@article {68665,
	title = {Fast and sensitive GCaMP calcium indicators for imaging neural populations.},
	journal = {Nature},
	year = {2023},
	month = {2023 Mar 15},
	abstract = {<p>Calcium imaging with protein-based indicators is widely used to follow neural activity in intact nervous systems, but current protein sensors report neural activity at timescales much slower than electrical signalling and are limited by trade-offs between sensitivity and kinetics. Here we used large-scale screening and structure-guided mutagenesis to develop and optimize several fast and sensitive GCaMP-type indicators. The resulting {\textquoteright}jGCaMP8{\textquoteright} sensors, based on the calcium-binding protein calmodulin and a fragment of endothelial nitric oxide synthase, have ultra-fast kinetics (half-rise times of 2 ms) and the highest sensitivity for neural activity reported for a protein-based calcium sensor. jGCaMP8 sensors will allow tracking of large populations of neurons on timescales relevant to neural computation.</p>
},
	issn = {1476-4687},
	doi = {10.1038/s41586-023-05828-9},
	author = {Zhang, Yan and Rozsa, Marton and Liang, Yajie and Bushey, Daniel and Wei, Ziqiang and Zheng, Jihong and Reep, Daniel and Broussard, Gerard Joey and Tsang, Arthur and Tsegaye, Getahun and Narayan, Sujatha and Obara, Christopher J and Lim, Jing-Xuan and Patel, Ronak and Zhang, Rongwei and Ahrens, Misha B and Turner, Glenn C and Wang, Samuel S-H and Korff, Wyatt L and Schreiter, Eric R and Svoboda, Karel and Hasseman, Jeremy P and Kolb, Ilya and Looger, Loren L}
}
@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 = {<p>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.</p>
},
	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}
}
