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PLoS Comput Biol. 2017 Aug 3;13(8):e1005685. doi: 10.1371/journal.pcbi.1005685. eCollection 2017 Aug.

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

Author information

1
Department of Statistics, Grossman Center for the Statistics of Mind, and Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America.
2
Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America.
3
NeuroTechnology Center, Department of Biological Sciences, Columbia University, New York, New York, United States of America.
4
Kavli Institute of Brain Science, Columbia University, New York, New York, United States of America.
5
Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America.

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.

PMID:
28771570
PMCID:
PMC5557609
DOI:
10.1371/journal.pcbi.1005685
[Indexed for MEDLINE]
Free PMC Article

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