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Synchronization non-linear dynamics and low-frequency fluctuations: Analogy between spontaneous brain activity and networked single-transistor chaotic oscillators

机译:同步非线性动力学和低频波动:自发性大脑活动与联网的单晶体管混沌振荡器之间的类比

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摘要

In this paper, the topographical relationship between functional connectivity (intended as inter-regional synchronization), spectral and non-linear dynamical properties across cortical areas of the healthy human brain is considered. Based upon functional MRI acquisitions of spontaneous activity during wakeful idleness, node degree maps are determined by thresholding the temporal correlation coefficient among all voxel pairs. In addition, for individual voxel time-series, the relative amplitude of low-frequency fluctuations and the correlation dimension (D2), determined with respect to Fourier amplitude and value distribution matched surrogate data, are measured. Across cortical areas, high node degree is associated with a shift towards lower frequency activity and, compared to surrogate data, clearer saturation to a lower correlation dimension, suggesting presence of non-linear structure. An attempt to recapitulate this relationship in a network of single-transistor oscillators is made, based on a diffusive ring (n = 90) with added long-distance links defining four extended hub regions. Similarly to the brain data, it is found that oscillators in the hub regions generate signals with larger low-frequency cycle amplitude fluctuations and clearer saturation to a lower correlation dimension compared to surrogates. The effect emerges more markedly close to criticality. The homology observed between the two systems despite profound differences in scale, coupling mechanism and dynamics appears noteworthy. These experimental results motivate further investigation into the heterogeneity of cortical non-linear dynamics in relation to connectivity and underline the ability for small networks of single-transistor oscillators to recreate collective phenomena arising in much more complex biological systems, potentially representing a future platform for modelling disease-related changes.
机译:在本文中,考虑了健康人脑皮质区域的功能连通性(意为区域间同步),光谱和非线性动力学特性之间的地形关系。基于功能性MRI采集的清醒空闲期间的自发活动,通过对所有体素对之间的时间相关系数进行阈值确定节点度图。另外,对于单个体素时间序列,测量相对于傅立叶振幅和值分布匹配的替代数据确定的低频波动的相对振幅和相关维数(D2)。在整个皮层区域中,高结点度与朝着较低频率活动的转变有关,并且与替代数据相比,饱和度向较低的相关维度更清晰,这表明存在非线性结构。基于一个扩散环(n = 90),并试图通过定义四个扩展的集线器区域的长距离链路,试图在单晶体管振荡器网络中重现这种关系。与大脑数据相似,发现与代理相比,集线器区域中的振荡器生成的信号具有更大的低频周期幅度波动和更清晰的饱和度,具有较低的相关维度。效果更加明显地接近临界。尽管在规模,耦合机制和动力学上存在巨大差异,但在两个系统之间观察到的同源性似乎值得注意。这些实验结果促使人们进一步研究皮质非线性动力学相对于连通性的异质性,并强调了单晶体管振荡器的小型网络能够重现复杂得多的生物系统中产生的集体现象的能力,这可能代表了未来的建模平台与疾病有关的变化。

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