Osanai, H. | Search bioRxiv

bioRxiv2024

Automated cell detection for immediate early gene-expressing neurons using inhomogeneous background subtraction in fluorescent images

Although many methods for automated fluorescent-labeled cell detection have been proposed, not all of them assume a highly inhomogeneous background arising from complex biological structures. Here, we propose an automated cell detection algorithm that accounts for and subtracts the inhomogeneous background by avoiding high-intensity pixels in the blur filtering calculation. Cells were detected by intensity thresholding in the background-subtracted image, and the algorithms performance was tested on NeuN- and c-Fos-stained images in the mouse prefrontal cortex and hippocampal dentate gyrus. In addition, applications in c-Fos positive cell counting and the quantification for the expression level in double-labeled cells were demonstrated. Our method of automated detection after background assumption (ADABA) offers the advantage of high-throughput and unbiased analysis in regions with complex biological structures that produce inhomogeneous background. Highlights- We proposed a method to assume and subtract inhomogeneous background pattern. (79/85) - Cells were automatically detected in the background-subtracted image. (71/85) - The automated detection results corresponded with the manual detection. (73/85) - Detection of IEG positive cells and overlapping with neural marker were demonstrated. (85/85)

neuroscience↗

bioRxiv2022

Extracting electromyographic signals from multi-channel local-field-potentials using independent component analysis without direct muscular recording

Electromyography (EMG) has been commonly used for precise identification of animal behavior. However, they are often not recorded together with in vivo electrophysiology due to the need for additional surgeries and setups and the high risk of mechanical wire disconnection. While independent component analysis (ICA) has been used to reduce noise from field potential data, there has been no attempt to proactively use the removed "noise", of which EMG signals are thought to be one of the major sources. Here, we demonstrate that EMG signals can be reconstructed without direct EMG recording, using the "noise" ICA component from local field potentials. The extracted component is highly correlated with directly measured EMG, termed as IC-EMG. IC-EMG is useful for measuring an animals sleep/wake, freezing response and NREM/REM sleep states consistently with actual EMG. Our method has advantages in precise and long-term behavioral measurement in wide-ranged in vivo electrophysiology experiments. Highlights- EMG signals can be extracted from LFP signals without direct muscular recording - The extracted signal is highly correlated with direct EMG recording signals - The extracted signal is useful in measuring animal behaviors as well as actual EMG - This method contributes to precise and stable long-term behavior measurement In briefOsanai et al. demonstrate electromyography (EMG) signals can be extracted from multi-channel local field potential (LFP) recordings using blind-source-separation technique without direct measurement of muscle activity. The proposed method adds precise and long-term behavioral measurements with EMG information in wide-ranged in vivo electrophysiology experiments.

neuroscience↗