Frontiers in network physiology最新文献

Arterial spin labelling (ASL) magnetic resonance imaging (MRI) enables cerebral perfusion measurement, which is crucial in detecting and managing neurological issues in infants born prematurely or after perinatal complications. However, cerebral blood flow (CBF) estimation in infants using ASL remains challenging due to the complex interplay of network physiology, involving dynamic interactions between cardiac output and cerebral perfusion, as well as issues with parameter uncertainty and data noise. We propose a new spatial uncertainty-based physics-informed neural network (PINN), SUPINN, to estimate CBF and other parameters from infant ASL data. SUPINN employs a multi-branch architecture to concurrently estimate regional and global model parameters across multiple voxels. It computes regional spatial uncertainties to weigh the signal. SUPINN can reliably estimate CBF (relative error $-0.3\pm 71.7$ ), bolus arrival time (AT) $\left(30.5\pm 257.8\right)$ , and blood longitudinal relaxation time $\left(T_{1b}\right)$ (-4.4 $\pm$ 28.9), surpassing parameter estimates performed using least squares or standard PINNs. Furthermore, SUPINN produces physiologically plausible spatially smooth CBF and AT maps. Our study demonstrates the successful modification of PINNs for accurate multi-parameter perfusion estimation from noisy and limited ASL data in infants. Frameworks like SUPINN have the potential to advance our understanding of the complex cardio-brain network physiology, aiding in the detection and management of diseases. Source code is provided at: https://github.com/cgalaz01/supinn.

Introduction: Accurate localization of the seizure onset zone (SOZ) is critical for successful epilepsy surgery but remains challenging with current techniques. We developed a novel seizure onset network characterization tool that combines dynamic biomarkers of resting-state intracranial stereoelectroencephalography (rs-iEEG) and resting-state functional magnetic resonance imaging (rs-fMRI), vetted against surgical outcomes. This approach aims to reduce reliance on capturing seizures during invasive monitoring to pinpoint the SOZ.

Methods: We computed the source-sink index (SSI) from rs-iEEG for all implanted regions and from rs-fMRI for regions identified as potential SOZs by noninvasive modalities. The SSI scores were evaluated in 17 pediatric drug-resistant epilepsy (DRE) patients (ages 3-15 years) by comparing outcomes classified as successful (Engel I or II) versus unsuccessful (Engel III or IV) at 1 year post-surgery.

Results: Of 30 reviewed patients, 17 met the inclusion criteria. The combined dynamic index (im-DNM) integrating rs-iEEG and rs-fMRI significantly differentiated good (Engel I-II) from poor (Engel III-IV) surgical outcomes, outperforming the predictive accuracy of individual biomarkers from either modality alone.

Conclusion: The combined dynamic network model demonstrated superior predictive performance than standalone rs-fMRI or rs-iEEG indices.

Significance: By leveraging interictal data from two complementary modalities, this combined approach has the potential to improve epilepsy surgical outcomes, increase surgical candidacy, and reduce the duration of invasive monitoring.

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.

All cells in the human body, including cancer cells, possess specific electrical properties crucial for their functions. These properties are notably different between normal and cancerous cells. Cancer cells are characterized by autonomous oscillations and damped electromagnetic field (EMF) activation. Cancer reduces physiological variability, implying a systemic disconnection that desynchronizes bodily systems and their inherent random processes. The dynamics of heart rate, in this context, could reflect global physiological network instability in the sense of entrainment. Using a medical device that employs an active closed-loop system, such as administering specifically modulated EMF frequencies at targeted intervals and at low energies, we can evaluate the periodic oscillations of the heart. This procedure serves as a closed-loop control mechanism leading to a temporary alteration in plasma membrane ionic flow and the heart's periodic oscillation dynamics. The understanding of this phenomenon is supported by computer simulations of a mathematical model, which are validated by experimental data. Heart dynamics can be quantified using difference logistic equations, and it correlates with improved overall survival rates in cancer patients.

The Constrained Disorder Principle (CDP) defines all systems in nature by their degree of inherent variability. Per the CDP, the intrinsic variability is mandatory for their proper function and is dynamically changed based on pressures. The CDP defines the boundaries of inherent variability as a mechanism for continuous adaptation to internal and external perturbations, enabling survival and function under dynamic conditions. The laws of nature govern the world's natural phenomena and underlie the function of all systems. Nevertheless, the laws of physics do not entirely explain systems' functionality under pressure, which is essential for determining the correct operation of complex systems in nature. Variability and noise are two broad sources of inherent unpredictability in biology and technology. This paper explores how the CDP defines the function of systems and provides examples from various areas in nature where the CDP applies, including climate, genetic, biology, and human behavioral variabilities. According to the CDP, system malfunction results from inappropriate performance of the boundaries of inherent variability. The environment influences the physiological variability, and species interactions influence eco-evolutionary outcomes. The CDP defines human behavior as being driven by randomness and accounts for malfunctions and their corrections. The paper reviews variability-based CDP algorithms and CDP-based second-generation artificial intelligence systems and their potential for improving systems' prediction and efficiency by using variability.

