To understand brain function, it is essential to identify how information is represented in neuronal population activity and how it is transformed by individual neurons as it flows through microcircuits. ?Two-photon (2P) microscopy is a core tool for this because it enables neuronal activity to be monitored at high spatial resolution deep within brain tissue in behaving animals?. ?However, ?t?he temporal resolution of conventional galvanometer-based 2P microscopy severely limits measurements of fast signaling in 3D neuronal circuits. Acousto-optic lens (AOL) microscopy, which enables fast focussing and selective imaging of regions of interest distributed within the imaging volume, has substantially improved the temporal resolution of 3D 2P microscopy. But current AOL microscopes, which rely on ?linear acoustic drive waveforms, suffer from limitations that make them ine?fficient to monitor signaling in structures that project in the Z dimension. ?Each change in the focus requires a 24 ??s ?dead time? to refill the AOL aperture and continuous line scanning is restricted to the selected X-Y focal plane, limiting imaging rates for 3D dendritic trees to a few Hz, rather than the 100-1000 Hz required for monitoring neurotransmitter reporters and voltage indicators. ?The main aim of this project is to optimize and disseminate ?nonlinear ?AOL 3D microscopy, a technology we have invented to overcome these limitations by enabling ultra-fast line scanning (up to 40 kHz) in any arbitrary direction in X, Y and Z. By developing a prototype ?nonlinear AOL 2P microscope with real time correction of brain movement, we have demonstrated the performance of this technology for high-speed multiscale 3D imaging of neural circuits in awake behaving animals. We will build on these results by optimizing ?nonlinear AOL microscopy for imaging entire 3D dendritic trees and the surrounding neuronal population at unprecedented speeds. We will develop variants of this dendritic ?arboreal imaging? approach to provide low spatial resolution, ultra-high-speed 3D imaging (up to 1 kHz) by combining the fast scanning and adaptive optics properties of ?nonlinear ?AOLs. We will also extend the real time FPGA analysis used in our closed loop 3D movement correction to enable ?attentional imaging? where active regions of a dendritic tree, or circuit, are rapidly detected and imaged at higher spatio-temporal resolution. These applications ?will provide the temporal resolution required for monitoring voltage across the entire 3D dendritic tree of pyramidal cells in awake animals for the first time. Moreover, attentional imaging will enable neurotransmitter release to be mapped at high spatiotemporal resolution. Low cost dissemination of this powerful new technology will be achieved by providing US labs and an imaging facility with compact ?nonlinear AOL modules that will be added to their existing conventional 2P microscopes. By extending our open source microscope GUI software, standardizing data formats with NWB2 and refining automated analysis pipelines, we will also deliver reliable user-friendly microscope control and a semiautomated data analysis framework for the collaborators to carry out experiments on a range of different neural circuits.

Public Health Relevance

Functional optical imaging is a key experimental method for investigating how neural circuits in the brain sense the outside world and control movement, providing knowledge that is critical for understanding what goes wrong during neurological disorders. This project will fund the refinement, dissemination and testing of a new type of optical microscope that can image in 3 dimensions much more rapidly than existing technologies, thereby enabling better measurements of spatially distributed neural signals in neuronal circuits.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01NS113273-02
Application #
10005501
Study Section
Special Emphasis Panel (ZNS1)
Program Officer
Talley, Edmund M
Project Start
2019-09-01
Project End
2022-08-31
Budget Start
2020-09-01
Budget End
2021-08-31
Support Year
2
Fiscal Year
2020
Total Cost
Indirect Cost
Name
University College London
Department
Type
DUNS #
225410919
City
London
State
Country
United Kingdom
Zip Code
WC1 6BT