An Autonomous Rapidly Adaptive Multiphoton Microscope for Neural Recording and Stimulation

Johns Hopkins University, Baltimore MD

Investigators

Abstract

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.