph theory metrics were calculated at rest and during tasks of reward processing, inhibition, and working memory.

Results

Greater attention-deficit/hyperactivity disorder symptoms were associated with reduced modularity across rest and tasks as well as reduced local efficiency in motor networks at rest. Results survived sensitivity analyses for medication and socioeconomic status. Greater conduct problem symptoms were associated with reduced modularity on working memory and reward processing tasks; however, these results did not persist after sensitivity analyses. General psychopathology and internalizing symptoms showed no significant network associations.

Conclusions

Our findings suggest reduced efficiency in topology in those with greater attention-deficit/hyperactivity disorder symptoms across 4 critical cognitive states, with conduct problems also showing network deficits, although less consistently. This may suggest that modularity deficits are a neurobiological marker of externalizing behavior in children. Such specificity has not been demonstrated before using graph theory metrics and has the potential to redefine our understanding of network deficits in children with psychopathology symptoms.

General and Specific Factors of Environmental Stress and Their Associations With Brain Structure and Dimensions of Psychopathology

Background

Early-life stressors can adversely affect the developing brain. While hierarchical modeling has established the existence of a general factor of psychopathology, no studies have modeled a general factor of environmental stress and related this factor to brain development. Using a large sample of children from the ABCD (Adolescent Brain Cognitive Development) Study, the current study aimed to identify general and specific factors of environmental stress and test their associations with brain structure and psychopathology.

Methods

In a sample of 11,878 children, bifactor modeling and higher-order (second-order) modeling identified general and specific factors of environmental stress: family dynamics, interpersonal support, neighborhood socioeconomic status deprivation, and urbanicity. Structural equation modeling was performed to examine associations between these factors and regional gray matter volume (GMV) and cortical thickness as well as general and specific factors of psychopathology.

Results

The general environmental stress factor was associated with globally smaller cortical and subcortical GMV as well as thinner cortices across widespread regions. Family dynamics and neighborhood socioeconomic status deprivation were associated with smaller GMV in focal regions. Urbanicity was associated with larger cortical and subcortical GMV and thicker cortices in frontotemporal regions. The environmental factors were associated with psychopathology in the expected directions. The general factors of environmental stress and psychopathology were both predictors of smaller GMV in children, while remaining distinct from each other.

Conclusions

This study reveals a unifying model of environmental influences that illustrates the inherent organization of environmental stressors and their relationship to brain structure and psychopathology.

Scalable Orthonormal Projective NMF via Diversified Stochastic Optimization

The increasing availability of large-scale neuroimaging initiatives opens exciting opportunities for discovery science of human brain structure and function. Data-driven techniques, such as Orthonormal Projective Non-negative Matrix Factorization (opNMF), are well positioned to explore multivariate relationships in big data towards uncovering brain organization. opNMF enjoys advantageous interpretability and reproducibility compared to commonly used matrix factorization methods like Principal Component Analysis (PCA) and Independent Component Analysis (ICA), which led to its wide adoption in clinical computational neuroscience. However, applying opNMF in large-scale cohort studies is hindered by its limited scalability caused by its accompanying computational complexity. In this work, we address the computational challenges of opNMF using a stochastic optimization approach that learns over mini-batches of the data. Additionally, we diversify the stochastic batches via repulsive point processes, which reduce redundancy in the mini-batches and in turn lead to lower variance in the updates. We validated our framework on gray matter tissue density maps estimated from 1000 subjects part of the Open Access Series of Imaging (OASIS) dataset. We demonstrated that operations over mini-batches of data yield significant reduction in computational cost. Importantly, we showed that our novel optimization does not compromise the accuracy or interpretability of factors when compared to standard opNMF. The proposed model enables new investigations of brain structure using big neuroimaging data that could improve our understanding of brain structure in health and disease.

Brain morphometry points to emerging patterns of psychosis, depression, and anxiety vulnerability over a 2-year period in childhood

BACKGROUND

Gray matter morphometry studies have lent seminal insights into the etiology of mental illness. Existing research has primarily focused on adults and then, typically on a single disorder. Examining brain characteristics in late childhood, when the brain is preparing to undergo significant adolescent reorganization and various forms of serious psychopathology are just first emerging, may allow for a unique and highly important perspective of overlapping and unique pathogenesis.

METHODS

A total of 8645 youth were recruited as part of the Adolescent Brain and Cognitive Development study. Magnetic resonance imaging scans were collected, and psychotic-like experiences (PLEs), depressive, and anxiety symptoms were assessed three times over a 2-year period. Cortical thickness, surface area, and subcortical volume were used to predict baseline symptomatology and symptom progression over time.

