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...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-12-01
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| Series: | Frontiers in Aging Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2021.804384/full |
| _version_ | 1819099395560308736 |
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| author | Jiahao Zhang Jiahao Zhang Haifeng Lu Lin Zhu Huixia Ren Huixia Ren Ge Dang Xiaolin Su Xiaoyong Lan Xin Jiang Xu Zhang Xu Zhang Jiansong Feng Jiansong Feng Xue Shi Taihong Wang Taihong Wang Xiping Hu Xiping Hu Yi Guo Yi Guo |
| author_facet | Jiahao Zhang Jiahao Zhang Haifeng Lu Lin Zhu Huixia Ren Huixia Ren Ge Dang Xiaolin Su Xiaoyong Lan Xin Jiang Xu Zhang Xu Zhang Jiansong Feng Jiansong Feng Xue Shi Taihong Wang Taihong Wang Xiping Hu Xiping Hu Yi Guo Yi Guo |
| author_sort | Jiahao Zhang |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-22T00:46:12Z |
| format | Article |
| id | doaj.art-a620a595ccd743ce98f7056235d1a44e |
| institution | Directory Open Access Journal |
| issn | 1663-4365 |
| language | English |
| last_indexed | 2024-12-22T00:46:12Z |
| publishDate | 2021-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Aging Neuroscience |
| spelling | doaj.art-a620a595ccd743ce98f7056235d1a44e2022-12-21T18:44:33ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652021-12-011310.3389/fnagi.2021.804384804384Classification of Cognitive Impairment and Healthy Controls Based on Transcranial Magnetic Stimulation Evoked PotentialsJiahao Zhang0Jiahao Zhang1Haifeng Lu2Lin Zhu3Huixia Ren4Huixia Ren5Ge Dang6Xiaolin Su7Xiaoyong Lan8Xin Jiang9Xu Zhang10Xu Zhang11Jiansong Feng12Jiansong Feng13Xue Shi14Taihong Wang15Taihong Wang16Xiping Hu17Xiping Hu18Yi Guo19Yi Guo20Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, ChinaSchool of Microelectronics, Southern University of Science and Technology, Shenzhen, ChinaGansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, ChinaDepartment of Neurology, Shenzhen People's Hospital (The First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University), Shenzhen, ChinaDepartment of Neurology, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen, ChinaThe First Affiliated Hospital, Jinan University, Guangzhou, ChinaDepartment of Neurology, Shenzhen People's Hospital (The First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University), Shenzhen, ChinaDepartment of Neurology, Shenzhen People's Hospital (The First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University), Shenzhen, ChinaDepartment of Neurology, Shenzhen People's Hospital (The First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University), Shenzhen, ChinaDepartment of Geratic, Shenzhen People's Hospital (The First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University), Shenzhen, ChinaDepartment of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, ChinaSchool of Microelectronics, Southern University of Science and Technology, Shenzhen, ChinaDepartment of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, ChinaSchool of Microelectronics, Southern University of Science and Technology, Shenzhen, ChinaDepartment of Neurology, Shenzhen People's Hospital (The First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University), Shenzhen, ChinaDepartment of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, ChinaSchool of Microelectronics, Southern University of Science and Technology, Shenzhen, ChinaGansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, ChinaSchool of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, ChinaDepartment of Neurology, Shenzhen People's Hospital (The First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University), Shenzhen, ChinaShenzhen Bay Laboratory, Shenzhen, ChinaBackgrounds: 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.https://www.frontiersin.org/articles/10.3389/fnagi.2021.804384/fullspatiotemporal featuresmachine learningcognitive impairmentTEPTMS-EEG |
| spellingShingle | Jiahao Zhang Jiahao Zhang Haifeng Lu Lin Zhu Huixia Ren Huixia Ren Ge Dang Xiaolin Su Xiaoyong Lan Xin Jiang Xu Zhang Xu Zhang Jiansong Feng Jiansong Feng Xue Shi Taihong Wang Taihong Wang Xiping Hu Xiping Hu Yi Guo Yi Guo Classification of Cognitive Impairment and Healthy Controls Based on Transcranial Magnetic Stimulation Evoked Potentials Frontiers in Aging Neuroscience spatiotemporal features machine learning cognitive impairment TEP TMS-EEG |
| 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 | spatiotemporal features machine learning cognitive impairment TEP TMS-EEG |
| url | https://www.frontiersin.org/articles/10.3389/fnagi.2021.804384/full |
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