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ORIGINAL RESEARCH article
Front. Psychiatry
Sec. Autism
Volume 15 - 2024 |
doi: 10.3389/fpsyt.2024.1463654
This article is part of the Research Topic Enhancing the Social Skills and Social Competence for Children and Adolescents with Autism Spectrum Disorder View all articles
Exploring the Most Discriminative Brain Structural Abnormalities in ASD with Multi-Stage Progressive Feature Refinement Approach
Provisionally accepted
Bingxi Sun
1
Yingying Xu
1*
SiuChing Kat
1
Anlan Sun
2*
Tingni Yin
1*
Liyang Zhao
1*
Xing Su
1*
Jialu Chen
1*
Hui Wang
1*
Xiaoyun Gong
1
Qinyi Liu
1*
Gangqiang Han
1*
Shuchen Peng
1*
Xue Li
1*
Jing Liu
1*- 1 Peking University Sixth Hospital, Beijing, Beijing Municipality, China
- 2 Yizhun Medical AI Co., Ltd, Beijing, China
Objective: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by increasing prevalence, diverse impairments, and unclear origins and mechanisms. To gain a better grasp of the origins of ASD, it is essential to identify the most distinctive structural brain abnormalities in individuals with ASD. Methods: A Multi-Stage Progressive Feature Refinement Approach was employed to identify the most pivotal structural magnetic resonance imaging (MRI) features that distinguish individuals with ASD from typically developing (TD) individuals. The study included 175 individuals with ASD and 69 TD individuals, all aged between 7 and 18 years, matched in terms of age and gender. Both cortical and subcortical features were integrated, with a particular focus on hippocampal subfields. Results: Out of 317 features, 9 had the most significant impact on distinguishing ASD from TD individuals. These structural features, which include a specific hippocampal subfield, are closely related to the brain areas associated with the reward system. Conclusion: Structural irregularities in the reward system may play a crucial role in the pathophysiology of ASD, and specific hippocampal subfields may also contribute uniquely, warranting further investigation.
Keywords: Autism Spectrum Disorder, structural magnetic resonance imaging, Feature Selection, machine learning, Support vector machine, Least absolute shrinkage and selection operator
Received: 12 Jul 2024; Accepted: 23 Sep 2024.
Copyright: © 2024 Sun, Xu, Kat, Sun, Yin, Zhao, Su, Chen, Wang, Gong, Liu, Han, Peng, Li and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yingying Xu, Peking University Sixth Hospital, Beijing, 100191, Beijing Municipality, China
Anlan Sun, Yizhun Medical AI Co., Ltd, Beijing, China
Tingni Yin, Peking University Sixth Hospital, Beijing, 100191, Beijing Municipality, China
Liyang Zhao, Peking University Sixth Hospital, Beijing, 100191, Beijing Municipality, China
Xing Su, Peking University Sixth Hospital, Beijing, 100191, Beijing Municipality, China
Jialu Chen, Peking University Sixth Hospital, Beijing, 100191, Beijing Municipality, China
Hui Wang, Peking University Sixth Hospital, Beijing, 100191, Beijing Municipality, China
Qinyi Liu, Peking University Sixth Hospital, Beijing, 100191, Beijing Municipality, China
Gangqiang Han, Peking University Sixth Hospital, Beijing, 100191, Beijing Municipality, China
Shuchen Peng, Peking University Sixth Hospital, Beijing, 100191, Beijing Municipality, China
Xue Li, Peking University Sixth Hospital, Beijing, 100191, Beijing Municipality, China
Jing Liu, Peking University Sixth Hospital, Beijing, 100191, Beijing Municipality, China
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