Melbourne School of Psychological Sciences - Research Publications
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ItemResearch Review: Language problems in children with Attention-Deficit Hyperactivity Disorder - a systematic meta-analytic review(WILEY, 2017-06)BACKGROUND: Children with Attention-Deficit Hyperactivity Disorder (ADHD) appear to have a higher risk of language problems compared with typically developing children, although the types of language problems experienced are less clear. This review aims to establish the types of language problems experienced by children with ADHD according to systematically reviewed literature and determine the empirical evidence for language problems in children with ADHD compared with non-ADHD controls. METHODS: A standardized search protocol was used on databases: CINAHL, Medline, and PsychINFO. We identified studies with the following inclusion criteria: (a) confirmed ADHD status at the time of the study, (b) inclusion of a non-ADHD control group, (c) use of a validated language measure, and (d) age ≤ 18. t-Tests, Pearson's r, and Hedges g effect sizes (ES) were calculated using summary statistics. Random effects meta-analyses were conducted for the language domain suitable for analysis. Publication bias was investigated using both the trim and fill and p-curve techniques. RESULTS: Twenty-one studies were included in the systematic review (ADHD = 1,209; Control = 1,101), within which 60 of 68 separate analyses found significant differences between the ADHD and control group on the language measures (p < .05). Follow-up meta-analyses found evidence for large deficits in the ADHD groups overall (10/11 studies met p < .05; weighted mean ES [WMES]: 1.04); expressive (10/10 met p < .05; WMES: 1.23); receptive (12/14 met p < .05; WMES: 0.97), and pragmatic language (4/4 studies met p < .05; WMES: 0.98) compared with controls. CONCLUSIONS: This study demonstrates that children with ADHD have poorer performance on measures of overall, expressive, receptive, and pragmatic language compared with controls. A screening of language functioning may be a valuable addition to the assessment of ADHD.
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ItemA network analysis approach to ADHD symptoms: More than the sum of its parts(PUBLIC LIBRARY SCIENCE, 2019-01-18)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 naïve 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.
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ItemDevelopmental brain trajectories in children with ADHD and controls: a longitudinal neuroimaging study(BMC, 2016-03-11)BACKGROUND: The symptom profile and neuropsychological functioning of individuals with Attention Deficit/Hyperactivity Disorder (ADHD), change as they enter adolescence. It is unclear whether variation in brain structure and function parallels these changes, and also whether deviations from typical brain development trajectories are associated with differential outcomes. This paper describes the Neuroimaging of the Children's Attention Project (NICAP), a comprehensive longitudinal multimodal neuroimaging study. Primary aims are to determine how brain structure and function change with age in ADHD, and whether different trajectories of brain development are associated with variations in outcomes including diagnostic persistence, and academic, cognitive, social and mental health outcomes. METHODS/DESIGN: NICAP is a multimodal neuroimaging study in a community-based cohort of children with and without ADHD. Approximately 100 children with ADHD and 100 typically developing controls will be scanned at a mean age of 10 years (range; 9-11years) and will be re-scanned at two 18-month intervals (ages 11.5 and 13 years respectively). Assessments include a structured diagnostic interview, parent and teacher questionnaires, direct child cognitive/executive functioning assessment and magnetic resonance imaging (MRI). MRI acquisition techniques, collected at a single site, have been selected to provide optimized information concerning structural and functional brain development. DISCUSSION: This study will allow us to address the primary aims by describing the neurobiological development of ADHD and elucidating brain features associated with differential clinical/behavioral outcomes. NICAP data will also be explored to assess the impact of sex, ADHD presentation, ADHD severity, comorbidities and medication use on brain development trajectories. Establishing which brain regions are associated with differential clinical outcomes, may allow us to improve predictions about the course of ADHD.