Beyond massive univariate tests: Covariance regression reveals complex patterns of functional connectivity related to attention-deficit/hyperactivity disorder, age, sex, and response control

Abstract

Background: Studies of brain functional connectivity (FC) typically involve massive univariate tests, performing statistical analysis on each individual connection. In this study we apply a novel whole-matrix regression approach referred to as Covariate Assisted Principal (CAP) regression to identify resting-state FC brain networks associated with attention-deficit/hyperactivity disorder (ADHD) and response control.

Methods: Participants included 8-12 year-old children with ADHD (n=115, 29 girls) and typically developing controls (n=102, 35 girls) who completed a resting-state fMRI scan and a go/no-go task (GNG). We modeled three sets of covariates to identify resting-state networks associated with an ADHD diagnosis, sex, and response inhibition (commission errors) and variability (ex-Gaussian parameter tau).

Results: The first network includes FC between striatal-cognitive control (CC) network subregions and thalamic-default mode network (DMN) subregions and is positively related to age. The second consists of FC between CC-visual-somatomotor regions and between CC-DMN subregions and is positively associated with response variability in boys with ADHD. The third consists of FC within the DMN and between DMN-CC-visual regions and differs between boys with and without ADHD. The fourth consists of FC between visual-somatomotor regions and between visual-DMN regions and differs between girls and boys with ADHD and is associated with response inhibition and variability in boys with ADHD. Unique networks were also identified in each of the three models suggesting some specificity to the covariates of interest.

Conclusions: These findings demonstrate the utility of our novel covariance regression approach to studying functional brain networks relevant for development, behavior, and psychopathology.

Keywords: ADHD; children; covariance regression; functional connectivity; response control; sex differences.

Figure 1

Significant effect of the components estimated from the covariate assisted principal model. The red color denotes a positive effect and blue for negative. DxSex signifies the behavior-free model; Tau signifies the model including Go/NoGo tau as one of the predictors; Com Rate signifies the model including Go/NoGo commission error rate as one of the predictors. ADHD, attention-deficit/hyperactivity disorder; C, component; GAI, General Ability Index; TD, typically developing.

Figure 2

Chord diagram compares the similarity between the components identified from the three models. DxSex signifies the behavior-free model; Tau signifies the model including Go/NoGo tau as one of the predictors; CR signifies the model including Go/NoGo commission error rate (CR) as one of the predictors. A red connection indicates that the two components are highly similar. C, component.

Figure 3

Reconstructed brain maps of the components identified by the covariate assisted principal method. DxSex signifies the behavior-free model; Tau signifies the model including Go/NoGo tau as one of the predictors; ComRate signifies the model including Go/NoGo commission error rate as one of the predictors. (A–D) Common components identified by all three models. (E) Unique component identified by the behavior-free model. (F, G) Unique components identified by the model with Go/NoGo tau as one of the predictors. (H–J) Unique components identified by the model with Go/NoGo commission error rate as one of the predictors. C, component; L, left; R, right.