Detection of Mild Cognitive Impairment with MEG Functional Connectivity Using Wavelet-Based Neuromarkers Article Swipe
Su Yang
,
José M. Sánchez‐Bornot
,
Ricardo Bruña
,
Farzin Deravi
,
Sanaul Hoque
,
KongFatt Wong‐Lin
,
Girijesh Prasad
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s21186210
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s21186210
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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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s21186210
- https://www.mdpi.com/1424-8220/21/18/6210/pdf?version=1631854392
- OA Status
- gold
- Cited By
- 4
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3200029388
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3200029388Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s21186210Digital Object Identifier
- Title
-
Detection of Mild Cognitive Impairment with MEG Functional Connectivity Using Wavelet-Based NeuromarkersWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-09-16Full publication date if available
- Authors
-
Su Yang, José M. Sánchez‐Bornot, Ricardo Bruña, Farzin Deravi, Sanaul Hoque, KongFatt Wong‐Lin, Girijesh PrasadList of authors in order
- Landing page
-
https://doi.org/10.3390/s21186210Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/21/18/6210/pdf?version=1631854392Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/21/18/6210/pdf?version=1631854392Direct OA link when available
- Concepts
-
Pattern recognition (psychology), Support vector machine, Artificial intelligence, Cross-validation, Region of interest, Classifier (UML), Cognitive impairment, Computer science, Wavelet, Mathematics, Cognition, Psychology, NeuroscienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 2, 2021: 1Per-year citation counts (last 5 years)
- References (count)
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46Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.participants. | 59 |
| abstract_inverted_index.significantly | 137 |
| abstract_inverted_index.wavelet-based | 107, 248 |
| abstract_inverted_index.classification | 221 |
| abstract_inverted_index.discriminating | 42 |
| abstract_inverted_index.hypo-connected | 146 |
| abstract_inverted_index.research-based | 288 |
| abstract_inverted_index.(hypo-connected | 98 |
| abstract_inverted_index.hyper-connected | 134, 179 |
| abstract_inverted_index.(hyper-connected | 86 |
| abstract_inverted_index.cross-validation | 191 |
| abstract_inverted_index.multi-resolution | 269 |
| abstract_inverted_index.cross-validation. | 165 |
| abstract_inverted_index.magnitude-squared | 27 |
| abstract_inverted_index.hyper-connections. | 240 |
| abstract_inverted_index.source-reconstructed | 175 |
| abstract_inverted_index.magnetoencephalographic | 7 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5040786747 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I39586589 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.6399999856948853 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile.value | 0.56634859 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |