NOSA, an Analytical Toolbox for Multicellular Optical Electrophysiology

Area of Science:

• Neuroscience
• Computational Neuroscience
• Biophysics

Background:

• Simultaneous monitoring of neural electrical activity is crucial for understanding neural network function.
• Genetically encoded voltage indicators (GEVIs) offer cell-type specific optical reporting of neuronal activity.
• Analyzing voltage imaging data is challenging due to low signal-to-noise ratios.

Purpose of the Study:

• To present NOSA (Neuro-Optical Signal Analysis), a novel open-source software for analyzing voltage imaging data.
• To provide analytical solutions for challenges in voltage imaging experiments.
• To facilitate the identification of temporal interactions between neuronal electrical activity patterns.

Main Methods:

• Development of NOSA software with baseline fitting, filtering, and movement correction algorithms.
• Implementation of oscillatory frequency identification and neuronal response parameter quantification.
• Integration of cross-correlation analysis for temporal relation identification between activity patterns.

Main Results:

• NOSA effectively compensates for baseline shifts and extracts electrical patterns from low signal-to-noise recordings.
• The software can identify oscillatory frequencies and quantify neuronal response parameters.
• Cross-correlation analysis in NOSA demonstrates utility in voltage imaging of *Drosophila* and mice.

Conclusions:

• NOSA is a valuable tool for analyzing voltage imaging data, overcoming challenges of low signal-to-noise ratios.
• The software aids in understanding cell-type specific connectivity and functional interactions.
• NOSA facilitates the use of GEVIs to their full potential in neuroscience research.