Department of Neurobiology, The Weizmann Institute of Science, 76100 Rehovot, Israel.
Neuroimage. 2013 Nov 15;82:237-51. doi: 10.1016/j.neuroimage.2013.05.045. Epub 2013 May 18.
Fundamental understanding of higher cognitive functions can greatly benefit from imaging of cortical activity with high spatiotemporal resolution in the behaving non-human primate. To achieve rapid imaging of high-resolution dynamics of cortical representations of spontaneous and evoked activity, we designed a novel data acquisition protocol for sensory stimulation by rapidly interleaving multiple stimuli in continuous sessions of optical imaging with voltage-sensitive dyes. We also tested a new algorithm for the "temporally structured component analysis" (TSCA) of a multidimensional time series that was developed for our new data acquisition protocol, but was tested only on simulated data (Blumenfeld, 2010). In addition to the raw data, the algorithm incorporates prior knowledge about the temporal structure of the data as well as input from other information. Here we showed that TSCA can successfully separate functional signal components from other signals referred to as noise. Imaging of responses to multiple visual stimuli, utilizing voltage-sensitive dyes, was performed on the visual cortex of awake monkeys. Multiple cortical representations, including orientation and ocular dominance maps as well as the hitherto elusive retinotopic representation of orientation stimuli, were extracted in only 10s of imaging, approximately two orders of magnitude faster than accomplished by conventional methods. Since the approach is rather general, other imaging techniques may also benefit from the same stimulation protocol. This methodology can thus facilitate rapid optical imaging explorations in monkeys, rodents and other species with a versatility and speed that were not feasible before.
对高级认知功能的基本理解可以从对行为非人类灵长类动物的皮质活动进行高时空分辨率成像中大大受益。为了实现对自发和诱发活动的皮质表示的高分辨率动力学的快速成像,我们设计了一种新的用于通过在具有电压敏感性染料的连续光学成像会话中快速交错多个刺激来进行感觉刺激的新型数据采集协议。我们还测试了一种新的算法,用于对多维时间序列进行“时间结构成分分析”(TSCA),该算法是为我们的新数据采集协议而开发的,但仅在模拟数据上进行了测试(Blumenfeld,2010 年)。除了原始数据外,该算法还将有关数据时间结构的先验知识以及来自其他信息的输入结合在一起。在这里,我们表明 TSCA 可以成功地将功能信号分量与其他称为噪声的信号区分开来。利用电压敏感性染料对清醒猴子的视觉皮层进行了对多种视觉刺激的响应成像。仅在 10 秒的成像中提取了多个皮质表示,包括方位和眼优势图以及以前难以捉摸的方位刺激的视网膜图,其速度比传统方法快大约两个数量级。由于该方法相当通用,因此其他成像技术也可能从相同的刺激协议中受益。因此,这种方法可以促进猴子、啮齿动物和其他物种的快速光学成像探索,其灵活性和速度是以前不可能实现的。