RESULTS

Some features could possibly signal common vulnerability, predicting progression across forms of psychopathology (e.g. superior frontal and middle temporal regions). However, there was a specific predictive value for emerging PLEs (lateral occipital and precentral thickness), anxiety (parietal thickness/area and cingulate), and depression (e.g. parahippocampal and inferior temporal).

CONCLUSION

Findings indicate common and distinct patterns of vulnerability for varying forms of psychopathology are present during late childhood, before the adolescent reorganization, and have direct relevance for informing novel conceptual models along with early prevention and intervention efforts.

Neuroanatomical Correlates of Emotion-Related Impulsivity

BACKGROUND

Emotion-related impulsivity (ERI) refers to chronically poor self-control during periods of strong emotion. ERI robustly predicts psychiatric disorders and related problems, yet its neuroanatomical correlates are largely unknown. We tested whether local brain morphometry in targeted brain regions that integrate emotion and control could explain ERI severity.

METHODS

One hundred twenty-two adults (ages 18-55 years) with internalizing or externalizing psychopathology completed a structural magnetic resonance imaging (MRI) scan, the Three-Factor Impulsivity Index, and the Structured Clinical Interview for DSM-5. The Three-Factor Impulsivity Index measures two types of ERI and a third type of impulsivity not linked to emotion. Cortical reconstruction yielded cortical thickness and local gyrification measurements. We evaluated whether morphometry in the orbitofrontal cortex (OFC), insula, amygdala, and nucleus accumbens was associated with ERI severity. Hypotheses and analyses were preregistered.

RESULTS

Lower cortical gyrification in the right lateral OFC was associated with high ERI severity in a full, preregistered model. Separate examinations of local gyrification and cortical thickness also showed a positive association between gyrification in the left lateral OFC and ERI. An integrated measure of hemispheric imbalance in lateral OFC gyrification (right < left) correlated with ERI severity. These findings were specific to ERI and did not appear with non-emotion-related impulsivity.

CONCLUSIONS

Local gyrification in the lateral OFC is associated with ERI severity. The current findings fit with existing theories of OFC function, strengthen the connections between the transdiagnostic literature in psychiatry and neuroscience, and may guide future treatment development.

A cross-sectional and longitudinal study of human brain development: The integration of cortical thickness, surface area, gyrification index, and cortical curvature into a unified analytical framework

Brain maturation studies typically examine relationships linking a single morphometric feature with cognition, behavior, age, or other demographic characteristics. However, the coordinated spatiotemporal arrangement of morphological features across development and their associations with behavior are unclear. Here, we examine covariation across multiple cortical features (cortical thickness [CT], surface area [SA], local gyrification index [GI], and mean curvature [MC]) using magnetic resonance images from the NIMH developmental cohort (ages 5-25). Neuroanatomical covariance was examined using non-negative matrix factorization (NMF), which decomposes covariance resulting in a parts-based representation. Cross-sectionally, we identified six components of covariation which demonstrate differential contributions of CT, GI, and SA in hetero- vs. unimodal areas. Using this technique to examine covariance in rates of change to identify longitudinal sources of covariance highlighted preserved SA in unimodal areas and changes in CT and GI in heteromodal areas. Using behavioral partial least squares (PLS), we identified a single latent variable (LV) that recapitulated patterns of reduced CT, GI, and SA related to older age, with limited contributions of IQ and SES. Longitudinally, PLS revealed three LVs that demonstrated a nuanced developmental pattern that highlighted a higher rate of maturational change in SA and CT in higher IQ and SES females. Finally, we situated the components in the changing architecture of cortical gradients. This novel characterization of brain maturation provides an important understanding of the interdependencies between morphological measures, their coordinated development, and their relationship to biological sex, cognitive ability, and the resources of the local environment.

Scalable NMF via linearly optimized data compression

Orthonormal projective non-negative matrix factorization (opNMF) has been widely used in neuroimaging and clinical neuroscience applications to derive representations of the brain in health and disease. The non-negativity and orthonormality constraints of opNMF result in intuitive and well-localized factors. However, the advantages of opNMF come at a steep computational cost that prohibits its use in large-scale data. In this work, we propose novel and scalable optimization schemes for orthonormal projective non-negative matrix factorization that enable the use of the method in large-scale neuroimaging settings. We replace the high-dimensional data matrix with its corresponding singular value decomposition (SVD) and QR decompositions and combine the decompositions with opNMF multiplicative update algorithm. Empirical validation of the proposed methods demonstrated significant speed-up in computation time while keeping memory consumption low without compromising the accuracy of the solution.

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.

Differences in cortical morphology and child internalizing or externalizing problems: Accounting for the co-occurrence

Background

Childhood internalizing and externalizing problems frequently co-occur. Many studies report neural correlates of either internalizing or externalizing problems, but few account for their co-occurrence. We aimed to assess specific cortical substrates of these psychiatric problems.

