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ref: -2017 tags: calcium imaging seeded iterative demixing light field microscopy mouse cortex hippocampus date: 02-13-2019 22:44 gmt revision:1 [0] [head]

PMID-28650477 Video rate volumetric Ca2+ imaging across cortex using seeded iterative demixing (SID) microscopy

  • Tobias Nöbauer, Oliver Skocek, Alejandro J Pernía-Andrade, Lukas Weilguny, Francisca Martínez Traub, Maxim I Molodtsov & Alipasha Vaziri
  • Cell-scale imaging at video rates of hundreds of GCaMP6 labeled neurons with light-field imaging followed by computationally-efficient deconvolution and iterative demixing based on non-negative factorization in space and time.
  • Utilized a hybrid light-field and 2p microscope, but didn't use the latter to inform the SID algorithm.
  • Algorithm:
    • Remove motion artifacts
    • Time iteration:
      • Compute the standard deviation versus time (subtract mean over time, measure standard deviance)
      • Deconvolve standard deviation image using Richardson-Lucy algo, with non-negativity, sparsity constraints, and a simulated PSF.
      • Yields hotspots of activity, putative neurons.
      • These neuron lcoations are convolved with the PSF, thereby estimating its ballistic image on the LFM.
      • This is converted to a binary mask of pixels which contribute information to the activity of a given neuron, a 'footprint'
        • Form a matrix of these footprints, p * n, S 0S_0 (p pixels, n neurons)
      • Also get the corresponding image data YY , p * t, (t time)
      • Solve: minimize over T ||YST|| 2|| Y - ST||_2 subject to T0T \geq 0
        • That is, find a non-negative matrix of temporal components TT which predicts data YY from masks SS .
    • Space iteration:
      • Start with the masks again, SS , find all sets O kO^k of spatially overlapping components s is_i (e.g. where footprints overlap)
      • Extract the corresponding data columns t it_i of T (from temporal step above) from O kO^k to yield T kT^k . Each column corresponds to temporal data corresponding to the spatial overlap sets. (additively?)
      • Also get the data matrix Y kY^k that is image data in the overlapping regions in the same way.
      • Minimize over S kS^k ||Y kS kT k|| 2|| Y^k - S^k T^k||_2
      • Subject to S k>=0S^k >= 0
        • That is, solve over the footprints S kS^k to best predict the data from the corresponding temporal components T kT^k .
        • They also impose spatial constraints on this non-negative least squares problem (not explained).
    • This process repeats.
    • allegedly 1000x better than existing deconvolution / blind source segmentation algorithms, such as those used in CaImAn