多通道局部场电位独立分量能量编码工作记忆事件的研究

目的 基于大鼠前额叶皮层多通道局部场电位（LFPs）的独立分量分析（ICA）,研究LEPs的各个独立分量（ICs）的能量如何编码工作记忆过程中的事件.方法 以事件发生点为零点,分别将±500 ms内在大鼠记忆皮层记录的15通道LEPs,通过ICA分解为15个ICs,计算各分量在窗宽为50 ms的窗口内的能量.以步长为25ms移动窗口,获得15个ICs能量的动态分布.选择在±200 ms内能量明显增高（即其能量改变编码了事件）的ICs,通过ICA逆变换可知ICs对应的记录通道.结果 在对同一段实验数据进行多次重复分析时,由于ICA算法分解结果的不确定性,在±200 ms内能量明显增高的ICs不尽相同,但其对应的空间位置均为同一通道处,表明该通道所在位置即为LFPs编码该次事件的主要功能区域;而对多段实验数据的分析结果显示,实验数据与分析所得的主要功能区域具有较为理想的一一对应性.结论 多通道LFPs独立分量的能量可以编码工作记忆过程中的事件;ICA对于识别多通道LFPs对工作记忆事件的编码模式是有效的;ICA可以对编码事件的主要功能区域进行空间定位,且鲁棒性较为理想.

Study on coding working memory event via independent components energies of multichannel local field potentials

Objective To investigate how the independent components(ICs)energies of multichannel local field potentials(LFPs) code event base on the analysis of ICA of the cortical LFPs of rats. Methods Taking the event point as the zero point, 15-channel LFPs between the span of ±500ms recorded from the prefrontal cortex of rats were decomposed into 15 ICs. The energies of the ICs were computed in a 50-ms window. By sliding the window with step of 25 ms, a dynamic distribution mapping of the 15 ICs' energies was established. ICs with distinctly increased energies during the span of ±200 ms, which indicating that these ICs energies coded event,were selected as the targets. The corresponding channels of these ICs were determined consequently via the inverse transformation of ICA. Results Considering each trail of the repetitious analysis for the same segment of data, the spatial localization of the dominate function region(s) turned out to be relatively stable in spite of the uncertainty of the number and sequence of the target IC(s) due to the ambiguities of the decomposition of ICA.Meanwhile, the analysis results of a series of data segments showed satisfactory correspondence between data segments and dominate function regions. Conclusion The ICs' energies of multichannel LFPs are able to code events in working memories; It is valid for ICA to identify the coding patterns of multichannel LFPs to events; ICA is capable to localize the dominate function regions of event coding with satisfactory robustness.