About this Research Topic
In recent decades, efforts have been made to understand the brain’s spatiotemporal organization from local neuronal assemblies to long-range interactions within large-scale distributed brain networks. It emerged that the brain exhibits state-space behavior characteristics of complex or chaotic systems that allows the fluid transition between “computational” states that are specific to sensory, motoric, or cognitive tasks. An important parameter defining complex or chaotic systems is the self-similar or “fractal” behavior across multiple measurement scales, and the tendency of the frequency spectra to show an inverse power-law (1/fn–like) scaling pattern. Scale-free activity is present at almost every temporal and spatial scale in the brain; it has been observed in neuronal spike trains, neurotransmitter release, spontaneous local field potential (LFP), electrocorticography (ECoG) and resting-state fMRI (rs-fMRI). In addition, similar “small world” topological structure of brain networks has been observed from micro- and mesoscopic circuits to large-scale brain networks. The fact that such scale-free spatiotemporal pattern “replicates” itself across different modalities and measurement scales offers a unique opportunity to bridge temporal dynamics of neurophysiologic signals and spatial organization of brain networks across cellular, circuit, and systems level.
In this Research Topic, we seek to showcase and summarize the recent advances in the development and application of nonlinear statistics and information theory for characterizing the complex dynamics of physiological systems of the brain. We will discuss the use of various complexity metrics, available toolboxes and appropriate parameters for characterizing scale-free patterns in both temporal and spatial domains across multiple measurement scales. Nonlinear dynamic analyses using power law scaling, fractal dimension (FD) and Hurst exponent (H), approximate entropy (ApEn), sample entropy (SampEn) and multi-scale entropy (MSE) as well as other emerging metrics in rs-fMRI and electrophysiological data along with their applications in aging and development, as well as neurologic and psychiatric disorders will be endorsed. In order to accurately interpret the observed complexity changes, animal models and human studies with concurrent recording of fMRI and electrophysiology in conjunction with pharmacological manipulations and/or neuromodulations that provide insights on the neurophysiological underpinning of complexity changes in noninvasive neuroimaging methods are also highly encouraged. Finally, theoretical work on modeling brain complexity across multiple scales is also welcome.
In this Research Topic, we seek to showcase and summarize the recent advances in the development and application of nonlinear statistics and information theory for characterizing the complex dynamics of physiological systems of the brain. We will discuss the use of various complexity metrics, available toolboxes and appropriate parameters for characterizing scale-free patterns in both temporal and spatial domains across multiple measurement scales. Nonlinear dynamic analyses using power law scaling, fractal dimension (FD) and Hurst exponent (H), approximate entropy (ApEn), sample entropy (SampEn) and multi-scale entropy (MSE) as well as other emerging metrics in rs-fMRI and electrophysiological data along with their applications in aging and development, as well as neurologic and psychiatric disorders will be endorsed. In order to accurately interpret the observed complexity changes, animal models and human studies with concurrent recording of fMRI and electrophysiology in conjunction with pharmacological manipulations and/or neuromodulations that provide insights on the neurophysiological underpinning of complexity changes in noninvasive neuroimaging methods are also highly encouraged. Finally, theoretical work on modeling brain complexity across multiple scales is also welcome.
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