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

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

Content Provider	Europe PMC
Author	Rubega, Maria Cecchetto, Claudia Vassanelli, Stefano Sparacino, Giovanni
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. Electronic supplementary material The online version of this article (doi:10.1186/s13029-017-0062-5) contains supplementary material, which is available to authorized users.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC5297145&blobtype=pdf
Volume Number	12
DOI	10.1186/s13029-017-0062-5
PubMed Central reference number	PMC5297145
PubMed reference number	28191033
Journal	Source Code for Biology and Medicine [Source Code Biol Med]
e-ISSN	17510473
Language	English
Publisher	BioMed Central
Publisher Date	2017-02-07
Publisher Place	London
Access Restriction	Open
Rights License	Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. © The Author(s). 2017
Subject Keyword	LFP Automated analysis Rat barrel cortex Whisker stimulation Phillips-Tikhonov regularization Neuroscience
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Health Informatics Information Systems Information Systems and Management

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