Close-loop, spatially addressable multiphoton functional imaging

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Abstract A major goal of brain research is to image the dynamics of groups of neurons during behavior. Although even the simplest behaviors involve interactions across multiple parts of the nervous system, our tools for assessing function at the level of individual neurons usually allow only access to small regions of the brain, and with limited temporal resolution. Optical recordings of activity are critical to probe neural systems because they provide high-resolution, non-invasive measurements, ranging from single neurons to entire populations in intact nervous systems, and are readily combined with genetic methods to provide cell type-specific recordings. Nevertheless, the limited spatial scale and temporal resolution remain a major challenge for optical imaging. Cellular-resolution imaging in scattering brains is typically achieved with multiphoton microscopy (MPM). The focus of this proposal is to develop, implement, and disseminate a new generation of multiphoton imaging tools and genetically encoded indicators that allow deep, fast, and large-scale imaging of structure and function with cellular and subcellular resolution. To approach fundamental limits defined by the 'photon budget', we will develop an adaptive excitation source (AES). By feeding the structural information of the sample to the source, and synchronizing the on-demand pulses with the microscope scanning system, the AES transforms a conventional MPM into a “random-access” MPM that only excites regions of interest. We will integrate the AES with high speed scanners, resulting in a new AES-MPM that will provide >10x improvement in imaging speed or the number of neurons imaged. We will combine the AES effort at Cornell with the development effort of genetically encoded voltage indicator (GEVI) at Stanford and Janelia Research Campus, and demonstrate the AES-MPM in imaging GEVI labeled neurons in mouse brains in vivo. We will also combine the AES with a multiphoton mesoscope to demonstrate large scale imaging of genetically encoded Ca indicator (GECI). The research involves close interactions between the PI (Xu), Co-investigator Michael Lin (Stanford), and Karel Svoboda (Janelia). Furthermore, the investigators will work closely with industrial partners to explore the potential of translating the AES into a commercially available system, which provides a direct path to broad dissemination.