Frontiers in network physiology最新文献

Purpose: Teamwork involves intricate interactions among individuals or groups with shared goals. It necessitates effective communication, defined roles, decision-making processes, and the allocation of cognitive and emotional resources. Objective teamwork assessment demands a comprehensive set of metrics. Although subjective and behavioral metrics, such as self-evaluation and task completion time, are generally applied, they are prone to bias and a lack of objectivity, highlighting the inherent limitations of capturing the unconscious processes of human behavior.

Methods: To mitigate these limitations, the present study proposed a novel approach to teamwork evaluation based on neurophysiological signals (electroencephalograms, EEGs) compatible with real-world applications, i.e., surgical teams engaged in real-world surgeries. To the best of our knowledge, there is no scientific evidence of an objective teamwork measure performed among more than two members and relying on neurophysiological signals in real-world environments. Therefore, the present work aimed at i) developing and investigating the reliability of an objective EEG-based teamwork index using mutual information (MI) methods and ii) providing additional and objective insights for surgeons' supervisors in healthcare training.

Findings: The results demonstrated the capability of the EEG-based training index to provide additional and objective information, along with its added value and reliability compared to conventional measures (all R > 0.62, all p < 0.002). Furthermore, the EEG-based teamwork index allowed the determination (all p < 0.001) of surgeons' experience levels (expert vs novice) in terms of cooperative behavior.

Conclusion: The results pave the way for targeted interventions, adaptive training sessions, and optimizations in team dynamics and open up opportunities for applying neurophysiological measurements for teamwork evaluation in all operational fields, where proper and granular teamwork optimization could play a crucial role in terms of safety.

Objectives: We propose a novel application of higher-order information-theoretic measures to assess the temporal interaction information (TII) between laser-evoked potentials (LEPs) and individual pain ratings in healthy subjects and patients with fibromyalgia (FM) affected by small fiber pathology.

Methods: Seventy-nine FM patients, categorized into three groups based on skin biopsy findings (normal innervation n 19, proximal denervation n 53, and both proximal and distal denervation n 7), and 14 control subjects were studied. We used cluster-based permutation tests (p < 0.05) to identify significant clusters of TII between cortical components recorded using a 64-channel electroencephalography (EEG) system - with focus on the Cz electrode - and subjective pain ratings to quantify synergy or redundancy between the LEP signal time points and Visual Analog Scale (VAS).

Results: Control subjects generally exhibited synergy clusters corresponding to N2 and P2 peaks, whereas patients with fibromyalgia (FM), particularly those with distal denervation, exhibited increased redundancy and decreased synergy.

Conclusion: Patients with FM and small fiber pathology exhibited an alteration in higher-order integration mechanisms due to a complex interaction between cortical processing of pain and denervation of nociceptive fibers.

Significance: These findings highlight the potential of TII in elucidating the complex interplay between peripheral nerve integrity and central sensitization in FM and other chronic pain disorders.

The human brain is a complex dynamical system which displays a wide range of macroscopic and mesoscopic patterns of neural activity, whose mechanistic origin remains poorly understood. Whole-brain modelling allows us to explore candidate mechanisms causing the observed patterns. However, it is not fully established how the choice of model type and the networks' spatial resolution influence the simulation results, hence, it remains unclear, to which extent conclusions drawn from these results are limited by modelling artefacts. Here, we compare the dynamics of a biophysically realistic, linear-nonlinear cascade model of whole-brain activity with a phenomenological Wilson-Cowan model using three structural connectomes based on the Schaefer parcellation scheme with 100, 200, and 500 nodes. Both neural mass models implement the same mechanistic hypotheses, which specifically address the interaction between excitation, inhibition, and a slow adaptation current which affects the excitatory populations. We quantify the emerging dynamical states in detail and investigate how consistent results are across the different model variants. Then we apply both model types to the specific phenomenon of slow oscillations, which are a prevalent brain rhythm during deep sleep. We investigate the consistency of model predictions when exploring specific mechanistic hypotheses about the effects of both short- and long-range connections and of the antero-posterior structural connectivity gradient on key properties of these oscillations. Overall, our results demonstrate that the coarse-grained dynamics is robust to changes in both model type and network resolution. In some cases, however, model predictions do not generalize. Thus, some care must be taken when interpreting model results.