Methods

We used data from 9635 children aged 9-11 years in the baseline Adolescent Brain Cognitive Development Study. Internalizing and externalizing problem composite scales scores were derived from the Child Behavior Checklist. We standardized FreeSurfer-derived volumes of 68 cortical regions. We examined internalizing and externalizing problems separately and jointly (covariate-adjustment) in relation to cortical volumes, with and without adjusting for total brain volume (TBV) in multivariate linear regressions adjusted for demographics and multiple comparisons. We fit bifactor models to confirm the consistency of patterns exploring specific internalizing and specific externalizing problems. Sensitivity analyses included a vertex-wide analysis and a replication in another large population-based study.

Results

In separate TBV-unadjusted analyses, externalizing and internalizing problems were associated with smaller cortical volumes. If adjusted for externalizing behavior, however, larger cortical volumes were associated with internalizing problems, while smaller cortical volumes remained associated with externalizing problems after adjustment for internalizing problems. The bifactor model produced similar results, which were consistently replicated in another pre-adolescent neuroimaging sample. These associations likely represent global effects: adjusting for TBV rendered most associations non-significant. Vertex-wise analyses confirmed global patterns.

Conclusion

Our results suggest that internalizing and externalizing problems have globally opposing, and non-specific associations with cortical morphology in childhood, which are only apparent if analyses account for their co-occurrence.

Deviations from normative brain white and gray matter structure are associated with psychopathology in youth

Combining imaging modalities and metrics that are sensitive to various aspects of brain structure and maturation may help identify individuals that show deviations in relation to same-aged peers, and thus benefit early-risk-assessment for mental disorders. We used one timepoint multimodal brain imaging, cognitive, and questionnaire data from 1280 eight- to twenty-one-year-olds from the Philadelphia Neurodevelopmental Cohort. We estimated age-related gray and white matter properties and estimated individual deviation scores using normative modeling. Next, we tested for associations between the estimated deviation scores, and with psychopathology domain scores and cognition. More negative deviations in DTI-based fractional anisotropy (FA) and the first principal eigenvalue of the diffusion tensor (L1) were associated with higher scores on psychosis positive and prodromal symptoms and general psychopathology. A more negative deviation in cortical thickness (CT) was associated with a higher general psychopathology score. Negative deviations in global FA, surface area, L1 and CT were also associated with poorer cognitive performance. No robust associations were found between the deviation scores based on CT and DTI. The low correlations between the different multimodal magnetic resonance imaging-based deviation scores suggest that psychopathological burden in adolescence can be mapped onto partly distinct neurobiological features.

Multiscale neural gradients reflect transdiagnostic effects of major psychiatric conditions on cortical morphology

It is increasingly recognized that multiple psychiatric conditions are underpinned by shared neural pathways, affecting similar brain systems. Here, we carried out a multiscale neural contextualization of shared alterations of cortical morphology across six major psychiatric conditions (autism spectrum disorder, attention deficit/hyperactivity disorder, major depression disorder, obsessive-compulsive disorder, bipolar disorder, and schizophrenia). Our framework cross-referenced shared morphological anomalies with respect to cortical myeloarchitecture and cytoarchitecture, as well as connectome and neurotransmitter organization. Pooling disease-related effects on MRI-based cortical thickness measures across six ENIGMA working groups, including a total of 28,546 participants (12,876 patients and 15,670 controls), we identified a cortex-wide dimension of morphological changes that described a sensory-fugal pattern, with paralimbic regions showing the most consistent alterations across conditions. The shared disease dimension was closely related to cortical gradients of microstructure as well as neurotransmitter axes, specifically cortex-wide variations in serotonin and dopamine. Multiple sensitivity analyses confirmed robustness with respect to slight variations in analytical choices. Our findings embed shared effects of common psychiatric conditions on brain structure in multiple scales of brain organization, and may provide insights into neural mechanisms of transdiagnostic vulnerability.

Association between mild traumatic brain injury, brain structure, and mental health outcomes in the Adolescent Brain Cognitive Development Study

BACKGROUND

Children that experience a mild traumatic brain injury (mTBI) are at an increased risk of neural alterations that can deteriorate mental health. We test the hypothesis that mTBI is associated with psychopathology and that structural brain metrics (e.g., volume, area) meaningfully mediate the relation in an adolescent population.

METHODS

We analyzed behavioral and brain MRI data from 11,876 children who participated in the Adolescent Brain Cognitive Development (ABCD) Study. Mixed-effects models were used to examine the longitudinal association between mTBI and mental health outcomes. Bayesian methods were used to investigate brain regions that are intermediate between mTBI and symptoms of poor mental health.

