Wavelet-based multi-resolution statistics for optical imaging signals: Application to automated detection of odour activated glomeruli in the mouse olfactory bulb
Wavelet-based multi-resolution statistics for optical imaging signals: Application to automated detection of odour activated glomeruli in the mouse olfactory bulb
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DOI：
10.1016/j.neuroimage.2006.10.038
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发表时间：
2007-02-01
期刊：
影响因子：
5.7
通讯作者：
Carleton, Alan
中科院分区：
文献类型：
--
作者：
Bathellier, Brice;De Ville, Dimitri Van;Carleton, Alan
光学成像技术为捕捉动物大脑网络的处理过程提供了强大的解决方案，特别是当活动分布在功能不同的空间区域时。尽管成像技术取得了进展，但针对此类数据的标准分析程序和统计评估仍然有限。在本文中，我们在小鼠嗅球中应用了两种体内非侵入性光学记录技术，使用了一种基因表达的活性报告荧光蛋白（突触pH荧光蛋白）和大脑的内源性信号。对于这两种成像技术，我们表明可以使用线性模型对气味触发的信号进行准确的参数化。与标准方法相比，拟合模型使我们能够提取出噪声水平更低的气味特异性信号。此外，这些模型用于评估统计显著性，使用了一种基于小波的框架，该框架利用了不同尺度的空间相关性。我们提出对该框架进行扩展，以提取特定小波尺度下的激活模式。这种方法对于检测在嗅球上分离成称为 glomeruli（肾小球；此处指嗅小球）的小球状结构中的气味输入特别有用。有趣的是，通过适当选择小波尺度，我们可以分离出显著激活的嗅小球，从而以自动化的方式确定气味图谱。与手动检测嗅小球的比较表明了所提出方法的高精度。因此，除了一般而言对现有的光学成像信号处理方法是一种有利的替代方案外，我们的框架还提出了一种有趣的程序，可在统计控制下剖析多个尺度上的大脑激活模式。（c）2006爱思唯尔公司。保留所有权利。
Optical imaging techniques offer powerful solutions to capture brain networks processing in animals, especially when activity is distributed in functionally distinct spatial domains. Despite the progress in imaging techniques, the standard analysis procedures and statistical assessments for this type of data are still limited. In this paper, we perform two in vivo non-invasive optical recording techniques in the mouse olfactory bulb, using a genetically expressed activity reporter fluorescent protein (synaptopHfluorin) and intrinsic signals of the brain. For both imaging techniques, we show that the odour-triggered signals can be accurately parameterized using linear models. Fitting the models allows us to extract odour specific signals with a reduced level of noise compared to standard methods. In addition, the models serve to evaluate statistical significance, using a wavelet-based framework that exploits spatial correlation at different scales. We propose an extension of this framework to extract activation patterns at specific wavelet scales. This method is especially interesting to detect the odour inputs that segregate on the olfactory bulb in small spherical structures called glomeruli. Interestingly, with proper selection of wavelet scales, we can isolate significantly activated glomeruli and thus determine the odour map in an automated manner. Comparison against manual detection of glomeruli shows the high accuracy of the proposed method. Therefore, beyond the advantageous alternative to the existing treatments of optical imaging signals in general, our framework propose an interesting procedure to dissect brain activation patterns on multiple scales with statistical control. (c) 2006 Elsevier Inc. All rights reserved.