A Method for Estimating Longitudinal Change in Motor Skill from Individualized Functional-Connectivity Measures
Pragmatic, objective, and accurate motor assessment tools could facilitate more frequent appraisal of longitudinal change in motor function and subsequent development of personalized therapeutic strategies. Brain functional connectivity (FC) has shown promise as an objective neurophysiological measu...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/24/9857 |
| _version_ | 1797455293946265600 |
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| author | Nader Riahi Ryan D’Arcy Carlo Menon |
| author_facet | Nader Riahi Ryan D’Arcy Carlo Menon |
| author_sort | Nader Riahi |
| collection | DOAJ |
| description | Pragmatic, objective, and accurate motor assessment tools could facilitate more frequent appraisal of longitudinal change in motor function and subsequent development of personalized therapeutic strategies. Brain functional connectivity (FC) has shown promise as an objective neurophysiological measure for this purpose. The involvement of different brain networks, along with differences across subjects due to age or existing capabilities, motivates an individualized approach towards the evaluation of FC. We advocate the use of EEG-based resting-state FC (rsFC) measures to address the pragmatic requirements. Pertaining to appraisal of accuracy, we suggest using the acquisition of motor skill by healthy individuals that could be quantified at small incremental change. Computer-based tracing tasks are a good candidate in this regard when using spatial error in tracing as an objective measure of skill. This work investigates the application of an individualized method that utilizes Partial Least Squares analysis to estimate the longitudinal change in tracing error from changes in rsFC. Longitudinal data from participants yielded an average accuracy of 98% (standard deviation of 1.2%) in estimating tracing error. The results show potential for an accurate individualized motor assessment tool that reduces the dependence on the expertise and availability of trained examiners, thereby facilitating more frequent appraisal of function and development of personalized training programs. |
| first_indexed | 2024-03-09T15:52:22Z |
| format | Article |
| id | doaj.art-33440e39f3764445975b11706bd04e0d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T15:52:22Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-33440e39f3764445975b11706bd04e0d2023-11-24T17:56:31ZengMDPI AGSensors1424-82202022-12-012224985710.3390/s22249857A Method for Estimating Longitudinal Change in Motor Skill from Individualized Functional-Connectivity MeasuresNader Riahi0Ryan D’Arcy1Carlo Menon2Schools of Engineering Science, Simon Fraser University, Burnaby, BC V5A 1S6, CanadaSchools of Engineering Science, Simon Fraser University, Burnaby, BC V5A 1S6, CanadaSchools of Engineering Science, Simon Fraser University, Burnaby, BC V5A 1S6, CanadaPragmatic, objective, and accurate motor assessment tools could facilitate more frequent appraisal of longitudinal change in motor function and subsequent development of personalized therapeutic strategies. Brain functional connectivity (FC) has shown promise as an objective neurophysiological measure for this purpose. The involvement of different brain networks, along with differences across subjects due to age or existing capabilities, motivates an individualized approach towards the evaluation of FC. We advocate the use of EEG-based resting-state FC (rsFC) measures to address the pragmatic requirements. Pertaining to appraisal of accuracy, we suggest using the acquisition of motor skill by healthy individuals that could be quantified at small incremental change. Computer-based tracing tasks are a good candidate in this regard when using spatial error in tracing as an objective measure of skill. This work investigates the application of an individualized method that utilizes Partial Least Squares analysis to estimate the longitudinal change in tracing error from changes in rsFC. Longitudinal data from participants yielded an average accuracy of 98% (standard deviation of 1.2%) in estimating tracing error. The results show potential for an accurate individualized motor assessment tool that reduces the dependence on the expertise and availability of trained examiners, thereby facilitating more frequent appraisal of function and development of personalized training programs.https://www.mdpi.com/1424-8220/22/24/9857motor skill assessmentEEG sensorsresting state functional connectivityphase lag indexpartial least squares correlation and regression |
| spellingShingle | Nader Riahi Ryan D’Arcy Carlo Menon A Method for Estimating Longitudinal Change in Motor Skill from Individualized Functional-Connectivity Measures Sensors motor skill assessment EEG sensors resting state functional connectivity phase lag index partial least squares correlation and regression |
| title | A Method for Estimating Longitudinal Change in Motor Skill from Individualized Functional-Connectivity Measures |
| title_full | A Method for Estimating Longitudinal Change in Motor Skill from Individualized Functional-Connectivity Measures |
| title_fullStr | A Method for Estimating Longitudinal Change in Motor Skill from Individualized Functional-Connectivity Measures |
| title_full_unstemmed | A Method for Estimating Longitudinal Change in Motor Skill from Individualized Functional-Connectivity Measures |
| title_short | A Method for Estimating Longitudinal Change in Motor Skill from Individualized Functional-Connectivity Measures |
| title_sort | method for estimating longitudinal change in motor skill from individualized functional connectivity measures |
| topic | motor skill assessment EEG sensors resting state functional connectivity phase lag index partial least squares correlation and regression |
| url | https://www.mdpi.com/1424-8220/22/24/9857 |
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