A New Breakthrough in Neuroinformatics: Research Report on Cadence Tool for Calcium Event Detection

Background Introduction

Calcium imaging technology has revolutionized the study of neuron ensembles, providing researchers with a powerful tool to simultaneously visualize and monitor multiple neuronal activities. Calcium imaging utilizes fluorescent calcium indicators, which emit light when intracellular calcium levels change, closely linked to neuronal activity. By imaging these fluorescence changes, researchers can obtain real-time dynamics of neuron ensembles, thereby studying complex neural circuits and networks.

Although calcium imaging can generate relative fluorescence change δf/f curves, scientists studying neuron ensembles often need to infer calcium events from these continuous δf/f curves to create raster representations of calcium events. To this end, scientists need a tool capable of inferring calcium events from these curves. In this paper, the research team introduces an open-source tool called Cadence, which can semi-automatically detect calcium events from calcium imaging data.

Authors and Source

This paper is authored by Nikolay Aseyev, Anastasia Borodinova, Svetlana Pavlova, Marina Roshchina, Matvey Roshchin, Evgeny Nikitin, and Pavel Balaban, all from the Institute of Higher Nervous Activity and Neurophysiology of RAS, Moscow, Russia. The paper was received on June 22, 2024, and published in the journal Neuroinformatics.

Research Process

Experimental Design and Methods

The research team’s experimental goal was to develop and validate the performance of the Cadence tool. The process included the following steps:

1. Animal Experiments and Cell Culture:

   • Primary Neuron Culture: The experiment was conducted on newborn Wistar rats, involving the extraction and culture of neurons from the entire cortex of rat neonates.
   • Virus Production and Purification: Recombinant adeno-associated virus (AAV2) carrying GCamp6s was used to infect primary neurons to express the fluorescent calcium sensor.

2. Calcium Imaging Experiments:

   • Calcium imaging experiments were conducted under various conditions, including rat primary neuron cultures, brain slices in vitro, and hippocampal neurons in vivo.

3. Development and Testing of the Cadence Tool:

   • The Cadence tool was developed using Python3, employing the PySide6 Qt6 framework for the graphical user interface design.
   • Calcium events were detected from δf/f curves of three different experiments: rat primary neuron cultures, in vivo mouse hippocampal region, and rat brain slice experiments.

Experimental Results

The study used the Cadence tool for calcium event detection and compared it with the Cascade algorithm. The results showed:

1. Primary Neuron Culture and Calcium Imaging:

   • Calcium imaging was performed using the Celena X high-content imaging system, and MP4 video files were generated for analysis.
   • Calcium events were detected using Cadence and compared with the Cascade algorithm, revealing Cadence’s advantage in detecting low-amplitude events.

2. In Vitro Brain Slice Experiments:

   • Imaging was conducted using the LSM 5 Live Confocal Microscope, calculating relative fluorescence changes and analyzing calcium events.
   • The Cadence tool performed well in detecting low-time resolution experimental data.

3. In Vivo Mouse Hippocampal Region Experiments:

   • Mice were placed in an open field for behavioral experiments and calcium imaging data recording.
   • The Cadence tool efficiently detected calcium events across multiple channels while maintaining good accuracy.

Data Processing and Output

The Cadence tool uses the signal.argrelextrema function from the signals module for local maxima detection and adjusts detection quality by setting thresholds and window parameters on the δf/f curves. The study shows that Cadence, compared to the fully automated Cascade algorithm, can identify useless signals in poor-quality channels, thus avoiding false peak detection.

Research Conclusion and Value

The research results clearly indicate that Cadence is a simple, fast, and free open-source tool with significant value for inferring events in calcium imaging experiments. Its semi-automated algorithm effectively avoids subjective bias and incorporates blind design in the analysis process to improve result accuracy. Cadence outperforms complex fully automated programs in channels with low-amplitude events or without valid signals.

Research Highlights

1. Innovation:

   • Developed a simple yet efficient semi-automated calcium event detection tool that excels in low-amplitude event detection.
   • Proposed a method to improve detection quality by adjusting thresholds in noisy data.

2. Application Value:

   • The Cadence tool provides a quick and accurate method for inferring neuronal electrophysiological activities with a user-friendly interface.
   • Its open-source and free nature makes it widely applicable in academia.

Other Valuable Information

The research team also developed a series of codes for Cadence data export for further analysis in advanced neural algorithm environments (e.g., Elephant). Additionally, the results demonstrate the application of the Cadence tool in real data sets (e.g., Celena X, Miniscope, and LSM 5 Live Confocal Microscope experimental data).

Through the detailed experimental design, validation, and result analysis described above, this study demonstrates the important application value of the Cadence tool in the field of neuroinformatics, providing strong support for further research on neuronal calcium imaging data.