Dynamic networks of cortico-muscular interactions in sleep and neurodegenerative disorders

Co-Community Network Analysis Reveals Alterations in Brain Networks in Alzheimer's Disease

Background: Alzheimer's disease (AD) is a common neurodegenerative disease. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain's intrinsic connectivity and capture dynamic changes in the brain. In this study, our research goal is to investigate how the brain network structure, as measured by resting-state fMRI, differs across distinct physiological states. Method: With the research goal of addressing the limitations of BOLD signal-based brain networks constructed using Pearson correlation coefficients, individual brain networks and community detection are used to study the brain networks based on co-community probability matrices (CCPMs). We used CCPMs and enrichment analysis to compare differences in brain network topological characteristics among three typical brain states. Result: The experimental results indicate that AD patients with increasing disease severity levels will experience the isolation of brain networks and alterations in the topological characteristics of brain networks, such as the Somatomotor Network (SMN), dorsal attention network (DAN), and Default Mode Network (DMN). Conclusion: This work suggests that using different data-driven methods based on CCPMs to study alterations in the topological characteristics of brain networks would provide better information complementarity, which can provide a novel analytical perspective for AD progression and a new direction for the extraction of neuro-biomarkers in the early diagnosis of AD.

Age-related breakdown in networks of inter-muscular coordination

Assessing inter-muscular coordination in older adults is crucial, as it directly impacts an individual's ability for independent functioning, injury prevention, and active engagement in daily activities. However, the precise mechanisms by which distinct muscle fiber types synchronize their activity across muscles to generate coordinated movements in older adults remain unknown. Our objective is to investigate how distinct muscle groups dynamically synchronize with each other in young and older adults during exercise. Thirty-five young adults and nine older adults performed one bodyweight squat set until exhaustion. Simultaneous surface electromyography (sEMG) recordings were taken from the left and right vastus lateralis, and left and right erector spinae. To quantify inter-muscular coordination, we first obtained ten time series of sEMG band power for each muscle, representing the dynamics of different muscle fiber types. Next, we calculated the bivariate equal-time Pearson's cross-correlation for each pair of sEMG band power time series across all leg and back muscles. The main results show (i) an overall reduction in the degree of inter-muscular coordination, and (ii) increased stratification of the inter-muscular network in older adults compared to young adults. These findings suggest that as individuals age, the global inter-muscular network becomes less flexible and adaptable, hindering its ability to reorganize effectively in response to fatigue or other stimuli. This network approach opens new avenues for developing novel network-based markers to characterize multilevel inter-muscular interactions, which can help target functional deficits and potentially reduce the risk of falls and neuro-muscular injuries in older adults.

Future of Sleep Medicine: Novel Insights on Sleep Regulation from Network Physiology (Part II)

The authors review recent progress in understanding fundamental aspects of physiologic regulation during wake and sleep based on modern data-driven, analytic, and computational approaches with focus on the complex dynamics of physiologic systems interactions, their coexisting and transient forms of coupling, and the role of network integration among physiologic systems in generating states and functions at the organism level. They underscore the importance of novel network-based integrative approaches and the network physiology framework to investigate the structure and dynamics of physiologic networks and to quantify emergent global states and behaviors in health and disease.

The Past and Future of Sleep Medicine

The past of sleep medicine is rich with seminal discoveries, from the recognition of clinical syndromes to measurement of sleep itself to classic and novel therapeutics. Advances in neurobiology have mapped a number of sleep circuits, described the central and peripheral circadian system, and identified the cause of narcolepsy with cataplexy. Sleep apnea endotypes and phenotypes now have established clinical relevance, though treatment implications are a work in progress. Artificial intelligence will continue to change sleep medicine in a number of domains from aiding scoring to health outcome predictions. There is a large gap between the known science and clinical translational.

