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Detection of Mild Cognitive Impairment with MEG Functional Connectivity using Wavelet-based Neuromarkers

Yang, Su, Sanchez Bornot, Jose Miguel, Bruna Fernandez, Ricardo, Deravi, Farzin, Hoque, Sanaul, Wong-Lin, KongFatt, Prasad, Girijesh (2021) Detection of Mild Cognitive Impairment with MEG Functional Connectivity using Wavelet-based Neuromarkers. Sensors, 21 (18). Article Number 6210. E-ISSN 1424-8220. (doi:10.3390/s21186210) (KAR id:90196)

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https://doi.org/10.3390/s21186210

Abstract

Studies on developing effective neuromarkers based on magnetoencephalographic (MEG) signals have been drawing increasing attention in the neuroscience community. This study explores the idea of using source-based magnitude-squared spectral coherence as a spatial indicator for effective regions of interest (ROIs) localization, subsequently discriminating the participants with mild cognitive impairment (MCI) from a group of age-matched healthy control (HC) elderly participants. We found that the cortical regions could be divided into two distinctive groups based on their coherence indices. Compared to HC, some ROIs showed increased connectivity (hyper-connected ROIs) for MCI participants, whereas the remaining ROIs demonstrated reduced connectivity (hypo-connected ROIs). Based on these findings, a series of wavelet-based source-level neuromarkers for MCI detection are proposed and explored, with respect to the two distinctive ROI groups. It was found that the neuromarkers extracted from the hyper-connected ROIs performed significantly better for MCI detection than those from the hypo-connected ROIs. The neuromarkers were classified using support vector machine (SVM) and k-NN classifiers and evaluated through Monte Carlo cross-validation. An average recognition rate of 93.83% was obtained using source-reconstructed signals from the hyper-connected ROI group. To better conform to clinical practice settings, a leave-one-out cross-validation

(LOOCV) approach was also employed to ensure that the data for testing was from a participant that the classifier has never seen. Using LOOCV, we found the best average classification accuracy was reduced to 83.80% using the same set of neuromarkers obtained from the ROI group with functional hyper-connections. This performance surpassed the results reported using wavelet-based features by approximately 15%. Overall, our work suggests that (1) certain ROIs are particularly effective for MCI detection, especially when multi-resolution wavelet biomarkers are employed for such diagnosis; (2) there exists a significant performance difference in system evaluation between research-based

experimental design and clinically accepted evaluation standards.

Item Type: Article
DOI/Identification number: 10.3390/s21186210
Uncontrolled keywords: MEG; connectivity coherence; wavelet-based neuromarker; hyperconnectivity; hypoconnectivity; MCI detection
Subjects: T Technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Signature Themes: Future Human
Depositing User: Sanaul Hoque
Date Deposited: 22 Sep 2021 19:48 UTC
Last Modified: 23 Sep 2021 08:48 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90196 (The current URI for this page, for reference purposes)
Deravi, Farzin: https://orcid.org/0000-0003-0885-437X
Hoque, Sanaul: https://orcid.org/0000-0001-8627-3429
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