A network analysis approach to ADHD symptoms: More than the sum of its parts

Timothy J. Silk, Charles B. Malpas, Richard Beare, Daryl Efron, Vicki Anderson, Philip Hazell, Brad Jongeling, Jan M. Nicholson, Emma Sciberras

Research output: Contribution to journalArticle

Abstract

In interpreting attention-deficit/hyperactivity disorder (ADHD) symptoms, categorical and dimensional approaches are commonly used. Both employ binary symptom counts which give equal weighting, with little attention to the combinations and relative contributions of individual symptoms. Alternatively, symptoms can be viewed as an interacting network, revealing the complex relationship between symptoms. Using a novel network modelling approach, this study explores the relationships between the 18 symptoms in the Diagnostic Statistical Manual (DSM-5) criteria and whether network measures are useful in predicting outcomes. Participants were from a community cohort, the Children's Attention Project. DSM ADHD symptoms were recorded in a face-to-face structured parent interview for 146 medication naive children with ADHD and 209 controls (aged 6-8 years). Analyses indicated that not all symptoms are equal. Frequencies of endorsement and configurations of symptoms varied, with certain symptoms playing a more important role within the ADHD symptom network. In total, 116,220 combinations of symptoms within a diagnosis of ADHD were identified, with 92% demonstrating a unique symptom configuration. Symptom association networks highlighted the relative importance of hyperactive/impulsive symptoms in the symptom network. In particular, the 'motoric'-type symptoms as well as interrupts as a marker of impulsivity in the hyperactive domain, as well as loses things and does not follow instructions in the inattentive domain, had high measures of centrality. Centrality-measure weighted symptom counts showed significant association with clinical but not cognitive outcomes, however the relationships were not significantly stronger than symptom count alone. The finding may help to explain heterogeneity in the ADHD phenotype.

Original languageEnglish
Article number0211053
Number of pages17
JournalPLoS One
Volume14
Issue number1
DOIs
Publication statusPublished - 18 Jan 2019

Cite this

Silk, T. J., Malpas, C. B., Beare, R., Efron, D., Anderson, V., Hazell, P., ... Sciberras, E. (2019). A network analysis approach to ADHD symptoms: More than the sum of its parts. PLoS One, 14(1), [0211053]. https://doi.org/10.1371/journal.pone.0211053
Silk, Timothy J. ; Malpas, Charles B. ; Beare, Richard ; Efron, Daryl ; Anderson, Vicki ; Hazell, Philip ; Jongeling, Brad ; Nicholson, Jan M. ; Sciberras, Emma. / A network analysis approach to ADHD symptoms : More than the sum of its parts. In: PLoS One. 2019 ; Vol. 14, No. 1.
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Silk, TJ, Malpas, CB, Beare, R, Efron, D, Anderson, V, Hazell, P, Jongeling, B, Nicholson, JM & Sciberras, E 2019, 'A network analysis approach to ADHD symptoms: More than the sum of its parts' PLoS One, vol. 14, no. 1, 0211053. https://doi.org/10.1371/journal.pone.0211053

A network analysis approach to ADHD symptoms : More than the sum of its parts. / Silk, Timothy J.; Malpas, Charles B.; Beare, Richard; Efron, Daryl; Anderson, Vicki; Hazell, Philip; Jongeling, Brad; Nicholson, Jan M.; Sciberras, Emma.

In: PLoS One, Vol. 14, No. 1, 0211053, 18.01.2019.

Research output: Contribution to journalArticle

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AU - Malpas, Charles B.

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Silk TJ, Malpas CB, Beare R, Efron D, Anderson V, Hazell P et al. A network analysis approach to ADHD symptoms: More than the sum of its parts. PLoS One. 2019 Jan 18;14(1). 0211053. https://doi.org/10.1371/journal.pone.0211053