@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} } @article{MayerThielemannCibaetal.2018, author = {Mayer, Margot and Thielemann, Christiane and Ciba, Manuel and Lieb, Florian and Ritter, Sylvia}, 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 = {https://doi.org/10.1016/j.bios.2017.09.034}, pages = {462 -- 468}, year = {2018}, subject = {Embryonale Stammzelle}, language = {en} } @article{BouilletCibaAlvesetal.2022, author = {Bouillet, Thomas and Ciba, Manuel and Alves, Caroline and Rodrigues, Francisco A. and Thielemann, Christiane and Colin, Morvane and Bu{\´e}e, Luc and Halliez, Sophie}, title = {Revisiting the involvement of tau in complex neural network remodeling: analysis of the extracellular neuronal activity in organotypic brain slice co-cultures}, series = {Journal of Neural Engineering}, volume = {2022}, journal = {Journal of Neural Engineering}, number = {November}, doi = {DOI 10.1088/1741-2552/aca261}, pages = {1 -- 2}, year = {2022}, abstract = {Objective: Tau ablation has a protective effect in epilepsy due to inhibition of the hyperexcitability/hypersynchrony. Protection may also occur in transgenic models of Alzheimer's disease by reducing the epileptic activity and normalizing the excitation/inhibition imbalance. However, it is difficult to determine the exact functions of tau, because tau knockout (tauKO) brain networks exhibit elusive phenotypes. In this study, we aimed to further explore the physiological role of tau using brain network remodeling. Approach: The effect of tau ablation was investigated in hippocampal-entorhinal slice co-cultures during network remodeling. We recorded the spontaneous extracellular neuronal activity over two weeks in single-slice cultures and co-cultures from control and tauKO mice. We compared the burst parameters and applied concepts and analytical tools intended for the analysis of the network synchrony and connectivity. Main results: Comparison of the control and tauKO co-cultures revealed that tau ablation had an anti-synchrony effect on the hippocampal-entorhinal two-slice networks at late stages of culture, in line with the literature. Differences were also found between the single-slice and co-culture conditions, which indicated that tau ablation had differential effects at the sub-network scale. For instance, tau ablation was found to have an anti-synchrony effect on the co-cultured hippocampal slices throughout the culture, possibly due to a reduction in the excitation/inhibition ratio. Conversely, tau ablation led to increased synchrony in the entorhinal slices at early stages of the co-culture, possibly due to homogenization of the connectivity distribution. Significance: The new methodology presented here proved useful for investigating the role of tau in the remodeling of complex brain-derived neural networks. The results confirm previous findings and hypotheses concerning the effects of tau ablation on neural networks. Moreover, the results suggest, for the first time, that tau has multifaceted roles that vary in different brain sub-networks.}, subject = {Neuronales Netz}, language = {de} } @phdthesis{Ciba2021, author = {Ciba, Manuel}, title = {Synchrony Measurement and Connectivity Estimation of Parallel Spike Trains from in vitro Neuronal Networks}, doi = {https://doi.org/10.25972/OPUS-22364}, school = {Technische Hochschule Aschaffenburg}, year = {2021}, subject = {Neuronales Netz}, language = {en} } @article{AlvesCibadeOToutainetal.2024, author = {Alves, Caroline and Ciba, Manuel and de O Toutain, Thaise GL and Moura Porto, Joel Augusto and de Sena, Eduardo Pond{\´e} and Thielemann, Christiane and Rodrigues, Francisco A}, title = {On the advances in machine learning and complex network measures to an EEG dataset from DMT experiments}, series = {Journal of Physics: Complexity}, volume = {2024}, journal = {Journal of Physics: Complexity}, number = {1}, doi = {10.1088/2632-072X/ad1c68}, pages = {1 -- 2}, year = {2024}, abstract = {There is a growing interest in the medical use of psychedelic substances, as preliminary studies using them for psychiatric disorders have shown positive results. In particular, one of these substances is N, N-dimethyltryptamine (DMT), an agonist serotonergic psychedelic that can induce profound alterations in the state of consciousness. In this work, we use an exploratory tool to reveal DMT-induced changes in brain activity using EEG data and provide new insights into the mechanisms of action of this psychedelic substance. We used a two-class classification based on (A) the connectivity matrix or (B) complex network measures derived from it as input to a support vector machine. We found that both approaches could detect changes in the brain's automatic activity, with case (B) showing the highest AUC (89\%), indicating that complex network measurements best capture the brain changes that occur due to DMT use. In the second step, we ranked the features that contributed the most to this result. For case (A), we found that differences in the high alpha, low beta, and delta frequency bands were most important in distinguishing between the state before and after DMT inhalation, which is consistent with the results described in the literature. Further, the connection between the temporal (TP8) and central cortex (C3) and between the precentral gyrus (FC5) and the lateral occipital cortex (P8) contributed most to the classification result. The connection between regions TP8 and C3 has been found in the literature associated with finger movements that might have occurred during DMT consumption. However, the connection between cortical areas FC5 and P8 has not been found in the literature and is presumably related to the volunteers' emotional, visual, sensory, perceptual, and mystical experiences during DMT consumption. For case (B), closeness centrality was the most crucial complex network measure. Furthermore, we discovered larger communities and longer average path lengths when DMT was used and the converse when not, showing that the balance between functional segregation and integration had been disrupted. These findings support the idea that cortical brain activity becomes more entropic under psychedelics. Overall, a robust computational workflow has been developed here with interpretability of how DMT (or other psychedelics) modify brain networks and insights into their mechanism of action. Finally, the same methodology applied here may help interpret EEG time series from patients who consumed other psychedelic drugs.}, subject = {Neuronales Netz}, language = {en} }