Automated identification of mouse visual areas with intrinsic signal imaging.
Automated identification of mouse visual areas with intrinsic signal imaging.
复制标题
通过内在信号成像自动识别小鼠视觉区域。
DOI：
10.1038/nprot.2016.158
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发表时间：
2017-01
期刊：
影响因子：
14.8
通讯作者：
Callaway EM
中科院分区：
文献类型：
--
作者：
Juavinett AL;Nauhaus I;Garrett ME;Zhuang J;Callaway EM
本征信号光学成像(ISI)是一种快速、非侵入性的方法，可以在大范围的大脑皮层上观察活体的脑活动。在这里，我们描述了我们的方案，映射视网膜，以识别小鼠的视觉皮质区域使用ISI。首先，手术将头框固定在小鼠的头骨上(~1小时)。第二天，在呈现水平和垂直方向的全视野漂移条(~2小时)的过程中，记录整个视皮层的内在活动。通过分析每个像素在刺激期间的响应来生成水平和垂直视网膜定位图。最后，算法使用这些视网膜地图来计算视野符号和覆盖范围，并在不需要人工输入的情况下自动构建视觉边界。与传统的间歇性显示相邻刺激的视网膜定位相比，连续周期性刺激更能抵抗生物伪影。此外，与手工绘制的方法不同，我们提出了一种自动分割视觉区域的方法，即使在小的鼠标皮质中也是如此。这个相对简单的程序和附带的开放源代码可以用最少的手术和计算经验来实现，并且对于任何希望在这个日益有价值的模型系统中瞄准视觉皮质区域的实验室来说都是有用的。
Intrinsic signal optical imaging (ISI) is a rapid and noninvasive method for observing brain activity in vivo over a large area of the cortex. Here we describe our protocol for mapping retinotopy to identify mouse visual cortical areas using ISI. First, surgery is performed to attach a head frame to the mouse skull (~1 h). The next day, intrinsic activity across the visual cortex is recorded during the presentation of a full-field drifting bar in the horizontal and vertical directions (~2 h). Horizontal and vertical retinotopic maps are generated by analyzing the response of each pixel during the period of the stimulus. Last, an algorithm uses these retinotopic maps to compute the visual field sign and coverage, and automatically construct visual borders without human input. Compared with conventional retinotopic mapping with episodic presentation of adjacent stimuli, a continuous, periodic stimulus is more resistant to biological artifacts. Furthermore, unlike manual hand-drawn approaches, we present a method for automatically segmenting visual areas, even in the small mouse cortex. This relatively simple procedure and accompanying open-source code can be implemented with minimal surgical and computational experience, and is useful to any laboratory wishing to target visual cortical areas in this increasingly valuable model system.