pyPhotometry: Open source Python based hardware and software for fiber photometry data acquisition
pyPhotometry: Open source Python based hardware and software for fiber photometry data acquisition
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DOI：
10.1038/s41598-019-39724-y
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发表时间：
2019-03-05
影响因子：
4.6
通讯作者：
Walton, Mark E.
中科院分区：
文献类型：
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作者：
Akam, Thomas;Walton, Mark E.
光纤光度法是通过光纤测量活动敏感指标（如GCaMP）的荧光变化来记录整体神经活动的过程。我们提出了一个系统的开源硬件和软件的光纤光度数据采集组成的一个紧凑的，低成本的，数据采集板周围的Micropython微控制器，和一个跨平台的图形用户界面（GUI）控制采集和可视化信号。该系统可以采集两个模拟和两个数字信号，并通过内置的LED驱动器控制两个外部LED。时分复用照明允许从单个光接收器信号独立读出由不同激发波长引起的荧光。验证实验表明，这种方法提供了更好的信号噪声为给定的平均激发光强度比正弦调制照明。pyPhotometry比商业硬件便宜得多，我们预计，作为一个开源和相对简单的工具，它将很容易适应，因此会引起广泛用户的广泛兴趣。
Fiber photometry is the process of recording bulk neural activity by measuring fluorescence changes in activity sensitive indicators such as GCaMP through an optical fiber. We present a system of open source hardware and software for fiber photometry data acquisition consisting of a compact, low cost, data acquisition board built around the Micropython microcontroller, and a cross platform graphical user interface (GUI) for controlling acquisition and visualising signals. The system can acquire two analog and two digital signals, and control two external LEDs via built in LED drivers. Time-division multiplexed illumination allows independent readout of fluorescence evoked by different excitation wavelengths from a single photoreceiver signal. Validation experiments indicate this approach offers better signal to noise for a given average excitation light intensity than sinusoidally-modulated illumination. pyPhotometry is substantially cheaper than commercial hardware filling the same role, and we anticipate, as an open source and comparatively simple tool, it will be easily adaptable and therefore of broad interest to a wide range of users.