Smith Keith, Azami Hamed, Escudero Javier, Parra Mario A, Starr John M
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:2207-10. doi: 10.1109/EMBC.2015.7318829.
We analyse the electroencephalogram signals in the beta band of working memory representation recorded from young healthy volunteers performing several different Visual Short-Term Memory (VSTM) tasks which have proven useful in the assessment of clinical and preclinical Alzheimer's disease. We compare network analysis using Maximum Spanning Trees (MSTs) with network analysis obtained using 20% and 25% connection thresholds on the VSTM data. MSTs are a promising method of network analysis negating the more classical use of thresholds which are so far chosen arbitrarily. However, we find that the threshold analyses outperforms MSTs for detection of functional network differences. Particularly, MSTs fail to find any significant differences. Further, the thresholds detect significant differences between shape and shape-colour binding tasks when these are tested in the left side of the display screen, but no such differences are detected when these tasks are tested for in the right side of the display screen. This provides evidence that contralateral activity is a significant factor in sensitivity for detection of cognitive task differences.
我们分析了从年轻健康志愿者身上记录的、与工作记忆表征的β波段相关的脑电图信号，这些志愿者执行了几种不同的视觉短期记忆（VSTM）任务，这些任务已被证明在临床和临床前阿尔茨海默病的评估中很有用。我们将使用最大生成树（MST）的网络分析与在VSTM数据上使用20%和25%连接阈值获得的网络分析进行比较。MST是一种很有前景的网络分析方法，它摒弃了迄今为止一直任意选择的更传统的阈值使用方法。然而，我们发现阈值分析在检测功能网络差异方面优于MST。特别是，MST未能发现任何显著差异。此外，当在显示屏左侧测试形状和形状 - 颜色绑定任务时，阈值检测到了显著差异，但当在显示屏右侧测试这些任务时，未检测到此类差异。这提供了证据，表明对侧活动是检测认知任务差异敏感性的一个重要因素。
