Research Fund Secretariat - Detail of Approved Project (Reference No.: 13240096)

Detail of Approved Project (Reference No.: 13240096)

Fund	:	HMRF - Advanced Medical Research
Project Status	:	Current
Reference No.	:	13240096
Project Title	:	Towards precision psychiatry: building transdiagnostic prediction model from neurophysiological pattern for ADHD children
Research Activity Code	:	Detection, screening and diagnosis
Health Category	:	Neurological
Applicant(s)	:	WONG Clive Ho Yin(1) YUNG Trevor Wai Kit(2) TSO Wan Yee Winnie(3) LAI Tsz Him(4)
Affiliation(s)	:	Department of Psychology, The Education University of Hong Kong(1) Psychology, The Education University of Hong Kong(2) Paediatrics & Adolescent Medicine, The University of Hong Kong(3) Psychiatry, The University of Hong Kong(4)
Approved Amount (HK$)	:	$500,000.00
Abstract	:	Objectives: This study aims to enhance the understanding of cognitive and neurophysiological variability in children with ADHD using the Research Domain Criteria (RDoC) framework. By developing EEG-based predictive models, the research evaluates machine learning algorithms and EEG-derived features in predicting cognitive abilities (e.g., processing speed, sustained attention, executive functions) and treatment response, focusing on transdiagnostic neurobiological mechanisms. Hypothesis to be Tested: 1. EEG-derived features (e.g., band power, ERP components, connectivity) can predict cognitive deficits associated with ADHD, mapped onto RDoC Cognitive Systems domain. 2. Baseline EEG characteristics can predict individualized treatment responses to neurofeedback training, informing mechanisms of neural plasticity and self-regulation. Design and Subjects: This is a cross-sectional study involving 110 children aged 7–11 years, including ADHD and typically developing controls. Participants will complete EEG recordings, cognitive tasks, and a single neurofeedback session, analyzed within an RDoC framework to link neural and cognitive measures. Study Instruments: Neurophysiological data will be collected via resting-state and task-based EEG/ERP recordings. Cognitive tasks will target constructs within RDoC domains, such as attention and working memory. Symptom profiling will use parent-rated instruments (e.g., ADHD Rating Scale, BRIEF, SWAN). Main Outcome Measures: The primary outcome is the predictive performance of machine learning algorithms (e.g., support vector regression, penalized regression, CPM) and EEG predictors (band power, ERP components, CPM) on cognitive abilities mapped to RDoC domains. Data Analysis and Expected Results: Using internal validation, machine learning models will identify neurophysiological markers predicting cognitive abilities and treatment responses, advancing precision psychiatry through transdiagnostic, individualized interventions.
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