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Ge LK, Man X, Cai K, Liu Z, Tsang WW, Chen A, Wei GX. Sharing Our World: Impact of Group Motor Skill Learning on Joint Attention in Children with Autism Spectrum Disorder. J Autism Dev Disord 2024:10.1007/s10803-024-06528-7. [PMID: 39230782 DOI: 10.1007/s10803-024-06528-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/16/2024] [Indexed: 09/05/2024]
Abstract
Impaired joint attention is a common feature of autism spectrum disorder (ASD), affecting social interaction and communication. We explored if group basketball learning could enhance joint attention in autistic children, and how this relates to brain changes, particularly white matter development integrity. Forty-nine autistic children, aged 4-12 years, were recruited from special education centers. The experimental group underwent a 12-week basketball motor skill learning, while the control group received standard care. Eye-tracking and brain scans were conducted. The 12-week basketball motor skill learning improved joint attention in the experimental group, evidenced by better eye tracking metrics and enhanced white matter integrity. Moreover, reduced time to first fixation correlated positively with decreased mean diffusivity of the left superior corona radiata and left superior fronto-occipital fasciculus in the experimental group. Basketball-based motor skill intervention effectively improved joint attention in autistic children. Improved white matter fiber integrity related to sensory perception, spatial and early attention function may underlie this effect. These findings highlight the potential of group motor skill learning within clinical rehabilitation for treating ASD.
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Affiliation(s)
- Li-Kun Ge
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiaoxia Man
- Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, 100875, China
- Shandong Sports Science Research Center, Jinan, 250100, China
| | - Kelong Cai
- College of Physical Education, Yangzhou University, Yangzhou, 225009, China
| | - Zhimei Liu
- College of Physical Education, Yangzhou University, Yangzhou, 225009, China
| | - William Wainam Tsang
- Department of Physiotherapy, School of Nursing and Health Studies, Hong Kong Metropolitan University, Kowloon, China
| | - Aiguo Chen
- Nanjing Institute of Physical Education, Nanjing, 210014, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou, 225127, China
| | - Gao-Xia Wei
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China.
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China.
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de Belen RAJ, Eapen V, Bednarz T, Sowmya A. Using visual attention estimation on videos for automated prediction of autism spectrum disorder and symptom severity in preschool children. PLoS One 2024; 19:e0282818. [PMID: 38346053 PMCID: PMC10861059 DOI: 10.1371/journal.pone.0282818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 12/17/2023] [Indexed: 02/15/2024] Open
Abstract
Atypical visual attention in individuals with autism spectrum disorders (ASD) has been utilised as a unique diagnosis criterion in previous research. This paper presents a novel approach to the automatic and quantitative screening of ASD as well as symptom severity prediction in preschool children. We develop a novel computational pipeline that extracts learned features from a dynamic visual stimulus to classify ASD children and predict the level of ASD-related symptoms. Experimental results demonstrate promising performance that is superior to using handcrafted features and machine learning algorithms, in terms of evaluation metrics used in diagnostic tests. Using a leave-one-out cross-validation approach, we obtained an accuracy of 94.59%, a sensitivity of 100%, a specificity of 76.47% and an area under the receiver operating characteristic curve (AUC) of 96% for ASD classification. In addition, we obtained an accuracy of 94.74%, a sensitivity of 87.50%, a specificity of 100% and an AUC of 99% for ASD symptom severity prediction.
