Temporal dynamics of animacy categorization in the brain of patients with mild cognitive impairment

Electroencephalography (EEG) has been commonly used to measure brain alterations in Alzheimer’s Disease (AD). However, reported changes are limited to those obtained from using univariate measures, including activation level and frequency bands. To look beyond the activation level, we used multivari...

Full description

Saved in:
Bibliographic Details
Published inPloS one Vol. 17; no. 2; p. e0264058
Main Authors Karimi, Hamed, Marefat, Haniyeh, Khanbagi, Mahdiyeh, Kalafatis, Chris, Modarres, Mohammad Hadi, Vahabi, Zahra, Khaligh-Razavi, Seyed-Mahdi
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 23.02.2022
Public Library of Science (PLoS)
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary:Electroencephalography (EEG) has been commonly used to measure brain alterations in Alzheimer’s Disease (AD). However, reported changes are limited to those obtained from using univariate measures, including activation level and frequency bands. To look beyond the activation level, we used multivariate pattern analysis (MVPA) to extract patterns of information from EEG responses to images in an animacy categorization task. Comparing healthy controls (HC) with patients with mild cognitive impairment (MCI), we found that the neural speed of animacy information processing is decreased in MCI patients. Moreover, we found critical time-points during which the representational pattern of animacy for MCI patients was significantly discriminable from that of HC, while the activation level remained unchanged. Together, these results suggest that the speed and pattern of animacy information processing provide clinically useful information as a potential biomarker for detecting early changes in MCI and AD patients.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
Competing Interests: SKR and CK serve as the CSO and CMO at Cognetivity ltd., and HModarres as the lead data scientist. These affiliations do not alter our adherence to PLOS ONE policies on sharing data and materials. Other authors declared no competing interests.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0264058