Unravelling Robust Brain-Behavior Links of Depressive Symptoms Through Granular Network Models: Understanding Heterogeneity and Clinical Implications

A Network Approach to Understanding the Role of Executive Functioning and Alpha Oscillations in Inattention and Hyperactivity-Impulsivity Symptoms of ADHD

OBJECTIVE

ADHD is a prevalent neurodevelopmental disorder characterized by symptoms of inattention and hyperactivity-impulsivity. Impairments in executive functioning (EF) are central to models of ADHD, while alpha-band spectral power event-related decreases (ERD) have emerged as a putative electroencephalography (EEG) biomarker of EF in ADHD. Little is known about the roles of EF and alpha ERD and their interactions with symptoms of ADHD.

METHOD

We estimated network models of ADHD symptoms and integrated alpha ERD measures into the symptom network.

RESULTS

EF emerges as a bridge network node connecting alpha ERD and the hyperactivity/impulsivity and inattention symptoms. We found that EF most closely relates to a subset of symptoms, namely the motoric symptoms, "seat" (difficulty staying seated), and "runs" (running or climbing excessively).

CONCLUSIONS

EF functions as a bridge node connecting alpha ERD and the ADHD symptom network. Motoric-type symptoms and EF deficits may constitute important nodes in the interplay between behavior/symptoms, cognition, and neurophysiological markers of ADHD.

Symptom Network Analysis Tools for Applied Researchers With Cross-Sectional and Panel Data - A Brief Overview and Multiverse Analysis

In recent years, there has been a growing interest in utilizing symptom-network models to study psychopathology and relevant risk factors, such as cognitive and physical health. Various methodological approaches can be employed by researchers analyzing cross-sectional and panel data (i.e., several time points over an extended period). This paper provides an overview of some commonly used analytical tools, including moderated network models, network comparison tests, cross-lagged network analysis, and panel graphical vector-autoregression (VAR) models. Using an easily accessible dataset (easySHARE), this study demonstrates the use of different analytical approaches when investigating (a) the association between mental health and cognitive functioning, and (b) the role of chronic disease in mediating or moderating this association. This multiverse analysis showcases both converging and diverging evidence from different analytical avenues. These findings underscore the importance of multiverse investigations to increase transparency and communicate the extent to which conclusions depend on analytical choices.