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
TITLE=Classification of Cognitive Impairment and Healthy Controls Based on Transcranial Magnetic Stimulation Evoked Potentials
JOURNAL=Frontiers in Aging Neuroscience
VOLUME=13
YEAR=2021
URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2021.804384
DOI=10.3389/fnagi.2021.804384
ISSN=1663-4365
ABSTRACT=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.