Biology subjects

Aonishi, T.

Publications and source records attributed to Aonishi, T..

2 recordsLinked to original sources

bioRxiv2020

Fast scanning high optical invariant two-photon microscopy for monitoring a large neural network activity with cellular resolution

Fast and wide imaging with single-cell resolution, high signal-to-noise ratio and no optical aberration has the potential to open up new avenues of investigation in biology. However, this imaging is challenging because of the inevitable tradeoffs among those parameters. Here, we overcome the tradeoffs by combining a resonant scanning system, a large objective with low magnification and high numerical aperture, and highly sensitive large-aperture photodetectors. The result is a practically aberration-free, fast scanning high optical invariant two-photon microscopy (FASHIO-2PM) that enables calcium imaging from a large network composed of [~]16k neurons at 7.5 Hz in a 9 mm2 contiguous image plane including more than 10 sensory-motor and higher-order regions of the cerebral cortex in awake mice. Through a network analysis based on single-cell activities, we discover that the brain exhibits small-world-ness rather than scale-freeness. FASHIO-2PM will enable revealing biological dynamics by simultaneous monitoring of macroscopic activity and its composing elements.

neuroscience↗

bioRxiv2018

Low Computational-cost Cell Detection Method for Calcium Imaging Data

The rapid progress of calcium imaging has reached a point where the activity of tens of thousands of cells can be recorded simultaneously. However, the huge amount of data in such records makes it difficult to carry out cell detection manually. Consequently, because the cell detection is the first step of multicellular data analysis, there is a pressing need for automatic cell detection methods for large-scale image data. Automatic cell detection algorithms have been pioneered by a handful of research groups. Such algorithms, however, assume a conventional field of view (FOV) (i.e. 512 x 512 pixels) and need a significantly higher computational power for a wider FOV to work within a practical period of time. To overcome this issue, we propose a method called low computational-cost cell detection (LCCD), which can complete its processing even on the latest ultra-large FOV data within a practical period of time. We compared it with two previously proposed methods, constrained non-negative matrix factorization (CNMF) and Suite2P. We found that LCCD makes it possible to detect cells from a huge-amount of high-density imaging data within a shorter period of time and with an accuracy comparable to or better than those of CNMF and Suite2P.

neuroscience↗