Working Memory Decline in Alzheimer's Disease Is Detected by Complexity Analysis of Multimodal EEG-fNIRS.
Alzheimer’s disease (AD)
Electroencephalography (EEG)
Raven’s progressive matrices
Rey–Osterrieth complex figure
complexity analysis
conditional entropy
functional Near-Infrared Spectroscopy (fNIRS)
multimodal neuroimaging
neurovascular coupling (NC)
sample entropy
Journal
Entropy (Basel, Switzerland)
ISSN: 1099-4300
Titre abrégé: Entropy (Basel)
Pays: Switzerland
ID NLM: 101243874
Informations de publication
Date de publication:
06 Dec 2020
06 Dec 2020
Historique:
received:
30
10
2020
revised:
30
11
2020
accepted:
03
12
2020
entrez:
6
12
2020
pubmed:
7
12
2020
medline:
7
12
2020
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 33279924
pii: e22121380
doi: 10.3390/e22121380
pmc: PMC7762102
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : H2020, ECSEL-04-2015-Smart Health, Advancing Smart Optical Imaging and Sensing for Health (ASTONISH)
ID : 692470
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