Light-field deep learning enables high-throughput, scattering-mitigated calcium imaging

Abstract

Light-field microscopy (LFM) enables high-throughput functional imaging by scanlessly encoding entire volumes in single snapshots. However, LFM’s computational burden and vulnerability to scattering limit its application to biological imaging. We present a light-field strategy for volumetric, scattering-mitigated neural circuit activity monitoring. A physics-based deep neural network, 2PiLnet, is trained with two-photon volumes and one-photon light fields. Light-field videos of jGCaMP8f-expressing neurons are acquired in neocortical brain slices. 2PiLnet reconstructs volumes with two-photon-like contrast and source confinement from scattered, blurry one-photon light fields from fields-of-view for which no two-photon images are provided. This enables automated segmentation and extraction of calcium signals with high signal-to-noise ratios and reduces optical crosstalk compared to conventional volume reconstruction methods. Imaging 100 volumes per second, we observe putative spikes fired at up to 10 Hz and the spatial intermingling of putative ensembles throughout 530 × 530 × 100 -micron volumes. Compared to iterative algorithms, 2PiLnet workflows reduce light-field video processing times by several-fold, advancing the goal of real-time, scattering-robust volumetric neural circuit imaging for closed-loop and adaptive experimental paradigms.