RESULTS

There were 199 children with mTBI and 527 with possible mTBI across the three ABCD Study visits. There was a 7% (IRR = 1.07, 95% CI: 1.01, 1.13) and 15% (IRR = 1.16, 95% CI: 1.05, 1.26) increased risk of emotional or behavioral problems in children that experienced possible mTBI or mTBI, respectively. Possible mTBI was associated with a 17% (IRR: 1.17, 95% CI: 0.99, 1.40) increased risk of experiencing distress following a psychotic-like experience. We did not find any brain regions that meaningfully mediated the relationship between mTBI and mental health outcomes. Analysis of volumetric measures found that approximately 2% to 5% of the total effect of mTBI on mental health outcomes operated through total cortical volume. Image intensity measure analyses determined that approximately 2% to 5% of the total effect was mediated through the left-hemisphere of the dorsolateral prefrontal cortex.

CONCLUSION

Results indicate an increased risk of emotional and behavioral problems in children that experienced possible mTBI or mTBI. Mediation analyses did not elucidate the mechanisms underlying the association between mTBI and mental health outcomes.

Linking cerebellar functional gradients to transdiagnostic behavioral dimensions of psychopathology

High co-morbidity and substantial overlap across psychiatric disorders encourage a transition in psychiatry research from categorical to dimensional approaches that integrate neuroscience and psychopathology. Converging evidence suggests that the cerebellum is involved in a wide range of cognitive functions and mental disorders. An important question thus centers on the extent to which cerebellar function can be linked to transdiagnostic dimensions of psychopathology. To address this question, we used a multivariate data-driven statistical technique (partial least squares) to identify latent dimensions linking human cerebellar connectome as assessed by functional MRI to a large set of clinical, cognitive, and trait measures across 198 participants, including healthy controls (n = 92) as well as patients diagnosed with attention-deficit/hyperactivity disorder (n = 35), bipolar disorder (n = 36), and schizophrenia (n = 35). Macroscale spatial gradients of connectivity at voxel level were used to characterize cerebellar connectome properties, which provide a low-dimensional representation of cerebellar connectivity, i.e., a sensorimotor-supramodal hierarchical organization. This multivariate analysis revealed significant correlated patterns of cerebellar connectivity gradients and behavioral measures that could be represented into four latent dimensions: general psychopathology, impulsivity and mood, internalizing symptoms and executive dysfunction. Each dimension was associated with a unique spatial pattern of cerebellar connectivity gradients across all participants. Multiple control analyses and 10-fold cross-validation confirmed the robustness and generalizability of the yielded four dimensions. These findings highlight the relevance of cerebellar connectivity as a necessity for the study and classification of transdiagnostic dimensions of psychopathology and call on researcher to pay more attention to the role of cerebellum in the dimensions of psychopathology, not just within the cerebral cortex.

Neural substrates of motivational dysfunction across neuropsychiatric conditions: Evidence from meta-analysis and lesion network mapping

Motivational dysfunction constitutes one of the fundamental dimensions of psychopathology cutting across traditional diagnostic boundaries. However, it is unclear whether there is a common neural circuit responsible for motivational dysfunction across neuropsychiatric conditions. To address this issue, the current study combined a meta-analysis on psychiatric neuroimaging studies of reward/loss anticipation and consumption (4308 foci, 438 contrasts, 129 publications) with a lesion network mapping approach (105 lesion cases). Our meta-analysis identified transdiagnostic hypoactivation in the ventral striatum (VS) for clinical/at-risk conditions compared to controls during the anticipation of both reward and loss. Moreover, the VS subserves a key node in a distributed brain network which encompasses heterogeneous lesion locations causing motivation-related symptoms. These findings do not only provide the first meta-analytic evidence of shared neural alternations linked to anticipatory motivation-related deficits, but also shed novel light on the role of VS dysfunction in motivational impairments in terms of both network integration and psychological functions. Particularly, the current findings suggest that motivational dysfunction across neuropsychiatric conditions is rooted in disruptions of a common brain network anchored in the VS, which contributes to motivational salience processing rather than encoding positive incentive values.

Developmental patterning of irritability enhances prediction of psychopathology in preadolescence: Improving RDoC with developmental science

