Diagnosis for autism spectrum disorder children using T1-based gray matter and arterial spin labeling-based cerebral blood flow network metrics
IntroductionAutism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by impairments in motor skills, communication, emotional expression, and social interaction. Accurate diagnosis of ASD remains challenging due to the reliance on subjective behavioral observations and...
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| Format: | Article |
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Frontiers Media S.A.
2024-04-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2024.1356241/full |
| _version_ | 1797202893775831040 |
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| author | Mingyang Liu Weibo Yu Dandan Xu Miaoyan Wang Bo Peng Haoxiang Jiang Yakang Dai |
| author_facet | Mingyang Liu Weibo Yu Dandan Xu Miaoyan Wang Bo Peng Haoxiang Jiang Yakang Dai |
| author_sort | Mingyang Liu |
| collection | DOAJ |
| description | IntroductionAutism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by impairments in motor skills, communication, emotional expression, and social interaction. Accurate diagnosis of ASD remains challenging due to the reliance on subjective behavioral observations and assessment scales, lacking objective diagnostic indicators.MethodsIn this study, we introduced a novel approach for diagnosing ASD, leveraging T1-based gray matter and ASL-based cerebral blood flow network metrics. Thirty preschool-aged patients with ASD and twenty-two typically developing (TD) individuals were enrolled. Brain network features, including gray matter and cerebral blood flow metrics, were extracted from both T1-weighted magnetic resonance imaging (MRI) and ASL images. Feature selection was performed using statistical t-tests and Minimum Redundancy Maximum Relevance (mRMR). A machine learning model based on random vector functional link network was constructed for diagnosis.ResultsThe proposed approach demonstrated a classification accuracy of 84.91% in distinguishing ASD from TD. Key discriminating network features were identified in the inferior frontal gyrus and superior occipital gyrus, regions critical for social and executive functions in ASD patients.DiscussionOur study presents an objective and effective approach to the clinical diagnosis of ASD, overcoming the limitations of subjective behavioral observations. The identified brain network features provide insights into the neurobiological mechanisms underlying ASD, potentially leading to more targeted interventions. |
| first_indexed | 2024-04-24T08:10:41Z |
| format | Article |
| id | doaj.art-81a0cf2c7f2647dcb5b5177b469062a6 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-24T08:10:41Z |
| publishDate | 2024-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-81a0cf2c7f2647dcb5b5177b469062a62024-04-17T04:59:08ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2024-04-011810.3389/fnins.2024.13562411356241Diagnosis for autism spectrum disorder children using T1-based gray matter and arterial spin labeling-based cerebral blood flow network metricsMingyang Liu0Weibo Yu1Dandan Xu2Miaoyan Wang3Bo Peng4Haoxiang Jiang5Yakang Dai6School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun, ChinaSchool of Electrical and Electronic Engineering, Changchun University of Technology, Changchun, ChinaDepartment of Radiology, Affiliated Children’s Hospital of Jiangnan University, Wuxi, ChinaDepartment of Radiology, Affiliated Children’s Hospital of Jiangnan University, Wuxi, ChinaSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, ChinaSchool of Electrical and Electronic Engineering, Changchun University of Technology, Changchun, ChinaSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, ChinaIntroductionAutism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by impairments in motor skills, communication, emotional expression, and social interaction. Accurate diagnosis of ASD remains challenging due to the reliance on subjective behavioral observations and assessment scales, lacking objective diagnostic indicators.MethodsIn this study, we introduced a novel approach for diagnosing ASD, leveraging T1-based gray matter and ASL-based cerebral blood flow network metrics. Thirty preschool-aged patients with ASD and twenty-two typically developing (TD) individuals were enrolled. Brain network features, including gray matter and cerebral blood flow metrics, were extracted from both T1-weighted magnetic resonance imaging (MRI) and ASL images. Feature selection was performed using statistical t-tests and Minimum Redundancy Maximum Relevance (mRMR). A machine learning model based on random vector functional link network was constructed for diagnosis.ResultsThe proposed approach demonstrated a classification accuracy of 84.91% in distinguishing ASD from TD. Key discriminating network features were identified in the inferior frontal gyrus and superior occipital gyrus, regions critical for social and executive functions in ASD patients.DiscussionOur study presents an objective and effective approach to the clinical diagnosis of ASD, overcoming the limitations of subjective behavioral observations. The identified brain network features provide insights into the neurobiological mechanisms underlying ASD, potentially leading to more targeted interventions.https://www.frontiersin.org/articles/10.3389/fnins.2024.1356241/fullautism spectrum disorderT1-weighted MRIASLgray matter networkcerebral blood flow networkmachine learning |
| spellingShingle | Mingyang Liu Weibo Yu Dandan Xu Miaoyan Wang Bo Peng Haoxiang Jiang Yakang Dai Diagnosis for autism spectrum disorder children using T1-based gray matter and arterial spin labeling-based cerebral blood flow network metrics Frontiers in Neuroscience autism spectrum disorder T1-weighted MRI ASL gray matter network cerebral blood flow network machine learning |
| title | Diagnosis for autism spectrum disorder children using T1-based gray matter and arterial spin labeling-based cerebral blood flow network metrics |
| title_full | Diagnosis for autism spectrum disorder children using T1-based gray matter and arterial spin labeling-based cerebral blood flow network metrics |
| title_fullStr | Diagnosis for autism spectrum disorder children using T1-based gray matter and arterial spin labeling-based cerebral blood flow network metrics |
| title_full_unstemmed | Diagnosis for autism spectrum disorder children using T1-based gray matter and arterial spin labeling-based cerebral blood flow network metrics |
| title_short | Diagnosis for autism spectrum disorder children using T1-based gray matter and arterial spin labeling-based cerebral blood flow network metrics |
| title_sort | diagnosis for autism spectrum disorder children using t1 based gray matter and arterial spin labeling based cerebral blood flow network metrics |
| topic | autism spectrum disorder T1-weighted MRI ASL gray matter network cerebral blood flow network machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2024.1356241/full |
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