Verinaz-Jadan, H. | bioRxiv Science

bioRxiv2025

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

Light field microscopy (LFM) enables volumetric, high throughput functional imaging. However, the computational burden and vulnerability to scattering limit LFMs application to neuroscience. We present a light-field strategy for volumetric, scattering-mitigated neural circuit activity monitoring. A physics-based deep neural network, LNet, is trained with two-photon volumes and one-photon light fields. A processing pipeline uses LNet to extract calcium activity from light-field videos of jGCaMP8f-expressing neurons in acute cortical slices. The extracted time series have high signal-to-noise ratios and reduced 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 x 530 x 100-micron volumes. Compared to iterative algorithms, LNet workflows reduce light-field video processing times by 2- to 12-fold, advancing the goal of real-time, scattering-robust volumetric neural circuit imaging for closed-loop and adaptive experimental paradigms.

bioengineering↗

bioRxiv2022

Physics-based Deep Learning for Imaging Neuronal Activity via Two-photon and Light Field Microscopy

Light Field Microscopy (LFM) is an imaging technique that offers the opportunity to study fast dynamics in biological systems due to its rapid 3D imaging rate. In particular, it is attractive to analyze neuronal activity in the brain. Unlike scanning-based imaging methods, LFM simultaneously encodes the spatial and angular information of light in a single snapshot. However, LFM is limited by a trade-off between spatial and angular resolution and is affected by scattering at deep layers in the brain tissue. In contrast, two-photon (2P) microscopy is a point-scanning 3D imaging technique that achieves higher spatial resolution, deeper tissue penetration, and reduced scattering effects. However, point-scanning acquisition limits the imaging speed in 2P microscopy and cannot be used to simultaneously monitor the activity of a large population of neurons. This work introduces a physics-driven deep neural network to image neuronal activity in scattering volume tissues using LFM. The architecture of the network is obtained by unfolding the ISTA algorithm and is based on the observation that the neurons in the tissue are sparse. The deep-network architecture is also based on a novel imaging system modeling that uses a linear convolutional neural network and fits the physics of the acquisition process. To achieve the high-quality reconstruction of neuronal activity in 3D brain tissues from temporal sequences of light field (LF) images, we train the network in a semi-supervised manner using generative adversarial networks (GANs). We use the TdTomato indicator to obtain static structural information of the tissue with the microscope operating in 2P scanning modality, representing the target reconstruction quality. We also use additional functional data in LF modality with GCaMP indicators to train the network. Our approach is tested under adverse conditions: limited training data, background noise, and scattering samples. We experimentally show that our method performs better than model-based reconstruction strategies and typical artificial neural networks for imaging neuronal activity in mammalian brain tissue, considering reconstruction quality, generalization to functional imaging, and reconstruction speed.

neuroscience↗