@article{CibaBestelNicketal.2020, author = {Ciba, Manuel and Bestel, Robert and Nick, Christoph and Ferraz de Arruda, Guilherme and Peron, Thomas and Henrique, Comin C{\´e}sar and da Fontoura Costa, Luciano and Aparecido Rodrigues, Francisco and Thielemann, Christiane}, title = {Comparison of Different Spike Train Synchrony Measures Regarding Their Robustness to Erroneous Data from Bicuculline-Induced Epileptiform Activity}, series = {Neural Computation}, volume = {32}, journal = {Neural Computation}, number = {3}, doi = {10.1162/neco_a_01277}, pages = {1 -- 25}, year = {2020}, abstract = {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.}, subject = {Neuronales Netz}, language = {en} } @article{DeBlasiCibaBahmeretal.2019, author = {De Blasi, Stefano and Ciba, Manuel and Bahmer, Andreas and Thielemann, Christiane}, title = {Total spiking probability edges: A cross-correlation based method for effective connectivity estimation of cortical spiking neurons}, series = {Journal of Neuroscience Methods}, volume = {312}, journal = {Journal of Neuroscience Methods}, number = {312}, doi = {10.1016/j.jneumeth.2018.11.013}, pages = {169 -- 181}, year = {2019}, abstract = {Background: Connectivity is a relevant parameter for the information flow within neuronal networks. Network connectivity can be reconstructed from recorded spike train data. Various methods have been developed to estimate connectivity from spike trains. New method: In this work, a novel effective connectivity estimation algorithm called Total Spiking Probability Edges (TSPE) is proposed and evaluated. First, a cross-correlation between pairs of spike trains is calculated. Second, to distinguish between excitatory and inhibitory connections, edge filters are applied on the resulting cross-correlogram. Results: TSPE was evaluated with large scale in silico networks and enables almost perfect reconstructions (true positive rate of approx. 99\% at a false positive rate of 1\% for low density random networks) depending on the network topology and the spike train duration. A distinction between excitatory and inhibitory connections was possible. TSPE is computational effective and takes less than 3 min on a high-performance computer to estimate the connectivity of an 1 h dataset of 1000 spike trains. Comparison of existing methods: TSPE was compared with connectivity estimation algorithms like Transfer Entropy based methods, Filtered and Normalized Cross-Correlation Histogram and Normalized Cross-Correlation. In all test cases, TSPE outperformed the compared methods in the connectivity reconstruction accuracy. Conclusions: The results show that the accuracy of functional connectivity estimation of large scale neuronal networks has been enhanced by TSPE compared to state of the art methods. Furthermore, TSPE enables the classification of excitatory and inhibitory synaptic effects.}, subject = {Neuronales Netz}, language = {en} } @article{KoehlerWoelfelCibaetal.2018, author = {K{\"o}hler, Tim and W{\"o}lfel, Maximilian and Ciba, Manuel and Bochtler, Ulrich and Thielemann, Christiane}, title = {Terrestrial Trunked Radio (TETRA) exposure of neuronal in vitro networks}, series = {Environmental Research}, volume = {2018}, journal = {Environmental Research}, number = {162}, doi = {10.1016/j.envres.2017.12.007}, pages = {1 -- 7}, year = {2018}, abstract = {Terrestrial Trunked Radio (TETRA) is a worldwide common mobile communication standard, used by authorities and organizations with security tasks. Previous studies reported on health effects of TETRA, with focus on the specific pulse frequency of 17.64 Hz, which affects calcium efflux in neuronal cells. Likewise among others, it was reported that TETRA affects heart rate variability, neurophysiology and leads to headaches. In contrast, other studies conclude that TETRA does not affect calcium efflux of cells and has no effect on people's health. In the present study we examine whether TETRA short- and long-term exposure could affect the electrophysiology of neuronal in vitro networks. Experiments were performed with a carrier frequency of 395 MHz, a pulse frequency of 17.64 Hz and a differential quaternary phase-shift keying (π/4 DQPSK) modulation. Specific absorption rates (SAR) of 1.17 W/kg and 2.21 W/kg were applied. In conclusion, the present results do not indicate any effect of TETRA exposure on electrophysiology of neuronal in vitro networks, neither for short-term nor long-term exposure. This applies to the examined parameters spike rate, burst rate, burst duration and network synchrony.