Abstract
The human brain consists of anatomically distant neuronal assemblies that are interconnected via a myriad of synapses. This anatomical network provides the neurophysiological wiring framework for functional connectivity (FC), which is essential for higher-order brain functions. While several studies have explored the scale-specific FC, the scale-free (i.e., multifractal) aspect of brain connectivity remains largely neglected. Here we examined the brain reorganization during a visual pattern recognition paradigm, using bivariate focus-based multifractal (BFMF) analysis. For this study, 58 young, healthy volunteers were recruited. Before the task, 3-3 min of resting EEG was recorded in eyes-closed (EC) and eyes-open (EO) states, respectively. The subsequent part of the measurement protocol consisted of 30 visual pattern recognition trials of 3 difficulty levels graded as Easy, Medium, and Hard. Multifractal FC was estimated with BFMF analysis of preprocessed EEG signals yielding two generalized Hurst exponent-based multifractal connectivity endpoint parameters, H(2) and ΔH15; with the former indicating the long-term cross-correlation between two brain regions, while the latter captures the degree of multifractality of their functional coupling. Accordingly, H(2) and ΔH15 networks were constructed for every participant and state, and they were characterized by their weighted local and global node degrees. Then, we investigated the between- and within-state variability of multifractal FC, as well as the relationship between global node degree and task performance captured in average success rate and reaction time. Multifractal FC increased when visual pattern recognition was administered with no differences regarding difficulty level. The observed regional heterogeneity was greater for ΔH15 networks compared to H(2) networks. These results show that reorganization of scale-free coupled dynamics takes place during visual pattern recognition independent of difficulty level. Additionally, the observed regional variability illustrates that multifractal FC is region-specific both during rest and task. Our findings indicate that investigating multifractal FC under various conditions – such as mental workload in healthy and potentially in diseased populations – is a promising direction for future research.
Highlights
The human brain is a complex system encompassing spatially distinct neuronal populations interconnected via an intricate axonal grid
Our findings show that: (i) the local and global functional connectivity measures increased during task when compared to resting conditions, indicating a reorganization of brain networks, and (ii) there was a substantial regional variability within the 5 different states
Despite the different channel density of the EEG devices and the different sampling populations, similar results were obtained in these studies, concluding that coupled dynamics between cortical regions are multifractal during rest
Summary
The human brain is a complex system encompassing spatially distinct neuronal populations interconnected via an intricate axonal grid. Visual pattern recognition requires coordinated interactions among disparate brain regions such as the visual cortex, where primary processing, and the association areas in the parietal and frontal cortices, where high-level cognitive evaluation takes place (Van Hoesen, 1993; Kandel et al, 2012). A paradigm shift regarding resting-state studies occurred after discovering that even in the absence of external stimuli the brain is organized in resting-state networks (RSNs) (Raichle et al, 2001). This restingstate neural architecture is altered during task through a series of activations and deactivations of brain regions (Fox et al, 2005). We believe that studying the brain under mental workload could reveal valuable information
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