High-speed, cortex-wide volumetric recording of neuroactivity at cellular resolution using light beads microscopy.

Two-photon microscopy has enabled high-resolution imaging of neuroactivity at depth within scattering brain tissue. However, its various realizations have not overcome the tradeoffs between speed and spatiotemporal sampling that would be necessary to enable mesoscale volumetric recording of neuroactivity at cellular resolution and speed compatible with resolving calcium transients. Here, we introduce light beads microscopy (LBM), a scalable and spatiotemporally optimal acquisition approach limited only by fluorescence lifetime, where a set of axially separated and temporally distinct foci record the entire axial imaging range near-simultaneously, enabling volumetric recording at 1.41 × 108 voxels per second. Using LBM, we demonstrate mesoscopic and volumetric imaging at multiple scales in the mouse cortex, including cellular-resolution recordings within ~3 × 5 × 0.5 mm volumes containing >200,000 neurons at ~5 Hz and recordings of populations of ~1 million neurons within ~5.4 × 6 × 0.5 mm volumes at ~2 Hz, as well as higher speed (9.6 Hz) subcellular-resolution volumetric recordings. LBM provides an opportunity for discovering the neurocomputations underlying cortex-wide encoding and processing of information in the mammalian brain.

Author Correction: High-speed volumetric imaging of neuronal activity in freely moving rodents.

In the version of this Brief Communication originally published online, ref. 21 included details for a conference paper (Pegard, N. C. et al. Paper presented at Novel Techniques in Microscopy: Optics in the Life Sciences, Vancouver, BC, Canada, 12-15 April 2015). The correct reference is the following: Pégard, N. C. et al. Optica 3, 517-524 (2016). This error has been corrected in the print, HTML and PDF versions of the paper.

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

Light-field microscopy (LFM) is a scalable approach for volumetric Ca2+ imaging with high volumetric acquisition rates (up to 100 Hz). Although the technology has enabled whole-brain Ca2+ imaging in semi-transparent specimens, tissue scattering has limited its application in the rodent brain. We introduce seeded iterative demixing (SID), a computational source-extraction technique that extends LFM to the mammalian cortex. SID can capture neuronal dynamics in vivo within a volume of 900 × 900 × 260 µm located as deep as 380 µm in the mouse cortex or hippocampus at a 30-Hz volume rate while discriminating signals from neurons as close as 20 µm apart, at a computational cost three orders of magnitude less than that of frame-by-frame image reconstruction. We expect that the simplicity and scalability of LFM, coupled with the performance of SID, will open up a range of applications including closed-loop experiments.

Simultaneous, cortex-wide and cellular-resolution neuronal population dynamics reveal an unbounded scaling of dimensionality with neuron number.

The brain's remarkable properties arise from collective activity of millions of neurons. Widespread application of dimensionality reduction to multi-neuron recordings implies that neural dynamics can be approximated by low-dimensional "latent" signals reflecting neural computations. However, what would be the biological utility of such a redundant and metabolically costly encoding scheme and what is the appropriate resolution and scale of neural recording to understand brain function? Imaging the activity of one million neurons at cellular resolution and near-simultaneously across mouse cortex, we demonstrate an unbounded scaling of dimensionality with neuron number. While half of the neural variance lies within sixteen behavior-related dimensions, we find this unbounded scaling of dimensionality to correspond to an ever-increasing number of internal variables without immediate behavioral correlates. The activity patterns underlying these higher dimensions are fine-grained and cortex-wide, highlighting that large-scale recording is required to uncover the full neural substrates of internal and potentially cognitive processes.

Simultaneous, cortex-wide dynamics of up to 1 million neurons reveal unbounded scaling of dimensionality with neuron number.

The brain's remarkable properties arise from the collective activity of millions of neurons. Widespread application of dimensionality reduction to multi-neuron recordings implies that neural dynamics can be approximated by low-dimensional "latent" signals reflecting neural computations. However, can such low-dimensional representations truly explain the vast range of brain activity, and if not, what is the appropriate resolution and scale of recording to capture them? Imaging neural activity at cellular resolution and near-simultaneously across the mouse cortex, we demonstrate an unbounded scaling of dimensionality with neuron number in populations up to 1 million neurons. Although half of the neural variance is contained within sixteen dimensions correlated with behavior, our discovered scaling of dimensionality corresponds to an ever-increasing number of neuronal ensembles without immediate behavioral or sensory correlates. The activity patterns underlying these higher dimensions are fine grained and cortex wide, highlighting that large-scale, cellular-resolution recording is required to uncover the full substrates of neuronal computations.