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Liu L, Wang Y, Chen W, Gao Y, Li H, Wang Y, Chan RCK, Qian Q. Network analysis of 18 attention-deficit/hyperactivity disorder symptoms suggests the importance of " Distracted" and " Fidget" as central symptoms: Invariance across age, gender, and subtype presentations. Front Psychiatry 2022; 13:974283. [PMID: 36339870 PMCID: PMC9633674 DOI: 10.3389/fpsyt.2022.974283] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 10/06/2022] [Indexed: 11/13/2022] Open
Abstract
The network theory of mental disorders conceptualizes psychiatric symptoms as networks of symptoms that causally interact with each other. Our present study aimed to explore the symptomatic structure in children with attention-deficit/hyperactivity disorder (ADHD) using network analyses. Symptom network based on 18 items of ADHD Rating Scale-IV was evaluated in 4,033 children and adolescents with ADHD. The importance of nodes was evaluated quantitatively by examining centrality indices, including Strength, Betweenness and Closeness, as well as Predictability and Expected Influence (EI). In addition, we compared the network structure across different subgroups, as characterized by ADHD subtypes, gender and age groups to evaluate its invariance. A three-factor-community structure was identified including inattentive, hyperactive and impulsive clusters. For the centrality indices, the nodes of "Distracted" and "Fidget" showed high closeness and betweenness, and represented a bridge linking the inattentive and hyperactive/impulsive domains. "Details" and "Fidget" were the most common endorsed symptoms in inattentive and hyperactive/impulsive domains respectively. On the contrary, the "Listen" item formed a peripheral node showing weak links with all other items within the inattentive cluster, and the "Loss" item as the least central node by all measures of centrality and with low predictability value. The network structure was relatively invariant across gender, age and ADHD subtypes/presentations. The 18 items of ADHD core symptoms appear not equivalent and interchangeable. "Distracted" and "Fidget" should be considered as central, or core, symptoms for further evaluation and intervention. The network-informed differentiation of these symptoms has the potentials to refine the phenotype and reduce heterogeneity.
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Affiliation(s)
- Lu Liu
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders, Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Yi Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Wai Chen
- Mental Health Service, Fiona Stanley Hospital, Perth, WA, Australia.,Curtin Medical School, Curtin University, Perth, WA, Australia.,Curtin enAble Institute, Curtin University, Bentley, WA, Australia.,Graduate School of Education, University of Western Australia, Perth, WA, Australia.,School of Medicine, University of Notre Dame Australia, Fremantle, WA, Australia.,School of Psychology, Murdoch University, Perth, WA, Australia
| | - Yuan Gao
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders, Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Haimei Li
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders, Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Yufeng Wang
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders, Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Raymond C K Chan
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Qiujin Qian
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders, Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
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Cotton J, Baker ST. A data mining and item response mixture modeling method to retrospectively measure Diagnostic and Statistical Manual of Mental Disorders-5 attention deficit hyperactivity disorder in the 1970 British Cohort Study. Int J Methods Psychiatr Res 2019; 28:e1753. [PMID: 30402897 PMCID: PMC6877163 DOI: 10.1002/mpr.1753] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 09/21/2018] [Accepted: 10/06/2018] [Indexed: 12/18/2022] Open
Abstract
OBJECTIVE To facilitate future outcome studies, we aimed to develop a robust and replicable method for estimating a categorical and dimensional measure of Diagnostic and Statistical Manual of Mental Disorders-5 (DSM-5) attention deficit hyperactivity disorder (ADHD) in the 1970 British Cohort Study (BCS70). METHOD Following a data mining framework, we mapped DSM-5 ADHD symptoms to age 10 BCS70 data (N = 11,426) and derived a 16-item scale (α = 0.85). Mapping was validated by an expert panel. A categorical subgroup was derived (n = 594, 5.2%), and a zero-inflated item response theory (IRT) mixture model fitted to estimate a dimensional measure. RESULTS Subgroup composition was comparable with other ADHD samples. Relative risk ratios (ADHD/not ADHD) included boys = 1.38, unemployed fathers = 2.07, below average reading = 2.58, and depressed parent = 3.73. Our estimated measures correlated with two derived reference scales: Strengths and Difficulties Questionnaire hyperactivity (r = 0.74) and a Rutter/Conners-based scale (r = 0.81), supporting construct validity. IRT model items (symptoms) had moderate to high discrimination (0.90-2.81) and provided maximum information at average to moderate theta levels of ADHD (0.5-1.75). CONCLUSION We extended previous work to identify ADHD in BCS70, derived scales from existing data, modeled ADHD items with IRT, and adjusted for a zero-inflated distribution. Psychometric properties were promising, and this work will enable future studies of causal mechanisms in ADHD.
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Affiliation(s)
- Joanne Cotton
- Faculty of EducationUniversity of CambridgeCambridgeUK
| | - Sara T. Baker
- Faculty of EducationUniversity of CambridgeCambridgeUK
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