Sparse decomposition light-field microscopy for high speed imaging of neuronal activity

Abstract

One of the major challenges in large scale optical imaging of neuronal activity is to simultaneously achieve sufficient temporal and spatial resolution across a large volume. Here, we introduce sparse decomposition light-field microscopy (SDLFM), a computational imaging technique based on light-field microscopy (LFM) that takes algorithmic advantage of the high temporal resolution of LFM and the inherent temporal sparsity of spikes to improve effective spatial resolution and signal-to-noise ratios (SNRs). With increased effective spatial resolution and SNRs, neuronal activity at the single-cell level can be recovered over a large volume. We demonstrate the single-cell imaging capability of SDLFM with in vivo imaging of neuronal activity of whole brains of larval zebrafish with estimated lateral and axial resolutions of ∼ 3.5 µ m and ∼ 7.4 µ m , respectively, acquired at volumetric imaging rates up to 50 Hz. We also show that SDLFM increases the quality of neural imaging in adult fruit flies.

Whole brain functional recordings at cellular resolution in zebrafish larvae with 3D scanning multiphoton microscopy

“…line Elavl:H2B-GCaMP6s. This version of the fluorescence reporter presents a characteristic response decay time between 3.5 and 4.1 s 8 . Hence, sampling cell activity at 1 vps still allows a good reconstruction of the neuronal activity profiles 9 .…”

“…Imaging whole brain activity in zebrafish larvae is currently possible with a few approaches: Selective Plane Illumination Microscopy (SPIM) 5 , 6 , Structured Illumination Microscopy (SIM) 7 and Light Field Microscopy (LFM) 8 , 9 . All these rely on the illumination of the sample with spatially extended illumination profiles, that can be either thin sheets of light, or a series of grating, or a bulk diffuse illumination.…”

Light-Field Microscopy for the Optical Imaging of Neuronal Activity: When model-based methods meet data-driven approaches

“…Considering that sparsity priors can improve reconstruction performance, spatial resolution and SNR, Yoon et al [ 16 ] propose a sparse decomposition LFM. This strategy converts the inherent temporal sparsity of neuronal activity into spatial sparsity of 2D images to achieve the level of resolution expected for sparse samples in densely packed samples.…”

Handheld and Cost-Effective Fourier Lightfield Microscope

“…Due to the linear and spatially shiftinvariant nature of captured views, FLMic is especially suited for easing the postprocessing and therefore for providing depth reconstructions with high and homogeneous resolution over a large depth of field [12][13][14][15]. Despite the short amount of time that has passed since FLMic was first reported [1], the number of applications for capturing dynamic biomedical images has increased significantly [16][17][18][19][20][21]. The FLMic can be built from scratch by aligning and adjusting many different elements, such as the illumination system, the sample holder, an infinity-corrected microscope objective (MO), the tube lens, relay lenses, the microlens array (MLA), and a digital camera.…”