Müller Viktor, Jirsa Viktor, Perdikis Dionysios, Sleimen-Malkoun Rita, von Oertzen Timo, Lindenberger Ulman
Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.
Aix Marseille University, INSERM, INS, The Institut de Neurosciences des Systèmes, Marseille, France.
Front Aging Neurosci. 2019 Jun 11;11:138. doi: 10.3389/fnagi.2019.00138. eCollection 2019.
Behavioral and physiological evidence suggests that developmental changes lead to enhanced cortical differentiation and integration through maturation and learning, and that senescent changes during aging result in dedifferentiation and reduced cortical specialization of neural cell assemblies. We used electroencephalographic (EEG) recordings to evaluate network structure and network topology dynamics during rest with eyes closed and open, and during auditory oddball task across the lifespan. For this evaluation, we constructed a hyper-frequency network (HFN) based on within- and cross-frequency coupling (WFC and CFC, respectively) at 10 oscillation frequencies ranging between 2 and 20 Hz. We found that WFC increased monotonously across the lifespan, whereas CFC showed a U-shaped relationship. These changes in WFC and CFC strengths coevolve with changes in network structure and network topology dynamics, namely the magnitude of graph-theoretical topology measures increased linearly with age (except for characteristic path length, which is going shorter), while their standard deviation showed an inverse U-shaped relationship with a peak in young adults. Temporal as well as structural or nodal similarity of network topology (with some exceptions) seems to coincide with variability changes, i.e., stronger variability is related to higher similarity between consecutive time windows or nodes. Furthermore, network complexity measures showed different lifespan-related patterns, which depended on the balance of WFC and CFC strengths. Both variability and complexity of HFNs were strongly related to the perceptual speed scores. Finally, investigation of the modular organization of the networks revealed higher number of modules and stronger similarity of community structures across time in young adults as compared with children and older adults. We conclude that network variability and complexity measures reflect temporal and structural topology changes in the functional organization and reorganization of neuronal cell assemblies across the lifespan.
行为和生理证据表明，发育变化通过成熟和学习导致皮质分化和整合增强，而衰老过程中的衰老变化则导致神经细胞集合的去分化和皮质特化降低。我们使用脑电图（EEG）记录来评估在闭眼和睁眼休息期间以及在整个生命周期的听觉Oddball任务期间的网络结构和网络拓扑动态。为了进行这种评估，我们基于2至20Hz范围内的10个振荡频率的内频和跨频耦合（分别为WFC和CFC）构建了一个超高频网络（HFN）。我们发现WFC在整个生命周期中单调增加，而CFC呈现出U形关系。WFC和CFC强度的这些变化与网络结构和网络拓扑动态的变化共同演变，即图论拓扑测量的幅度随年龄线性增加（特征路径长度除外，其变短），而它们的标准差与年轻人中的峰值呈现反U形关系。网络拓扑的时间以及结构或节点相似性（有一些例外）似乎与变异性变化一致，即更强的变异性与连续时间窗口或节点之间的更高相似性相关。此外，网络复杂性测量显示出不同的与寿命相关的模式，这取决于WFC和CFC强度的平衡。HFN的变异性和复杂性都与感知速度得分密切相关。最后，对网络模块化组织的研究表明，与儿童和老年人相比，年轻人在不同时间的模块数量更多，社区结构的相似性更强。我们得出结论，网络变异性和复杂性测量反映了神经元细胞集合在整个生命周期中的功能组织和重组中的时间和结构拓扑变化。
