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Autism Spectrum Disorder Studies Using fMRI Data and Machine Learning: A Review
Machine learning methods have been frequently applied in the field of cognitive neuroscience in the last decade. A great deal of attention has been attracted to introduce machine learning methods to study the autism spectrum disorder (ASD) in order to find out its neurophysiological underpinnings. I...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8480393/ https://www.ncbi.nlm.nih.gov/pubmed/34602966 http://dx.doi.org/10.3389/fnins.2021.697870 |
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author | Liu, Meijie Li, Baojuan Hu, Dewen |
author_facet | Liu, Meijie Li, Baojuan Hu, Dewen |
author_sort | Liu, Meijie |
collection | PubMed |
description | Machine learning methods have been frequently applied in the field of cognitive neuroscience in the last decade. A great deal of attention has been attracted to introduce machine learning methods to study the autism spectrum disorder (ASD) in order to find out its neurophysiological underpinnings. In this paper, we presented a comprehensive review about the previous studies since 2011, which applied machine learning methods to analyze the functional magnetic resonance imaging (fMRI) data of autistic individuals and the typical controls (TCs). The all-round process was covered, including feature construction from raw fMRI data, feature selection methods, machine learning methods, factors for high classification accuracy, and critical conclusions. Applying different machine learning methods and fMRI data acquired from different sites, classification accuracies were obtained ranging from 48.3% up to 97%, and informative brain regions and networks were located. Through thorough analysis, high classification accuracies were found to usually occur in the studies which involved task-based fMRI data, single dataset for some selection principle, effective feature selection methods, or advanced machine learning methods. Advanced deep learning together with the multi-site Autism Brain Imaging Data Exchange (ABIDE) dataset became research trends especially in the recent 4 years. In the future, advanced feature selection and machine learning methods combined with multi-site dataset or easily operated task-based fMRI data may appear to have the potentiality to serve as a promising diagnostic tool for ASD. |
format | Online Article Text |
id | pubmed-8480393 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84803932021-09-30 Autism Spectrum Disorder Studies Using fMRI Data and Machine Learning: A Review Liu, Meijie Li, Baojuan Hu, Dewen Front Neurosci Neuroscience Machine learning methods have been frequently applied in the field of cognitive neuroscience in the last decade. A great deal of attention has been attracted to introduce machine learning methods to study the autism spectrum disorder (ASD) in order to find out its neurophysiological underpinnings. In this paper, we presented a comprehensive review about the previous studies since 2011, which applied machine learning methods to analyze the functional magnetic resonance imaging (fMRI) data of autistic individuals and the typical controls (TCs). The all-round process was covered, including feature construction from raw fMRI data, feature selection methods, machine learning methods, factors for high classification accuracy, and critical conclusions. Applying different machine learning methods and fMRI data acquired from different sites, classification accuracies were obtained ranging from 48.3% up to 97%, and informative brain regions and networks were located. Through thorough analysis, high classification accuracies were found to usually occur in the studies which involved task-based fMRI data, single dataset for some selection principle, effective feature selection methods, or advanced machine learning methods. Advanced deep learning together with the multi-site Autism Brain Imaging Data Exchange (ABIDE) dataset became research trends especially in the recent 4 years. In the future, advanced feature selection and machine learning methods combined with multi-site dataset or easily operated task-based fMRI data may appear to have the potentiality to serve as a promising diagnostic tool for ASD. Frontiers Media S.A. 2021-09-15 /pmc/articles/PMC8480393/ /pubmed/34602966 http://dx.doi.org/10.3389/fnins.2021.697870 Text en Copyright © 2021 Liu, Li and Hu. 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 Liu, Meijie Li, Baojuan Hu, Dewen Autism Spectrum Disorder Studies Using fMRI Data and Machine Learning: A Review |
title | Autism Spectrum Disorder Studies Using fMRI Data and Machine Learning: A Review |
title_full | Autism Spectrum Disorder Studies Using fMRI Data and Machine Learning: A Review |
title_fullStr | Autism Spectrum Disorder Studies Using fMRI Data and Machine Learning: A Review |
title_full_unstemmed | Autism Spectrum Disorder Studies Using fMRI Data and Machine Learning: A Review |
title_short | Autism Spectrum Disorder Studies Using fMRI Data and Machine Learning: A Review |
title_sort | autism spectrum disorder studies using fmri data and machine learning: a review |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8480393/ https://www.ncbi.nlm.nih.gov/pubmed/34602966 http://dx.doi.org/10.3389/fnins.2021.697870 |
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