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Frequency dependent whole-brain coactivation patterns analysis in Alzheimer's disease.

Authors :
Zhang SP
Mao B
Zhou T
Su CW
Li C
Jiang J
An S
Yao N
Li Y
Huang ZG
Source :
Frontiers in neuroscience [Front Neurosci] 2023 Oct 25; Vol. 17, pp. 1198839. Date of Electronic Publication: 2023 Oct 25 (Print Publication: 2023).
Publication Year :
2023

Abstract

Background: The brain in resting state has complex dynamic properties and shows frequency dependent characteristics. The frequency-dependent whole-brain dynamic changes of resting state across the scans have been ignored in Alzheimer's disease (AD).<br />Objective: Coactivation pattern (CAP) analysis can identify different brain states. This paper aimed to investigate the dynamic characteristics of frequency dependent whole-brain CAPs in AD.<br />Methods: We utilized a multiband CAP approach to model the state space and study brain dynamics in both AD and NC. The correlation between the dynamic characteristics and the subjects' clinical index was further analyzed.<br />Results: The results showed similar CAP patterns at different frequency bands, but the occurrence of patterns was different. In addition, CAPs associated with the default mode network (DMN) and the ventral/dorsal visual network (dorsal/ventral VN) were altered significantly between the AD and NC groups. This study also found the correlation between the altered dynamic characteristics of frequency dependent CAPs and the patients' clinical Mini-Mental State Examination assessment scale scores.<br />Conclusion: This study revealed that while similar CAP spatial patterns appear in different frequency bands, their dynamic characteristics in subbands vary. In addition, delineating subbands was more helpful in distinguishing AD from NC in terms of CAP.<br />Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.<br /> (Copyright © 2023 Zhang, Mao, Zhou, Su, Li, Jiang, An, Yao, Li and Huang.)

Details

Language :
English
ISSN :
1662-4548
Volume :
17
Database :
MEDLINE
Journal :
Frontiers in neuroscience
Publication Type :
Academic Journal
Accession number :
37946728
Full Text :
https://doi.org/10.3389/fnins.2023.1198839