Key result
Fuzzy dispersion entropy best distinguishes Alzheimer's disease resting-state MEG patterns from controls and MCI.
Why the study?
Nonlinear dynamical measures have been studied individually in MEG recordings of patients with Alzheimer's disease, but systematic comparisons among conventional and recently introduced measures are lacking.
Do nonlinear dynamical measures of resting-state MEGs distinguish between Alzheimer's disease, mild cognitive impairment, and healthy controls?
Observational (n=80)
Do nonlinear dynamical measures of resting-state MEGs distinguish between Alzheimer's disease, mild cognitive impairment, and healthy controls?
Fuzzy dispersion entropy is a consistent nonlinear measure for distinguishing MEG dynamical patterns in Alzheimer's disease compared to healthy controls and mild cognitive impairment.
BACKGROUND: Nonlinear dynamical measures, such as fractal dimension (FD), entropy, and Lempel-Ziv complexity (LZC), have been extensively investigated individually for detecting information content in magnetoencephalograms (MEGs) from patients with Alzheimer's disease (AD). OBJECTIVE: To compare systematically the performance of twenty conventional and recently introduced nonlinear dynamical measures in studying AD versus mild cognitive impairment (MCI) and healthy control (HC) subjects using MEG. METHODS: We compared twenty nonlinear measures to distinguish MEG recordings from 36 AD (mean age = 74.06±6.95 years), 18 MCI (mean age = 74.89±5.57 years), and 26 HC subjects (mean age = 71.77±6.38 years) in different brain regions and also evaluated the effect of the length of MEG epochs on their performance. We also studied the correlation between these measures and cognitive performance based on the Mini-Mental State Examination (MMSE). RESULTS: The results obtained by LZC, zero-crossing rate (ZCR), FD, and dispersion entropy (DispEn) measures showed significant differences among the three groups. There was no significant difference between HC and MCI. The highest Hedge's g effect sizes for HC versus AD and MCI versus AD were respectively obtained by Higuchi's FD (HFD) and fuzzy DispEn (FuzDispEn) in the whole brain and was most prominent in left lateral. The results obtained by HFD and FuzDispEn had a significant correlation with the MMSE scores. DispEn-based techniques, LZC, and ZCR, compared with HFD, were less sensitive to epoch length in distinguishing HC form AD. CONCLUSIONS: FuzDispEn was the most consistent technique to distinguish MEG dynamical patterns in AD compared with HC and MCI.
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Azami et al. (2023) conducted an observational in Alzheimer's disease and Mild Cognitive Impairment (n=80). Nonlinear dynamical measures of MEG (e.g., FuzDispEn, HFD) vs. Healthy controls and Mild Cognitive Impairment was evaluated on Distinguishing MEG recordings among AD, MCI, and HC subjects. Fuzzy dispersion entropy (FuzDispEn) was the most consistent nonlinear dynamical measure to distinguish resting-state MEG patterns in Alzheimer's disease compared with healthy controls and MCI.
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