Seeds Cleansing CNMF for Spatiotemporal Neural Signals Extraction of Miniscope Imaging Data

TL;DR

This paper introduces sc-CNMF, a novel framework that enhances neural signal extraction from miniscope calcium imaging data by incorporating neural enhancement and seed cleansing modules, outperforming existing methods.

Contribution

The paper presents a new seeds cleansing CNMF method with modules tailored for miniscope data, improving accuracy and stability in neural signal extraction.

Findings

Achieves higher accuracy than existing methods

Provides stable neural activity extraction in noisy data

Outperforms current techniques in benchmark tests

Abstract

Miniscope calcium imaging is increasingly being used to monitor large populations of neuronal activities in freely behaving animals. However, due to the high background and low signal-to-noise ratio of the single-photon based imaging used in this technique, extraction of neural signals from the large numbers of imaged cells automatically has remained challenging. Here we describe a highly accurate framework for automatically identifying activated neurons and extracting calcium signals from the miniscope imaging data, seeds cleansing Constrained Nonnegative Matrix Factorization (sc-CNMF). This sc-CNMF extends the conventional CNMF with two new modules: i) a neural enhancing module to overcome miniscope-specific limitations, and ii) a seeds cleansing module combining LSTM to rigorously select and cleanse the set of seeds for detecting regions-of-interest. Our sc-CNMF yields highly stable and superior performance in analyzing miniscope calcium imaging data compared to existing methods.