The transdiagnostic importance of irritability in psychopathology has been demonstrated. However, the contribution of developmentally unfolding irritability patterns to specific clinical and neural outcomes remains an important and unanswered question. To address this gap in the literature, irritability patterns of 110 youth from a large, diverse cohort were assessed at preschool age and again at early school age (∼2.5 years later) with a dimensional irritability scale designed to capture the normal:abnormal spectrum. At preadolescence (∼6 years later), clinical outcomes (internalizing/externalizing symptoms) derived from a semistructured clinical interview and neural outcomes (characterized as gray-matter-volume abnormalities) were assessed. For clinical outcomes, preschool-age irritability alone was a transdiagnostic predictor of internalizing and externalizing symptoms at preadolescence. However, in a model including both preschool and early school age, irritability provided greater specificity, suggesting that higher irritability at early school age related to elevated preadolescent externalizing but not internalizing symptoms. In terms of neural outcomes, elevated preschool irritability did not predict preadolescent gray-matter-volume abnormality; however, irritability at early school age demonstrated an interactive effect among regions, with reduced volume in preadolescence emotional regions (e.g., amygdala, medial orbitofrontal cortex) and increased volume in other regions (e.g., cerebellum). These complex patterns highlight the contribution of a developmentally informed approach, the National Institute of Mental Health's Research Domain Criteria (RDoC) approach, to yield transdiagnostic phenotypes and multiple units of analysis. Capturing these individual differences and developmental heterogeneity can provide critical insight into the unfolding of mechanisms underlying emerging psychopathology. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The relationship between brain structure and general psychopathology in preadolescents

BACKGROUND

An emerging body of literature has indicated that broad, transdiagnostic dimensions of psychopathology are associated with alterations in brain structure across the life span. The current study aimed to investigate the relationship between brain structure and broad dimensions of psychopathology in the critical preadolescent period when psychopathology is emerging.

METHODS

This study included baseline data from the Adolescent Brain and Cognitive Development (ABCD) Study^® (n = 11,875; age range = 9-10 years; male = 52.2%). General psychopathology, externalizing, internalizing, and thought disorder dimensions were based on a higher-order model of psychopathology and estimated using Bayesian plausible values. Outcome variables included global and regional cortical volume, thickness, and surface area.

RESULTS

Higher levels of psychopathology across all dimensions were associated with lower volume and surface area globally, as well as widespread and pervasive alterations across the majority of cortical and subcortical regions studied, after adjusting for sex, race/ethnicity, parental education, income, and maternal psychopathology. The relationships between general psychopathology and brain structure were attenuated when adjusting for cognitive functioning. There were no statistically significant relationships between psychopathology and cortical thickness in this sample of preadolescents.

CONCLUSIONS

The current study identified lower cortical volume and surface area as transdiagnostic biomarkers for general psychopathology in preadolescence. Future research may focus on whether the widespread and pervasive relationships between general psychopathology and brain structure reflect cognitive dysfunction that is a feature across a range of mental illnesses.

Clinical, Brain, and Multilevel Clustering in Early Psychosis and Affective Stages

Importance

Approaches are needed to stratify individuals in early psychosis stages beyond positive symptom severity to investigate specificity related to affective and normative variation and to validate solutions with premorbid, longitudinal, and genetic risk measures.

Objective

To use machine learning techniques to cluster, compare, and combine subgroup solutions using clinical and brain structural imaging data from early psychosis and depression stages.

Design, Setting, and Participants

A multisite, naturalistic, longitudinal cohort study (10 sites in 5 European countries; including major follow-up intervals at 9 and 18 months) with a referred patient sample of those with clinical high risk for psychosis (CHR-P), recent-onset psychosis (ROP), recent-onset depression (ROD), and healthy controls were recruited between February 1, 2014, to July 1, 2019. Data were analyzed between January 2020 and January 2022.

Main Outcomes and Measures

A nonnegative matrix factorization technique separately decomposed clinical (287 variables) and parcellated brain structural volume (204 gray, white, and cerebrospinal fluid regions) data across CHR-P, ROP, ROD, and healthy controls study groups. Stability criteria determined cluster number using nested cross-validation. Validation targets were compared across subgroup solutions (premorbid, longitudinal, and schizophrenia polygenic risk scores). Multiclass supervised machine learning produced a transferable solution to the validation sample.

Results

There were a total of 749 individuals in the discovery group and 610 individuals in the validation group. Individuals included those with CHR-P (n = 287), ROP (n = 323), ROD (n = 285), and healthy controls (n = 464), The mean (SD) age was 25.1 (5.9) years, and 702 (51.7%) were female. A clinical 4-dimensional solution separated individuals based on positive symptoms, negative symptoms, depression, and functioning, demonstrating associations with all validation targets. Brain clustering revealed a subgroup with distributed brain volume reductions associated with negative symptoms, reduced performance IQ, and increased schizophrenia polygenic risk scores. Multilevel results distinguished between normative and illness-related brain differences. Subgroup results were largely validated in the external sample.

Conclusions and Relevance

The results of this longitudinal cohort study provide stratifications beyond the expression of positive symptoms that cut across illness stages and diagnoses. Clinical results suggest the importance of negative symptoms, depression, and functioning. Brain results suggest substantial overlap across illness stages and normative variation, which may highlight a vulnerability signature independent from specific presentations. Premorbid, longitudinal, and genetic risk validation suggested clinical importance of the subgroups to preventive treatments.

