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

Abstract

IntroductionResearch 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.MethodsEnergy 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.ResultsAD 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.DiscussionThe 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.

Energy landscape analysis of brain network dynamics in Alzheimer’s disease

“…It has been reported that functional organizations of CEN and DMN were impaired in AD ( Joo et al, 2016 ), and functional connectivity between the SN and the other two networks (CEN and DMN) was also altered in AD patients ( He et al, 2014 ). Furthermore, a triple-network (SN, CEN and DMN) analysis of Alzheimer’s disease, focusing on the energy landscape of these networks, revealed that the dynamics of patients with AD tend to be unstable, suggesting fluctuations in network interactions ( Li et al, 2023 ). An increased occupancy rate in State 1 of AD patients possibly suggests a potential brain compensatory mechanism for enhancing cognitive control and coordination.…”

“…Energy landscape analysis has been utilized to analyze abnormalities in brain dynamic dysfunction, including autism spectrum disorder ( Watanabe and Rees, 2017 ), poststroke aphasia ( Fan et al, 2022 ) and AD ( Klepl et al, 2022 ; Li et al, 2023 ). For the AD patient, it was found that the dynamics of AD patients’ EEG were shown to be more constrained - with more local minima, less variation in basin size, and smaller basins ( Klepl et al, 2022 ).…”

“…Due to poor spatial resolution of EEG, the EEG study cannot reveal the dynamic changes of brain networks in AD patients. Li et al (2023) applied the energy landscape analysis to the resting fMRI data of AD and investigate the dynamic changes of the DMN, the SN, and the ECN. The study revealed that the dynamics of patients with AD tend to be unstable, with an unusually high flexibility in switching between states ( Li et al, 2023 ).…”

“… Li et al (2023) applied the energy landscape analysis to the resting fMRI data of AD and investigate the dynamic changes of the DMN, the SN, and the ECN. The study revealed that the dynamics of patients with AD tend to be unstable, with an unusually high flexibility in switching between states ( Li et al, 2023 ). However, Li’s study analyzes the DMN, SN and ECN separately by using the energy landscape method and ignored the interaction between the three networks.…”