Volumetric two-photon imaging of neurons using stereoscopy (vTwINS)

Song, Alexander; Charles, Adam S.; Koay, S. A.; Gauthier, Jeffrey; Thiberge, Stephan Y.; Pillow, Jonathan W.; Tank, David W.

doi:10.1038/nmeth.4226

Cited by 128 publications

(64 citation statements)

References 48 publications

(55 reference statements)

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“…This approach gains temporal resolution (160 fps over 500 × 500 μm) and can use statistical algorithms 65,67,68 to extract the neural activity from the seemingly decreased spatial resolution images. Other examples include using Bessel beams, which do not spread out laterally over an extended depth of field and can thus image all dendrites or neurons that the beams intersect 69,70 . It aims to acquire 3D volumetric imaging by a 2D projection scan for sparsely labeled samples.…”

Section: Multiplexing Two-photon Microscopymentioning

confidence: 99%

In vivo imaging of neural activity

2017

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Since the introduction of calcium imaging to monitor neuronal activity with single-cell resolution, optical imaging methods have revolutionized neuroscience by enabling systematic recordings of neuronal circuits in living animals. The plethora of methods for functional neural imaging can be daunting to the nonexpert to navigate. Here we review advanced microscopy techniques for in vivo functional imaging and offer guidelines for which technologies are best suited for particular applications.

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Section: Multiplexing Two-photon Microscopymentioning

confidence: 99%

In vivo imaging of neural activity

2017

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“…More recently, several groups have started using rodents to tackle such questions (Brunton et al, 2013 ; Carandini and Churchland, 2013 ; Raposo et al, 2014 ; Hanks et al, 2015 ; Scott et al, 2015 ; Morcos and Harvey, 2016 ; Licata et al, 2017 ; Odoemene et al, 2017 ). Rodents provide many complementary advantages to the use of primates, such as lower cost, larger scalability, and, particularly for mice, the wide availability of an ever-expanding arsenal of tools to record from and manipulate circuits with great spatiotemporal and genetic specificity in behaving animals (Svoboda and Yasuda, 2006 ; Dombeck et al, 2007 ; Luo et al, 2008 ; Deisseroth, 2011 ; Chen et al, 2013 ; Guo et al, 2014 ; Rickgauer et al, 2014 ; Sofroniew et al, 2016 ; Song et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

An Accumulation-of-Evidence Task Using Visual Pulses for Mice Navigating in Virtual Reality

Pinto

Koay

Engelhard

et al. 2018

Front. Behav. Neurosci.

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The gradual accumulation of sensory evidence is a crucial component of perceptual decision making, but its neural mechanisms are still poorly understood. Given the wide availability of genetic and optical tools for mice, they can be useful model organisms for the study of these phenomena; however, behavioral tools are largely lacking. Here, we describe a new evidence-accumulation task for head-fixed mice navigating in a virtual reality (VR) environment. As they navigate down the stem of a virtual T-maze, they see brief pulses of visual evidence on either side, and retrieve a reward on the arm with the highest number of pulses. The pulses occur randomly with Poisson statistics, yielding a diverse yet well-controlled stimulus set, making the data conducive to a variety of computational approaches. A large number of mice of different genotypes were able to learn and consistently perform the task, at levels similar to rats in analogous tasks. They are sensitive to side differences of a single pulse, and their memory of the cues is stable over time. Moreover, using non-parametric as well as modeling approaches, we show that the mice indeed accumulate evidence: they use multiple pulses of evidence from throughout the cue region of the maze to make their decision, albeit with a small overweighting of earlier cues, and their performance is affected by the magnitude but not the duration of evidence. Additionally, analysis of the mice's running patterns revealed that trajectories are fairly stereotyped yet modulated by the amount of sensory evidence, suggesting that the navigational component of this task may provide a continuous readout correlated to the underlying cognitive variables. Our task, which can be readily integrated with state-of-the-art techniques, is thus a valuable tool to study the circuit mechanisms and dynamics underlying perceptual decision making, particularly under more complex behavioral contexts.

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“…In a compressed sensing framework, [ 40 ] proposed to image randomized projections of the spatial calcium concentration at each timestep, instead of measuring the concentration at individual locations. In [ 41 ], [ 42 ], and [ 43 ], information is integrated primarily across depth, either by creating multiple foci, axially extended point spread functions (PSFs), or both, respectively. In contrast to these methods, [ 7 ] instead scanned an enlarged near-isotropic PSF, generated with temporal focusing, to quickly interrogate cells in a single plane at low spatial resolution.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2017

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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.

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Volumetric two-photon imaging of neurons using stereoscopy (vTwINS)

Cited by 128 publications

References 48 publications

In vivo imaging of neural activity

In vivo imaging of neural activity

An Accumulation-of-Evidence Task Using Visual Pulses for Mice Navigating in Virtual Reality

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

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