Chinese adult brain atlas with functional and white matter parcellation

Brain atlases play important roles in studying anatomy and function of the brain. As increasing interests in multi-modal magnetic resonance imaging (MRI) approaches, such as combining structural MRI, diffusion weighted imaging (DWI), and resting-state functional MRI (rs-fMRI), there is a need to construct integrated brain atlases based on these three imaging modalities. This study constructed a multi-modal brain atlas for a Chinese aging population (n = 180, age: 22-79 years), which consists of a T1 atlas showing the brain morphology, a high angular resolution diffusion imaging (HARDI) atlas delineating the complex fiber architecture, and a rs-fMRI atlas reflecting brain intrinsic functional organization in one stereotaxic coordinate. We employed large deformation diffeomorphic metric mapping (LDDMM) and unbiased diffeomorphic atlas generation to simultaneously generate the T1 and HARDI atlases. Using spectral clustering, we generated 20 brain functional networks from rs-fMRI data. We demonstrated the use of the atlas to explore the coherent markers among the brain morphology, functional networks, and white matter tracts for aging and gender using joint independent component analysis.

Neurobiological, familial and genetic risk factors for dimensional psychopathology in the Adolescent Brain Cognitive Development study

BACKGROUND

Adolescence is a key period for brain development and the emergence of psychopathology. The Adolescent Brain Cognitive Development (ABCD) study was created to study the biopsychosocial factors underlying healthy and pathological brain development during this period, and comprises the world's largest youth cohort with neuroimaging, family history and genetic data.

METHODS

We examined 9856 unrelated 9-to-10-year-old participants in the ABCD study drawn from 21 sites across the United States, of which 7662 had multimodal magnetic resonance imaging scans passing quality control, and 4447 were non-Hispanic white and used for polygenic risk score analyses. Using data available at baseline, we associated eight 'syndrome scale scores' from the Child Behavior Checklist-summarizing anxious/depressed symptoms, withdrawn/depressed symptoms, somatic complaints, social problems, thought problems, attention problems, rule-breaking behavior, and aggressive behavior-with resting-state functional and structural brain magnetic resonance imaging measures; eight indicators of family history of psychopathology; and polygenic risk scores for major depression, bipolar disorder, schizophrenia, attention deficit hyperactivity disorder (ADHD) and anorexia nervosa. As a sensitivity analysis, we excluded participants with clinically significant (>97th percentile) or borderline (93rd-97th percentile) scores for each dimension.

RESULTS

Most Child Behavior Checklist dimensions were associated with reduced functional connectivity within one or more of four large-scale brain networks-default mode, cingulo-parietal, dorsal attention, and retrosplenial-temporal. Several dimensions were also associated with increased functional connectivity between the default mode, dorsal attention, ventral attention and cingulo-opercular networks. Conversely, almost no global or regional brain structural measures were associated with any of the dimensions. Every family history indicator was associated with every dimension. Major depression polygenic risk was associated with six of the eight dimensions, whereas ADHD polygenic risk was exclusively associated with attention problems and externalizing behavior (rule-breaking and aggressive behavior). Bipolar disorder, schizophrenia and anorexia nervosa polygenic risk were not associated with any of the dimensions. Many associations remained statistically significant even after excluding participants with clinically significant or borderline psychopathology, suggesting that the same risk factors that contribute to clinically significant psychopathology also contribute to continuous variation within the clinically normal range.

CONCLUSIONS

This study codifies neurobiological, familial and genetic risk factors for dimensional psychopathology across a population-scale cohort of community-dwelling preadolescents. Future efforts are needed to understand how these multiple modalities of risk intersect to influence trajectories of psychopathology into late adolescence and adulthood.

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).

Timing and Type of Early Psychopathology Symptoms Predict Longitudinal Change in Cortical Thickness From Middle Childhood Into Early Adolescence

BACKGROUND

Early-life experiences have profound effects on functioning in adulthood. Altered cortical development may be one mechanism through which early-life experiences, including poverty and psychopathology symptoms, affect outcomes. However, there is little prospective research beginning early in development that combines clinician-rated psychopathology symptoms and multiwave magnetic resonance imaging to examine when these relationships emerge.

METHODS

Children from the Preschool Depression Study who completed diagnostic interviews at three different developmental stages (preschool, school age, early adolescent) and up to three magnetic resonance imaging scans beginning in middle childhood participated in this study (N = 138). Multilevel models were used to calculate intercepts and slopes of cortical thickness within a priori cortical regions of interest. Linear regressions probed how early-life poverty and psychopathology (depression, anxiety, and externalizing symptoms at separate developmental periods) related to intercept/slope.

