Sakaki Kelly D R, Coleman Patrick, Toth Tristan Dellazizzo, Guerrier Claire, Haas Kurt
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:1-7. doi: 10.1109/EMBC.2018.8512983.
Determining how a neuron computes requires an understanding of the complex spatiotemporal relationship between its input (e.g. synaptic input as a result of external stimuli) and action potential output. Recent advances in in vivo, laser-scanning multiphoton technology, known as random-access microscopy (RAM), can capture this relationship by imaging fluorescent light, emitted from calcium-sensitive biosensors responding to synaptic and action potential firing in a neuron's full dendritic arbor and cell body. Ideally, a continuous output of fluorescent intensities from the neuron would be converted to a binary output (`event', 'or no-event'). These binary events can be used to correlate temporal and spatial associations between the input and output. However, neurons contain hundreds-to-thousands of synapses on the dendritic arbors generating an enormous quantity of data composed of physiological signals, which vary greatly in shape and size. Thus, automating data-processing tasks is essential to support high-throughput analysis for real-time/post-processing operations and to improve operators' comprehension of the data used to decipher neuron computations. Here, we describe an automated software algorithm to detect brain neuron events in real-time using an acousto-optic, multiphoton, laser scanning RAM developed in our laboratory. The fluorescent light intensities, from a genetically encoded, calcium biosensor (GCAMP 6m), are measured by our RAM system and are input to our 'event-detector', which converts them to a binary output meant for real-time applications. We evaluate three algorithms for this purpose: exponentially weighted moving average, cumulative sum, and template matching; present each algorithm's performance; and discuss user-feasibility of each. We validated our system in vivo, using the visual circuit of the Xenopus laevis.
确定神经元如何进行计算需要了解其输入（例如外部刺激导致的突触输入）与动作电位输出之间复杂的时空关系。体内激光扫描多光子技术（称为随机存取显微镜，RAM）的最新进展，可以通过对荧光成像来捕捉这种关系，荧光由钙敏生物传感器发出，这些传感器响应神经元完整树突分支和细胞体中的突触活动和动作电位发放。理想情况下，神经元发出的荧光强度连续输出将被转换为二进制输出（“事件”或“无事件”）。这些二进制事件可用于关联输入和输出之间的时间和空间关联。然而，神经元在树突分支上有数百到数千个突触，会产生大量由生理信号组成的数据，这些信号在形状和大小上差异很大。因此，自动化数据处理任务对于支持实时/后处理操作的高通量分析以及提高操作人员对用于解读神经元计算的数据的理解至关重要。在这里，我们描述了一种自动化软件算法，该算法使用我们实验室开发的声光多光子激光扫描RAM实时检测脑神经元事件。来自基因编码钙生物传感器（GCAMP 6m）的荧光强度由我们的RAM系统测量，并输入到我们的“事件检测器”，该检测器将其转换为用于实时应用的二进制输出。为此，我们评估了三种算法：指数加权移动平均、累积求和和模板匹配；展示了每种算法的性能；并讨论了每种算法的用户可行性。我们使用非洲爪蟾的视觉回路在体内验证了我们的系统。
