Identifying Boys With Autism Spectrum Disorder Based on Whole-Brain Resting-State Interregional Functional Connections Using a Boruta-Based Support Vector Machine Approach
An increasing number of resting-state functional magnetic resonance neuroimaging (R-fMRI) studies have used functional connections as discriminative features for machine learning to identify patients with brain diseases. However, it remains unclear which functional connections could serve as highly...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-02-01
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| Series: | Frontiers in Neuroinformatics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fninf.2022.761942/full |
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| author | Lei Zhao Lei Zhao Lei Zhao Yun-Kai Sun Shao-Wei Xue Shao-Wei Xue Shao-Wei Xue Hong Luo Hong Luo Hong Luo Xiao-Dong Lu Xiao-Dong Lu Xiao-Dong Lu Lan-Hua Zhang |
| author_facet | Lei Zhao Lei Zhao Lei Zhao Yun-Kai Sun Shao-Wei Xue Shao-Wei Xue Shao-Wei Xue Hong Luo Hong Luo Hong Luo Xiao-Dong Lu Xiao-Dong Lu Xiao-Dong Lu Lan-Hua Zhang |
| author_sort | Lei Zhao |
| collection | DOAJ |
| description | An increasing number of resting-state functional magnetic resonance neuroimaging (R-fMRI) studies have used functional connections as discriminative features for machine learning to identify patients with brain diseases. However, it remains unclear which functional connections could serve as highly discriminative features to realize the classification of autism spectrum disorder (ASD). The aim of this study was to find ASD-related functional connectivity patterns and examine whether these patterns had the potential to provide neuroimaging-based information to clinically assist with the diagnosis of ASD by means of machine learning. We investigated the whole-brain interregional functional connections derived from R-fMRI. Data were acquired from 48 boys with ASD and 50 typically developing age-matched controls at NYU Langone Medical Center from the publicly available Autism Brain Imaging Data Exchange I (ABIDE I) dataset; the ASD-related functional connections identified by the Boruta algorithm were used as the features of support vector machine (SVM) to distinguish patients with ASD from typically developing controls (TDC); a permutation test was performed to assess the classification performance. Approximately, 92.9% of participants were correctly classified by a combined SVM and leave-one-out cross-validation (LOOCV) approach, wherein 95.8% of patients with ASD were correctly identified. The default mode network (DMN) exhibited a relatively high network degree and discriminative power. Eight important brain regions showed a high discriminative power, including the posterior cingulate cortex (PCC) and the ventrolateral prefrontal cortex (vlPFC). Significant correlations were found between the classification scores of several functional connections and ASD symptoms (p < 0.05). This study highlights the important role of DMN in ASD identification. Interregional functional connections might provide useful information for the clinical diagnosis of ASD. |
| first_indexed | 2024-12-13T11:28:29Z |
| format | Article |
| id | doaj.art-d9470fa8bbbc4261ac7c0fd5b32fa797 |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-12-13T11:28:29Z |
| publishDate | 2022-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-d9470fa8bbbc4261ac7c0fd5b32fa7972022-12-21T23:48:03ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962022-02-011610.3389/fninf.2022.761942761942Identifying Boys With Autism Spectrum Disorder Based on Whole-Brain Resting-State Interregional Functional Connections Using a Boruta-Based Support Vector Machine ApproachLei Zhao0Lei Zhao1Lei Zhao2Yun-Kai Sun3Shao-Wei Xue4Shao-Wei Xue5Shao-Wei Xue6Hong Luo7Hong Luo8Hong Luo9Xiao-Dong Lu10Xiao-Dong Lu11Xiao-Dong Lu12Lan-Hua Zhang13Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, ChinaInstitute of Psychological Science, Hangzhou Normal University, Hangzhou, ChinaZhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, ChinaDepartment of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaCentre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, ChinaInstitute of Psychological Science, Hangzhou Normal University, Hangzhou, ChinaZhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, ChinaCentre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, ChinaInstitute of Psychological Science, Hangzhou Normal