Introduction: Pulmonary fibrosis (PF) is a heterogeneous progressive lung disease characterized by excessive extracellular matrix (ECM) deposition and cross-linking, leading to irreversible tissue stiffening and loss of function. Previous evidence suggests that percolation behavior, where increasing local stiffness facilitates the emergence of stiff regions that span the tissue, underlies the stiffening of the ECM and drives the irreversible mechanical dysfunction. However, it is not fully understood how percolation emerges from the complex interactions between cells and the ECM.

Methods: In this study, we investigated a previously published agent-based spring network model of PF that exhibited bifurcation behavior between healing and fully developed fibrosis as network members were gradually stiffened. By systematically analyzing the configuration of the initial tissue injury, we identify key structural determinants that govern whether an injury heals or transitions into fibrosis.

Results: Results demonstrate that fibrosis is strongly associated with increased initial clustering of injured springs, reduced intercluster distances, and the presence of critical stiffening sites, or hotspots, that act as bifurcation points for disease progression. Furthermore, we show that selectively modifying the stiffness of pivotal network regions at the time of injury can shift the network's trajectory from fibrosis to healing, highlighting potential intervention targets. These findings suggest that the network structure of tissue injury may serve as a predictive marker for fibrosis susceptibility and provide a mechanistic basis for understanding the nonlinear progression of PF.

Collective behavior is among the most fascinating complex dynamics in coupled networks with applications in various fields. Recent works have shown that higher-order interactions widely exist in complex systems. Both positive couplings among nodes, as the majority of studies have assumed, and negative couplings are very common in real-world systems, like physiological networks. Positive coupling (excitatory coupling) promotes synchronization and drives excitatory synaptic transmission between neurons. Meanwhile, negative coupling (inhibitory coupling) inhibits synchronization and sustains inhibitory synaptic transmission between neurons. Since high-order coupling patterns and different coupling patterns strongly affect the synchronous performance of complex systems, this article develops a globally coupled higher-order oscillatory system model that incorporates both positive and negative couplings. It is shown that, in the case of positive couplings, a second-order interaction has a negligible impact on the synchronization capability of a network within a certain range. In contrast, a higher-order network with purely negative couplings exhibits asynchronous states for any values of the second-order interactions. However, the synchronous region gradually shrinks with the increase of the negative coupling in the case of mixed couplings. This indicates a prominent role of coupling patterns on the onset of globally higher-order network synchronization.

Introduction: Periodic brain stimulation (PBS) techniques, either intracranial or non-invasive, electrical or magnetic, represent promising neuromodulatory tools for the treatment of neurological and neuropsychiatric disorders. Through the modulation of endogenous oscillations, PBS may engage synaptic plasticity, hopefully leading to persistent lasting effects. However, stabilizing such effects represents an important challenge: the interaction between induced electromagnetic fields and neural circuits may yield highly variable responses due to heterogeneous neuronal and synaptic biophysical properties, limiting PBS clinical potential.

Methods: In this study, we explored the conditions on which transcranial alternating current stimulation (tACS) as a common type of non-invasive PBS leads to amplified post-stimulation oscillatory power, persisting once stimulation has been turned off. We specifically examined the effects of heterogeneity in neuron time scales on post-stimulation dynamics in a population of balanced Leaky-Integrate and Fire (LIF) neurons that exhibit synchronous-irregular spiking activity.

Results: Our analysis reveals that such heterogeneity enables tACS to engage synaptic plasticity, amplifying post-stimulation power. Our results show that such post-stimulation aftereffects result from selective frequency- and cell-type-specific synaptic modifications. We evaluated the relative importance of stimulation-induced plasticity amongst and between excitatory and inhibitory populations.

Discussion: Our results indicate that heterogeneity in neurons' time scales and synaptic plasticity are both essential for stimulation to support post-stimulation aftereffects, notably to amplify the power of endogenous rhythms.

