Etiological Networks of Attention-Deficit/Hyperactivity Disorder during Childhood and Adolescence

ABSTRACT

Objective

The purpose of the current study was to use network analysis techniques to parse relations between attention-deficit/hyperactivity disorder (ADHD) symptom domains, domains of executive function, and temperament traits.

Methods

Participants were 420 children aged 6–17 years (55% boys). The majority of the participants were Caucasian (72.86%) and 50% of the sample met diagnostic criteria for ADHD. Both parents and teachers provided ratings of participants’ ADHD symptom severity. Parents completed questionnaires pertaining to participants’ temperament traits, and participants completed well-validated laboratory measures of executive function.

Results

Results suggested effortful control as demonstrating the strongest relations with ADHD, particularly the parent-reported inattentive symptom domain. Additionally, negative effects appeared to demonstrate weaker but still notable relations primarily with the parent-reported hyperactive/impulsive symptom domain. Measures of executive function did not appear to demonstrate relations with any measures of ADHD symptoms or temperament traits. The results were generally replicated in a distinct sample (n = 732, 7–13 years, 63% boys, 81% White), although differences emerged pertaining to the role of surgency (i.e., related to the hyperactive/impulsive symptom domain in the replication but not the primary sample).

Conclusions

Overall, findings provided support for the primary role of effortful control, as well as secondary roles for negative affect and surgency, as key risk markers for the characterization of ADHD. Additional exploration of the overlap between temperament and executive function, as pertaining to ADHD, may help clarify heterogeneity in phenotypes and suggest priorities for targeted interventions outside of traditional symptoms.

Acknowledgments

The authors thank all participants for making this work possible. The data that support the findings of this study are available from the corresponding author upon reasonable request.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Supplementary Material

Supplemental material for this article can be accessed online at https://doi.org/10.1080/15374416.2021.1946820.

Notes

¹ Partial correlations range from −1 to 1 and correspond with the remaining association between two elements within a network after controlling for all other relations among elements. This contrasts with bivariate correlations which do not account for these other relations. However, spurious or false positive relations are still possible in networks given the high number of parameters that are estimated. Hence, regularization techniques apply a “penalty” to the strength of all relations within a network, decreasing their strength and removing weaker edges. Together, these two techniques are thought to increase the likelihood of creating a network structure that minimizes the number of spurious relations while highlighting the strongest relations (see Epskamp & Fried, 2018).

² To test for redundancy between variables, we utilized the Goldbricker function in the R package networktools (Jones, 2018). This package identified variable pairs that were correlated (r > .50) with each other and in highly similar patterns with other variables (less than 25% of overlapping correlations with other items being significantly different [p < .05]). Results suggested no cross-construct redundancy between traits, executive function domains, and ADHD symptom domains. As a secondary test, we replicated analyses removing the Attention scale from Effortful Control scores to reduce item overlap, and results were generally consistent with analyses presented in the manuscript. Detailed results related to these “non-overlapping” analyses are available in the Supplemental Material. Further, we replicated analyses after removing effortful control from analyses completely. Results suggested general consistency with those presented in the main body of the text, as no measures of executive function emerged as bridge risk markers. Detailed results of these analyses are available upon request to the corresponding author.

³ To test the validity of findings in clinical versus non-clinical samples, we compared network structure in those diagnosed with versus without ADHD. Results suggested robustness in network structure regardless of ADHD diagnosis (see Supplemental Material). We also conducted another supplementary analysis by adding two other variables that have been found to be relevant to the development of ADHD to the primary sample’s networks: behavioral genetics and martial conflict. Results were consistent with those presented in the manuscript, with genetic markers and marital conflict not appearing to exhibit meaningful relations with ADHD symptom domains. Detailed results are available in the Supplemental Material.

Additional information

Funding

Support was provided by NIH [R37-MH-59105-12 and R01-MH59105].