| Autor: |
Ciba M; Biomems Lab, University of Applied Science Aschaffenburg, 63743 Aschaffenburg, Germany manuel.ciba@th-ab.de., Bestel R; Biomems Lab, University of Applied Science Aschaffenburg, 63743 Aschaffenburg, Germany robert.bestel@mailbox.org., Nick C; Biomems Lab, University of Applied Science Aschaffenburg, 63743 Aschaffenburg, Germany christoph.nick@web.de., de Arruda GF; ISI Foundation, 10126 Turin, Italy gui.f.arruda@gmail.com., Peron T; Institute of Mathematics and Computer Science, University of São Paulo, São Carlos SP 13566-590, Brazil thomaskaue@gmail.com., Henrique CC; Department of Computer Science, Federal University of São Carlos, São Carlos SP 13565-905, Brazil chcomin@gmail.com., Costa LDF; Instituto de Física de São Carlos, University of São Paulo, São Carlos SP 13566-590, Brazil ldfcosta@gmail.com., Rodrigues FA; Institute of Mathematics and Computer Science, University of São Paulo, São Carlos SP 13566-590, Brazil francisco.rodrigues.usp@gmail.com., Thielemann C; Biomems Lab, University of Applied Science Aschaffenburg, 63743 Aschaffenburg, Germany Christiane.Thielemann@th-ab.de. |
| Abstrakt: |
As synchronized activity is associated with basic brain functions and pathological states, spike train synchrony has become an important measure to analyze experimental neuronal data. Many measures of spike train synchrony have been proposed, but there is no gold standard allowing for comparison of results from different experiments. This work aims to provide guidance on which synchrony measure is best suited to quantify the effect of epileptiform-inducing substances (e.g., bicuculline, BIC) in in vitro neuronal spike train data. Spike train data from recordings are likely to suffer from erroneous spike detection, such as missed spikes (false negative) or noise (false positive). Therefore, different timescale-dependent (cross-correlation, mutual information, spike time tiling coefficient) and timescale-independent (Spike-contrast, phase synchronization (PS), A-SPIKE-synchronization, A-ISI-distance, ARI-SPIKE-distance) synchrony measures were compared in terms of their robustness to erroneous spike trains. For this purpose, erroneous spike trains were generated by randomly adding (false positive) or deleting (false negative) spikes (in silico manipulated data) from experimental data. In addition, experimental data were analyzed using different spike detection threshold factors in order to confirm the robustness of the synchrony measures. All experimental data were recorded from cortical neuronal networks on microelectrode array chips, which show epileptiform activity induced by the substance BIC. As a result of the in silico manipulated data, Spike-contrast was the only measure that was robust to false-negative as well as false-positive spikes. Analyzing the experimental data set revealed that all measures were able to capture the effect of BIC in a statistically significant way, with Spike-contrast showing the highest statistical significance even at low spike detection thresholds. In summary, we suggest using Spike-contrast to complement established synchrony measures because it is timescale independent and robust to erroneous spike trains. |
