Multitask fMRI and machine learning approach improve prediction of differential brain activity pattern in patients with insomnia disorder

Abstract We investigated the differential spatial covariance pattern of blood oxygen level-dependent (BOLD) responses to single-task and multitask functional magnetic resonance imaging (fMRI) between patients with psychophysiological insomnia (PI) and healthy controls (HCs), and evaluated features g...

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Autores principales:	Mi Hyun Lee, Nambeom Kim, Jaeeun Yoo, Hang-Keun Kim, Young-Don Son, Young-Bo Kim, Seong Min Oh, Soohyun Kim, Hayoung Lee, Jeong Eun Jeon, Yu Jin Lee
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1497f9ba340f453b8c4012f95fc262d2
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spelling	oai:doaj.org-article:1497f9ba340f453b8c4012f95fc262d22021-12-02T17:39:30ZMultitask fMRI and machine learning approach improve prediction of differential brain activity pattern in patients with insomnia disorder10.1038/s41598-021-88845-w2045-2322https://doaj.org/article/1497f9ba340f453b8c4012f95fc262d22021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88845-whttps://doaj.org/toc/2045-2322Abstract We investigated the differential spatial covariance pattern of blood oxygen level-dependent (BOLD) responses to single-task and multitask functional magnetic resonance imaging (fMRI) between patients with psychophysiological insomnia (PI) and healthy controls (HCs), and evaluated features generated by principal component analysis (PCA) for discrimination of PI from HC, compared to features generated from BOLD responses to single-task fMRI using machine learning methods. In 19 patients with PI and 21 HCs, the mean beta value for each region of interest (ROIbval) was calculated with three contrast images (i.e., sleep-related picture, sleep-related sound, and Stroop stimuli). We performed discrimination analysis and compared with features generated from BOLD responses to single-task fMRI. We applied support vector machine analysis with a least absolute shrinkage and selection operator to evaluate five performance metrics: accuracy, recall, precision, specificity, and F2. Principal component features showed the best classification performance in all aspects of metrics compared to BOLD response to single-task fMRI. Bilateral inferior frontal gyrus (orbital), right calcarine cortex, right lingual gyrus, left inferior occipital gyrus, and left inferior temporal gyrus were identified as the most salient areas by feature selection. Our approach showed better performance in discriminating patients with PI from HCs, compared to single-task fMRI.Mi Hyun LeeNambeom KimJaeeun YooHang-Keun KimYoung-Don SonYoung-Bo KimSeong Min OhSoohyun KimHayoung LeeJeong Eun JeonYu Jin LeeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mi Hyun Lee Nambeom Kim Jaeeun Yoo Hang-Keun Kim Young-Don Son Young-Bo Kim Seong Min Oh Soohyun Kim Hayoung Lee Jeong Eun Jeon Yu Jin Lee Multitask fMRI and machine learning approach improve prediction of differential brain activity pattern in patients with insomnia disorder
description	Abstract We investigated the differential spatial covariance pattern of blood oxygen level-dependent (BOLD) responses to single-task and multitask functional magnetic resonance imaging (fMRI) between patients with psychophysiological insomnia (PI) and healthy controls (HCs), and evaluated features generated by principal component analysis (PCA) for discrimination of PI from HC, compared to features generated from BOLD responses to single-task fMRI using machine learning methods. In 19 patients with PI and 21 HCs, the mean beta value for each region of interest (ROIbval) was calculated with three contrast images (i.e., sleep-related picture, sleep-related sound, and Stroop stimuli). We performed discrimination analysis and compared with features generated from BOLD responses to single-task fMRI. We applied support vector machine analysis with a least absolute shrinkage and selection operator to evaluate five performance metrics: accuracy, recall, precision, specificity, and F2. Principal component features showed the best classification performance in all aspects of metrics compared to BOLD response to single-task fMRI. Bilateral inferior frontal gyrus (orbital), right calcarine cortex, right lingual gyrus, left inferior occipital gyrus, and left inferior temporal gyrus were identified as the most salient areas by feature selection. Our approach showed better performance in discriminating patients with PI from HCs, compared to single-task fMRI.
format	article
author	Mi Hyun Lee Nambeom Kim Jaeeun Yoo Hang-Keun Kim Young-Don Son Young-Bo Kim Seong Min Oh Soohyun Kim Hayoung Lee Jeong Eun Jeon Yu Jin Lee
author_facet	Mi Hyun Lee Nambeom Kim Jaeeun Yoo Hang-Keun Kim Young-Don Son Young-Bo Kim Seong Min Oh Soohyun Kim Hayoung Lee Jeong Eun Jeon Yu Jin Lee
author_sort	Mi Hyun Lee
title	Multitask fMRI and machine learning approach improve prediction of differential brain activity pattern in patients with insomnia disorder
title_short	Multitask fMRI and machine learning approach improve prediction of differential brain activity pattern in patients with insomnia disorder
title_full	Multitask fMRI and machine learning approach improve prediction of differential brain activity pattern in patients with insomnia disorder
title_fullStr	Multitask fMRI and machine learning approach improve prediction of differential brain activity pattern in patients with insomnia disorder
title_full_unstemmed	Multitask fMRI and machine learning approach improve prediction of differential brain activity pattern in patients with insomnia disorder
title_sort	multitask fmri and machine learning approach improve prediction of differential brain activity pattern in patients with insomnia disorder
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1497f9ba340f453b8c4012f95fc262d2
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Multitask fMRI and machine learning approach improve prediction of differential brain activity pattern in patients with insomnia disorder

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