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

Si-Ping Zhang; Bi Mao; Tianlin Zhou; Chun-Wang Su; Chenxi Li; Junjie Jiang; Simeng An; Nan Yao; Youjun Li; Zi-Gang Huang; Alzheimer’s Disease Neuroimaging Initiative

doi:10.3389/fnins.2023.1198839

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

Front Neurosci. 2023 Oct 25:17:1198839. doi: 10.3389/fnins.2023.1198839. eCollection 2023.

Authors

Si-Ping Zhang^{1

2}, Bi Mao^{1

2}, Tianlin Zhou^{1

2}, Chun-Wang Su^{1

2}, Chenxi Li³, Junjie Jiang^{1

2}, Simeng An^{1

2}, Nan Yao^{2

4}, Youjun Li^{1

2}, Zi-Gang Huang^{1

2

5}; Alzheimer’s Disease Neuroimaging Initiative

Affiliations

¹ The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-Informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China.
² Research Center for Brain-Inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
³ Department of Military Medical Psychology, Air Force Medical University, Xi'an, Shaanxi, China.
⁴ Department of Applied Physics, Xi'an University of Technology, Xi'an, China.
⁵ The State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.

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).

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.

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.

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.

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.

Keywords: Alzheimer’s disease; MMSE score; coactivation pattern; resting state fMRI; subbands.

Grants and funding

This work was supported by the National Key Program of China (Grants 2022ZD0208500, 2021ZD0201300), Natural Science Foundation of China (No. 11975178), Natural Science Basic Research Program of Shaanxi (No. 2023-JC-YB-07) and Shaanxi Fundamental Science Research Project for Mathematics and Physics (Grant No.22JSQ037), Scientific Research Program Funded by Shaanxi Provincial Education Department (No. 22JP053), Fundamental Research Funds for the Central Universities (xtr062022004) and K. C. Wong Education Foundation.