Multi-scale approaches for high-speed imaging and analysis of large neural populations.

Johannes Friedrich,Misha B Ahrens,Weijian Yang,Daniel Soudry,Yu Mu,Rafael Yuste,Liam Paninski,Darcy S Peterka,Lyle J Graham

doi:10.1371/journal.pcbi.1005685

Johannes Friedrich, Misha B Ahrens + Show 7 more

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https://doi.org/10.1371/journal.pcbi.1005685

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Abstract

Progress in modern neuroscience critically depends on our ability to observe the activity of large neuronal populations with cellular spatial and high temporal resolution. However, two bottlenecks constrain efforts towards fast imaging of large populations. First, the resulting large video data is challenging to analyze. Second, there is an explicit tradeoff between imaging speed, signal-to-noise, and field of view: with current recording technology we cannot image very large neuronal populations with simultaneously high spatial and temporal resolution. Here we describe multi-scale approaches for alleviating both of these bottlenecks. First, we show that spatial and temporal decimation techniques based on simple local averaging provide order-of-magnitude speedups in spatiotemporally demixing calcium video data into estimates of single-cell neural activity. Second, once the shapes of individual neurons have been identified at fine scale (e.g., after an initial phase of conventional imaging with standard temporal and spatial resolution), we find that the spatial/temporal resolution tradeoff shifts dramatically: after demixing we can accurately recover denoised fluorescence traces and deconvolved neural activity of each individual neuron from coarse scale data that has been spatially decimated by an order of magnitude. This offers a cheap method for compressing this large video data, and also implies that it is possible to either speed up imaging significantly, or to “zoom out” by a corresponding factor to image order-of-magnitude larger neuronal populations with minimal loss in accuracy or temporal resolution.

Highlights

A major goal of neuroscience is to understand interactions within large populations of neurons, including their network dynamics and emergent behavior
Multi-scale approaches for neuronal population imaging snf.ch) Research Award P300P2_158428, and the research of DS by the Gruss Lipper Charitable Foundation
Recent advances have enabled faster light-sheet imaging in cortex [6] and fast volumetric 2P imaging [7], but we must still contend with critical trade-offs between temporal and spatial resolution—and the need for even faster imaging of even larger neural populations

Summary

Introduction

A major goal of neuroscience is to understand interactions within large populations of neurons, including their network dynamics and emergent behavior. This ideally requires the observation of neural activity over large volumes. Light-sheet microscopy and genetically encoded indicators have enabled unprecedented whole-brain imaging of tens of thousands of neurons at cellular resolution [1]. Recent advances have enabled faster light-sheet imaging in cortex [6] and fast volumetric 2P imaging [7], but we must still contend with critical trade-offs between temporal and spatial resolution—and the need for even faster imaging of even larger neural populations

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