File Download
Supplementary
-
Citations:
- Appears in Collections:
postgraduate thesis: Neurophysiological correlates of executive function and theory of mind in autistic and neurotypical children : EEG and machine learning
Title | Neurophysiological correlates of executive function and theory of mind in autistic and neurotypical children : EEG and machine learning |
---|---|
Authors | |
Advisors | |
Issue Date | 2023 |
Publisher | The University of Hong Kong (Pokfulam, Hong Kong) |
Citation | Lee, H. K. R.. (2023). Neurophysiological correlates of executive function and theory of mind in autistic and neurotypical children : EEG and machine learning. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. |
Abstract | Autism is a neurodevelopmental disorder characterized by impaired cognitive control and social interactions that manifests poor executive function (EF) and theory of mind (ToM) abilities. Separate theoretical accounts—such as the executive dysfunction and ToM deficit hypotheses—have highlighted that dysfunctional EF and ToM are critical factors underlying the cognitive and social deficits of autism. However, despite their significance in school-age children’s socio-cognitive development, evidence for autistic children’s neural deficits in cognitive-emotional control and ToM reasoning are limited, as are the automated autism diagnostic tools based on time-sensitive neural markers of these socio-cognitive skills.
To address these research gaps, study 1 examined the neurophysiological correlates of cognitive-emotional control in 52 autistic and 52 neurotypical children aged 10–12 years, addressing whether inhibition control is affected by face realism and emotional valence. The analyses of inhibition-emotion and face-specific event-related potential (ERP) components revealed that autistic children required greater cognitive efforts inhibiting responses to Nogo trials and real but not cartoon emotional faces. Moreover, autistic children exhibited reduced activations to only real face emotions. Furthermore, correlation results showed autistic children with reduced face-related ERPs exhibited better behavioural inhibition and emotion recognition. These findings suggest that the neural mechanisms of inhibitory control in autistic children are less efficient and more disrupted during real face processing, which may affect their age-appropriate socio-emotional development.
Study 2 investigated the neural indicators of cognitive and affective components of ToM in 41 autistic and 52 neurotypical children. Using cartoon-based vignettes and auditory ToM questions, this study found that autistic relative to neurotypical children exhibited generally reduced and delayed ERPs for false-belief reasoning, particularly for cognitive ToM. Moreover, autistic children showed enhanced and faster activations for affective ToM than cognitive ToM, whereas neurotypical children showed the opposite pattern. Furthermore, the autistic tendency for local bias and cognitive inflexibility significantly correlated with autistic children’s cognitive and affective ToM processing. These findings suggest that autistic children exhibit apparent deficits in cognitive ToM while they use compensatory strategies for affective ToM, and their cognitive and affective ToM mechanisms underlying empathic functioning are closely associated with their autistic traits.
Study 3 integrated these EF- and ToM-related ERPs in machine learning models to evaluate their effectiveness in classifying autistic from neurotypical children. Using datasets of 38 autistic and 49 neurotypical children, a sequential backward feature selection-based linear support vector machine and least absolute shrinkage and selection operator logistic regression model obtained 100.0% classification accuracies with the 19 and 14 most discriminative features, respectively. Additionally, early cartoon face perception and later real face processing during inhibition, as well as cognitive and affective ToM during false-belief reasoning, were significant predictors of autism. These findings demonstrated the practical application of machine learning algorithms in detecting autism based on ERP correlates associated with EF and ToM.
