@article {69723, title = {The first complete 3D reconstruction and morphofunctional mapping of an insect eye}, year = {2025}, month = {01/2025}, doi = {10.7554/elife.103247.1}, url = {http://dx.doi.org/10.7554/eLife.103247.1}, author = {Makarova, Anastasia A and Chua, Nicholas J and Diakova, Anna V and Desyatirkina, Inna A and Gunn, Pat and Pang, Song and Xu, C Shan and Hess, Harald and Chklovskii, Dmitri B and Polilov, Alexey A} } @article {69629, title = {The first complete 3D reconstruction and morphofunctional mapping of an insect eye}, journal = {bioRxiv}, year = {2024}, month = {2024 Oct 01}, abstract = {

The structure of compound eyes in arthropods has been the subject of many studies revealing important biological principles. However, until recently, these studies were constrained by the two-dimensional nature of available ultrastructural data. Here, by taking advantage of the novel three-dimensional ultrastructural dataset obtained using volume electron microscopy (vEM), we present the first cellular-level reconstruction of the whole compound eye of an insect, the extremely miniaturized parasitoid wasp Megaphragma viggianii. The compound eye of the female M. viggianii consists of 29 ommatidia and contains 478 cells. Despite the almost anucleate brain, all cells of the compound eye possess nuclei. Like in larger insects, the dorsal rim area (DRA) of the M. viggianii eye contains ommatidia that putatively specialize in the polarized light detection as reflected in their corneal and retinal morphology. We report the presence of three {\textquoteright}ectopic{\textquoteright} photoreceptors. Our results offer new insights into the miniaturization of compound eyes and scaling of sensory organs in general.

}, doi = {10.1101/2024.09.30.615804}, url = {https://doi.org/10.1101/2024.09.30.615804}, author = {Makarova, Anastasia A. and Chua, Nicholas J. and Diakova, Anna V. and Desyatirkina, Inna A. and Gunn, Pat and Pang, Song and Xu, C Shan and Hess, Harald F and Chklovskii, Dmitri B. and Polilov, Alexey A.} } @article {69022, title = {A complete reconstruction of the early visual system of an adult insect.}, journal = {Current Biology}, volume = {33}, year = {2023}, month = {2023 Nov 06}, pages = {4611-4623}, abstract = {

For most model organisms in neuroscience, research into visual processing in the brain is difficult because of a lack of high-resolution maps that capture complex neuronal circuitry. The microinsect Megaphragma viggianii, because of its small size and non-trivial behavior, provides a unique opportunity for tractable whole-organism connectomics. We image its whole head using serial electron microscopy. We reconstruct its compound eye and analyze the optical properties of the ommatidia as well as the connectome of the first visual neuropil-the lamina. Compared with the fruit fly and the honeybee, Megaphragma visual system is highly simplified: it has 29 ommatidia per eye and 6 lamina neuron types. We report features that are both stereotypical among most ommatidia and specialized to some. By identifying the "barebones" circuits critical for flying insects, our results will facilitate constructing computational models of visual processing in insects.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2023.09.021}, author = {Chua, Nicholas J and Makarova, Anastasia A and Gunn, Pat and Villani, Sonia and Cohen, Ben and Thasin, Myisha and Wu, Jingpeng and Shefter, Deena and Pang, Song and Xu, C Shan and Hess, Harald F and Polilov, Alexey A and Chklovskii, Dmitri B} } @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.} }