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Light-field deep learning enables high-throughput, scattering-mitigated calcium imaging
File(s)
Author(s)
Howe, Carmel L
Zhao, Kate LY
Verinaz-Jadan, Herman
Song, Pingfan
Barnes, Samuel J
Type
Journal Article
Abstract
Light-field microscopy (LFM) enables high-throughput functional imaging byscanlessly encoding entire volumes in single snapshots. However, LFM’s computationalburden and vulnerability to scattering limit its application to biological imaging. Wepresent a light-field strategy for volumetric, scattering-mitigated neural circuit activitymonitoring. A physics-based deep neural network, 2PiLnet, is trained with two-photonvolumes and one-photon light fields. Light-field videos of jGCaMP8f-expressingneurons are acquired in neocortical brain slices. 2PiLnet reconstructs volumes with two-photon-like contrast and source confinement from scattered, blurry one-photon lightfields from fields-of-view for which no two-photon images are provided. This enablesautomated segmentation and extraction of calcium signals with high signal-to-noiseratios and reduces optical crosstalk compared to conventional volume reconstructionmethods. Imaging 100 volumes per second, we observe putative spikes fired at up to10 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 experimentalparadigms
Date Issued
2025-12-02
Date Acceptance
2025-10-21
Citation
Proceedings of the National Academy of Sciences, 2025, 122 (48)
URI
URL
DOI
ISSN
0027-8424
Publisher
Proceedings of the National Academy of Sciences
Journal / Book Title
Proceedings of the National Academy of Sciences
Volume
122
Issue
48
Copyright Statement
© 2025 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/41289378
Subjects
calcium imaging
deep learning
light-field microscopy
neural circuits
two-photon imaging
Deep Learning
Calcium
Animals
Neurons
Mice
Neocortex
Image Processing, Computer-Assisted
Signal-To-Noise Ratio
Publication Status
Published
Coverage Spatial
United States
Article Number
e2510337122
Date Publish Online
2025-11-25