Musician's dystonia: a perspective on the strongest evidence towards new prevention and mitigation treatments

This perspective article addresses the critical and up-to-date problem of task-specific musician's dystonia (MD) from both theoretical and practical perspectives. Theoretically, MD is explored as a result of impaired sensorimotor interplay across different brain circuits, supported by the most frequently cited scientific evidence-each referenced dozens of times in Scopus. Practically, MD is a significant issue as it occurs over 60 times more frequently in musicians compared to other professions, underscoring the influence of individual training as well as environmental, social, and emotional factors. To address these challenges, we propose a novel application of the FeeSyCy principle (feedback-synchrony-plasticity), which emphasizes the pivotal role of feedback in guiding inter-neuronal synchronization and plasticity-the foundation of learning and memory. This model integrates with established literature to form a comprehensive framework for understanding MD as an impaired FeeSyCy-mediated relationship between the individual and their environment, ultimately leading to trauma. The proposed approach provides significant advantages by enabling the development of innovative therapeutic and preventive strategies. Specifically, it lays the groundwork for multimodal psycho-physical therapies aimed at restoring balance in the neural circuits affected by MD. These strategies include personalized psychotherapy combined with physical rehabilitation to address both the psychological and physiological dimensions of MD. This integration offers a practical and value-added solution to this pressing problem, with potential for broad applicability across similar conditions.

Dynamics of cardio-muscular networks in exercise and fatigue

A fundamental question in cardiovascular and muscle physiology is how the heart operates in synchrony with distinct muscles to regulate homeostasis, enable movement and adapt to exercise demands and fatigue. Here we investigate how autonomic regulation of cardiac function synchronizes and integrates as a network with the activity of distinct muscles during exercise. Further, we establish how the network of cardio-muscular interactions reorganizes with fatigue. Thirty healthy young adults performed two body weight squat tests until exhaustion. Simultaneous recordings were taken of a 3-lead electrocardiogram (EKG) along with electromyography (EMG) signals from the left and right vastus lateralis, and left and right erector spinae. We first obtained instantaneous heart rate (HR) derived from the EKG signal and decomposed the EMG recordings in 10 frequency bands (F1-F10). We next quantified pair-wise coupling (cross-correlation) between the time series for HR and all EMG spectral power frequency bands in each leg and back muscle. We uncovered the first profiles of cardio-muscular network interactions, which depend on the role muscles play during exercise and muscle fibre histochemical characteristics. Additionally, we observed a significant decline in the degree of cardio-muscular coupling with fatigue, characterized by complex transitions from synchronous to asynchronous behaviour across a range of timescales. The network approach we utilized introduces new avenues for the development of novel network-based markers, with the potential to characterize multilevel cardio-muscular interactions to assess global health, levels of fatigue, fitness status or the effectiveness of cardiovascular and muscle injury rehabilitation programmes. KEY POINTS: The heart operates in synchrony with muscles to regulate homeostasis, enable movement, and adapt to exercise demands and fatigue. However, the precise mechanisms regulating cardio-muscular coupling remain unknown. This study introduces a pioneering approach to assess cardio-muscular network interactions by examining the synchronization of cardiac function with muscle activity during exercise and fatigue. We uncover the first profiles of cardio-muscular interactions characterized by specific hierarchical organization of link strength. We observe a significant decline in the degree of cardio-muscular coupling with fatigue, marked by complex transitions from synchronous to asynchronous behaviour. This network approach offers new network-based markers to characterize multilevel cardio-muscular interactions to assess global health, levels of fatigue, fitness status or the effectiveness of cardiovascular and muscle injury rehabilitation programmes.

Physiological network approach to prognosis in cirrhosis: A shifting paradigm

Decompensated liver disease is complicated by multi-organ failure and poor prognosis. The prognosis of patients with liver failure often dictates clinical management. Current prognostic models have focused on biomarkers considered as individual isolated units. Network physiology assesses the interactions among multiple physiological systems in health and disease irrespective of anatomical connectivity and defines the influence or dependence of one organ system on another. Indeed, recent applications of network mapping methods to patient data have shown improved prediction of response to therapy or prognosis in cirrhosis. Initially, different physical markers have been used to assess physiological coupling in cirrhosis including heart rate variability, heart rate turbulence, and skin temperature variability measures. Further, the parenclitic network analysis was recently applied showing that organ systems connectivity is impaired in patients with decompensated cirrhosis and can predict mortality in cirrhosis independent of current prognostic models while also providing valuable insights into the associated pathological pathways. Moreover, network mapping also predicts response to intravenous albumin in patients hospitalized with decompensated cirrhosis. Thus, this review highlights the importance of evaluating decompensated cirrhosis through the network physiologic prism. It emphasizes the limitations of current prognostic models and the values of network physiologic techniques in cirrhosis.