Triple-network analysis of Alzheimer's disease based on the energy landscape

Youjun Li; Simeng An; Tianlin Zhou; Chunwang Su; Siping Zhang; Chenxi Li; Junjie Jiang; Yunfeng Mu; Nan Yao; Zi-Gang Huang; Alzheimer’s Disease Neuroimaging Initiative

doi:10.3389/fnins.2023.1171549

Triple-network analysis of Alzheimer's disease based on the energy landscape

Front Neurosci. 2023 May 23:17:1171549. doi: 10.3389/fnins.2023.1171549. eCollection 2023.

Authors

Youjun Li^{1

2}, Simeng An^{1

2}, Tianlin Zhou^{1

2}, Chunwang Su^{1

2}, Siping Zhang^{1

2}, Chenxi Li³, Junjie Jiang^{1

2}, Yunfeng Mu⁴, Nan Yao^{2

5}, Zi-Gang Huang^{1

2

6}; 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 and 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 Gynecological Oncology, Shaanxi Provincial Cancer Hospital, Xi'an, China.
⁵ Department of Applied Physics, Xi'an University of Technology, Xi'an, China.
⁶ The State Key Laboratory of Congnitive Neuroscience and Learning, Beijing Normal University, Beijing, China.

Abstract

Introduction: Research on the brain activity during resting state has found that brain activation is centered around three networks, including the default mode network (DMN), the salient network (SN), and the central executive network (CEN), and switches between multiple modes. As a common disease in the elderly, Alzheimer's disease (AD) affects the state transitions of functional networks in the resting state.

Methods: Energy landscape, as a new method, can intuitively and quickly grasp the statistical distribution of system states and information related to state transition mechanisms. Therefore, this study mainly uses the energy landscape method to study the changes of the triple-network brain dynamics in AD patients in the resting state.

Results: AD brain activity patterns are in an abnormal state, and the dynamics of patients with AD tend to be unstable, with an unusually high flexibility in switching between states. Also , the subjects' dynamic features are correlated with clinical index.

Discussion: The atypical balance of large-scale brain systems in patients with AD is associated with abnormally active brain dynamics. Our study are helpful for further understanding the intrinsic dynamic characteristics and pathological mechanism of the resting-state brain in AD patients.

Keywords: Alzheimer’s disease; dynamics analysis; energy landscape; resting-state fMRI; triple-network.