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Affiliation(s)
- Ryan Anthony J. de Belen
- School of Computer Science and Engineering, University of New South Wales, New South Wales, Australia
| | - Valsamma Eapen
- School of Psychiatry, University of New South Wales, New South Wales, Australia
| | - Tomasz Bednarz
- School of Art & Design, University of New South Wales, New South Wales, Australia
| | - Arcot Sowmya
- School of Computer Science and Engineering, University of New South Wales, New South Wales, Australia
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Shan J, Gu Y, Zhang J, Hu X, Wu H, Yuan T, Zhao D. A scoping review of physiological biomarkers in autism. Front Neurosci 2023; 17:1269880. [PMID: 37746140 PMCID: PMC10512710 DOI: 10.3389/fnins.2023.1269880] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 08/23/2023] [Indexed: 09/26/2023] Open
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by pervasive deficits in social interaction, communication impairments, and the presence of restricted and repetitive behaviors. This complex disorder is a significant public health concern due to its escalating incidence and detrimental impact on quality of life. Currently, extensive investigations are underway to identify prospective susceptibility or predictive biomarkers, employing a physiological biomarker-based framework. However, knowledge regarding physiological biomarkers in relation to Autism is sparse. We performed a scoping review to explore putative changes in physiological activities associated with behaviors in individuals with Autism. We identified studies published between January 2000 and June 2023 from online databases, and searched keywords included electroencephalography (EEG), magnetoencephalography (MEG), electrodermal activity markers (EDA), eye-tracking markers. We specifically detected social-related symptoms such as impaired social communication in ASD patients. Our results indicated that the EEG/ERP N170 signal has undergone the most rigorous testing as a potential biomarker, showing promise in identifying subgroups within ASD and displaying potential as an indicator of treatment response. By gathering current data from various physiological biomarkers, we can obtain a comprehensive understanding of the physiological profiles of individuals with ASD, offering potential for subgrouping and targeted intervention strategies.
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Affiliation(s)
- Jiatong Shan
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Arts and Sciences, New York University Shanghai, Shanghai, China
| | - Yunhao Gu
- Graduate School of Education, University of Pennsylvania, Philadelphia, PA, United States
| | - Jie Zhang
- Department of Neurology, Institute of Neurology, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaoqing Hu
- Department of Psychology, The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
- HKU, Shenzhen Institute of Research and Innovation, Shenzhen, China
| | - Haiyan Wu
- Center for Cognitive and Brain Sciences and Department of Psychology, Macau, China
| | - Tifei Yuan
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Di Zhao
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Wang H, Zhao X, Yu D. Nonlinear features of gaze behavior during joint attention in children with autism spectrum disorder. Autism Res 2023; 16:1786-1798. [PMID: 37530201 DOI: 10.1002/aur.3000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 07/16/2023] [Indexed: 08/03/2023]
Abstract
Since children with autism spectrum disorder (ASD) might exhibit a variety of aberrant response to joint attention (RJA) behaviors, there is growing interest in identifying robust, reliable and valid eye-tracking metrics for determining differences in RJA behaviors between typically developing (TD) children and those with ASD. Previous eye-tracking studies have not been deeply investigated nonlinear features of gaze time-series during RJA. As a main motivation, this study aimed to extract three nonlinear features (i.e., complexity, long-range correlation, and local instability) of gaze time-series during RJA in children with ASD, which can be measured by fractal dimension (FD), Hurst exponent (H), and largest Lyapunov exponent (LLE), respectively. To illustrate our idea, this study adopted a publicly accessible database, including eye-tracking data collected during RJA from 19 children with ASD (7.74 ± 2.73) and 30 TD children (8.02 ± 2.89), and conducted a battery of nonparametric analysis of covariance (ANCOVA), where gender was used as covariable. Findings showed that gaze time-series during RJA in autistic children may generally have greater FD but lower H than that in TD controls. This implies that children with ASD possess more complex and unpredictable gaze behaviors during RJA than TD children. Furthermore, nonlinear metrics outperformed traditional eye-tracking metrics in obtaining higher identification performance with an accuracy of 82% and an AUC value of 0.81, distinguishing the differences between successful and failed RJA trails, and predicting the severity of ASD symptoms. Findings might bring some new insights into the understanding of the impairments in RJA behaviors for children with ASD.
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Affiliation(s)
- Hongan Wang
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Xin Zhao
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Dongchuan Yu
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
- Henan Provincial Medical Key Lab of Child Developmental Behavior and Learning, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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