Classification of cognitive reserve in healthy older adults based on brain activity using support vector machine.
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| Title: | Classification of cognitive reserve in healthy older adults based on brain activity using support vector machine. |
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| Authors: | Yang, Chia-Yen (AUTHOR) cyyang@mail.mcu.edu.tw, Lin, Ching-Po (AUTHOR) |
| Source: | Physiological Measurement. Jun2020, Vol. 41 Issue 6, p1-10. 10p. |
| Subjects: | Support vector machines, Older people, Occupational prestige |
| Abstract: | Objective: Cognitive reserve (CR) refers to the capacity of the brain to actively cope with damage via the implementation of remedial cognitive processes. Traditional CR measurements focus on static proxies, which may not be able to appropriately estimate dynamic changes in CR. This study therefore investigated the cognitive performance and characteristics of brain activity of low- and high-CR healthy adults during resting and n-back task states and categorized subjects according to magnetoencephalographic (MEG) information using a support vector machine (SVM) classifier. Approach: Forty-one volunteers were divided into groups with low and high CR indexes based on their education, occupational attainment, leisure and social activities. Main results: The results can be summarized as follows. First, subjects with a higher CR had higher accuracies and faster reaction times in the task. Second, subjects with a lower CR had a higher M300 intensity but a constant M300 latency. Third, subjects with a higher CR had a higher beta intensity in the parietal and occipital regions during the task, whereas subjects with a higher CR had a higher gamma intensity in the right temporal region in the resting state. Finally, subjects with a higher CR had negative gamma asymmetry between the right and left occipital regions, whereas subjects with a lower CR had positive values in the resting state. Significance: These MEG results were subsequently used to classify subjects into high-/low-CR subjects using an SVM classifier, and a mean accuracy of 88.89% was obtained. This objective and nonstatic method for determining CR level warrants further research for a wider variety of future clinical applications. [ABSTRACT FROM AUTHOR] |
| Copyright of Physiological Measurement is the property of IOP Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 145295162 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Classification of cognitive reserve in healthy older adults based on brain activity using support vector machine. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yang%2C+Chia-Yen%22">Yang, Chia-Yen</searchLink> (AUTHOR)<i> cyyang@mail.mcu.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Lin%2C+Ching-Po%22">Lin, Ching-Po</searchLink> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Physiological+Measurement%22">Physiological Measurement</searchLink>. Jun2020, Vol. 41 Issue 6, p1-10. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Older+people%22">Older people</searchLink><br /><searchLink fieldCode="DE" term="%22Occupational+prestige%22">Occupational prestige</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Objective: Cognitive reserve (CR) refers to the capacity of the brain to actively cope with damage via the implementation of remedial cognitive processes. Traditional CR measurements focus on static proxies, which may not be able to appropriately estimate dynamic changes in CR. This study therefore investigated the cognitive performance and characteristics of brain activity of low- and high-CR healthy adults during resting and n-back task states and categorized subjects according to magnetoencephalographic (MEG) information using a support vector machine (SVM) classifier. Approach: Forty-one volunteers were divided into groups with low and high CR indexes based on their education, occupational attainment, leisure and social activities. Main results: The results can be summarized as follows. First, subjects with a higher CR had higher accuracies and faster reaction times in the task. Second, subjects with a lower CR had a higher M300 intensity but a constant M300 latency. Third, subjects with a higher CR had a higher beta intensity in the parietal and occipital regions during the task, whereas subjects with a higher CR had a higher gamma intensity in the right temporal region in the resting state. Finally, subjects with a higher CR had negative gamma asymmetry between the right and left occipital regions, whereas subjects with a lower CR had positive values in the resting state. Significance: These MEG results were subsequently used to classify subjects into high-/low-CR subjects using an SVM classifier, and a mean accuracy of 88.89% was obtained. This objective and nonstatic method for determining CR level warrants further research for a wider variety of future clinical applications. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Physiological Measurement is the property of IOP Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1088/1361-6579/ab979e Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 1 Subjects: – SubjectFull: Support vector machines Type: general – SubjectFull: Older people Type: general – SubjectFull: Occupational prestige Type: general Titles: – TitleFull: Classification of cognitive reserve in healthy older adults based on brain activity using support vector machine. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yang, Chia-Yen – PersonEntity: Name: NameFull: Lin, Ching-Po IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 09673334 Numbering: – Type: volume Value: 41 – Type: issue Value: 6 Titles: – TitleFull: Physiological Measurement Type: main |
| ResultId | 1 |