RESULTS

Collectively, experiences during the preschool period predicted reduced cortical thickness, via either reduced intercept or accelerated thinning (slope). Early-life poverty predicted intercepts within sensory and sensory-motor integration regions. Beyond poverty, preschool anxiety symptoms predicted intercepts within the insula, subgenual cingulate, and inferior parietal cortex. Preschool externalizing symptoms predicted accelerated thinning within prefrontal and parietal cortices. Depression and anxiety/externalizing symptoms at later ages were not significant predictors.

CONCLUSIONS

Early childhood is a critical period of risk; experiences at this developmental stage specifically have the potential for prolonged influence on brain development. Negative early experiences collectively predicted reduced cortical thickness, but the specific neural systems affected aligned with those typically implicated in these individual disorders/experiences.

Effects of prenatal nutrient supplementation and early life exposures on neurodevelopment at age 10: a randomised controlled trial - the COPSYCH study protocol

INTRODUCTION

Nutrient deficiency and immune and inflammatory disturbances in early life may compromise neurodevelopment and be implicated in the aetiology of psychiatric disorders. However, current evidence is limited by its predominantly observational nature. COpenhagen Prospective Study on Neuro-PSYCHiatric Development (COPSYCH) is a research alliance between Copenhagen Prospective Studies on Asthma in Childhood (COPSAC) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research with the overall aim to investigate effects of prenatal and early life exposures on neurodevelopment at 10 years. COPSYCH will investigate the impact of prenatal n-3 long-chain polyunsaturated fatty acids (n-3 LCPUFA) and high-dose vitamin D supplementation on neurodevelopment reflected by brain development, neurocognition and psychopathology. Moreover, the neurodevelopmental impact of early life exposures such as infections, low grade inflammation and the gut microbiome will be scrutinised.

METHODS AND ANALYSIS

COPSYCH is based on the prospective and ongoing COPSAC2010 birth cohort of 700 mother-child pairs. Randomised controlled trials of supplementation with n-3 LCPUFA and/or high-dose vitamin D or placebo in the third trimester were embedded in a factorial 2×2 design (ClinicalTrials.gov: NCT01233297 and NCT00856947). This unique cohort provides deep phenotyping data from 14 previous clinical follow-up visits and exposure assessments since birth. The ongoing 10-year visit is a 2-day visit. Day 1 includes a comprehensive neurocognitive examination, and assessment of psychopathological dimensions, and assessment of categorical psychopathology. Day 2 includes acquisition of brain structural, diffusion and functional sequences using 3 Tesla MRI. Study outcomes are neurocognitive, psychopathological and MRI measures.

ETHICS AND DISSEMINATION

This study has been approved by the Danish National Committee on Health Research Ethics and The Danish Data Protection Agency. The study is conducted in accordance with the guiding principles of the Declaration of Helsinki. Parents gave written informed consent before enrolment.

Neural Signatures of Data-Driven Psychopathology Dimensions at the Transition to Adolescence

Background

One of the challenges in human neuroscience is to uncover associations between brain organization and psychopathology in order to better understand the biological underpinnings of mental disorders. Here, we aimed to characterize the neural correlates of psychopathology dimensions obtained using two conceptually different data-driven approaches.

Methods

Dimensions of psychopathology that were either maximally dissociable or correlated were respectively extracted by independent component analysis (ICA) and exploratory factor analysis (EFA) applied to the Childhood Behavior Checklist items from 9- to 10-year-olds (n = 9983; 47.8% female, 50.8% white) participating in the Adolescent Brain Cognitive Development study. The patterns of brain morphometry, white matter integrity and resting-state connectivity associated with each dimension were identified using kernel-based regularized least squares and compared between dimensions using Spearman’s correlation coefficient.

Results

ICA identified three psychopathology dimensions, representing opposition–disinhibition, cognitive dyscontrol, and negative affect, with distinct brain correlates. Opposition–disinhibition was negatively associated with cortical surface area, cognitive dyscontrol was negatively associated with anatomical and functional dysconnectivity while negative affect did not show discernable associations with any neuroimaging measure. EFA identified three dimensions representing broad externalizing, neurodevelopmental, and broad Internalizing problems with partially overlapping brain correlates. All EFA-derived dimensions were negatively associated with cortical surface area, whereas measures of functional and structural connectivity were associated only with the neurodevelopmental dimension.

Conclusions

This study highlights the importance of cortical surface area and global connectivity for psychopathology in preadolescents and provides evidence for dissociable psychopathology dimensions with distinct brain correlates.

