Frontiers in network physiology最新文献

Interictal epileptiform discharges (IEDs) are pathologic hallmarks of epilepsy which frequently arise and spread through networks of functionally-connected brain regions. Recent studies demonstrate that the sequential recruitment of brain regions by propagating IEDs is highly conserved across repeated discharges, suggesting that IED propagation is spatiotemporally constrained by features of the underlying epileptic network. Understanding how repetitive IED sequences relate to the spatiotemporal organization of the epileptic network may reveal key insights into the pathophysiological role of IEDs during epileptogenesis. Delivery of exogenous electrical current allows for direct experimental probing of epileptic network circuitry and correlation with spontaneous epileptiform activity (e.g., IEDs). In this pilot study of human subjects with refractory epilepsy, we performed cortical stimulation via invasive depth electrodes to test whether spatiotemporal patterns observed during spontaneous IEDs are reproduced by evoked cortical potentials. We found that evoked potentials were accentuated following stimulation of early-activating "upstream" IED regions (anterograde) and attenuated with stimulation of late-activating "downstream" IED regions (retrograde). Concordance between IED latencies and evoked potentials suggests that these distinct network phenomena share common spatiotemporal constraints in the human epileptic brain.

Objectives: Current approaches to objective measurement of sleep disturbances in children overlook the period prior to sleep, or the settling down time. Using machine learning techniques, we identified key features that characterize differences in activity during the settling down period that differentiate children with sensory sensitivities to tactile input (SS) and children without sensitivities (NSS).

Methods: Actigraphy data were collected from children with SS (n = 17) and children with NSS (n = 18) over 2 weeks (a total of 430 evenings). The settling down period, indicated using caregiver report and actigraphy indices, was isolated each evening and seven features (mean magnitude, maximum magnitude, kurtosis, skewness, Shannon entropy, standard deviation, and interquartile range) were extracted. 10-fold cross-validation with random forests were used to determine accuracy, sensitivity, and specificity of differentiating groups.

Results: We could accurately differentiate groups (accuracy = 83%, specificity = 83%, sensitivity = 84%). Feature importance maps identify that children with SS have higher maximum bouts of activity (U = -2.23, p = 0.026) during the settling down time and a higher variance in activity for the children with SS (e.g., interquartile range, Shannon entropy) that sets them apart from their peers.

Conclusion: We present a novel use of machine learning techniques that successfully uncovered differentiating features within the settling down period for our groups. These differences have been difficult to capture using standard sleep and rest-activity metrics. Our data suggests that activity during the settling down period may be a unique target for future research for children with SS.

Ventricular fibrillation is a lethal condition where the heartbeat becomes too disorganised to maintain proper circulation. It is treated with defibrillation, which applies an electric shock in an attempt to reset the heart rhythm. As the high energy of this shock risks long-term harm to the patient, means of reducing it without compromising treatment efficacy are of great interest. One approach to maintaining efficacy is to improve the success rate of such low-energy shocks (i.e., pulses) through the proper timing of their application as defibrillation protocols, which consist of one or more pulses with predetermined inter-pulse periods. In practice, however, the effects of application timing remain to be tested for any of the multi-pulse protocols proposed in literature. We use (de)fibrillation simulations to show that such timing matters: The success rate of single-pulse protocols can vary by as much as 80 percentage points depending on timing, and using more shocks in succession only lessens this sensitivity up to a point. We also present evidence that feedback-based defibrillation on a shock-by-shock basis may be the only practical means of using timing to increase treatment efficacy, as we also generally find any optimal application timings to be specific to each combination of protocol and fibrillation episode.

Introduction: More than 50 million people worldwide suffer from epilepsy. Approximately 30% of epileptic patients suffer from medically refractory epilepsy (MRE), which means that over 15 million people must seek extensive treatment. One such treatment involves surgical removal of the epileptogenic zone (EZ) of the brain. However, because there is no clinically validated biomarker of the EZ, surgical success rates vary between 30%-70%. The current standard for EZ localization often requires invasive monitoring of patients for several weeks in the hospital during which intracranial EEG (iEEG) data is captured. This process is time-consuming as the clinical team must wait for seizures and visually interpret the iEEG during these events. Hence, an iEEG biomarker that does not rely on seizure observations is desirable to improve EZ localization and surgical success rates. Recently, the source-sink index (SSI) was proposed as an interictal (between seizure) biomarker of the EZ, which captures regional interactions in the brain and in particular identifies the EZ as regions being inhibited ("sinks") by neighbors ("sources") when patients are not seizing. The SSI only requires 5-min snapshots of interictal iEEG recordings. However, one limitation of the SSI is that it is computed heuristically from the parameters of dynamical network models (DNMs).

Methods: In this work, we propose a formal method for detecting sink regions from DNMs, which has a strong foundation in linear systems theory. In particular, the steady-state solution of the DNM highlights the sinks and is characterized by the leading eigenvector of the state-transition matrix of the DNM. To test this, we build patient-specific DNMs from interictal iEEG data collected from 65 patients treated across 6 centers. From each DNM, we compute the average leading eigenvectors and evaluate their potential as a biomarker to accurately predict EZ and surgical success.

