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

Tobias Nöbauer1, Oliver Skocek1, Alejandro J Pernía-Andrade2

  • 1Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, New York, USA.

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Summary

Light-field microscopy (LFM) now images deep into the mouse cortex using seeded iterative demixing (SID). This computational technique captures fast neuronal dynamics in vivo, overcoming tissue scattering for broader neuroscience applications.

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Area of Science:

  • Neuroscience
  • Biophotonics
  • Computational Imaging

Background:

  • Light-field microscopy (LFM) offers high-speed volumetric Ca2+ imaging.
  • Tissue scattering limits LFM's depth penetration in dense tissues like the rodent brain.
  • Existing methods struggle with signal discrimination in closely packed neuronal structures.

Purpose of the Study:

  • To extend the application of LFM to deep tissue imaging in the mammalian cortex.
  • To develop a computational method for improving signal extraction in light-field microscopy.
  • To enable in vivo neuronal activity monitoring at high spatiotemporal resolution within scattering brain tissue.

Main Methods:

  • Introduction of seeded iterative demixing (SID), a novel computational source-extraction technique.
  • Application of SID to light-field microscopy (LFM) for volumetric Ca2+ imaging.
  • In vivo imaging experiments in the mouse cortex and hippocampus.

Main Results:

  • SID successfully extends LFM to image neuronal dynamics in vivo within the mouse cortex and hippocampus.
  • Imaging achieved a volume of 900 × 900 × 260 μm at depths up to 380 μm.
  • The method captures neuronal activity at 30 Hz, resolving neurons as close as 20 μm, with significantly reduced computational cost.

Conclusions:

  • Seeded iterative demixing (SID) enhances light-field microscopy (LFM) for deep brain imaging in mammals.
  • The technique overcomes scattering limitations, enabling high-resolution, high-speed neuronal activity recording.
  • This advancement promises broader applications for LFM in neuroscience research, including closed-loop experiments.