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.

Highlights

  • Cognitive impairment (CI) refers to a cognitive function decline beyond typical aging, which is increasingly prevalent in the elderly and loom as a public health issue (Montine et al, 2021).Clinically, Montreal Cognitive Assessment (MoCA) and MiniMental State Examination (MMSE) are commonly used for routine cognitive screening

  • The highest accuracy of 88.37% was achieved by Support Vector Machine (SVM), with a specificity of 95.45%, the sensitivity of 80.95%, and the F1-score of 87.18%

  • Classification Results Based on Regions of interest were divided according to the section 2.2.4 and we extracted features from each regions of interest (ROIs) to explore which ROIs are more sensitive to CI

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Summary

Introduction

Montreal Cognitive Assessment (MoCA) and MiniMental State Examination (MMSE) are commonly used for routine cognitive screening. MoCA is more sensitive than MMSE in detecting mild cognitive impairment (MCI) (Ciesielska et al, 2016). For Alzheimer’s disease (AD) diagnosis, there are two main types of biomarkers: biophysiological biomarkers such as β amyloid in cerebrospinal fluid (CSF)/plasma/serum and brain imaging markers (Frisoni et al, 2017; Ng et al, 2019; Cullen et al, 2021). Aβ42/Aβ40 ratio in CSF and blood (Buchhave et al, 2012; Hansson et al, 2018), Positron Emission Tomography (PET) of beta-amyloid and tau proteins (Leuzy et al, 2019; Rabinovici et al, 2019). Multimodal fusion combines the advantages of each modality, but it is undeniable that complex data fusion algorithms impose huge challenges for clinical application

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