Minati Ludovico, Chiesa Pietro, Tabarelli Davide, D'Incerti Ludovico, Jovicich Jorge
Scientific Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy.
Center for Mind/Brain Sciences, University of Trento, Trento, Italy.
Chaos. 2015 Mar;25(3):033107. doi: 10.1063/1.4914938.
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.
本文研究了健康人脑皮质区域间功能连接（定义为区域间同步）、频谱及非线性动力学特性之间的拓扑关系。基于清醒静息状态下自发活动的功能磁共振成像数据，通过对所有体素对之间的时间相关系数进行阈值处理来确定节点度图。此外，对于个体体素时间序列，测量相对于傅里叶振幅和值分布匹配的替代数据确定的低频波动相对振幅和关联维数（D2）。在皮质区域中，高节点度与向低频活动的转变相关，并且与替代数据相比，关联维数向更低值的饱和更明显，表明存在非线性结构。基于一个具有定义四个扩展枢纽区域的长距离链接的扩散环（n = 90）单晶体管振荡器网络，尝试重现这种关系。与脑数据类似，发现枢纽区域中的振荡器产生的信号与替代数据相比具有更大的低频周期振幅波动和更明显的向更低关联维数的饱和。这种效应在接近临界状态时更为明显。尽管在尺度、耦合机制和动力学方面存在显著差异，但两个系统之间观察到的同源性似乎值得关注。这些实验结果促使进一步研究皮质非线性动力学与连接性相关的异质性，并强调单晶体管振荡器小网络再现更复杂生物系统中出现的集体现象的能力，这可能代表了一个未来用于模拟疾病相关变化的平台。