Results: Our findings show the ability of the leading eigenvector to accurately predict EZ (average accuracy 66.81% ± 0.19%) and surgical success (average accuracy 71.9% ± 0.22%) with data from 65 patients across 6 centers from 5 min of data, which we show is comparable with the current method of localizing the EZ over several weeks.

Discussion: This eigenvector biomarker has the potential to assist clinicians in localizing the EZ quickly and thus increase surgical success in patients with MRE, resulting in an improvement in patient care and quality of life.

Introduction: We present a theoretical foundation based on the spontaneous self-organized temporal criticality (SOTC) and multifractal dimensionality $\mu$ to model complex neurophysiological and behavioral systems to infer the optimal empirical transfer of information among them. We hypothesize that heterogeneous time series characterizing the brain, heart, and lung organ-networks (ONs) are necessarily multifractal, whose level of complexity and, therefore, their information content is measured by their multifractal dimensions.

Methods: We apply modified diffusion entropy analysis (MDEA) to assess multifractal dimensions of ON time series (ONTS), and complexity synchronization (CS) analysis to infer information transfer among ONs that are part of a network-of-organ-networks (NoONs). An automated parameter selection process is proposed that relies on the Kolmogorov-Smirnov statistic to properly choose stripe sizes which are crucial in the MDEA analysis using synthetic duration times derived from the Mittag-Leffler map, shows the strength of KS-based stripe size selection to track changes in the IPL parameter $\mu$ . The purpose of this paper is to advance the validation, standardization, and reconstruct-ability of MDEA and CS analysis of heterogeneous neurophysiological time series data.

Results: Results from processing these datasets show that the complexity of brain, heart, and lung ONTS co-vary over time during cognitive task performance in 44% of subjects, while complexity of brain-heart interactions significantly co-vary in 85% of subjects.

Discussion: We conclude that certain principles, guidelines, and strategies for the application of MDEA analysis need further consideration. We conclude with a summary of the MDEA's limitations and future research directions.

Introduction: Focal resective surgery can be an effective treatment option for patients with refractory epilepsy if the seizure onset zone is accurately identied through intracranial EEG recordings. The traditional concept of the epileptogenic zone has expanded to the notion of an epileptogenic network, emphasizing the role of interconnected brain regions in seizure generation. Precise delineation of this network is essential for optimizing surgical outcomes. Over the past 3 decades, several quantitative connectivity methods have been developed to study the interactions between the seizure onset zone and non-involved regions. Despite these advances, the mechanisms governing the transition from interictal to ictal periods remain poorly understood. In this study, we investigated preictal interactions between the seizure onset zone and the broader network using directed connectivity measures.

Methods: We evaluated their effectiveness in identifying seizure onset zones using a multicenter intracranial EEG dataset, encompassing 243 seizures from 61 patients. Directed transfer function and partial directed coherence were used to extract connectivity matrices during 28-seconds of preictal period in patients with good surgery outcomes. Inflow and outflow metrics were computed for iEEG electrode contact annotated as seizure onset zone and the performance of each metric is assessed in disentangling these electrodes from the rest of the network.

Results: We observed two distinct patterns of network connectivity preceding seizure onset. While there was an increase in inflow of information to seizure onset electrodes in one subset of patients, in the other subset, there was a prominent outflow of information from seizure onset electrodes to the rest of the network, suggesting distinct connectivity patterns associated with the seizure onset zone across patients. Further analyses showed that patients who underwent the grid/strip/depth implantation approach exhibited significantly higher area under curve (AUC) than those with electrocorticography (ECoG) or stereo-electroencephalography (sEEG) alone. Finally, examining the influence of lesional vs non-lesional neuroimaging status demonstrated that a greater proportion of high-inflow and high-outflow were lesional.

Conclusion: Our findings reinforce the notion that seizure generation is driven by dynamic shifts in information flow within the brain's functional network. The preictal connectivity patterns observed --either increased inflow to the seizure onset zone or high outflow from it --underscore the network reorganization involved in epileptic transitions. These results emphasize epilepsy as a network-level phenomenon, supporting the use of network concepts for better understanding seizure dynamics and improving surgical localization strategies.

