Characteristics and variability of functional brain networks

Sheng, Jinhua; Liu, Qingqiang; Wang, Bo-Cheng; Wang, Luyun; Shao, Meiling; Yu, Xinguang

doi:10.1016/j.neulet.2020.134954

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…We specifically tested two-classes and four-classes scenarios according to the typical number of mental states used in BCIs. To reproduce the fact that nodes might not correspond exactly to the same brain areas across different subjects-because of a natural individual spatial and functional variability [228]-we further performed a random permutation of the node labels. Notably, this procedure did not alter the intrinsic topology of the generated networks.…”

Section: Theoretical Advantage Of Network Metricsmentioning

confidence: 99%

Network-based brain–computer interfaces: principles and applications

et al. 2021

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Brain–computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basic neuroscience questions but also to unlock a variety of applications from exoskeleton control to neurofeedback rehabilitation. In general, BCI usability depends on the ability to comprehensively characterize brain functioning and correctly identify the user’s mental state. To this end, much of the efforts have focused on improving the classification algorithms taking into account localized brain activities as input features. Despite considerable improvement BCI performance is still unstable and, as a matter of fact, current features represent oversimplified descriptors of brain functioning. In the last decade, growing evidence has shown that the brain works as a networked system composed of multiple specialized and spatially distributed areas that dynamically integrate information. While more complex, looking at how remote brain regions functionally interact represents a grounded alternative to better describe brain functioning. Thanks to recent advances in network science, i.e. a modern field that draws on graph theory, statistical mechanics, data mining and inferential modeling, scientists have now powerful means to characterize complex brain networks derived from neuroimaging data. Notably, summary features can be extracted from brain networks to quantitatively measure specific organizational properties across a variety of topological scales. In this topical review, we aim to provide the state-of-the-art supporting the development of a network theoretic approach as a promising tool for understanding BCIs and improve usability.

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Section: Theoretical Advantage Of Network Metricsmentioning

confidence: 99%

Network-based brain–computer interfaces: principles and applications

et al. 2021

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A Unified Theory of Response Sparsity and Variability for Energy-Efficient Neural Coding

Huang,

Lin,

Roe

et al. 2024

Preprint

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Understanding how cortical neurons use dynamic firing patterns to represent sensory signals is a central challenge in neuroscience. Decades of research have shown that cortical neuronal activities exhibit high variance, typically quantified by the coefficient of variation (CV), suggesting intrinsic randomness. Conversely, substantial evidence indicates that cortical neurons display high response sparseness, indicative of efficient encoding. The apparent contradiction between these neural coding properties, stochastic yet efficient, has lacked a unified theoretical framework. This study aims to resolve this discrepancy. We conducted a series of analyses to establish a direct relational function between CV and sparseness, proving they are intrinsically correlated or equivalent across different statistical distributions in neural activities. We further derive a function showing that both irregularity and sparsity in neuronal activities are positive functions of energy-efficient coding capacity, quantified by Information-Cost Efficiency (ICE). This suggests that the observed high irregularity and sparsity in cortical activities result from a shared mechanism optimized for maximizing information encoding capacity while minimizing cost. Furthermore, we introduce a CV-maximization algorithm to generate kernel functions replicating the receptive fields of the primary visual cortex. This finding indicates that the neuronal functions in the visual cortex are optimal energy-efficient coding operators for natural images. Hence, this framework unifies the concepts of irregularity and sparsity in neuronal activities by linking them to a common mechanism of coding efficiency, offering deeper insights into neural coding strategies.

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Characteristics and variability of functional brain networks

Cited by 2 publications

References 22 publications

Network-based brain–computer interfaces: principles and applications

Network-based brain–computer interfaces: principles and applications

A Unified Theory of Response Sparsity and Variability for Energy-Efficient Neural Coding

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