@article {45064, title = {Virtual finger boosts three-dimensional imaging and microsurgery as well as terabyte volume image visualization and analysis.}, journal = {Nature Communications}, volume = {5}, year = {2014}, month = {2014 Jul 11}, pages = {4342}, abstract = {

Three-dimensional (3D) bioimaging, visualization and data analysis are in strong need of powerful 3D exploration techniques. We develop virtual finger (VF) to generate 3D curves, points and regions-of-interest in the 3D space of a volumetric image with a single finger operation, such as a computer mouse stroke, or click or zoom from the 2D-projection plane of an image as visualized with a computer. VF provides efficient methods for acquisition, visualization and analysis of 3D images for roundworm, fruitfly, dragonfly, mouse, rat and human. Specifically, VF enables instant 3D optical zoom-in imaging, 3D free-form optical microsurgery, and 3D visualization and annotation of terabytes of whole-brain image volumes. VF also leads to orders of magnitude better efficiency of automated 3D reconstruction of neurons and similar biostructures over our previous systems. We use VF to generate from images of 1,107 Drosophila GAL4 lines a projectome of a Drosophila brain.

}, issn = {2041-1723}, doi = {10.1038/ncomms5342}, author = {Peng, Hanchuan and Tang, Jianyong and Xiao, Hang and Bria, Alessandro and Zhou, Jianlong and Butler, Victoria and Zhou, Zhi and Gonzalez-Bellido, Paloma T and Oh, Seung W and Chen, Jichao and Mitra, Ananya and Tsien, Richard W and Zeng, Hongkui and Ascoli, Giorgio A and Iannello, Giulio and Hawrylycz, Michael and Myers, Eugene and Long, Fuhui} } @article {6100, title = {A distance-field based automatic neuron tracing method.}, journal = {BMC Bioinformatics}, volume = {14}, year = {2013}, month = {2013 Mar 12}, pages = {93}, abstract = {

Automatic 3D digital reconstruction (tracing) of neurons embedded in noisy microscopic images is challenging, especially when the cell morphology is complex.

}, issn = {1471-2105}, doi = {10.1186/1471-2105-14-93}, author = {Yang, Jinzhu and Gonzalez-Bellido, Paloma T and Peng, Hanchuan} } @article {5523, title = {Eight pairs of descending visual neurons in the dragonfly give wing motor centers accurate population vector of prey direction.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {110}, year = {2013}, month = {2013 Jan 8}, pages = {696-701}, chapter = {696}, abstract = {

Intercepting a moving object requires prediction of its future location. This complex task has been solved by dragonflies, who intercept their prey in midair with a 95\% success rate. In this study, we show that a group of 16 neurons, called target-selective descending neurons (TSDNs), code a population vector that reflects the direction of the target with high accuracy and reliability across 360{\textdegree}. The TSDN spatial (receptive field) and temporal (latency) properties matched the area of the retina where the prey is focused and the reaction time, respectively, during predatory flights. The directional tuning curves and morphological traits (3D tracings) for each TSDN type were consistent among animals, but spike rates were not. Our results emphasize that a successful neural circuit for target tracking and interception can be achieved with few neurons and that in dragonflies this information is relayed from the brain to the wing motor centers in population vector form.

}, issn = {1091-6490}, doi = {10.1073/pnas.1210489109}, author = {Gonzalez-Bellido, Paloma T and Peng, Hanchuan and Yang, Jinzhu and Georgopoulos, Apostolos P and Olberg, Robert M} }