Stylianou Orestis, Racz Frigyes Samuel, Kim Keumbi, Kaposzta Zalan, Czoch Akos, Yabluchanskiy Andriy, Eke Andras, Mukli Peter
Department of Physiology, Semmelweis University, Budapest, Hungary.
Institute of Translational Medicine, Semmelweis University, Budapest, Hungary.
Front Hum Neurosci. 2021 Oct 18;15:740225. doi: 10.3389/fnhum.2021.740225. eCollection 2021.
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, (2) and Δ ; 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, (2) and Δ 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 Δ networks compared to (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.
人类大脑由在解剖学上相距甚远的神经元集合组成，这些集合通过无数突触相互连接。这个解剖网络为功能连接（FC）提供了神经生理布线框架，而功能连接对于高阶脑功能至关重要。虽然有几项研究探讨了特定尺度的FC，但大脑连接性的无标度（即多重分形）方面在很大程度上仍被忽视。在这里，我们使用基于双变量焦点的多重分形（BFMF）分析，研究了视觉模式识别范式期间的大脑重组。在这项研究中，招募了58名年轻健康的志愿者。在任务之前，分别在闭眼（EC）和睁眼（EO）状态下记录3 - 3分钟的静息脑电图。测量方案的后续部分包括30次视觉模式识别试验，分为容易、中等和困难三个难度级别。通过对预处理脑电图信号进行BFMF分析来估计多重分形FC，得出两个基于广义赫斯特指数的多重分形连接端点参数，（2）和Δ；前者表示两个脑区之间的长期互相关，而后者反映它们功能耦合的多重分形程度。因此，为每个参与者和状态构建了（2）和Δ网络，并通过其加权局部和全局节点度对其进行表征。然后，我们研究了多重分形FC在状态间和状态内的变异性，以及全局节点度与以平均成功率和反应时间衡量的任务表现之间的关系。当进行视觉模式识别时，无论难度级别如何，多重分形FC都会增加。与（2）网络相比，Δ网络观察到的区域异质性更大。这些结果表明，在视觉模式识别过程中发生了无标度耦合动力学的重组，且与难度级别无关。此外，观察到的区域变异性表明，多重分形FC在休息和任务期间都是区域特异性的。我们的研究结果表明，在各种条件下——如健康人群以及可能患病群体的精神工作量——研究多重分形FC是未来研究的一个有前景的方向。