A steadily increasing number of publications support the concept of physiological networks, and how cellular bioelectrical properties drive cell proliferation and cell synchronization. All cells, especially cancer cells, are known to possess characteristic electrical properties critical for physiological behavior, with major differences between normal and cancer cell counterparts. This opportunity can be explored as a novel treatment modality in Oncology. Cancer cells exhibit autonomous oscillations, deviating from normal rhythms. In this context, a shift from a static view of cellular processes is required for a better understanding of the dynamic connections between cellular metabolism, gene expression, cell signaling and membrane polarization as states in constant flux in realistic human models. In oncology, radiofrequency electromagnetic fields have produced sustained responses and improved quality of life in cancer patients with minimal side effects. This review aims to show how non-thermal systemic radiofrequency electromagnetic fields leads to promising therapeutic responses at cellular and tissue levels in humans, supporting this newly emerging cancer treatment modality with early favorable clinical experience specifically in advanced cancer.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a multisystemic disease with a multifactorial pathogenesis involving dietary, environmental, and genetic factors. Previous mouse models suggested that circadian misalignment may additionally influence its development as it influences metabolism in diverse organs including the liver. Further, data from sleep questionnaires proved sleep-wake disruption in patients with MASLD. We objectively assessed sleep-wake rhythms in patients with biopsy-proven MASLD (n = 35) and healthy controls (HC, n = 16) using actigraphy 24/7 for 4 weeks. With the aim to re-align sleep rhythms a single standardized sleep hygiene education session was performed after 2 weeks. Actigraphy data revealed that MASLD patients had more awakenings per night (MASLD vs. HC 8.5 vs. 5.5, p = 0.0036), longer wakefulness after sleep onset (MASLD vs. HC 45.4 min vs. 21.3 min, p = 0.0004), and decreased sleep efficiency (MASLD vs. HC 86.5% vs. 92.8%, p = 0.0008) compared with HC despite comparable sleep duration. Patients with MASLD self-reported shorter sleep duration (MASLD vs. HC 6 h vs. 6 h 45 min, p = 0.01) and prolonged sleep latency contributing to poorer sleep quality. Standardized sleep hygiene education did not produce significant changes in sleep parameters. Our findings indicate fragmented nocturnal sleep in patients with MASLD, characterized by increased wakefulness and reduced sleep efficiency, perceived subjectively as shortened sleep duration and delayed onset. A single sleep hygiene education session did not improve sleep parameters.

To date, there is no neurophysiologic or neuroimaging biomarker that can accurately delineate the epileptogenic network. High-frequency oscillations (HFO) have been proposed as biomarkers for epileptogenesis and the epileptogenic network. The pathological HFO have been associated with areas of seizure onset and epileptogenic tissue. Several studies have demonstrated that the resection of areas with high rates of pathological HFO is associated with favorable postoperative outcomes. Recent studies have demonstrated the spatiotemporal organization of HFO into networks and their potential role in defining epileptogenic networks. Our review will present the existing literature on HFO-associated networks, specifically focusing on their role in defining epileptogenic networks and their potential significance in surgical planning.

Human group connectome analysis relies on combining individual connectome data to construct a single representative network which can be used to describe brain organisation and identify differences between subject groups. Existing methods adopt different strategies to select the network structural features to be retained or optimised at group level. In the absence of ground truth, however, it is unclear which structural features are the most suitable and how to evaluate the consequences on the group network of applying any given strategy. In this investigation, we consider the impact of defining a connectome as representative if it can recapitulate not just the structure of the individual networks in the cohort tested but also their dynamical behaviour, which we measured using a model of coupled oscillators. We applied the widely used approach of consensus thresholding to a dataset of individual structural connectomes from a healthy adult cohort to construct group networks for a range of thresholds and then identified the most dynamically representative group connectome as that having the least deviation from the individual connectomes given a dynamical measure of the system. We found that our dynamically representative network recaptured aspects of structure for which it did not specifically optimise, with no significant difference to other group connectomes constructed via methods which did optimise for those metrics. Additionally, these other group connectomes were either as dynamically representative as our chosen network or less so. While we suggest that dynamics should be at least one of the criteria for representativeness, given that the brain has evolved under the pressure of carrying out specific functions, our results suggest that the question persists as to which of these criteria are valid and testable.

In this mini review, we propose the use of the Julia programming language and its software as a strong candidate for reproducible, efficient, and sustainable physiological signal analysis. First, we highlight available software and Julia communities that provide top-of-the-class algorithms for all aspects of physiological signal processing despite the language's relatively young age. Julia can significantly accelerate both research and software development due to its high-level interactive language and high-performance code generation. It is also particularly suited for open and reproducible science. Openness is supported and welcomed because the overwhelming majority of Julia software programs are open source and developed openly on public platforms, primarily through individual contributions. Such an environment increases the likelihood that an individual not (originally) associated with a software program would still be willing to contribute their code, further promoting code sharing and reuse. On the other hand, Julia's exceptionally strong package manager and surrounding ecosystem make it easy to create self-contained, reproducible projects that can be instantly installed and run, irrespective of processor architecture or operating system.