EEG (electroencephalogram) modes in autistic trait
This interdisciplinary research project aims to identify modes in the resting state and task processing neural activity that are linked to the behavioural symptoms of a broad spectrum of autistic traits. A group of physicists (PI Prof. Changsong Zhou) will analyse brain signal data obtained by psychologists (Co-I Dr. Ming Lui) at the Department of Education Studies with systems analysis approaches. We aim to examine the neural correlates of autism traits in a large sample (N =150 adults and 50 children). The data collection is in progress and supported by the RC Interdisciplinary Research Matching Scheme (RC-IRMS) granted to PI and Co-I. The RC-IRMS project aimed to examine the continuum of autistic tendencies among a population of children and adults including both clinical and non-clinical cases. The project collected data from different sources including clinical assessment, behavioural measurements and neural activities measured by electroencephalogram (EEG) during resting states and social cognition tasks.
The current collaborative project will capitalize on the data from RC-IRMS, and aim to develop further analysis of the data focusing on the spatiotemporal patterns. We plan to apply and develop harmonic mode analysis, a method used in Physics, to decompose the spatiotemporal patterns. We aim to identify the modes that can distinguish individuals with different levels of autistic traits, and to develop measures from such differential modes which are associated with the continuous spectrum of autistic traits, extending to the extreme clinical cases in our sample.
Together with other measures and analysis ongoing in the RC-IRMS project, the findings of this interdisciplinary project will contribute to reveal multi-dimensional descriptions of autistic traits at neural and behavioral levels, which may advance diagnostic criteria and the search for biomarkers to promote early identification and intervention of Autism Spectrum Disorders (ASD).
- Apply and develop methods of harmonic mode analysis from physical science to the spatiotemporal pattern in EEG data under resting state and social cognition tasks.
- Compare the modes of individuals with low autistic traits to those with high autistic traits to identify the differential modes between the two groups.
- Further develop measures from the differential modes to associate with the autistic trait across the individuals, aiming to identify dynamics-based markers related to the clinically confirmed autistic symptoms.
- Provide interdisciplinary brain research training to PhD students, including physical science analysis approaches, brain signal processing, data interpretation, and scientific presentation and writing.
The project aims to use the physics concept and approaches of harmonic modes in the analysis of spatiotemporal complex data. Students with physics or computer science background are preferred.