A Unified Framework for Inattention Estimation From Resting State Phase Synchrony Using Machine Learning
Inattention is one of the most significant clinical symptoms for evaluating attention deficit hyperactivity disorder (ADHD). Previous inattention estimations were performed using clinical scales. Recently, predictive models for inattention have been established for brain-behavior estimation using ne...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-09-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2021.728913/full |
| _version_ | 1831561014092496896 |
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| author | Xun-Heng Wang Lihua Li |
| author_facet | Xun-Heng Wang Lihua Li |
| author_sort | Xun-Heng Wang |
| collection | DOAJ |
| description | Inattention is one of the most significant clinical symptoms for evaluating attention deficit hyperactivity disorder (ADHD). Previous inattention estimations were performed using clinical scales. Recently, predictive models for inattention have been established for brain-behavior estimation using neuroimaging features. However, the performance of inattention estimation could be improved for conventional brain-behavior models with additional feature selection, machine learning algorithms, and validation procedures. This paper aimed to propose a unified framework for inattention estimation from resting state fMRI to improve the classical brain-behavior models. Phase synchrony was derived as raw features, which were selected with minimum-redundancy maximum-relevancy (mRMR) method. Six machine learning algorithms were applied as regression methods. 100 runs of 10-fold cross-validations were performed on the ADHD-200 datasets. The relevance vector machines (RVMs) based on the mRMR features for the brain-behavior models significantly improve the performance of inattention estimation. The mRMR-RVM models could achieve a total accuracy of 0.53. Furthermore, predictive patterns for inattention were discovered by the mRMR technique. We found that the bilateral subcortical-cerebellum networks exhibited the most predictive phase synchrony patterns for inattention. Together, an optimized strategy named mRMR-RVM for brain-behavior models was found for inattention estimation. The predictive patterns might help better understand the phase synchrony mechanisms for inattention. |
| first_indexed | 2024-12-17T06:54:49Z |
| format | Article |
| id | doaj.art-47147d90825b4eaa91c6250e49681158 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-17T06:54:49Z |
| publishDate | 2021-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-47147d90825b4eaa91c6250e496811582022-12-21T21:59:27ZengFrontiers Media S.A.Frontiers in Genetics1664-80212021-09-011210.3389/fgene.2021.728913728913A Unified Framework for Inattention Estimation From Resting State Phase Synchrony Using Machine LearningXun-Heng WangLihua LiInattention is one of the most significant clinical symptoms for evaluating attention deficit hyperactivity disorder (ADHD). Previous inattention estimations were performed using clinical scales. Recently, predictive models for inattention have been established for brain-behavior estimation using neuroimaging features. However, the performance of inattention estimation could be improved for conventional brain-behavior models with additional feature selection, machine learning algorithms, and validation procedures. This paper aimed to propose a unified framework for inattention estimation from resting state fMRI to improve the classical brain-behavior models. Phase synchrony was derived as raw features, which were selected with minimum-redundancy maximum-relevancy (mRMR) method. Six machine learning algorithms were applied as regression methods. 100 runs of 10-fold cross-validations were performed on the ADHD-200 datasets. The relevance vector machines (RVMs) based on the mRMR features for the brain-behavior models significantly improve the performance of inattention estimation. The mRMR-RVM models could achieve a total accuracy of 0.53. Furthermore, predictive patterns for inattention were discovered by the mRMR technique. We found that the bilateral subcortical-cerebellum networks exhibited the most predictive phase synchrony patterns for inattention. Together, an optimized strategy named mRMR-RVM for brain-behavior models was found for inattention estimation. The predictive patterns might help better understand the phase synchrony mechanisms for inattention.https://www.frontiersin.org/articles/10.3389/fgene.2021.728913/fullpredictive modelsinattentionfeature selectionregression algorithmsphase synchrony |
| spellingShingle | Xun-Heng Wang Lihua Li A Unified Framework for Inattention Estimation From Resting State Phase Synchrony Using Machine Learning Frontiers in Genetics predictive models inattention feature selection regression algorithms phase synchrony |
| title | A Unified Framework for Inattention Estimation From Resting State Phase Synchrony Using Machine Learning |
| title_full | A Unified Framework for Inattention Estimation From Resting State Phase Synchrony Using Machine Learning |
| title_fullStr | A Unified Framework for Inattention Estimation From Resting State Phase Synchrony Using Machine Learning |
| title_full_unstemmed | A Unified Framework for Inattention Estimation From Resting State Phase Synchrony Using Machine Learning |
| title_short | A Unified Framework for Inattention Estimation From Resting State Phase Synchrony Using Machine Learning |
| title_sort | unified framework for inattention estimation from resting state phase synchrony using machine learning |
| topic | predictive models inattention feature selection regression algorithms phase synchrony |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2021.728913/full |
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