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A Day in the Lab: Analysing the Electrical Activity of Neurons using Computer Code

✍️ Author: Dr Eleni Christoforidou

🕒 Approximate reading time: 3 minutes

Today in the lab was primarily a day of analysis, dedicated to the computer, scrutinising the electrical activity of neurons that I had previously recorded during my electrophysiology experiments (you can refer to my previous post here). Today's agenda involved a considerable amount of coding via Matlab software, to compute various parameters like amplitude, frequency, and charge of the neuronal electrical activity. Performing these calculations necessitates a range of intricate mathematical functions, but the procedure is semi-automated thanks to computer code and software specifically designed for this type of analysis. The final product? Intricate graphs visually demonstrating the different components of the data analysis, as depicted below.

Detection of mEPSCs in a recording from a neuron. (A) Sum of two exponentials template (red) and mean event overlay (green). (B) Deconvoluted wave of the recording (blue), detection threshold (green), and detected events (red dots). (C) All-point histogram of the deconvoluted trace (blue bars) fitted with a Gaussian function (red curve). The detection threshold (green) was set to 4x the standard deviation of the Gaussian. (D) Continuous trace of detected mEPSCs (red). (E) Superimposed detected mEPSCs (grey curves) and the median mEPSC (black curve) during a continuous recording, aligned horizontally to the peak of the deconvolution function. (F) Merged events from entire recording wave with the mean ensemble (blue) and fit (red).
Detection of mEPSCs in a recording from a neuron. (A) Sum of two exponentials template (red) and mean event overlay (green). (B) Deconvoluted wave of the recording (blue), detection threshold (green), and detected events (red dots). (C) All-point histogram of the deconvoluted trace (blue bars) fitted with a Gaussian function (red curve). The detection threshold (green) was set to 4x the standard deviation of the Gaussian. (D) Continuous trace of detected mEPSCs (red). (E) Superimposed detected mEPSCs (grey curves) and the median mEPSC (black curve) during a continuous recording, aligned horizontally to the peak of the deconvolution function. (F) Merged events from entire recording wave with the mean ensemble (blue) and fit (red).

An Insight into Today's Data Analysis

Miniature excitatory postsynaptic currents (mEPSCs; essentially the basal electrical activity of neurons) were examined using a deconvolution-based method specifically developed for detecting spontaneous mEPSCs. This approach entails fitting events with a sum of two exponential functions (one each for rise and decay). This template is generated based on the time course of spontaneous synaptic events in the recorded waves, which are then deconvolved from the template using Fourier transformation algorithms. The deconvoluted wave is fitted with a Gaussian function, representing the noise distribution, and the detection threshold is set to four times the standard deviation of this Gaussian. The deconvoluted trace is subsequently scanned for local maxima, which correspond to the detected mEPSCs and their onset times. Subsequently, the median amplitude, frequency, charge, rise time and decay time were calculated for each individual neuron.

Disclaimer: The information provided in this blog post is intended for educational and informational purposes only and should not be considered as a substitute for professional advice. While the author holds a PhD in Neuroscience and has extensive experience in the field, the content of this post may not be fully exhaustive or up-to-date, and in-text citations or a bibliography are not always provided. Readers are encouraged to consult relevant primary research articles, textbooks, and other reliable sources for comprehensive information on the topic. The author is not responsible for any errors or omissions, or for any actions taken based on the information provided in this post.

Published 10 Dec 2018