Cargando…
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...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8901599/ https://www.ncbi.nlm.nih.gov/pubmed/35273487 http://dx.doi.org/10.3389/fninf.2022.761942 |
_version_ | 1784664399843164160 |
---|---|
author | Zhao, Lei Sun, Yun-Kai Xue, Shao-Wei Luo, Hong Lu, Xiao-Dong Zhang, Lan-Hua |
author_facet | Zhao, Lei Sun, Yun-Kai Xue, Shao-Wei Luo, Hong Lu, Xiao-Dong Zhang, Lan-Hua |
author_sort | Zhao, Lei |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-8901599 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89015992022-03-09 Identifying Boys With Autism Spectrum Disorder Based on Whole-Brain Resting-State Interregional Functional Connections Using a Boruta-Based Support Vector Machine Approach Zhao, Lei Sun, Yun-Kai Xue, Shao-Wei Luo, Hong Lu, Xiao-Dong Zhang, Lan-Hua Front Neuroinform Neuroscience 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. Frontiers Media S.A. 2022-02-22 /pmc/articles/PMC8901599/ /pubmed/35273487 http://dx.doi.org/10.3389/fninf.2022.761942 Text en Copyright © 2022 Zhao, Sun, Xue, Luo, Lu and Zhang. https://creativecommons.org/licenses/by/4.0/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) and the copyright owner(s) 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. |
spellingShingle | Neuroscience Zhao, Lei Sun, Yun-Kai Xue, Shao-Wei Luo, Hong Lu, Xiao-Dong Zhang, Lan-Hua Identifying Boys With Autism Spectrum Disorder Based on Whole-Brain Resting-State Interregional Functional Connections Using a Boruta-Based Support Vector Machine Approach |
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 | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8901599/ https://www.ncbi.nlm.nih.gov/pubmed/35273487 http://dx.doi.org/10.3389/fninf.2022.761942 |
work_keys_str_mv | AT zhaolei identifyingboyswithautismspectrumdisorderbasedonwholebrainrestingstateinterregionalfunctionalconnectionsusingaborutabasedsupportvectormachineapproach AT sunyunkai identifyingboyswithautismspectrumdisorderbasedonwholebrainrestingstateinterregionalfunctionalconnectionsusingaborutabasedsupportvectormachineapproach AT xueshaowei identifyingboyswithautismspectrumdisorderbasedonwholebrainrestingstateinterregionalfunctionalconnectionsusingaborutabasedsupportvectormachineapproach AT luohong identifyingboyswithautismspectrumdisorderbasedonwholebrainrestingstateinterregionalfunctionalconnectionsusingaborutabasedsupportvectormachineapproach AT luxiaodong identifyingboyswithautismspectrumdisorderbasedonwholebrainrestingstateinterregionalfunctionalconnectionsusingaborutabasedsupportvectormachineapproach AT zhanglanhua identifyingboyswithautismspectrumdisorderbasedonwholebrainrestingstateinterregionalfunctionalconnectionsusingaborutabasedsupportvectormachineapproach |