@article {69957, title = {Compressive streak microscopy for fast sampling of fluorescent reporters of neural activity.}, journal = {Neurophotonics}, volume = {12}, year = {2025}, month = {2025 Apr 12}, pages = {025013}, abstract = {

SIGNIFICANCE: one-photon fluorescence imaging of calcium and voltage indicators expressed in neurons enables noninvasive recordings of neural activity with submillisecond precision. However, data acquisition speed is limited by the frame rate of cameras.

AIM: We developed a compressive streak fluorescence microscope to record fluorescence in individual neurons at high speeds ( frames per second) exceeding the nominal frame rate of the camera by trading off spatial pixels for temporal resolution.

APPROACH: Our microscope leverages a digital micromirror device for targeted illumination, a galvo mirror for temporal scanning, and a ridge regression algorithm for fast computational reconstruction of fluorescence traces with high temporal resolution.

RESULTS: In simulations, the ridge regression algorithm reconstructs traces of high temporal resolution with limited signal loss. Validation experiments with fluorescent beads and experiments in larval zebrafish demonstrate accurate reconstruction with a data compression ratio of 10 and accurate recordings of neural activity with 200- to 400-Hz sampling speeds.

CONCLUSIONS: Our compressive microscopy enables new experimental capabilities to monitor activity at a sampling speed that outpaces the nominal frame rate of the camera.

}, issn = {2329-423X}, doi = {10.1117/1.NPh.12.2.025013}, url = {https://doi.org/10.1117/1.nph.12.2.025013}, author = {Cai, Changjia and Traubert, Owen and Tormes-Vaquerano, Jovan and Eybposh, M Hossein and Turaga, Srinivas C and Rodriguez-Romaguera, Jose and Naumann, Eva A and P{\'e}gard, Nicolas C} } @article {67230, title = {VolPy: Automated and scalable analysis pipelines for voltage imaging datasets.}, journal = {PLoS Computational Biology}, volume = {17}, year = {2021}, month = {2021 Apr 14}, pages = {e1008806}, abstract = {

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{\textquoteright}s performance in spike extraction and scalability are state-of-the-art.

}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1008806}, author = {Cai, Changjia and Friedrich, Johannes and Singh, Amrita and Eybposh, M Hossein and Pnevmatikakis, Eftychios A and Podgorski, Kaspar and Giovannucci, Andrea} }