Understanding how sensory inputs are dynamically mapped onto the functional activity of neuronal populations and how their processing leads to cognitive functions and behavior requires tools for non-invasive interrogation of neuronal circuits with high spatiotemporal resolution1,2. A number of approaches for 3D neural activity imaging that take advantage of chemical and genetically encoded fluorescent reporters exist3,4. Whereas some are based on scanning the excitation light in a volume, either sequentially5,6,7 or randomly8,9, others try to capture 3D image data simultaneously by mapping axial information onto a single lateral plane using a range of approaches10,11,12,13,14.

Light-field microscopy (LFM)12 is one such simultaneous 3D imaging method that has been applied to nonbiological and fixed biological samples12,13. In contrast to conventional imaging schemes, a light-field microscope captures both the 2D location and 2D angle of the incident light. This is done by placing a microlens array in the native image plane such that sensor pixels capture the rays of the light field simultaneously. Such 4D light fields allow the synthesis of a focal stack computationally. In LFM, single sensor images are used to retrieve information for the entire 3D volume, a scheme that enables high-speed volumetric acquisition. However, despite its potentially superb temporal resolution, LFM has not to date been used for functional biological imaging. This is because capturing the 4D light-field information via a single sensor image comes at the cost of reduced spatial resolution and because of inherent trade-offs between axial imaging range and the spatial and axial resolution12.

Here we report that neural tissues expressing calcium sensors can be imaged at volume rates of up to 50 Hz and at single-neuron resolution, using a 3D deconvolution algorithm15,16 applied to LFM. We achieved effective resolutions up to ∼1.4 μm and 2.6 μm in the lateral and axial dimensions, respectively, inside biological samples. To build our light-field deconvolution microscope (LFDM), we placed a microlens array at the image plane of an epifluorescence microscope (Fig. 1a and Online Methods), which captured the different perspectives of the sample (Fig. 1b) on the camera sensor. To overcome the trade-off between axial and lateral spatial resolution in LFM12, we exploited aliasing of the recorded data and used computational reconstruction methods based on 3D deconvolution to effectively obtain improved lateral and axial resolution15,16 (Online Methods, Supplementary Notes 1 and 2 and Supplementary Software).

Figure 1: Light-field deconvolution microscopy. (a) A microlens array was appended to the camera port of a wide-field microscope and placed in the primary image plane of the fluorescence microscope. The array itself was imaged with a 1:1 relay lens system onto the chip of a scientific complementary metal-oxide semiconductor (sCMOS) camera (Online Methods). Inset, close-up of the microlens array. (b) Point spread function (PSF) of a subdiffraction-sized bead located at z = 7.5 μm off the focal plane, as seen through the microlens array. (c) Axial (xz) PSF at z = 7.5 μm, reconstructed using the LFDM, and corresponding x and z profiles showing lateral and axial resolution, respectively. a.u., arbitrary units. (d) Maximum-intensity projection (MIP) of a deconvolved wide-field focal stack taken without microlenses. The sample consists of 6-μm-sized fluorescent beads in agarose. (e) Red box in d; xz- and yz-section MIPs are shown. (f,g) Corresponding volume of the same beads in d,e, 4–28 μm off the focal plane, reconstructed via 15 iterations of the light-field deconvolution algorithm. Scale bars, 150 μm (a,b), 3 μm (c) and 10 μm (d–g). Full size image

To evaluate the spatial resolution of our LFDM, we imaged subdiffraction-sized beads and reconstructed the point spread function (PSF) of our system (Fig. 1b,c). Using a 40× objective, we found resolutions of ∼1.4 μm and 2.6 μm in the lateral and axial dimensions, respectively. To verify the suitability of the LFDM for capturing the activity of individual neurons, we imaged a sample consisting of 6-μm-diameter fluorescent beads randomly distributed in three dimensions in agarose and compared a conventional focal stack (taken without microlenses) (Fig. 1d,e) with the deconvolved light-field images (Fig. 1f,g).