Levodopa is the most common therapy to reduce motor symptoms of parkinsonism. However, levodopa has potential to exacerbate cardiovascular autonomic (CVA) dysfunction that may co-occur in patients. Heart rate variability (HRV) is the most common method for assessing CVA function, but broader monitoring of CVA function and levodopa effects is typically limited to clinical settings and symptom reporting, which fail to capture its holistic nature. In this study, we evaluated the feasibility of a multimodal wearable chest patch for monitoring changes in CVA function during clinical and 24-h ambulatory (at home) conditions in 14 patients: 11 with Parkinson's disease (PD) and 3 with multiple system atrophy (MSA). In-clinic data were analyzed to examine the effects of orally administered levodopa on CVA function using a pre (OFF) and 60-min (ON) post-exposure protocol. Wearable-derived physiological markers related to the electrical and mechanical activity of the heart alongside vascular function were extracted. Pre-ejection period (PEP) and ratio of PEP to left ventricular ejection time index (LVETi) increased significantly (p $<$ 0.05) following levodopa, indicating a decrease in cardiac contractility. We further explored dose-response relationships and how CVA responses differed between participants with orthostatic hypotension (OH) from those without OH. Heart rate variability, specifically root-mean-square-of-successive-differences (RMSSD), following levodopa decreased significantly more in participants with OH (n = 7) compared to those without (no-OH, n = 7). The results suggest that the wearable patch's measures are sensitive to CVA dynamics and provide exploratory insights into levodopa's potential role in inducing a negative inotropic effect and exacerbating CVA dysfunction. This work encourages further evaluation of these wearable-derived physiomarkers for quantifying CVA and informing individualized care of individuals with parkinsonism.

Cerebral physiological signals embody complex neural, vascular, and metabolic processes that provide valuable insight into the brain's dynamic nature. Profound comprehension and analysis of these signals are essential for unraveling cerebral intricacies, enabling precise identification of patterns and anomalies. Therefore, the advancement of computational models in cerebral physiology is pivotal for exploring the links between measurable signals and underlying physiological states. This review provides a detailed explanation of computational models, including their mathematical formulations, and discusses their relevance to the analysis of cerebral physiology dynamics. It emphasizes the importance of linear multivariate statistical models, particularly autoregressive (AR) models and the Kalman filter, in time series modeling and prediction of cerebral processes. The review focuses on the analysis and operational principles of multivariate statistical models such as AR models and the Kalman filter. These models are examined for their ability to capture intricate relationships among cerebral parameters, offering a holistic representation of brain function. The use of multivariate statistical models enables the capturing of complex relationships among cerebral physiological signals. These models provide valuable insights into the dynamic nature of the brain by representing intricate neural, vascular, and metabolic processes. The review highlights the clinical implications of using computational models to understand cerebral physiology, while also acknowledging the inherent limitations, including the need for stationary data, challenges with high dimensionality, computational complexity, and limited forecasting horizons.

Objective: The global rise of autoimmune diseases presents a significant medical challenge, with inadequate treatment options, high morbidity risks, and escalating healthcare costs. While the underlying mechanisms of autoimmune disease development are not fully understood, both genetic predispositions and lifestyle factors, particularly sleep, play critical roles. Insomnia and circadian rhythm sleep disorders not only impair sleep but also disrupt multi-organ interactions by dysregulating sympathetic nervous system activity, altering immune responses, and influencing neuroendocrine function. These disruptions can contribute to immune system dysregulation, increasing the risk of autoimmune disease development.

Methods: To assess the impact of impaired sleep on the risk of developing autoimmune diseases, a global population-based retrospective cohort study was conducted using electronic health records from the TriNetX US Global Collaborative Network, including 351,366 subjects in each propensity score matched group. Twenty autoimmune diseases were examined, and propensity score matching was employed to reduce bias. Three sensitivity analyses were conducted to test the robustness of the results.

Results: The study identified significantly increased risks for several autoimmune diseases associated with impaired sleep, likely mediated by dysregulated neuroimmune and autonomic interactions. Specifically, cutaneous lupus erythematosus [hazard ratio (HR) = 2.119; confidence interval (CI) 1.674-2.682; p < 0.0001], rheumatoid arthritis (HR = 1.404; CI 1.313-1.501; p < 0.0001), Sjögren syndrome (HR = 1.84; CI 1.64-2.066; p < 0.0001), and autoimmune thyroiditis (HR = 1.348; CI 1.246-1.458; p < 0.0001) showed significantly increased risks. No diseases demonstrated reduced risks, and 4 out of 20 tested diseases did not show significant HR increases in any analysis.

Conclusion: This study highlights the integral role of sleep in maintaining immune homeostasis through multi-organ interactions involving the autonomic nervous system, immune signalling pathways, and endocrine regulation. Disruptions in these systems due to chronic sleep impairment may predispose individuals to autoimmune diseases by altering inflammatory responses and immune tolerance. These findings underscore the necessity of recognizing and treating sleep disorders not only for general wellbeing but also as a potential strategy to mitigate the long-term risk of autoimmune disease development.

Introduction: The hypothesis of the loss of complexity with aging and disease has received strong attention. Especially, the decrease of complexity of stride interval series in older people, during walking, was shown to correlate with falling propensity. However, recent experiments showed that a restoration of walking complexity in older people could occur through the prolonged experience of synchronized walking with a younger companion. This result was interpreted as the consequence of a complexity matching effect. Experiment: The aim of the present study was to analyze the link between the restoration of walking complexity in older people and clinical measures usually used in the context of rehabilitation or follow-up of older people. Results: We evidenced a link between restoring complexity, improving overall health and reducing fear of falling. In addition, we showed that 3 weeks of complexity matching training can have a positive effect on complexity up to 2 months post-protocol. Finally, we showed that the restoration of walking complexity obtained in the previous works is not guide-dependent.