University, Hangzhou, ChinaZhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, ChinaCentre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, ChinaInstitute of Psychological Science, Hangzhou Normal University, Hangzhou, ChinaZhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, ChinaCollege of Medical Information and Engineering, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an, ChinaAn increasing number of resting-state functional magnetic resonance neuroimaging (R-fMRI) studies have used functional connections as discriminative features for machine learning to identify patients with brain diseases. However, it remains unclear which functional connections could serve as highly discriminative features to realize the classification of autism spectrum disorder (ASD). The aim of this study was to find ASD-related functional connectivity patterns and examine whether these patterns had the potential to provide neuroimaging-based information to clinically assist with the diagnosis of ASD by means of machine learning. We investigated the whole-brain interregional functional connections derived from R-fMRI. Data were acquired from 48 boys with ASD and 50 typically developing age-matched controls at NYU Langone Medical Center from the publicly available Autism Brain Imaging Data Exchange I (ABIDE I) dataset; the ASD-related functional connections identified by the Boruta algorithm were used as the features of support vector machine (SVM) to distinguish patients with ASD from typically developing controls (TDC); a permutation test was performed to assess the classification performance. Approximately, 92.9% of participants were correctly classified by a combined SVM and leave-one-out cross-validation (LOOCV) approach, wherein 95.8% of patients with ASD were correctly identified. The default mode network (DMN) exhibited a relatively high network degree and discriminative power. Eight important brain regions showed a high discriminative power, including the posterior cingulate cortex (PCC) and the ventrolateral prefrontal cortex (vlPFC). Significant correlations were found between the classification scores of several functional connections and ASD symptoms (p < 0.05). This study highlights the important role of DMN in ASD identification. Interregional functional connections might provide useful information for the clinical diagnosis of ASD.https://www.frontiersin.org/articles/10.3389/fninf.2022.761942/fullautism spectrum disordersupport vector machineresting-state functional magnetic resonance neuroimaging (R-fMRI)functional connection (FC)Boruta |
| spellingShingle | Lei Zhao Lei Zhao Lei Zhao Yun-Kai Sun Shao-Wei Xue Shao-Wei Xue Shao-Wei Xue Hong Luo Hong Luo Hong Luo Xiao-Dong Lu Xiao-Dong Lu Xiao-Dong Lu Lan-Hua Zhang Identifying Boys With Autism Spectrum Disorder Based on Whole-Brain Resting-State Interregional Functional Connections Using a Boruta-Based Support Vector Machine Approach Frontiers in Neuroinformatics autism spectrum disorder support vector machine resting-state functional magnetic resonance neuroimaging (R-fMRI) functional connection (FC) Boruta |
| title | Identifying Boys With Autism Spectrum Disorder Based on Whole-Brain Resting-State Interregional Functional Connections Using a Boruta-Based Support Vector Machine Approach |
| title_full | Identifying Boys With Autism Spectrum Disorder Based on Whole-Brain Resting-State Interregional Functional Connections Using a Boruta-Based Support Vector Machine Approach |
| title_fullStr | Identifying Boys With Autism Spectrum Disorder Based on Whole-Brain Resting-State Interregional Functional Connections Using a Boruta-Based Support Vector Machine Approach |
| title_full_unstemmed | Identifying Boys With Autism Spectrum Disorder Based on Whole-Brain Resting-State Interregional Functional Connections Using a Boruta-Based Support Vector Machine Approach |
| title_short | Identifying Boys With Autism Spectrum Disorder Based on Whole-Brain Resting-State Interregional Functional Connections Using a Boruta-Based Support Vector Machine Approach |
| title_sort | identifying boys with autism spectrum disorder based on whole brain resting state interregional functional connections using a boruta based support vector machine approach |
| topic | autism spectrum disorder support vector machine resting-state functional magnetic resonance neuroimaging (R-fMRI) functional connection (FC) Boruta |
| url | https://www.frontiersin.org/articles/10.3389/fninf.2022.761942/full |
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