An Autonomous Rapidly Adaptive Multiphoton Microscope for Neural Recording and Stimulation

用于神经记录和刺激的自主快速自适应多光子显微镜

• 批准号: 10739050
• 负责人: Mark A Foster
• 金额: $ 200.86万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-20 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10739050
• 关键词: 3-Dimensional; Action Potentials; Algorithms; Architecture; Auditory area; Behavior; Behavioral; Benchmarking; Brain; Cells; Code; Computer software; Coupled; Detection; Dimensions; Feedback; Fluorescence; Generations; Goals; Hybrids; Image; In Vitro; Label; Light; Maps; Methods; Microscope; Microscopy; Monitor; Motion; Nature; Neurons; Neuropil; Noise; Optics; Output; Pattern; Recovery; Research; Resolution; Resources; Running; Sampling; Scanning; Signal Transduction; Slice; Source; Speed; Spottings; Stimulus; Structure; System; Techniques; Technology; Time; Tissues; Validation; Visualization; calcium indicator; code development; detector; improved; in vivo; lightspeed; machine learning algorithm; multi-photon; multiphoton microscopy; neural; neural network; neurotransmission; novel; parallelization; prevent; programs; response; sound; spatiotemporal; temporal measurement; theories

PROJECT SUMMARY/ABSTRACT Multiphoton microscopy of cells labeled with genetically encoded calcium indicators (GECIs) enables detection and correlation of fine neuronal structure to functional activity with cellular resolution. However, the point-scanning nature of conventional multiphoton systems makes it difficult to achieve sufficient temporal resolution for activity mapping over volumes spanning multiple circuits. Advances have largely come from developing faster methods of raster scanning. To this end, recent techniques focus on developing passive optical scanners that sequentially scan a focused spot in one dimension and these techniques demonstrate recording from an impressive 2.2 million neurons/sec. These unparalleled recording rates are enabled by passive axial multiplexing and optimized spatial sampling to maximize SNR, leading the microscope to be limited in speed by the fluorescence lifetime. Given this limit, further improvements in neurons/sec activity recording will necessitate either bright fluorescent indicators with shorter lifetime or better use of the fluorescence lifetime limited sampling rate. Since labeled neurons occupy a small volume fraction (< 5%) of a typical FOV, significant gains in neuronal recording rates are possible through the combination of these optimized scanning techniques with parallelized coded excitation. Through this research program, we will develop such an adaptive multiphoton microscope. Our approach will leverage a hybrid volumetric scanning architecture that combines the benefits of passive axial multiplexing and optimal sampling with simultaneous multi-beam volumetrically patterned excitation. This rapid and highly agile microscope platform, which we term Coded Raster Scanning Hybrid (CRaSH), will be coupled with machine learning algorithms and high-speed feedback circuits to adaptively adjust the scan conditions in response to the observed experimentally relevant activity and motion. Our goal is to develop a microscope that scans smarter and autonomously optimizes the use of resources to maximize the number and SNR of recorded neurons in response to their motion and activity. First (Aim 1), we will develop and construct the CRaSH microscope system. Our approach leverages a novel axial multiplexing approach that we term Binary Expansion Axial Multiplexing Module (BEAMM). We plan to develop the microscope in three stages starting with a non-adaptive scanning BEAMM microscope, then moving to an adaptive 2D CRaSH, and finally moving to the full adaptive 3D CRaSH. Second (Aim 2) we will develop an adaptive, hardware/software solution that uses computationally efficient algorithms running on FPGAs, to recover neural signals and adapt the excitation codes of our CRaSH microscopes. At first, we will optimize the acquisition hardware and software architecture for in vitro application. Subsequently, we will extend our algorithms to tackle in vivo challenges such as motion and uncorrelated activities. Lastly (Aim 3), we will benchmark are various microscope realizations (scanning BEAMM, 2D CRaSH, and 3D CRaSH) in brain slices and in vivo and then investigate the application of each realization to studying the functional representation of sounds in the auditory cortex.

项目摘要/摘要 标记有遗传编码钙指示剂(GECI)的细胞的多光子显微镜能够检测到精细的神经元结构，并将其与细胞分辨率的功能活动相关联。然而，传统的多光子系统的点扫描特性使得很难获得足够的时间分辨率来在跨越多个电路的体积上进行活动映射。进步在很大程度上来自于开发更快的栅格扫描方法。为此，最近的技术集中在开发被动光学扫描仪，它在一维上顺序扫描聚焦的斑点，这些技术展示了令人印象深刻的220万神经元/秒的记录。这些无与伦比的记录速率通过被动轴向多路复用和优化的空间采样来实现，以最大限度地提高SNR，从而导致显微镜的速度受到荧光寿命的限制。考虑到这一限制，神经元/秒活动记录的进一步改进将需要寿命更短的明亮荧光指示器或更好地使用荧光寿命有限的采样率。由于标记的神经元只占典型视野的一小部分(&lt；5%)，通过将这些优化的扫描技术与并行编码激发相结合，神经元记录率可能显著提高。通过这项研究计划，我们将开发出这样一种自适应多光子显微镜。我们的方法将利用混合体积扫描架构，该架构结合了无源轴向多路复用和最佳采样与同步多光束体积图案化激发的优点。这个快速且高度灵活的显微镜平台，我们称之为光栅扫描混合(CRASH)，将与机器学习算法和高速反馈电路相结合，以根据观察到的实验相关活动和运动自适应地调整扫描条件。我们的目标是开发一种能够更智能地扫描并自主优化资源使用的显微镜，以最大限度地提高记录的神经元的数量和信噪比，以响应它们的运动和活动。首先(目标1)，我们将开发和构建碰撞显微镜系统。我们的方法利用了一种新的轴向复用方法，我们称之为二进制扩展轴向复用模块(BEAMM)。我们计划分三个阶段开发显微镜，从非自适应扫描BEAMM显微镜开始，然后转向自适应2D碰撞，最后转向完全自适应3D碰撞。第二(目标2)我们将开发一种自适应的硬件/软件解决方案，该解决方案使用在现场可编程门阵列上运行的计算高效算法，以恢复神经信号并调整我们碰撞显微镜的激励代码。首先，我们将为体外应用优化采集硬件和软件架构。随后，我们将扩展我们的算法来处理活体挑战，如运动和不相关的活动。最后(目标3)，我们将对各种显微镜实现(扫描BEAMM、2D CRASH和3D CRASH)在脑切片和活体中进行基准测试，然后研究每种实现在研究声音在听觉皮质中的功能表征方面的应用。