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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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Autores principales: Wang, Xun-Heng, Li, Lihua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8495194/
https://www.ncbi.nlm.nih.gov/pubmed/34630522
http://dx.doi.org/10.3389/fgene.2021.728913
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author Wang, Xun-Heng
Li, Lihua
author_facet Wang, Xun-Heng
Li, Lihua
author_sort Wang, Xun-Heng
collection PubMed
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.
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spelling pubmed-84951942021-10-08 A Unified Framework for Inattention Estimation From Resting State Phase Synchrony Using Machine Learning Wang, Xun-Heng Li, Lihua Front Genet Genetics 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. Frontiers Media S.A. 2021-09-23 /pmc/articles/PMC8495194/ /pubmed/34630522 http://dx.doi.org/10.3389/fgene.2021.728913 Text en Copyright © 2021 Wang and Li. 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 Genetics
Wang, Xun-Heng
Li, Lihua
A Unified Framework for Inattention Estimation From Resting State Phase Synchrony Using Machine Learning
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 Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8495194/
https://www.ncbi.nlm.nih.gov/pubmed/34630522
http://dx.doi.org/10.3389/fgene.2021.728913
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