Dimitriadis Stavros I, Laskaris Nikolaos A, Del Rio-Portilla Yolanda, Koudounis George Ch
Artificial Intelligence & Information Analysis Laboratory, Department of Informatics, Aristotle University, Biology Building, BOX 451, Thessaloniki, 54124, Greece.
Brain Topogr. 2009 Sep;22(2):119-33. doi: 10.1007/s10548-008-0071-4. Epub 2008 Nov 13.
Following a nonlinear dynamics approach, we investigated the emergence of functional clusters which are related with spontaneous brain activity during sleep. Based on multichannel EEG traces from 10 healthy subjects, we compared the functional connectivity across different sleep stages. Our exploration commences with the conjecture of a small-world patterning, present in the scalp topography of the measured electrical activity. The existence of such a communication pattern is first confirmed for our data and then precisely determined by means of two distinct measures of non-linear interdependence between time-series. A graph encapsulating the small-world network structure along with the relative interdependence strength is formed for each sleep stage and subsequently fed to a suitable clustering procedure. Finally the delineated graph components are comparatively presented for all stages revealing novel attributes of sleep architecture. Our results suggest a pivotal role for the functional coupling during the different stages and indicate interesting dynamic characteristics like its variable hemispheric asymmetry and the isolation between anterior and posterior cortical areas during REM.
采用非线性动力学方法，我们研究了与睡眠期间自发脑活动相关的功能簇的出现。基于10名健康受试者的多通道脑电图记录，我们比较了不同睡眠阶段的功能连接性。我们的探索始于对测量的电活动头皮地形图中存在的小世界模式的推测。这种通信模式的存在首先在我们的数据中得到证实，然后通过时间序列之间两种不同的非线性相互依赖度量精确确定。为每个睡眠阶段形成一个封装小世界网络结构以及相对相互依赖强度的图，随后将其输入到合适的聚类过程中。最后，针对所有阶段比较呈现所描绘的图组件，揭示睡眠结构的新属性。我们的结果表明功能耦合在不同阶段起着关键作用，并表明了有趣的动态特征，如可变的半球不对称性以及快速眼动期间前后皮质区域之间的隔离。