Using the same objective with C. elegans, we were able to image the majority of a worm (∼350 μm × 350 μm × 30 μm) while maintaining single-neuron resolution (Fig. 2a–c, Supplementary Figs. 1,2,3,4 and Supplementary Videos 1,2,3,4,5). We could record activity of neurons in the brain region surrounding the nerve ring and the ventral cord at a 5-Hz volume rate. We note that our LFDM allows for substantially higher volume rates than this, which we demonstrated by recording unrestrained worms at 50 Hz (Supplementary Fig. 4 and Supplementary Video 3). Such volume rates would in principle be sufficient for performing whole-brain imaging in freely moving worms, especially if additional tracking is employed as previously shown for single neurons17. However, as Ca2+ signals in C. elegans typically occur at timescales of up to 1 Hz, we chose slower volume rates (5 Hz) in order to maximize the signal-to-noise ratio and reduce potential photobleaching.

Figure 2: Whole-animal Ca2+ imaging of C. elegans using LFDM. (a) Wide-field image of the worm inside a microfluidic poly(dimethylsiloxane) (PDMS) device used for immobilization. The head is at the bottom right. (b) Maximum-intensity projection (MIP) of a light-field deconvolved image (15 iterations) containing 14 distinct z planes. Arrows and numbers indicate individual neurons in the head ganglia and ventral cord. (c) Ca2+ intensity traces (ΔF/F 0 ) of NLS-GCaMP5K fluorescence of selected neurons as marked in b and imaged volumetrically at 5 Hz for 200 s (Supplementary Video 1). (d) Close-up of the brain region, with the MIP of the xy plane as well as xz and yz cross-sections indicated by the dashed lines (Supplementary Video 2). (e) Individual z planes of a typical recording of the worm's brain, reconstructed from a single camera exposure (see Supplementary Fig. 2 for neuron IDs). In this recording, the worm's center along the lateral (left–right) (z) axis was placed at the focal plane of the objective. (f) Activity of all 74 neurons identified in e (Supplementary Video 4). Each row shows a time-series heat map of an individual neuron. Color indicates percent fluorescence changes (ΔF/F 0 ); scaling is indicated by the color bar on the right. Scale bars, 50 μm (b,e), 10 μm (d). Full size image Download Excel source data

The wide field of view (FOV) of the LFDM and the intrinsic simultaneity of the acquisition allow one to study correlations in activity of neurons across the whole animal, which would not be feasible with other unbiased Ca2+-imaging techniques. In our experiments, we observed correlated and anticorrelated activity patterns between the premotor interneurons in the head and motor neurons located along the ventral nerve cord, which connect to body-wall muscles according to the WormAtlas (Fig. 2a–c).

We used the location, morphology and activity patterns of some of these neurons to identify specific premotor interneuron classes such as AVA, AVE, RIM, AIB and AVB, and A- and B-class motor neurons that have been associated with motor-program selection18 (Supplementary Fig. 3). AVA neurons have been associated with a switch from forward to backward directed crawling, which depends on A-class motor neurons19 and is associated with a change in the relative activities of A- and B-class motor neurons18. What we observed was consistent with these findings: a high correlation of AVA and A-class motor neuron activity and an anticorrelation of AVA and B-class motor neuron activity. Further, we used the LDFM and sensory stimulation to identify neuron classes (Supplementary Fig. 3 and Supplementary Video 5). Applying consecutive 30-s intervals of high and low oxygen levels, we observed two neuron classes with increasing Ca2+ transients upon oxygen up- and downshift, respectively. Morphology, location and activity patterns of these neuron classes matched those of the oxygen chemosensory neurons BAG and URX5.

We also recorded exclusively from brain regions surrounding the nerve ring (Fig. 2d–f and Supplementary Fig. 2). Imaging smaller FOVs (∼200 μm × 70 μm × 30 μm) led to faster volume reconstructions and better image quality owing to the lack of undesired fluorescence from coelomocytes, which were partially labeled in our transgenes. Similarly to previous findings5, we were able to resolve up to 74 individual neurons in a typical recording, around 30 of which showed pronounced activity over the recording time of 200 s (Fig. 2d–f and Supplementary Fig. 2).

