@article {68973, title = {FIOLA: an accelerated pipeline for fluorescence imaging online analysis.}, journal = {Nature Methods}, volume = {20}, year = {2023}, month = {2023 Sep 01}, pages = {1417-1425}, abstract = {

Optical microscopy methods such as calcium and voltage imaging enable fast activity readout of large neuronal populations using light. However, the lack of corresponding advances in online algorithms has slowed progress in retrieving information about neural activity during or shortly after an experiment. This gap not only prevents the execution of real-time closed-loop experiments, but also hampers fast experiment-analysis-theory turnover for high-throughput imaging modalities. Reliable extraction of neural activity from fluorescence imaging frames at speeds compatible with indicator dynamics and imaging modalities poses a challenge. We therefore developed FIOLA, a framework for fluorescence imaging online analysis that extracts neuronal activity from calcium and voltage imaging movies at speeds one order of magnitude faster than state-of-the-art methods. FIOLA exploits algorithms optimized for parallel processing on GPUs and CPUs. We demonstrate reliable and scalable performance of FIOLA on both simulated and real calcium and voltage imaging datasets. Finally, we present an online experimental scenario to provide guidance in setting FIOLA parameters and to highlight the trade-offs of our approach.

}, keywords = {Algorithms, Calcium, Microscopy, Optical Imaging}, issn = {1548-7105}, doi = {10.1038/s41592-023-01964-2}, author = {Cai, Changjia and Dong, Cynthia and Friedrich, Johannes and Rozsa, Marton and Pnevmatikakis, Eftychios A and Giovannucci, Andrea} } @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} } @article {67280, title = {CaImAn an open source tool for scalable calcium imaging data analysis}, journal = {eLife}, volume = {8}, year = {2019}, month = {01/2019}, abstract = {

Advances in fluorescence microscopy enable monitoring larger brain areas in-vivo with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. We present CaImAn, an open-source library for calcium imaging data analysis. CaImAn\ provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minimal user intervention, with good scalability on computers ranging from laptops to high-performance computing clusters. CaImAn\ is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data. To benchmark the performance of CaImAn\ we collected and combined a corpus of manual annotations from multiple labelers on nine mouse two-photon datasets. We demonstrate that CaImAn\ achieves near-human performance in detecting locations of active neurons

}, doi = {10.7554/eLife.38173}, url = {https://elifesciences.org/articles/38173}, author = {Giovannucci, Andrea and Friedrich, Johannes and Gunn, Pat and Kalfon, J{\'e}r{\'e}mie and Brown, Brandon L and Koay, Sue Ann and Taxidis, Jiannis and Najafi, Farzaneh and Gauthier, Jeffrey L and Zhou, Pengcheng and Khakh, Baljit S and Tank, David W and Chklovskii, Dmitri B and Pnevmatikakis, Eftychios A} } @article {67289, title = {CalmAn: An open source tool for scalable Calcium Imaging data Analysis}, journal = {bioRxiv}, year = {2018}, month = {06/2018}, abstract = {

Advances in fluorescence microscopy enable monitoring larger brain areas in-vivo with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. Here we present CaImAn, an open-source library for calcium imaging data analysis. CaImAn provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minimal user intervention, with good performance on computers ranging from laptops to high-performance computing clusters. CaImAn is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data. To benchmark the performance of CaImAn we collected a corpus of ground truth annotations from multiple labelers on nine mouse two-photon datasets. We demonstrate that CaImAn achieves near-human performance in detecting locations of active neurons.

}, doi = {10.1101/339564}, url = {https://www.biorxiv.org/content/early/2018/06/05/339564}, author = {Giovannucci, Andrea and Friedrich, Johannes and Gunn, Pat and Kalfon, J{\'e}r{\'e}mie and Koay, Sue Ann and Taxidis, Jiannis and Najafi, Farzaneh and Gauthier, Jeffrey L. and Zhou, Pengcheng and Tank, David W. and Chklovskii, Dmitri and Pnevmatikakis, Eftychios A.} } @article {68485, title = {SnapShot: Optical control and imaging of brain activity.}, journal = {Cell}, volume = {149}, year = {2012}, month = {06/2012}, pages = {1650-1650.e2}, abstract = {

No abstract available.

}, keywords = {Animals, Brain, Microscopy, Fluorescence, Molecular Imaging, Optics and Photonics}, issn = {1097-4172}, doi = {10.1016/j.cell.2012.06.009}, author = {Richard Sun, X and Giovannucci, Andrea and Sgro, Allyson E and Wang, Samuel S-H} }