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

Different psychiatric disorders and symptoms are highly correlated in the general population. A general psychopathology factor (or "P-factor") has been proposed to efficiently describe this covariance of psychopathology. Recently, genetic and neuroimaging studies also derived general dimensions that reflect densely correlated genomic and neural effects on behaviour and psychopathology. While these three types of general dimensions show striking parallels, it is unknown how they are conceptually related. Here, we provide an overview of these three general dimensions, and suggest a unified interpretation of their nature and underlying mechanisms. We propose that the general dimensions reflect, in part, a combination of heritable 'environmental' factors, driven by a dense web of gene-environment correlations. This perspective calls for an update of the traditional endophenotype framework, and encourages methodological innovations to improve models of gene-brain-environment relationships in all their complexity. We propose concrete approaches, which by taking advantage of the richness of current large databases will help to better disentangle the complex nature of causal factors underlying psychopathology.

Associations between Brain Structural Alterations, Executive Dysfunction, and General Psychopathology in a Healthy and Cross-Diagnostic Adult Patient Sample

BACKGROUND

A general psychopathology 'p' factor captures shared variance across mental disorders in diverse samples and may partly reflect executive dysfunction. Higher p factor scores have been related to structural alterations within the visual association cortex (VAC) and a cerebello-thalamo-cerebrocortical circuit (CTCC), both of which are important for executive control. Here, we tested replicability of these direct associations as well as the indirect role of executive functioning in a sample of healthy and cross-diagnostic adult patients.

METHODS

We conducted hypothesis-driven (i.e., region-of-interest) and exploratory whole-brain structural neuroimaging analyses using data from the Consortium for Neuropsychiatric Phenomics study of 272 adults who met diagnostic criteria for schizophrenia, bipolar disorder, or attention deficit-hyperactivity disorder or were healthy controls. Using structural equation modeling, we examined direct and indirect relations between structural neural alterations (within regions-of-interest and regions identified from exploratory analyses) and p and executive function factors.

RESULTS

Higher levels of p were associated with decreased executive functioning and VAC grey matter volume, replicating previous research. In contrast, we failed to replicate prior negative relations between the p factor and CTCC structure. A significant indirect relation between VAC grey matter volume and p via executive function also emerged. Whole-brain analyses identified additional structural alterations in supplementary motor area/cingulate cortex, anterior corona radiata, and corpus callosum genu related to the p factor.

CONCLUSIONS

Executive dysfunction may be one mechanism underlying relations between brain structure and general psychopathology. Replication of VAC structural alterations related to p encourages further focus on this brain structure.

Neurodevelopment of the association cortices: Patterns, mechanisms, and implications for psychopathology

The human brain undergoes a prolonged period of cortical development that spans multiple decades. During childhood and adolescence, cortical development progresses from lower-order, primary and unimodal cortices with sensory and motor functions to higher-order, transmodal association cortices subserving executive, socioemotional, and mentalizing functions. The spatiotemporal patterning of cortical maturation thus proceeds in a hierarchical manner, conforming to an evolutionarily rooted, sensorimotor-to-association axis of cortical organization. This developmental program has been characterized by data derived from multimodal human neuroimaging and is linked to the hierarchical unfolding of plasticity-related neurobiological events. Critically, this developmental program serves to enhance feature variation between lower-order and higher-order regions, thus endowing the brain's association cortices with unique functional properties. However, accumulating evidence suggests that protracted plasticity within late-maturing association cortices, which represents a defining feature of the human developmental program, also confers risk for diverse developmental psychopathologies.

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.

Linking RDoC and HiTOP: A new interface for advancing psychiatric nosology and neuroscience

The Research Domain Criteria (RDoC) and the Hierarchical Taxonomy of Psychopathology (HiTOP) represent major dimensional frameworks proposing two alternative approaches to accelerate progress in the way psychopathology is studied, classified, and treated. RDoC is a research framework rooted in neuroscience aiming to further the understanding of transdiagnostic biobehavioral systems underlying psychopathology and ultimately inform future classifications. HiTOP is a dimensional classification system, derived from the observed covariation among symptoms of psychopathology and maladaptive traits, which seeks to provide more informative research and treatment targets (i.e., dimensional constructs and clinical assessments) than traditional diagnostic categories. This article argues that the complementary strengths of RDoC and HiTOP can be leveraged in order to achieve their respective goals. RDoC's biobehavioral framework may help elucidate the underpinnings of the clinical dimensions included in HiTOP, whereas HiTOP may provide psychometrically robust clinical targets for RDoC-informed research. We present a comprehensive mapping between dimensions included in RDoC (constructs and subconstructs) and HiTOP (spectra and subfactors) based on narrative review of the empirical literature. The resulting RDoC-HiTOP interface sheds light on the biobehavioral correlates of clinical dimensions and provides a broad set of dimensional clinical targets for etiological and neuroscientific research. We conclude with future directions and practical recommendations for using this interface to advance clinical neuroscience and psychiatric nosology. Ultimately, we envision that this RDoC-HiTOP interface has the potential to inform the development of a unified, dimensional, and biobehaviorally-grounded psychiatric nosology.

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.