Together, the findings of the present thesis demonstrated that school-age autism is characterized by aberrant neural activity associated with cognitive-emotional control of real faces and false-belief reasoning of cognitive ToM, thus requiring interventions targeting these traits to improve these children’s socio-cognitive functioning. |
Degree | Doctor of Philosophy |
Subject | Autistic children Executive functions (Neuropsychology) Philosophy of mind in children |
Dept/Program | Education |
Persistent Identifier | http://hdl.handle.net/10722/344398 |
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Tong, X | - |
dc.contributor.advisor | Ouyang, G | - |
dc.contributor.author | Lee, Hyun Kyung Rachel | - |
dc.date.accessioned | 2024-07-30T05:00:37Z | - |
dc.date.available | 2024-07-30T05:00:37Z | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | Lee, H. K. R.. (2023). Neurophysiological correlates of executive function and theory of mind in autistic and neurotypical children : EEG and machine learning. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. | - |
dc.identifier.uri | http://hdl.handle.net/10722/344398 | - |
dc.description.abstract | Autism is a neurodevelopmental disorder characterized by impaired cognitive control and social interactions that manifests poor executive function (EF) and theory of mind (ToM) abilities. Separate theoretical accounts—such as the executive dysfunction and ToM deficit hypotheses—have highlighted that dysfunctional EF and ToM are critical factors underlying the cognitive and social deficits of autism. However, despite their significance in school-age children’s socio-cognitive development, evidence for autistic children’s neural deficits in cognitive-emotional control and ToM reasoning are limited, as are the automated autism diagnostic tools based on time-sensitive neural markers of these socio-cognitive skills. To address these research gaps, study 1 examined the neurophysiological correlates of cognitive-emotional control in 52 autistic and 52 neurotypical children aged 10–12 years, addressing whether inhibition control is affected by face realism and emotional valence. The analyses of inhibition-emotion and face-specific event-related potential (ERP) components revealed that autistic children required greater cognitive efforts inhibiting responses to Nogo trials and real but not cartoon emotional faces. Moreover, autistic children exhibited reduced activations to only real face emotions. Furthermore, correlation results showed autistic children with reduced face-related ERPs exhibited better behavioural inhibition and emotion recognition. These findings suggest that the neural mechanisms of inhibitory control in autistic children are less efficient and more disrupted during real face processing, which may affect their age-appropriate socio-emotional development. Study 2 investigated the neural indicators of cognitive and affective components of ToM in 41 autistic and 52 neurotypical children. Using cartoon-based vignettes and auditory ToM questions, this study found that autistic relative to neurotypical children exhibited generally reduced and delayed ERPs for false-belief reasoning, particularly for cognitive ToM. Moreover, autistic children showed enhanced and faster activations for affective ToM than cognitive ToM, whereas neurotypical children showed the opposite pattern. Furthermore, the autistic tendency for local bias and cognitive inflexibility significantly correlated with autistic children’s cognitive and affective ToM processing. These findings suggest that autistic children exhibit apparent deficits in cognitive ToM while they use compensatory strategies for affective ToM, and their cognitive and affective ToM mechanisms underlying empathic functioning are closely associated with their autistic traits. Study 3 integrated these EF- and ToM-related ERPs in machine learning models to evaluate their effectiveness in classifying autistic from neurotypical children. Using datasets of 38 autistic and 49 neurotypical children, a sequential backward feature selection-based linear support vector machine and least absolute shrinkage and selection operator logistic regression model obtained 100.0% classification accuracies with the 19 and 14 most discriminative features, respectively. Additionally, early cartoon face perception and later real face processing during inhibition, as well as cognitive and affective ToM during false-belief reasoning, were significant predictors of autism. These findings demonstrated the practical application of machine learning algorithms in detecting autism based on ERP correlates associated with EF and ToM. Together, the findings of the present thesis demonstrated that school-age autism is characterized by aberrant neural activity associated with cognitive-emotional control of real faces and false-belief reasoning of cognitive ToM, thus requiring interventions targeting these traits to improve these children’s socio-cognitive functioning. | - |
dc.language | eng | - |
dc.publisher | The University of Hong Kong (Pokfulam, Hong Kong) | - |
dc.relation.ispartof | HKU Theses Online (HKUTO) | - |
dc.rights | The author retains all proprietary rights, (such as patent rights) and the right to use in future works. | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject.lcsh | Autistic children | - |
dc.subject.lcsh | Executive functions (Neuropsychology) | - |
dc.subject.lcsh | Philosophy of mind in children | - |
dc.title | Neurophysiological correlates of executive function and theory of mind in autistic and neurotypical children : EEG and machine learning | - |
dc.type | PG_Thesis | - |
dc.description.thesisname | Doctor of Philosophy | - |
dc.description.thesislevel | Doctoral | - |
dc.description.thesisdiscipline | Education | - |
dc.description.nature | published_or_final_version | - |
dc.date.hkucongregation | 2023 | - |
dc.identifier.mmsid | 991044736495503414 | - |