}, subject = {Neuronales Netz}, language = {en} } @article{MayerArrizabalagaLiebetal.2018, author = {Mayer, Margot and Arrizabalaga, Onetsine and Lieb, Florian and Ciba, Manuel and Ritter, Sylvia and Thielemann, Christiane}, title = {Electrophysiological investigation of human embryonic stem cell derived neurospheres using a novel spike detection algorithm}, series = {Biosensors and Bioelectronics}, volume = {2018}, journal = {Biosensors and Bioelectronics}, number = {100}, doi = {10.1016/j.bios.2017.09.034}, pages = {462 -- 468}, year = {2018}, abstract = {Microelectrode array (MEA) technology in combination with three-dimensional (3D) neuronal cell models derived from human embryonic stem cells (hESC) provide an excellent tool for neurotoxicity screening. Yet, there are significant challenges in terms of data processing and analysis, since neuronal signals have very small amplitudes and the 3D structure enhances the level of background noise. Thus, neuronal signal analysis requires the application of highly sophisticated algorithms. In this study, we present a new approach optimized for the detection of spikes recorded from 3D neurospheres (NS) with a very low signal-to-noise ratio. This was achieved by extending simple threshold-based spike detection utilizing a highly sensitive algorithm named SWTTEO. This analysis procedure was applied to data obtained from hESC-derived NS grown on MEA chips. Specifically, we examined changes in the activity pattern occurring within the first ten days of electrical activity. We further analyzed the response of NS to the GABA receptor antagonist bicuculline. With this new algorithm method we obtained more reliable results compared to the simple threshold-based spike detection.}, subject = {Embryonale Stammzelle}, language = {en} } @misc{CibaMayerThielemann2019, author = {Ciba, Manuel and Mayer, Margot and Thielemann, Christiane}, title = {Experimental setup to investigate the effect of psychedelics on in vitro neuronal networks}, doi = {10.6084/m9.figshare.11980434.v1}, year = {2019}, abstract = {Experimental setup to investigate the effect of psychedelics on in vitro neuronal networks: A demonstration of the application of in vitro neuronal networks on high-density-microelectrode arrays (HDMEA) to study electrophysiological properties of neuronal networks in response to psychedelics.}, subject = {Neuronales Netz}, language = {en} } @inproceedings{CibaThielemann2018, author = {Ciba, Manuel and Thielemann, Christiane}, title = {Synchrony changes on different time-scales during in vitro neuronal network development}, year = {2018}, subject = {Neuronales Netz}, language = {en} } @article{CibaIsomuraJimboetal.2017, author = {Ciba, Manuel and Isomura, Takuya and Jimbo, Yasuhiko and Bahmer, Andreas and Thielemann, Christiane}, title = {Spike-contrast: A novel time scale independent and multivariate measure of spike train synchrony}, series = {Journal of Neuroscience Methods}, volume = {2018}, journal = {Journal of Neuroscience Methods}, number = {293}, doi = {10.1016/j.jneumeth.2017.09.008}, pages = {136 -- 143}, year = {2017}, abstract = {Background: Synchrony within neuronal networks is thought to be a fundamental feature of neuronal networks. In order to quantify synchrony between spike trains, various synchrony measures were developed. Most of them are time scale dependent and thus require the setting of an appropriate time scale. Recently, alternative methods have been developed, such as the time scale independent SPIKE-distance by Kreuz et al. New method: In this study, a novel time-scale independent spike train synchrony measure called Spike-contrast is proposed. The algorithm is based on the temporal "contrast" (activity vs. non-activity in certain temporal bins) and not only provides a single synchrony value, but also a synchrony curve as a function of the bin size. Results: For most test data sets synchrony values obtained with Spike-contrast are highly correlated with those of the SPIKE-distance (Spearman correlation value of 0.99). Correlation was lower for data containing multiple time scales (Spearman correlation value of 0.89). When analyzing large sets of data, Spike-contrast performed faster. Comparison of existing method: Spike-contrast is compared to the SPIKE-distance algorithm. The test data consisted of artificial spike trains with various levels of synchrony, including Poisson spike trains and bursts, spike trains from simulated neuronal Izhikevich networks, and bursts made of smaller bursts (sub-bursts). Conclusions: The high correlation of Spike-contrast with the established SPIKE-distance for most test data, suggests the suitability of the proposed measure. Both measures are complementary as SPIKE-distance provides a synchrony profile over time, whereas Spike-contrast provides a synchrony curve over bin size.