Department of Experimental Psychology, Oxford University, Oxford, UK.
Champalimaud Neuroscience Program, Champalimaud Center for the Unknown, Lisbon, Portugal.
Sci Rep. 2019 Mar 5;9(1):3521. doi: 10.1038/s41598-019-39724-y.
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.
光纤光度测定法是通过光纤测量活性敏感指示剂（如 GCaMP）的荧光变化来记录大量神经活动的过程。我们提出了一种用于光纤光度测定数据采集的开源硬件和软件系统，该系统由一个紧凑、低成本的数据采集板组成，该采集板围绕 Micropython 微控制器构建，以及一个用于控制采集和可视化信号的跨平台图形用户界面 (GUI)。该系统可以采集两个模拟信号和两个数字信号，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中独立读取不同激发波长激发的荧光。验证实验表明，与正弦调制照明相比，这种方法在给定的平均激发光强度下提供了更好的信噪比。pyPhotometry 比具有相同功能的商业硬件便宜得多，我们预计，作为一个开源且相对简单的工具，它将很容易适应，因此将广泛吸引广泛的用户。