In order to highlight the temporal resolution and the broader applicability of our technique for capturing dynamics of large populations of spiking neurons, we performed Ca2+ imaging in live zebrafish larvae brains expressing the Ca2+ indicator GCaMP5 pan-neuronally. Employing a 20× objective, we demonstrated whole-brain Ca2+ imaging for volumes spanning ∼700 μm × 700 μm × 200 μm at a 20-Hz volume rate. Although in this case optical single-cell resolution had to be compromised in favor of larger FOVs, we could still recover spatially resolved cellular signals over the entire time series using standard signal extraction and unmixing techniques20. Implementing this approach, we extracted neuronal activity for ∼5,000 cells across the brain and followed their fast Ca2+ transients on a millisecond timescale (Fig. 3 and Supplementary Video 6).

Figure 3: Whole-brain Ca2+ imaging of larval zebrafish in vivo. (a) Axial point spread function (PSF) of a 0.5-μm-sized bead located at z = 28 μm off the focal plane for the 20×/0.5–numerical aperture (NA) lens, and corresponding x and z profiles. a.u., arbitrary units. (b) Maximum-intensity projection (MIP) of a light-field deconvolved volume (eight iterations) containing 51 z planes, captured at an exposure time of 50 ms per frame and spaced 4 μm apart, showing the xy plane and xz and yz cross-sections. Spatial filters, each representing individual cells, identified using principal-component and independent-component analysis20 are shown. In total, 5,379 filters were automatically identified, most of which correspond to individual neurons. (c) Extracted Ca2+ intensity signal (ΔF/F 0 ) of GCaMP5 fluorescence using spatial filters shown in b. Each row shows a time-series heat map. Color bars denote encircled regions in b, which include the olfactory epithelium, olfactory bulb and telencephalon. The arrow at ∼15 s denotes the addition of an aversive odor. A close-up of the dashed box is shown (right, lower panel); neurons with subtle differences in response onset are highlighted by colored arrows. The location of these neurons in the MIP is also shown (right, upper panel). (d) Overlay of the MIP with randomly selected spatial filters (colored dots and arrows). (e) Ca2+ intensity traces of selected cells shown in d. Neurons were manually selected from the olfactory system, midbrain and hindbrain. Trace color matches spatial-filter color in d. Also see Supplementary Video 6. Scale bars, 10 μm (a) and 100 μm b–d. Full size image Download Excel source data

By applying an aversive odor to the fish (Online Methods), we evoked neuronal activity and inferred dynamics of Ca2+ signals across the olfactory system, the midbrain and parts of the hindbrain, results consistent with previous demonstrations of the neuronal dynamics in these regions6,7,21,22,23. The high temporal resolution of the LFDM revealed subtle differences in the exact timing of the onset of the response for different groups of neurons located close to each other (Fig. 3c). Whereas the neurons in each group exhibited a nearly synchronous onset of their activity, the collective response of each group was delayed with respect to those of the other groups. Overall, our imaging speed, which was more than an order of magnitude faster than in previous whole-brain functional imaging6,7, was thus able to reliably capture the dynamic activity of a large number of cells with high spatial and temporal resolution.

In summary, we have implemented an LFDM and demonstrated its ability to capture the neuronal activity of the entire nervous system of C. elegans simultaneously at single-cell resolution as well as record dynamics of spiking neurons by performing whole-brain Ca+2 imaging in larval zebrafish at 20 Hz. The increase in spatial resolution compared to that of LFM was achieved by performing deconvolution during postprocessing. The simultaneity of acquisition of volumes in LFDM imaging eliminates spatiotemporal ambiguity associated with sequentially recorded approaches and decouples temporal resolution from volume size. Resolutions in all three dimensions are set by the objective and microlens properties, and FOV and acquisition rate are determined by the camera chip size, frame rates and signal intensity. The LFDM is easy to set up and is cost effective and compatible with standard microscopes. Both the temporal resolution and the obtainable FOVs make light-field deconvolution microscopy an attractive technique for future combination with behavioral studies. Future work will focus on obtaining higher spatial resolutions and larger FOVs as well as faster and more efficient computational reconstruction techniques, both of which of are expected to improve with technological advancements in camera sensors and processors. Finally, the use of red-shifted Ca2+ sensors24 and the combination of the LFDM with techniques for imaging at depth in biological tissue25 bears further potential for widespread use of this method.