}, subject = {Neuronales Netz}, language = {en} } @inproceedings{CibaBahmerThielemann2017, author = {Ciba, Manuel and Bahmer, Andreas and Thielemann, Christiane}, title = {Application of spike train synchrony measure Spike‑contrast to quantify the effect of bicuculline on cortical networks grown on microelectrode arrays}, series = {BMC Neuroscience}, volume = {2017}, booktitle = {BMC Neuroscience}, number = {18}, doi = {10.1186/s12868-017-0372-1}, pages = {269}, year = {2017}, subject = {Mikroelektrode}, language = {en} } @article{FlachsCiba2016, author = {Flachs, Dennis and Ciba, Manuel}, title = {Cell-based sensor chip for neurotoxicity measurements in drinking water}, series = {L{\´e}kař a technika - Clinician and Technology}, volume = {46}, journal = {L{\´e}kař a technika - Clinician and Technology}, number = {2}, pages = {46 -- 50}, year = {2016}, abstract = {Our drinking water contains residues of pharmaceuticals. A sub-group of these contaminants are neuro-active substances, the antiepileptic carbamazepine being one of the most relevant. For assessment of the neurotoxicity of this drug at a sub-therapeutic level, a cell-based sensor chip platform has been realized and characterized. For this purpose, a microelectrode array chip was designed and processed in a clean room and optimized in terms of low processing costs and good recording properties. For characterization of the system neuronal cells were plated on microelectrode array chips and electrical activity was measured as a function of applied carbamazepine concentration. We found that the relative spike rate decreased with increasing drug concentration resulted in IC50 values of around 36 μM. This value is five orders of magnitude higher than the maximal dose found in drinking water. IC50 values for burst rate, burst duration and synchrony were slightly higher, suggesting spike rate being a more sensitive parameter to carbamazepine.}, subject = {Mikroelektrode}, language = {en} } @article{AlvesCuryRosteretal.2022, author = {Alves, Caroline and Cury, Rubens G. and Roster, Kirstin and Pineda, Aruane M. and Rodrigues, Francisco A. and Thielemann, Christiane and Ciba, Manuel}, title = {Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments}, series = {PLOS ONE}, volume = {2022}, journal = {PLOS ONE}, number = {12}, doi = {https://doi.org/10.1371/journal. pone.0277257}, pages = {1 -- 26}, year = {2022}, abstract = {Ayahuasca is a blend of Amazonian plants that has been used for traditional medicine by the inhabitants of this region for hundreds of years. Furthermore, this plant has been demon� strated to be a viable therapy for a variety of neurological and mental diseases. EEG experi� ments have found specific brain regions that changed significantly due to ayahuasca. Here, we used an EEG dataset to investigate the ability to automatically detect changes in brain activity using machine learning and complex networks. Machine learning was applied at three different levels of data abstraction: (A) the raw EEG time series, (B) the correlation of the EEG time series, and (C) the complex network measures calculated from (B). Further, at the abstraction level of (C), we developed new measures of complex networks relating to community detection. As a result, the machine learning method was able to automatically detect changes in brain activity, with case (B) showing the highest accuracy (92\%), followed by (A) (88\%) and (C) (83\%), indicating that connectivity changes between brain regions are more important for the detection of ayahuasca. The most activated areas were the frontal and temporal lobe, which is consistent with the literature. F3 and PO4 were the most impor� tant brain connections, a significant new discovery for psychedelic literature. This connec� tion may point to a cognitive process akin to face recognition in individuals during ayahuasca-mediated visual hallucinations. Furthermore, closeness centrality and assorta� tivity were the most important complex network measures. These two measures are also associated with diseases such as Alzheimer's disease, indicating a possible therapeutic mechanism. Moreover, the new measures were crucial to the predictive model and sug� gested larger brain communities associated with the use of ayahuasca. This suggests that the dissemination of information in functional brain networks is slower when this drug is present. Overall, our methodology was able to automatically detect changes in brain activity during ayahuasca consumption and interpret how these psychedelics alter brain networks, as well as provide insights into their mechanisms of action}, subject = {Ayahuasca}, language = {en} }