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Detection of Mild Cognitive Impairment with MEG Functional Connectivity Using Wavelet-Based Neuromarkers
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Jose Miguel Sanchez Bornot,
Ricardo Bruña Fernandez,
Farzin Deravi,
Sanaul Hoque,
KongFatt Wong-Lin,
Girijesh Prasad
Sensors, Volume: 21, Issue: 18, Start page: 6210
Swansea University Author:
Scott Yang
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DOI (Published version): 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 in...
| Published in: | Sensors |
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| ISSN: | 1424-8220 |
| Published: |
MDPI AG
2021
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa58109 |
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| 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. |
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| Keywords: |
MEG; connectivity coherence; wavelet-based neuromarker; hyperconnectivity; hypoconnectivity; MCI detection |
| College: |
Faculty of Science and Engineering |
| Funders: |
This work was supported by the Northern Ireland Functional Brain Mapping Project Facility (1303/101154803), funded by Invest Northern Ireland and the University of Ulster, Alzheimer’s
Research UK (ARUK) NI Pump Priming, EU’s INTERREG VA Programme, managed by the Special
EU Programs Body (SEUPB), Ulster University Research Challenge Fund, and the COST Action
Open Multiscale Systems Medicine (OpenMultiMed) supported by COST (European Cooperation in
Science and Technology) |
| Issue: |
18 |
| Start Page: |
6210 |