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Classification of Cognitive Impairment and Healthy Controls Based on Transcranial Magnetic Stimulation Evoked Potentials

Backgrounds: Nowadays, risks of Cognitive Impairment (CI) [highly suspected Alzheimer's disease (AD) in this study] threaten the quality of life for more older adults as the population ages. The emergence of Transcranial Magnetic Stimulation-Electroencephalogram (TMS-EEG) enables noninvasive ne...

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Autores principales: Zhang, Jiahao, Lu, Haifeng, Zhu, Lin, Ren, Huixia, Dang, Ge, Su, Xiaolin, Lan, Xiaoyong, Jiang, Xin, Zhang, Xu, Feng, Jiansong, Shi, Xue, Wang, Taihong, Hu, Xiping, Guo, Yi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8740294/
https://www.ncbi.nlm.nih.gov/pubmed/35002684
http://dx.doi.org/10.3389/fnagi.2021.804384
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author Zhang, Jiahao
Lu, Haifeng
Zhu, Lin
Ren, Huixia
Dang, Ge
Su, Xiaolin
Lan, Xiaoyong
Jiang, Xin
Zhang, Xu
Feng, Jiansong
Shi, Xue
Wang, Taihong
Hu, Xiping
Guo, Yi
author_facet Zhang, Jiahao
Lu, Haifeng
Zhu, Lin
Ren, Huixia
Dang, Ge
Su, Xiaolin
Lan, Xiaoyong
Jiang, Xin
Zhang, Xu
Feng, Jiansong
Shi, Xue
Wang, Taihong
Hu, Xiping
Guo, Yi
author_sort Zhang, Jiahao
collection PubMed
description Backgrounds: Nowadays, risks of Cognitive Impairment (CI) [highly suspected Alzheimer's disease (AD) in this study] threaten the quality of life for more older adults as the population ages. The emergence of Transcranial Magnetic Stimulation-Electroencephalogram (TMS-EEG) enables noninvasive neurophysiological investi-gation of the human cortex, which might be potentially used for CI detection. Objectives: The aim of this study is to explore whether the spatiotemporal features of TMS Evoked Potentials (TEPs) could classify CI from healthy controls (HC). Methods: Twenty-one patients with CI and 22 HC underwent a single-pulse TMS-EEG stimulus in which the pulses were delivered to the left dorsolateral prefrontal cortex (left DLPFC). After preprocessing, seven regions of interest (ROIs) and two most reliable TEPs' components: N100 and P200 were selected. Next, seven simple and interpretable linear features of TEPs were extracted for each region, three common machine learning algorithms including Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN) were used to detect CI. Meanwhile, data augmentation and voting strategy were used for a more robust model. Finally, the performance differences of features in classifiers and their contributions were investigated. Results: 1. In the time domain, the features of N100 had the best performance in the SVM classifier, with an accuracy of 88.37%. 2. In the aspect of spatiality, the features of the right frontal region and left parietal region had the best performance in the SVM classifier, with an accuracy of 83.72%. 3. The Local Mean Field Power (LMFP), Average Value (AVG), Latency and Amplitude contributed most in classification. Conclusions: The TEPs induced by TMS over the left DLPFC has significant differences spatially and temporally between CI and HC. Machine learning based on the spatiotemporal features of TEPs have the ability to separate the CI and HC which suggest that TEPs has potential as non-invasive biomarkers for CI diagnosis.
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spelling pubmed-87402942022-01-08 Classification of Cognitive Impairment and Healthy Controls Based on Transcranial Magnetic Stimulation Evoked Potentials Zhang, Jiahao Lu, Haifeng Zhu, Lin Ren, Huixia Dang, Ge Su, Xiaolin Lan, Xiaoyong Jiang, Xin Zhang, Xu Feng, Jiansong Shi, Xue Wang, Taihong Hu, Xiping Guo, Yi Front Aging Neurosci Aging Neuroscience Backgrounds: Nowadays, risks of Cognitive Impairment (CI) [highly suspected Alzheimer's disease (AD) in this study] threaten the quality of life for more older adults as the population ages. The emergence of Transcranial Magnetic Stimulation-Electroencephalogram (TMS-EEG) enables noninvasive neurophysiological investi-gation of the human cortex, which might be potentially used for CI detection. Objectives: The aim of this study is to explore whether the spatiotemporal features of TMS Evoked Potentials (TEPs) could classify CI from healthy controls (HC). Methods: Twenty-one patients with CI and 22 HC underwent a single-pulse TMS-EEG stimulus in which the pulses were delivered to the left dorsolateral prefrontal cortex (left DLPFC). After preprocessing, seven regions of interest (ROIs) and two most reliable TEPs' components: N100 and P200 were selected. Next, seven simple and interpretable linear features of TEPs were extracted for each region, three common machine learning algorithms including Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN) were used to detect CI. Meanwhile, data augmentation and voting strategy were used for a more robust model. Finally, the performance differences of features in classifiers and their contributions were investigated. Results: 1. In the time domain, the features of N100 had the best performance in the SVM classifier, with an accuracy of 88.37%. 2. In the aspect of spatiality, the features of the right frontal region and left parietal region had the best performance in the SVM classifier, with an accuracy of 83.72%. 3. The Local Mean Field Power (LMFP), Average Value (AVG), Latency and Amplitude contributed most in classification. Conclusions: The TEPs induced by TMS over the left DLPFC has significant differences spatially and temporally between CI and HC. Machine learning based on the spatiotemporal features of TEPs have the ability to separate the CI and HC which suggest that TEPs has potential as non-invasive biomarkers for CI diagnosis. Frontiers Media S.A. 2021-12-24 /pmc/articles/PMC8740294/ /pubmed/35002684 http://dx.doi.org/10.3389/fnagi.2021.804384 Text en Copyright © 2021 Zhang, Lu, Zhu, Ren, Dang, Su, Lan, Jiang, Zhang, Feng, Shi, Wang, Hu and Guo. 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 Aging Neuroscience
Zhang, Jiahao
Lu, Haifeng
Zhu, Lin
Ren, Huixia
Dang, Ge
Su, Xiaolin
Lan, Xiaoyong
Jiang, Xin
Zhang, Xu
Feng, Jiansong
Shi, Xue
Wang, Taihong
Hu, Xiping
Guo, Yi
Classification of Cognitive Impairment and Healthy Controls Based on Transcranial Magnetic Stimulation Evoked Potentials
title Classification of Cognitive Impairment and Healthy Controls Based on Transcranial Magnetic Stimulation Evoked Potentials
title_full Classification of Cognitive Impairment and Healthy Controls Based on Transcranial Magnetic Stimulation Evoked Potentials
title_fullStr Classification of Cognitive Impairment and Healthy Controls Based on Transcranial Magnetic Stimulation Evoked Potentials
title_full_unstemmed Classification of Cognitive Impairment and Healthy Controls Based on Transcranial Magnetic Stimulation Evoked Potentials
title_short Classification of Cognitive Impairment and Healthy Controls Based on Transcranial Magnetic Stimulation Evoked Potentials
title_sort classification of cognitive impairment and healthy controls based on transcranial magnetic stimulation evoked potentials
topic Aging Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8740294/
https://www.ncbi.nlm.nih.gov/pubmed/35002684
http://dx.doi.org/10.3389/fnagi.2021.804384
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