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Article

Working Memory Decline in Alzheimer’s Disease Is Detected by Complexity Analysis of Multimodal EEG-fNIRS

1
Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy
2
Department of Medicine and Science of Ageing, University G. D’Annunzio, Via Dei Vestini 31, 66100 Chieti, Italy
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(12), 1380; https://doi.org/10.3390/e22121380
Received: 30 October 2020 / Revised: 30 November 2020 / Accepted: 3 December 2020 / Published: 6 December 2020
(This article belongs to the Special Issue What Limits Working Memory Performance?)
Alzheimer’s disease (AD) is characterized by working memory (WM) failures that can be assessed at early stages through administering clinical tests. Ecological neuroimaging, such as Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS), may be employed during these tests to support AD early diagnosis within clinical settings. Multimodal EEG-fNIRS could measure brain activity along with neurovascular coupling (NC) and detect their modifications associated with AD. Data analysis procedures based on signal complexity are suitable to estimate electrical and hemodynamic brain activity or their mutual information (NC) during non-structured experimental paradigms. In this study, sample entropy of whole-head EEG and frontal/prefrontal cortex fNIRS was evaluated to assess brain activity in early AD and healthy controls (HC) during WM tasks (i.e., Rey–Osterrieth complex figure and Raven’s progressive matrices). Moreover, conditional entropy between EEG and fNIRS was evaluated as indicative of NC. The findings demonstrated the capability of complexity analysis of multimodal EEG-fNIRS to detect WM decline in AD. Furthermore, a multivariate data-driven analysis, performed on these entropy metrics and based on the General Linear Model, allowed classifying AD and HC with an AUC up to 0.88. EEG-fNIRS may represent a powerful tool for the clinical evaluation of WM decline in early AD. View Full-Text
Keywords: Alzheimer’s disease (AD); Electroencephalography (EEG); functional Near-Infrared Spectroscopy (fNIRS); multimodal neuroimaging; neurovascular coupling (NC); complexity analysis; sample entropy; conditional entropy; Rey–Osterrieth complex figure; Raven’s progressive matrices Alzheimer’s disease (AD); Electroencephalography (EEG); functional Near-Infrared Spectroscopy (fNIRS); multimodal neuroimaging; neurovascular coupling (NC); complexity analysis; sample entropy; conditional entropy; Rey–Osterrieth complex figure; Raven’s progressive matrices
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MDPI and ACS Style

Perpetuini, D.; Chiarelli, A.M.; Filippini, C.; Cardone, D.; Croce, P.; Rotunno, L.; Anzoletti, N.; Zito, M.; Zappasodi, F.; Merla, A. Working Memory Decline in Alzheimer’s Disease Is Detected by Complexity Analysis of Multimodal EEG-fNIRS. Entropy 2020, 22, 1380. https://doi.org/10.3390/e22121380

AMA Style

Perpetuini D, Chiarelli AM, Filippini C, Cardone D, Croce P, Rotunno L, Anzoletti N, Zito M, Zappasodi F, Merla A. Working Memory Decline in Alzheimer’s Disease Is Detected by Complexity Analysis of Multimodal EEG-fNIRS. Entropy. 2020; 22(12):1380. https://doi.org/10.3390/e22121380

Chicago/Turabian Style

Perpetuini, David, Antonio M. Chiarelli, Chiara Filippini, Daniela Cardone, Pierpaolo Croce, Ludovica Rotunno, Nelson Anzoletti, Michele Zito, Filippo Zappasodi, and Arcangelo Merla. 2020. "Working Memory Decline in Alzheimer’s Disease Is Detected by Complexity Analysis of Multimodal EEG-fNIRS" Entropy 22, no. 12: 1380. https://doi.org/10.3390/e22121380

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