|ZFIN ID: ZDB-PUB-170829-6|
Multi-scale approaches for high-speed imaging and analysis of large neural populations
Friedrich, J., Yang, W., Soudry, D., Mu, Y., Ahrens, M.B., Yuste, R., Peterka, D.S., Paninski, L.
|Source:||PLoS Computational Biology 13: e1005685 (Journal)|
|Registered Authors:||Ahrens, Misha, Mu, Yu|
|Keywords:||Neurons, Neuroimaging, Calcium imaging, Algorithms, Fluorescence imaging, Gaussian noise, Imaging techniques, Single neuron function|
|PubMed:||28771570 Full text @ PLoS Comput. Biol.|
Friedrich, J., Yang, W., Soudry, D., Mu, Y., Ahrens, M.B., Yuste, R., Peterka, D.S., Paninski, L. (2017) Multi-scale approaches for high-speed imaging and analysis of large neural populations. PLoS Computational Biology. 13:e1005685.
ABSTRACTProgress 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.