@article{zhu_grier_tandon_cai_agarwal_giovannucci_kaufman_pandarinath_2022, title={A deep learning framework for inference of single-trial neural population dynamics from calcium imaging with subframe temporal resolution}, ISSN={["1546-1726"]}, DOI={10.1038/s41593-022-01189-0}, abstractNote={In many areas of the brain, neural populations act as a coordinated network whose state is tied to behavior on a millisecond timescale. Two-photon (2p) calcium imaging is a powerful tool to probe such network-scale phenomena. However, estimating the network state and dynamics from 2p measurements has proven challenging because of noise, inherent nonlinearities and limitations on temporal resolution. Here we describe Recurrent Autoencoder for Discovering Imaged Calcium Latents (RADICaL), a deep learning method to overcome these limitations at the population level. RADICaL extends methods that exploit dynamics in spiking activity for application to deconvolved calcium signals, whose statistics and temporal dynamics are quite distinct from electrophysiologically recorded spikes. It incorporates a new network training strategy that capitalizes on the timing of 2p sampling to recover network dynamics with high temporal precision. In synthetic tests, RADICaL infers the network state more accurately than previous methods, particularly for high-frequency components. In 2p recordings from sensorimotor areas in mice performing a forelimb reach task, RADICaL infers network state with close correspondence to single-trial variations in behavior and maintains high-quality inference even when neuronal populations are substantially reduced.}, journal={NATURE NEUROSCIENCE}, author={Zhu, Feng and Grier, Harrison A. and Tandon, Raghav and Cai, Changjia and Agarwal, Anjali and Giovannucci, Andrea and Kaufman, Matthew T. and Pandarinath, Chethan}, year={2022}, month={Nov} }
 @article{liu_lu_villette_gou_colbert_lai_guan_land_lee_assefa_et al._2022, title={Sustained deep-tissue voltage recording using a fast indicator evolved for two-photon microscopy}, volume={185}, ISSN={["1097-4172"]}, DOI={10.1016/j.cell.2022.07.013}, abstractNote={Genetically encoded voltage indicators are emerging tools for monitoring voltage dynamics with cell-type specificity. However, current indicators enable a narrow range of applications due to poor performance under two-photon microscopy, a method of choice for deep-tissue recording. To improve indicators, we developed a multiparameter high-throughput platform to optimize voltage indicators for two-photon microscopy. Using this system, we identified JEDI-2P, an indicator that is faster, brighter, and more sensitive and photostable than its predecessors. We demonstrate that JEDI-2P can report light-evoked responses in axonal termini of Drosophila interneurons and the dendrites and somata of amacrine cells of isolated mouse retina. JEDI-2P can also optically record the voltage dynamics of individual cortical neurons in awake behaving mice for more than 30 min using both resonant-scanning and ULoVE random-access microscopy. Finally, ULoVE recording of JEDI-2P can robustly detect spikes at depths exceeding 400 μm and report voltage correlations in pairs of neurons.}, number={18}, journal={CELL}, author={Liu, Zhuohe and Lu, Xiaoyu and Villette, Vincent and Gou, Yueyang and Colbert, Kevin L. and Lai, Shujuan and Guan, Sihui and Land, Michelle A. and Lee, Jihwan and Assefa, Tensae and et al.}, year={2022}, month={Sep}, pages={3408-+} }
 @article{cai_friedrich_singh_eybposh_pnevmatikakis_podgorski_giovannucci_2021, title={VolPy: Automated and scalable analysis pipelines for voltage imaging datasets}, volume={17}, ISSN={["1553-7358"]}, DOI={10.1371/journal.pcbi.1008806}, abstractNote={Voltage imaging enables monitoring neural activity at sub-millisecond and sub-cellular scale, unlocking the study of subthreshold activity, synchrony, and network dynamics with unprecedented spatio-temporal resolution. However, high data rates (>800MB/s) and low signal-to-noise ratios create bottlenecks for analyzing such datasets. Here we present VolPy, an automated and scalable pipeline to pre-process voltage imaging datasets. VolPy features motion correction, memory mapping, automated segmentation, denoising and spike extraction, all built on a highly parallelizable, modular, and extensible framework optimized for memory and speed. To aid automated segmentation, we introduce a corpus of 24 manually annotated datasets from different preparations, brain areas and voltage indicators. We benchmark VolPy against ground truth segmentation, simulations and electrophysiology recordings, and we compare its performance with existing algorithms in detecting spikes. Our results indicate that VolPy’s performance in spike extraction and scalability are state-of-the-art.}, number={4}, journal={PLOS COMPUTATIONAL BIOLOGY}, author={Cai, Changjia and Friedrich, Johannes and Singh, Amrita and Eybposh, M. Hossein and Pnevmatikakis, Eftychios A. and Podgorski, Kaspar and Giovannucci, Andrea}, year={2021}, month={Apr} }