Disrupted Energy Landscape in Individuals with Mild Cognitive Impairment: Insights from Network Control Theory

Abstract

Introduction: Patients with mild cognitive impairment (MCI) have shown disruptions in both brain structure and function, often studied separately. However, understanding the relationship between brain structure and function can provide valuable insights into this early stage of cognitive decline for better treatment strategies to avoid its progression. Network Control Theory (NCT) is a multi-modal approach that captures the alterations in the brain's energetic landscape by combining the brain's functional activity and the structural connectome. Our study aims to explore the differences in the brain's energetic landscape between people with MCI and healthy controls (HC).

Methods: Four hundred ninety-nine HC and 55 MCI patients were included. First, k-means was applied to functional MRI (fMRI) time series to identify commonly recurring brain activity states. Second, NCT was used to calculate the minimum energy required to transition between these brain activity states, otherwise known as transition energy (TE). The entropy of the fMRI time series as well as PET-derived amyloid beta (Aβ) and tau deposition were measured for each brain region. The TE and entropy were compared between MCI and HC at the network, regional, and global levels using linear models where age, sex, and intracranial volume were added as covariates. The association of TE and entropy with Aβ and tau deposition was investigated in MCI patients using linear models where age, sex, and intracranial volume were controlled.

Results: Commonly recurring brain activity states included those with high and low amplitude activity in visual (+/-), default mode (+/-), and dorsal attention (+/-) networks. Compared to HC, MCI patients required lower transition energy in the limbic network (adjusted p = 0.028). Decreased global entropy was observed in MCI patients compared to HC (p = 7.29e-7). There was a positive association between TE and entropy in the frontoparietal network (p = 7.03e-3). Increased global Aβ was associated with higher global entropy in MCI patients (ρ = 0.632, p = 0.041).

Conclusion: Lower TE in the limbic network in MCI patients may indicate either neurodegeneration-related neural loss and atrophy or a potential functional upregulation mechanism in this early stage of cognitive impairment. Future studies that include people with AD are needed to better characterize the changes in the energetic landscape in the later stages of cognitive impairment.

Keywords: entropy; functional MRI; mild cognitive impairment; network control theory.

Figure 1:

Brain plots and radial plots used to visualize the most recurring brain activity states. The radial plots show the mean of the positive and negative functional activity values over the Yeo functional network. States were named based on the network having the maximum magnitude in the radial plot. Visual (VIS), Dorsal Attention (DAN), Somatomotor (SMN), Salient Ventral Attention (VAN), Frontoparietal Control (FPN), Limbic (LIM), Default Mode (DMN) Network.

Figure 2:

(A) Distribution of global transition energy in HC and MCI. The p-value (p = 0.796) was computed using the linear model where the output was the global TE and the inputs were the group assignment (MCI vs. HC), age, sex, and ICV. (B) The beta estimates from the linear model adjusted for age, sex, and ICV were used to represent the amplitude and the direction of the difference in the state-wise TE between HC and MCI groups. State pairs with significantly different TE before correction are marked with an asterisk. None of the state pairs were significant after correction. (C) The beta estimates from the linear model adjusted for age, sex, and ICV were used to represent the amplitude and the direction of the difference in the network-wise TE between HC and MCI groups. The limbic network (marked with *) was significant after multiple comparison p-value corrections. (D) The beta estimates from the linear model adjusted for age, sex, and ICV are used to represent the amplitude and the direction of the difference in the regional TE between HC and MCI groups. In figures B-D, positive values represent higher TE in MCI compared to HC, while negative values represent higher TE in HC compared to the MCI group.

Figure 3:

(A) Distribution of global entropy in HC and MCI. (B) The beta estimates from the linear model adjusted for age, sex, and ICV were used to represent the amplitude and the direction of the difference in the regional entropy between HC and MCI groups. Positive values represent higher regional entropy in MCI compared to HC, while negative values represent higher regional entropy in HC compared to the MCI group.

Figure 4:

(A) The scatterplot shows the association between average entropy and average transition energy in all subjects, and in HC and MCI groups separately. (B) The beta estimates from the linear model adjusted for age, sex, and ICV were used to represent the amplitude and direction of association between global entropy and global transition energy. (C) The beta estimates from the linear model adjusted for age, sex, and ICV were used to represent the amplitude and the direction of the association between network-wise TE and network-wise entropy. The frontoparietal control network (marked with an asterisk) was significant after multiple comparison p-value corrections.

Figure 5:

The scatter plots represent the association between (A) global TE and global tau protein levels, (B) global TE and global Aβ plaque levels, (C) global entropy and global tau protein levels, and (D) global entropy and global Aβ plaque levels. The data shown in this figure only represents MCI patients. Aβ plaque and tau protein levels are given in units of standard uptake value ratio (SUVR). The β and the p-value from the linear model adjusted for age, sex, and ICV are presented on the scatter plots.