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

Abstract

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

Socioeconomic Resources are Associated with Distributed Alterations of the Brain’s Intrinsic Functional Architecture in Youth

“…Bubble graphs (Figure 5) capture the proportion of significant cells associated with each network for each variable. Consistent with our previous reports, the general factor of psychopathology implicated interconnections between the default mode network and a number of control networks (28), while general cognitive ability implicated highly distributed connectivity patterns involving all networks (33). Though there was some overlap across the three variables, parental education preferentially implicated somatosensory/subcortical networks, with especially prominent involvement of the visual and subcortical networks.…”

“…The general psychopathology factor (P-factor) used here is based on the parent-rated Child Behavior Checklist (CBCL) (28), age 6 to 18 form. A bifactor model was fit to eight CBCL scales, with a general P factor that all scales loaded onto (average scale loading = .69) and two specific factors.…”

“…Given evidence of robust associations between parental education and resting state functional connectivity, we next compared the parental education connectivity pattern with corresponding connectivity patterns observed for general cognitive ability and the general factor of psychopathology, i.e., “P factor”, which were recently studied by our group in this same ABCD sample (28, 33). We focused on connectivity patterns observed with network contingency analysis, which is well-suited for localizing associations to sets of connections linking pairs of networks.…”

“…NCA is a count-based approach to quantifying altered connectivity (see Figure S3), see our prior work (27, 28, 84) for more details, and see also (29) for a related statistical treatment. In the current application, we applied the approach to “cells”; each cell is the set of connections linking a pair of the 16 networks in the Gordon parcellation augmented with additional networks (120 total cells).…”

“…To further assess potential confounding with head motion, we performed “resampling difference tests” (28) on a low motion subsample (with mean framewise displacement < 0.2 mm). First, we generated 10,000 random subsamples of the same size as the low motion subsample and performed multivariate predictive modeling for the parental education variable in each of these random subsamples.…”