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The energetic and entropic organization of the brain's functional activity in mild cognitive impairment (MCI) has yet to be fully characterized. Network Control Theory (NCT) is a multi‐modal approach that captures alterations in the brain's energetic landscape by combining the brain's functional activity and the structural connectome. Entropy is another complementary metric that can quantify the complexity and predictability in a neural time series, offering insights into the brain's dynamic functional activity. Our study aims to explore the differences in the brain's energetic and entropic landscape between people with MCI and healthy controls (HC). Four hundred ninety‐nine HC and 55 MCI patients were included. First, k‐means clustering 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. 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). 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 Alzheimer's Disease (AD) are needed to better characterize the changes in the energetic landscape in the later stages of cognitive impairment. We examined the shifts in the brain energetics computed with network control theory and entropy in people with mild cognitive impairment (MCI) compared to healthy controls (HC). MCI patients exhibit lower transition energy in the limbic network, reduced global entropy, and a positive Aβ‐entropy association compared to HC, suggesting a disrupted energetic and entropic landscape as potential neuroimaging biomarkers of MCI.

Background Mild cognitive impairment (MCI) is a clinical cognitive deficit that is not severe enough to meet the threshold for Alzheimer's Disease (AD); however, MCI patients have an increased risk of developing AD. Therefore, a diagnosis of MCI may represent a critical turning point in the trajectory of developing AD. Establishing neurological signatures of MCI using network control theory (NCT) may allow more informed diagnosis, and an understanding of its underlying mechanisms could pave the way for novel treatments. Method Functional MRI (fMRI) metrics were collected in MCI patients (n = 57, mean age = 66.68) and healthy controls (HC) (n = 500, mean age = 72.25). The average structural connectivity matrix was obtained using age‐matched controls from the Human Connectome Project‐Aging dataset. Commonly recurring brain states were identified via k‐means clustering of activation matrices over 200 regions using the Schafer atlas. NCT was used to compute the transition energy (TE): the minimum energy required to transition between each pair of brain states. The entropy of each region’s activity was calculated using SampEn, and was then correlated with TE using Pearson’s correlation. Pairwise/global (average of all brain state pairs) TE and global entropy (average of all regions) were compared between MCI and HC using ANCOVA with age and sex as covariates. Result The brain states identified via k‐means clustering were high and low amplitude activity in the visual, somatomotor, and limbic networks (Figure 1). While there were no significant differences in pairwise or global TE between MCI and HC (Figures 2‐3), MCI had significantly lower global entropy than HC. ANCOVA revealed that increased age is associated with increased entropy. Pearson correlation showed a significant inverse relationship between global TE and entropy across individuals (r = −0.13) (Figure 3). Conclusion Entropy of brain activity measured with fMRI is a promising neuroimaging biomarker of MCI, which is often underdiagnosed or diagnosed with delay. Future work will investigate regional entropy reduction patterns between MCI and AD patients to establish the use of these metrics in disease progression, and to get a more detailed picture of brain activity changes in individuals with these diagnoses.

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). 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. 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 = 0.028). Decreased global entropy was observed in MCI patients compared to HC ( = 7.29e-7). There was a positive association between TE and entropy in the frontoparietal network ( = 7.03e-3). Increased global Aβ was associated with higher global entropy in MCI patients (ρ = 0.632, = 0.041). 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.

This chapter describes the clinical epidemiology of shoulder impairment in older adults, standard approach to management, and the current ambiguity of the literature on the effectiveness of interventions for optimizing long‐term outcomes. Preliminary data is presented grounded within a theoretical model, supporting the hypothesis that patients use \"out‐of‐date\" self‐prototypes to establish expectations for outcomes, which has significant implications for long‐term self‐management behaviors and may explain lack of adherence to therapy. Finally, the authors describe feasibility work on theory‐based interventions that have the potential to address and inform unrealistic patient expectations.