Department of Mathematical Sciences and Center for Mathematical Biosciences, Indiana University Purdue University Indianapolis, Indiana 46032, USA.
Chaos. 2013 Mar;23(1):013138. doi: 10.1063/1.4794793.
Neural synchronization is believed to be critical for many brain functions. It frequently exhibits temporal variability, but it is not known if this variability has a specific temporal patterning. This study explores these synchronization/desynchronization patterns. We employ recently developed techniques to analyze the fine temporal structure of phase-locking to study the temporal patterning of synchrony of the human brain rhythms. We study neural oscillations recorded by electroencephalograms in α and β frequency bands in healthy human subjects at rest and during the execution of a task. While the phase-locking strength depends on many factors, dynamics of synchrony has a very specific temporal pattern: synchronous states are interrupted by frequent, but short desynchronization episodes. The probability for a desynchronization episode to occur decreased with its duration. The transition matrix between synchronized and desynchronized states has eigenvalues close to 0 and 1 where eigenvalue 1 has multiplicity 1, and therefore if the stationary distribution between these states is perturbed, the system converges back to the stationary distribution very fast. The qualitative similarity of this patterning across different subjects, brain states and electrode locations suggests that this may be a general type of dynamics for the brain. Earlier studies indicate that not all oscillatory networks have this kind of patterning of synchronization/desynchronization dynamics. Thus, the observed prevalence of short (but potentially frequent) desynchronization events (length of one cycle of oscillations) may have important functional implications for the brain. Numerous short desynchronizations (as opposed to infrequent, but long desynchronizations) may allow for a quick and efficient formation and break-up of functionally significant neuronal assemblies.
神经同步被认为对许多大脑功能至关重要。它经常表现出时间可变性,但目前尚不清楚这种可变性是否具有特定的时间模式。本研究探讨了这些同步/去同步模式。我们采用最近开发的技术来分析锁相的精细时间结构,以研究人类大脑节律同步的时间模式。我们研究了健康人体在休息和执行任务时记录的脑电图的α和β频带中的神经振荡。虽然锁相强度取决于许多因素,但同步的动态具有非常特定的时间模式:同步状态被频繁但短暂的去同步事件打断。去同步事件发生的概率随其持续时间而降低。同步和去同步状态之间的转移矩阵的特征值接近 0 和 1,其中特征值 1 的重数为 1,因此,如果这些状态之间的稳定分布受到干扰,系统会非常快速地收敛回稳定分布。这种模式在不同的受试者、大脑状态和电极位置上的定性相似性表明,这可能是大脑的一种通用类型的动力学。早期的研究表明,并非所有的振荡网络都具有这种同步/去同步动力学的模式。因此,观察到的短暂(但可能频繁)去同步事件(振荡周期的长度)的普遍性可能对大脑具有重要的功能意义。大量的短去同步(而不是不频繁但长的去同步)可能允许快速有效地形成和打破功能上重要的神经元集合。