Processing Speed is Related to the General Psychopathology Factor in Youth.

The relationship between the p factor and cognition in youth has largely focused on general cognition (IQ) and executive functions (EF). Another cognitive construct, processing speed (PS), is dissociable from IQ and EF, but has received less research attention despite being related to many different mental health symptoms. The present sample included 795 youth, ages 11-16 from the Colorado Learning Disabilities Research Center (CLDRC) sample. Confirmatory factor analyses tested multiple p factor models, with the primary model being a second-order, multi-reporter p factor. We then tested the correlation between the p factor and a latent PS factor. There was a significant, negative correlation between the p factor and PS (r(87) = -0.42, p < .001), indicating that slower processing speed is associated with higher general mental health symptoms. This association is stronger than previously reported associations with IQ or EF. This finding was robust across models that used different raters (youth and caregiver) and modeling approaches (second-order vs. bifactor). Our findings indicate that PS is related to general psychopathology symptoms. This research points to processing speed as an important transdiagnostic construct that warrants further exploration across development.

Diagnostic Associations of Processing Speed in a Transdiagnostic, Pediatric Sample.

INTRODUCTION: The present study examines the relationships between processing speed (PS), mental health disorders, and learning disorders. Prior work has tended to explore relationships between PS deficits and specific diagnoses in isolation of one another. Here, we simultaneously investigated PS associations with five diagnoses (i.e., anxiety, autism, ADHD, depressive, specific learning) in a large-scale, transdiagnostic, community self-referred sample. METHOD: 843 children, ages 8-16 were included from the Healthy Brain Network (HBN) Biobank. Principal component analysis (PCA) was employed to create a composite measure of four PS tasks, referred to as PC1. Intraclass correlation coefficient (ICC) between the four PS measures, as well as PC1, were calculated to assess reliability. RESULTS: ICCs were moderate between WISC-V tasks (0.663), and relatively modest between NIH Toolbox Pattern Comparison and other PS scales (0.14-0.27). Regression analyses revealed specific significant relationships between PS and reading and math disabilities, ADHD-inattentive presentation (ADHD-I), and ADHD-combined presentation (ADHD-C). After accounting for inattention, the present study did not find a significant relationship with Autism Spectrum Disorder. DISCUSSION: Our examination of PS in a large, transdiagnostic sample suggested more specific associations with ADHD and learning disorders than the literature currently suggests. Implications for understanding how PS interacts with a highly heterogeneous childhood sample are discussed.

Prospective Associations Between Sleep Disturbance and Repetitive Negative Thinking: The Mediating Roles of Focusing and Shifting Attentional Control.

Although considerable evidence has linked sleep disturbance to symptoms of psychopathology, including repetitive negative thinking, few studies have examined how sleep disturbance may predict repetitive negative thinking over time. Further, no study to date has examined specific mechanisms that may account for this relationship. The present study sought to address these gaps in the literature by testing focusing and shifting attentional control as two potential mediators of the relationship between sleep disturbance and repetitive negative thinking over a 6-month period. A final sample of 445 unselected community participants completed measures of sleep disturbance and repetitive negative thinking at Time 1, measures of focusing and shifting attentional control 3 months later, and measures of repetitive negative thinking again 6 months later. Results revealed that focusing, but not shifting, attentional control mediated the relationship between sleep disturbance and repetitive negative thinking, specifically, worry, rumination, and obsessions. These findings provide preliminary evidence for focusing attentional control as a candidate mechanism that may explain the causal role of sleep disturbance in the development of repetitive negative thinking observed in various disorders.

An open resource for transdiagnostic research in pediatric mental health and learning disorders.

Technological and methodological innovations are equipping researchers with unprecedented capabilities for detecting and characterizing pathologic processes in the developing human brain. As a result, ambitions to achieve clinically useful tools to assist in the diagnosis and management of mental health and learning disorders are gaining momentum. To this end, it is critical to accrue large-scale multimodal datasets that capture a broad range of commonly encountered clinical psychopathology. The Child Mind Institute has launched the Healthy Brain Network (HBN), an ongoing initiative focused on creating and sharing a biobank of data from 10,000 New York area participants (ages 5-21). The HBN Biobank houses data about psychiatric, behavioral, cognitive, and lifestyle phenotypes, as well as multimodal brain imaging (resting and naturalistic viewing fMRI, diffusion MRI, morphometric MRI), electroencephalography, eye-tracking, voice and video recordings, genetics and actigraphy. Here, we present the rationale, design and implementation of HBN protocols. We describe the first data release (n=664) and the potential of the biobank to advance related areas (e.g., biophysical modeling, voice analysis).