How Predictable are Symptoms in Psychopathological Networks? A Reanalysis of 18 Published Datasets

TL;DR

This study introduces a new measure called predictability to assess how much individual symptoms in psychopathological networks are determined by their neighbors, providing insights beyond traditional centrality measures.

Contribution

The paper presents the concept of predictability as an absolute measure of node determination in psychopathological networks and applies it to 18 datasets, revealing its variability and clinical relevance.

Findings

Predictability varies considerably within and between datasets.

Higher predictability in community samples compared to clinical samples.

Predictability is highest in mood and anxiety disorders, lowest in psychosis.

Abstract

Background Network analyses on psychopathological data focus on the network structure and its derivatives such as node centrality. One conclusion one can draw from centrality measures is that the node with the highest centrality is likely to be the node that is determined most by its neighboring nodes. However, centrality is a relative measure: knowing that a node is highly central gives no information about the extent to which it is determined by its neighbors. Here we provide an absolute measure of determination (or controllability) of a node - its predictability. We introduce predictability, estimate the predictability of all nodes in 18 prior empirical network papers on psychopathology, and statistically relate it to centrality. Methods We carried out a literature review and collected 25 datasets from 18 published papers in the field (several mood and anxiety disorders, substance abuse, psychosis, autism, and transdiagnostic data). We fit state-of-the-art net- work models to all datasets, and computed the predictability of all nodes. Results Predictability was unrelated to sample size, moderately high in most symptom networks, and differed considerable both within and between datasets. Predictability was higher in community than clinical samples, highest for mood and anxiety disorders, and lowest for psychosis. Conclusions Predictability is an important additional characterization of symptom networks because it gives an absolute measure of the controllability of each node. It allows conclusions about how self-determined a symptom network is, and may help to inform intervention strategies. Limitations of predictability along with future directions are discussed.