Algorithm and software to automatically identify latency and amplitude features of local field potentials recorded in electrophysiological investigation

Maria Rubega, Claudia Cecchetto, Stefano Vassanelli, Giovanni Sparacino
Department of Information Engineering
Via Gradenigo 6/B
35131 Padova, Italy

e-mail: maria.rubega@gmail.com

Abstract: Background: Local field potentials (LFPs) evoked by sensory stimulation are particularly useful in electrophysiological research. For instance, spike timing and current transmembrane current flow estimated from LFPs recorded in the barrel cortex in rats and mice are exploited to investigate how the brain represents sensory stimuli. Recent improvements in microelectrodes technology enable neuroscientists to acquire a great amount of LFPs during the same experimental session, calling for algorithms for their quantitative automatic analysis. Several computer tools were proposed for LFP analysis, but many of them incorporate algorithms that are not open to inspection or modification/personalization. We present a MATLAB software to automatically detect some important LFP features (latency, amplitude, time-derivative value in the inflection-point) for a quantitative analysis. The software features can be customized by the user according to his/her personal research needs. The incorporated algorithm is based on Phillips-Tikhonov regularization to deal with noise amplification due to ill-conditioning. In particular, its accuracy in the estimation of the features of interest is assessed in a Monte Carlo simulation mimicking the acquisition of LFPs in different SNR (signal-to-noise-ratio) conditions. Then, the algorithm is tested by analyzing a real set of 2500 LFPs recorded in rat, after whisker stimulation, at different depths in the primary somatosensory (S1) cortex, i.e., the region involved in the cortical representation of touch in mammals.
Results: Automatic identification of LFP features by the presented software is easy and fast. As far as accuracy is concerned, error indices from simulated data suggest that the algorithm provides reliable estimates. Indeed, results obtained from LFPs recorded in rat after whisker stimulation are in line with the known sequential activation of the microcircuits of the S1 cortex.
Conclusion: A MATLAB software implementing an algorithm to automatically detect the main LFPs features was presented. Simulated and real case studies showed that the employed algorithm is accurate and robust against measurement noise. The available code can be used as it is, but the reported description of the algorithms allows users to easily modify the code to cope with specific requirements.

Software: The software was developed in MATLAB R2014b. The source code files and a limited set of data to test the software are provided as additional files of the presented paper. To facilitate people who do not want to handle MATLAB code, a GUI is provided to guide the user step-by-step in inserting the information requested during the running of the software. Briefly, the user has to specify:

The path of the folder containing the data.
The name of the .mat file containing the data.
The name of the experiment (used to name the .mat file and .xls file that will contain the extracted features).
The depth of recording in order to name the .xls sheet (thus the .xls file will contain a sheet for each depth of recording referred to the same experiment).
The sampling frequency of the data.
The starting and the finishing time of the window of analysis.
The factor n to possibly decrease the sampling rate (if the sampling frequency of the acquired data is unnecessarily high for the purpose).
The position of the onset between the first maximum and the main negative peak, expressed in the range [0 1]. A latency parameter is computed by subtracting the stimulus onset to the negative peak time.
The minimum distance hypothesized by the experimenter from the onset and the main negative peak.

The .mat file containing the data has to be organized as follows: a matrix, in which each column stands for a single recording, and a time vector in ms. If the data are not in the .mat format, a script to convert .txt file to .mat file is provided. The .txt file has to be structured in columns, in which the first one contains the time vector and the others the amplitudes of the recordings. Eventually, the software displays the results, and produces a .mat file and a .xls file containing the features extracted. This approach lets the user import the results into MATLAB or in Microsoft Excel for a further off-line processing.

Download here software and documentation (last update 01/05/2017)