Frigyes Samuel Racz1, Peter Mukli, Zoltan Nagy, Andras Eke. 1. Department of Physiology, Semmelweis University, 37-43 Tűzoltó Street, 1094 Budapest, Hungary. Institute of Clinical Experimental Research, Semmelweis University, 37-43 Tűzoltó Street, Budapest 1094, Hungary.
Abstract
Brain function is organized as a network of functional connections between different neuronal populations with connection strengths dynamically changing in time and space. Studies investigating functional connectivity (FC) usually follow a static approach when describing FC by considering the connectivity strengths constant, however a dynamic approach seems more reasonable, as this way the spatio-temporal dynamics of the underlying system can also be captured. OBJECTIVE: The scale-free, i.e. fractal nature of neural dynamics is an inherent property of the nervous system. The aim of this study was to determine if dynamic functional connectivity (DFC) in the prefrontal cortex shows not only scale-free but indeed multifractal dynamics. APPROACH: Functional near-infrared spectroscopy (fNIRS) was used to monitor resting-state brain activity in young healthy volunteers. Sliding window correlation (SWC) analysis and graph theory approach were utilized to capture the functional connection networks for every time point, whose topology was subsequently characterized with three network metrics-Density, Clustering Coefficient and Efficiency-each capturing a different aspect of the given network. The temporal structuring of the obtained network metric time series was then described by multifractal time series analysis. MAIN RESULTS: We found the DFC in the prefrontal cortex fluctuating according to scale-free, specifically multifractal dynamics. Moreover, different topological properties of the network showed different multifractal characteristics. All the results were reproducible in all window sizes used in the SWC analysis, however we found that the actual values of the given multifractal properties depended significantly on the window size. SIGNIFICANCE: Our results may well be another indication of a self-organized critical state underlying resting-state brain activity. The proposed analysis of functional brain dynamics can also open new perspectives for future clinical applications.
Brain function is organized as a network of functional connections between different neuronal populations with connection strengths dynamically changing in time and space. Studies investigating functional connectivity (FC) usually follow a static approach when describing FC by considering the connectivity strengths constant, however a dynamic approach seems more reasonable, as this way the spatio-temporal dynamics of the underlying system can also be captured. OBJECTIVE: The scale-free, i.e. fractal nature of neural dynamics is an inherent property of the nervous system. The aim of this study was to determine if dynamic functional connectivity (DFC) in the prefrontal cortex shows not only scale-free but indeed multifractal dynamics. APPROACH: Functional near-infrared spectroscopy (fNIRS) was used to monitor resting-state brain activity in young healthy volunteers. Sliding window correlation (SWC) analysis and graph theory approach were utilized to capture the functional connection networks for every time point, whose topology was subsequently characterized with three network metrics-Density, Clustering Coefficient and Efficiency-each capturing a different aspect of the given network. The temporal structuring of the obtained network metric time series was then described by multifractal time series analysis. MAIN RESULTS: We found the DFC in the prefrontal cortex fluctuating according to scale-free, specifically multifractal dynamics. Moreover, different topological properties of the network showed different multifractal characteristics. All the results were reproducible in all window sizes used in the SWC analysis, however we found that the actual values of the given multifractal properties depended significantly on the window size. SIGNIFICANCE: Our results may well be another indication of a self-organized critical state underlying resting-state brain activity. The proposed analysis of functional brain dynamics can also open new perspectives for future clinical applications.