Abstract
Multiscale entropy (MSE) analysis is a novel entropy-based analysis method for quantifying the complexity of dynamic neural signals and physiological systems across multiple temporal scales. This approach may assist in elucidating the pathophysiologic mechanisms of amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD). Using resting-state fNIRS imaging, we recorded spontaneous brain activity from 31 healthy controls (HC), 27 patients with aMCI, and 24 patients with AD. The quantitative analysis of MSE revealed that reduced brain signal complexity in AD patients in several networks, namely, the default, frontoparietal, ventral and dorsal attention networks. For the default and ventral attention networks, the MSE values also showed significant positive correlations with cognitive performances. These findings demonstrated that the MSE-based analysis method could serve as a novel tool for fNIRS study in characterizing and understanding the complexity of abnormal cortical signals in AD cohorts.
Highlights
Alzheimer’s disease (AD) is the most common progressive neurodegenerative disease and one of the greatest healthcare challenges of the 21st century [1]
Eighty-seven right-handed participants were recruited for this study, comprising 27 AD patients (12 men and 15 women), 29 Amnestic mild cognitive impairment (aMCI) patients (14 men and 15 women) and 31 sex, age, and education-matched healthy controls (HC: 11 men and 20 women)
The current study shows that AD patients, compared to healthy controls, exhibited a reduced resting-state Functional near-infrared spectroscopy (fNIRS) complexity in most brain regions (Fig. 3(A)) and several typical cognitive networks (Fig. 4), e.g., the default mode and ventral attention networks
Summary
Alzheimer’s disease (AD) is the most common progressive neurodegenerative disease and one of the greatest healthcare challenges of the 21st century [1]. Network science, combined with non-invasive functional imaging, has generated unprecedented insights regarding the adult brain's functional organization and promises to help elucidate the development of the functional architectures that support complex behavior In this context, AD is increasingly viewed as a disease with multiple dysfunctional large-scale neuronal networks rather than a localized abnormality [4]. A meta-analysis of 75 fMRI studies [5] suggested that MCI and AD showed different hypoactivation patterns, whereas similar compensatory large-scale networks are used to fulfill cognitive tasks. This large-scale network approach may help evaluating the physiopathological progression of AD at a system level
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