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ref: -2014 tags: dopamine medium spiny neurons calcium STDP PKA date: 01-07-2020 03:43 gmt revision:2 [1] [0] [head]

PMID-25258080 A critical time window for dopamine actions on the structural plasticity of dendritic spines

  • Remarkably short time window for dopamine to modulate / modify (aggressive) STDP protocol.
  • Showed with the low-affinity calcium indicator Fluo4-FF that peak calcium concentrations in spines is not affected by optogenetic stimulation of dopamine fibers.
  • However, CaMKII activity is modulated by DA activity -- when glutamate uncaging and depolarization was followed by optogenetic stimulation of DA fibers followed, the FRET sensor Camui-CR reported significant increases of CaMKII activity.
  • This increase was abolished by the application of DRAPP-32 inhibiting peptide, which blocks the interaction of dopamine and cAMP-regulated phospoprotein - 32kDa (DRAPP-32) with protein phosphatase 1 (PP-1)
    • Spine enlargement was induced in the absence of optogenetic dopamine when PP-1 was inhibited by calculin A...
    • Hence, phosphorylation of DRAPP-32 by PKA inhibits PP-1 and disinihibts CaMKII. (This causal inference seems loopy; they reference a hippocampal paper, [18])
  • To further test this, they used a FRET probe of PKA activity, AKAR2-CR. This sensor showed that PKA activity extends throughout the dendrite, not just the stimulated spine, and can respond to DA release directly.

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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