Objective Thresholding of MEA Data for Action Potential Detection
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1
Tampere University of Technology, Computational Biophysics and Imaging Group, Finland
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2
Tampere University of Technology, Computational Biophysics and Imaging Group, Finland
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3
Tampere University of Technology, Computational Biophysics and Imaging Group, Finland
Motivation
Thresholding is probably the most used neuronal action potential detection method [1-4] for MEA data, and thresholds (THs) are often set by convention. Spike time stamps are sometimes the only input to subsequent analysis (c.f., [5]). Here, we propose a fully automatic and objective TH setting method based on spike count histogram features, and demonstrate it with MEA measurements from neuronal cultures.
Material and Methods
Dissociated mouse cortical cells (A15586, Gibco, Thermo Fisher) were plated on poly-L-lysine and laminin coated MEAs (60MEA200/30iR-Ti, Multi Channel Systems MCS). Recordings were made with a MEA60 USB system (MCS) regularly from 4 to 29 days in vitro.
The proposed method: (1) MEA signal is high pass filtered (stop band cutoff 150 Hz) to remove baseline fluctuations and line noise. (2) A spike count histogram is formed using 500 amplitude bins between the maximum and minimum of the signal. (3) Gradient of the histogram is calculated and (4) smoothed by a moving averager. (5) Local minima and maxima of the smoothed gradient are found. (6) Negative TH is the local minimum of the smoothed gradient closest to the global maximum (GM) at amplitude lower than GM (c.f. Fig. 1B). Positive TH is the local maximum of the gradient closes to GM at amplitude higher than GM. (7) If a TH is not found, it is set symmetrically if possible, or as per convention, and a warning is displayed.
Results
Here for the first time, the method is demonstrated with MEA measurements from dissociated neuronal cell cultures. For a continuously spiking signal (Fig. 1A), the method found THs at approx. –22 µV and 21 µV, corresponding to –5.2 and 5.1 times the standard deviation of the signal (STD), respectively, alike an expert might have set THs. For a highly noisy MEA signal with no detectable action potentials the positive TH was set beyond noise at 18 µV (4.4 times STD) (Fig. 1C). Negative TH was not found and the signal minimum at –17 µV (–4.1 times STD) was used at the negative TH. An expert operator could probably agree with THs. For a bursting signal (Fig. 1E), the method found only negative TH at –36 µV (–5.3 times STD) and used a symmetric positive TH.
A Matlab function implementation of the method is provided freely in Matlab Central File Exchange (http://www.mathworks.com/matlabcentral/fileexchange/55227-autothreshold-signal-smplfreq-varargin-). Compared to the earlier versions of the function [6,7], the implementation described here includes also baseline fluctuation removal, additional checks on found THs, setting THs as per convention if needed, and bug fixes. The default parameters can be found in the function.
Discussion
Our method finds appropriate THs in many cases. Next version will include automatic signal segmentation for TH analysis to overcome the phenomenon seen in Fig. 1E. It is to be noted that function parameters (e.g., segment and averager lengths) may sometimes greatly affect the outcome; thus, input signal based parameter adaptation will be developed.
Conclusion
A fully automatic objective method to set neuronal action potential spike detection THs was proposed and demonstrated.
References
[1] M. S. Lewicki, “A review of methods for spike sorting: the detection and classification of neural action potentials,“ Netw. Comput. Neural Syst., vol. 9, no. 4, 1998, pp. R53-R78. http://dx.doi.org/10.1088/0954-898X_9_4_001
[2] M. Aghagolzadeh, A. Mohebi, and K. G. Oweiss, “Sorting and tracking neuronal spikes via simple thresholding,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 22, no. 4, July 2014, pp. 858-869. http://dx.doi.org/10.1109/TNSRE.2013.2289918
[3] E. Biffi, D. Ghezzi, A. Pedrocchi, and G. Ferrigno, “Development and validation of a spike detection and classification algorithm aimed at implementation on hardware devices,” Comput. Intell. Neurosci., vol. 2010, article 659050. http://dx.doi.org/10.1155/2010/659050
[4] S. B. Wilson and R. Emerson, “Spike detection: a review and comparison of algorithms,” Clin. Neurophysiol., vol. 113, no. 12, Dec. 2002, pp. 1873-1881. http://dx.doi.org/10.1016/S1388-2457(02)00297-3
[5] F. E. Kapucu, J. M. A. Tanskanen, J. E Mikkonen, L. Ylä-Outinen, S. Narkilahti, and J. A. K. Hyttinen, “Burst analysis tool for developing neuronal networks exhibiting highly varying action potential dynamics,” Front. Comput. Neurosci., vol. 6, article 38, June 2012. http://dx.doi.org/10.3389/fncom.2012.00038
[6] J. M. A. Tanskanen, F. E. Kapucu, and J. A. K. Hyttinen, “On the threshold based neuronal spike detection, and an objective criterion for setting the threshold,” in Proc. 7th Ann. Int. IEEE EMBS Conf. Neural Engineering, Montpellier, France, Apr. 2015, pp. 1016-1019. http://dx.doi.org/10.1109/NER.2015.7146799
[7] J. M. A. Tanskanen, F. E. Kapucu, I. Vornanen, and J. A. K. Hyttinen, “Automatic objective thresholding to Detect Neuronal Action Potentials,“ submitted for publication in Proc. 24th European Signal Processing Conference, Aug.-Sept. 2016, Budapest, Hungary.
Figure Legend
Fig. 1. MEA signals with the found THs for exemplary cases: (A) continuously spiking signal, (C) noisy signal without detectable spikes, and (E) bursting, and (B), (D), and (F) the respectively corresponding spike count histograms (black) and their gradients (red), and the found thresholds (purple and green).
Acknowledgements
The work of JMAT has been supported by Jane and Aatos Erkko Foundation, Finland, under the project Biological Neuronal Communications and Computing with ICT. The work of FEK has been supported by the Academy of Finland under the project Bio-integrated Software Development for Adaptive Sensor Networks, project number 278882, by Human Spare Parts Project funded by Tekes – the Finnish Funding Agency for Innovation, and by Ella and Georg Ehrnrooth Foundation, Finland. The works of IV and KL have been supported by Human Spare Parts 2 Project funded by Tekes – the Finnish Funding Agency for Innovation. The works of JMAT, FEK, IV, and KL, have also been supported by the 3DNeuroN project in the European Union's Seventh Framework Programme, Future and Emerging Technologies, grant agreement n°296590.
Keywords:
Spike Detection,
thresholding,
Action potential detection,
neuronal action potential
Conference:
MEA Meeting 2016 |
10th International Meeting on Substrate-Integrated Electrode Arrays, Reutlingen, Germany, 28 Jun - 1 Jul, 2016.
Presentation Type:
Poster Presentation
Topic:
MEA Meeting 2016
Citation:
A. Tanskanen
JM,
Kapucu
FE,
Vornanen
I,
Lenk
K and
Hyttinen
J
(2016). Objective Thresholding of MEA Data for Action Potential Detection.
Front. Neurosci.
Conference Abstract:
MEA Meeting 2016 |
10th International Meeting on Substrate-Integrated Electrode Arrays.
doi: 10.3389/conf.fnins.2016.93.00028
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Received:
22 Jun 2016;
Published Online:
24 Jun 2016.
*
Correspondence:
Dr. Jari Hyttinen, Tampere University of Technology, Computational Biophysics and Imaging Group, Tampere, Finland, jari.hyttinen@tuni.fi