Conventional scientific paradigms predominantly emphasize upward causality, often overlooking or dismissing the role of downward causality. This approach is also prevalent in neuroscience, where cortical neurodynamics and higher cognitive functions are typically viewed as consequences of neuronal or even ion channel activity. Conversely, mental phenomena are generally assumed to lack causal efficacy over neural processes-an assumption that is increasingly being questioned. The causality associated with volition may be analyzed at three organizational levels: (1) neuronal interactions within cortical networks, (2) interregional dynamics between distinct brain areas, and (3) the reciprocal relationship between the nervous system and its environmental context. Across all these domains, circular rather than strictly linear causality appears to be at play. This paper examines the implications of such circular causality for volition and the longstanding problem of free will, with particular reference to insights derived from neurocomputational modeling.

Introduction: Transcranial direct current stimulation (tDCS) is increasingly used to modulate motor learning. Current polarity and intensity, electrode montage, and application before or during learning had mixed effects. Both Hebbian and homeostatic plasticity were proposed to account for the observed effects, but the explanatory power of these models is limited. In a previous modeling study, we showed that homeostatic structural plasticity (HSP) model can explain long-lasting after-effects of tDCS and transcranial magnetic stimulation (TMS). The interference between motor learning and tDCS, which are both based on HSP in our model, is a candidate mechanism to resolve complex and seemingly contradictory experimental observations.

Methods: We implemented motor learning and tDCS in a spiking neural network subject to HSP. The anatomical connectivity of the engram induced by motor learning was used to quantify the impact of tDCS on motor learning.

Results: Our modeling results demonstrated that transcranial direct current stimulation applied before learning had weak modulatory effects. It led to a small reduction in connectivity if it was applied uniformly. When applied during learning, targeted anodal stimulation significantly strengthened the engram, while targeted cathodal or uniform stimulation weakened it. Applied after learning, targeted cathodal, but not anodal, tDCS boosted engram connectivity. Strong tDCS would distort the engram structure if not applied in a targeted manner.

Discussion: Our model explained both Hebbian and homeostatic phenomena observed in human tDCS experiments by assuming memory strength positively correlates with engram connectivity. This includes applications with different polarity, intensity, electrode montage, and timing relative to motor learning. The HSP model provides a promising framework for unraveling the dynamic interaction between learning and transcranial DC stimulation.

Understanding the relationship between structure and function in the human brain is essential for revealing how brain organization influences cognition, perception, emotion, and behavior. To this end, we introduce an interactive web tool and underlying database for Yale Brain Atlas, a high-resolution anatomical parcellation designed to facilitate precise localization and generalizable analyses of multimodal neuroimaging data. The tool supports parcel-level exploration of structural and functional data through dedicated interactive pages for each modality. For structural data, it incorporates white matter connectomes of 1,065 subjects and cortical thickness profiles of 200 subjects both from the Human Connectome Project. For functional data, it includes resting-state fMRI connectivity matrices for 34 healthy subjects and task-specific fMRI activation data acquired from two meta-analytic resources-Neurosynth and NeuroQuery-which, once translated into Yale Brain Atlas space and modified to include 334 function-specific terms, form Parcelsynth and ParcelQuery, respectively. Altogether, to support investigation of brain structure-function relationships, this study presents a web tool and database for the Yale Brain Atlas that enable scalable, interactive exploration of multimodal neuroimaging data.

This paper describes a discrete state-space model and accompanying methods for generative modeling. This model generalizes partially observed Markov decision processes to include paths as latent variables, rendering it suitable for active inference and learning in a dynamic setting. Specifically, we consider deep or hierarchical forms using the renormalization group. The ensuing renormalizing generative models (RGM) can be regarded as discrete homologs of deep convolutional neural networks or continuous state-space models in generalized coordinates of motion. By construction, these scale-invariant models can be used to learn compositionality over space and time, furnishing models of paths or orbits: that is, events of increasing temporal depth and itinerancy. This technical note illustrates the automatic discovery, learning, and deployment of RGMs using a series of applications. We start with image classification and then consider the compression and generation of movies and music. Finally, we apply the same variational principles to the learning of Atari-like games.