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

Overview of attention for article published in Source Code for Biology and Medicine, February 2017
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Title
Algorithm and software to automatically identify latency and amplitude features of local field potentials recorded in electrophysiological investigation
Published in
Source Code for Biology and Medicine, February 2017
DOI 10.1186/s13029-017-0062-5
Pubmed ID
Authors

Maria Rubega, Claudia Cecchetto, Stefano Vassanelli, Giovanni Sparacino

Abstract

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. 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. 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.

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Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 27%
Student > Ph. D. Student 4 15%
Student > Bachelor 2 8%
Student > Postgraduate 2 8%
Professor > Associate Professor 2 8%
Other 4 15%
Unknown 5 19%
Readers by discipline Count As %
Engineering 4 15%
Neuroscience 4 15%
Biochemistry, Genetics and Molecular Biology 3 12%
Medicine and Dentistry 3 12%
Psychology 3 12%
Other 3 12%
Unknown 6 23%