Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments.

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 demonstrated to be a viable therapy for a variety of neurological and mental diseases. EEG experiments 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 important brain connections, a significant new discovery for psychedelic literature. This connection may point to a cognitive process akin to face recognition in individuals during ayahuasca-mediated visual hallucinations. Furthermore, closeness centrality and assortativity 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 suggested 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.

Revisiting the involvement of tau in complex neural network remodeling: analysis of the extracellular neuronal activity in organotypic brain slice co-cultures.

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 2 weeks in single-slice cultures and co-cultures from control andtauKOmice. We compared the burst activity and applied concepts and analytical tools intended for the analysis of the network synchrony and connectivity.Main results.Comparison of the control andtauKOco-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.

Novel in vitro assay to investigate radiation induced changes in the functionality of human embryonic stem cell-derived neurospheres.

Ionizing radiation (IR) is increasingly used for diagnostics and therapy of severe brain diseases. However, IR also has adverse effects on the healthy brain tissue, particularly on the neuronal network. This is true for adults but even more pronounced in the developing brain of unborn and pediatric patients. Epidemiological studies on children receiving radiotherapy showed an increased risk for cognitive decline ranging from mild deficits in academic functioning to severe late effects in intellectual ability and language as a consequence of altered neuronal development and connectivity. To provide a comprehensive approach for the analysis of radiation-induced alterations in human neuronal functionality, we developed an in vitro assay by combining microelectrode array (MEA) analyses and human embryonic stem cell (hESC) derived three-dimensional neurospheres (NS). In our proof of principle study, we irradiated hESC with 1 Gy X-rays and let them spontaneously differentiate into neurons within NS. After the onset of neuronal activity, we recorded and analyzed the activity pattern of the developing neuronal networks. The network activity in NS derived from irradiated hESC was significantly reduced, whereas no differences in molecular endpoints such as cell proliferation and transcript or protein expression analyses were found. Thus, the combination of MEA analysis with a 3D model for neuronal functionality revealed radiation sequela that otherwise would not have been detected. We therefore strongly suggest combining traditional biomolecular methods with the new functional assay presented in this work to improve the risk assessment for IR-induced effects on the developing brain.

Comparison of Different Spike Train Synchrony Measures Regarding Their Robustness to Erroneous Data From Bicuculline-Induced Epileptiform Activity.

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.

Total spiking probability edges: A cross-correlation based method for effective connectivity estimation of cortical spiking neurons.

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.

Spike-contrast: A novel time scale independent and multivariate measure of spike train synchrony.

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.

Electrophysiological investigation of human embryonic stem cell derived neurospheres using a novel spike detection algorithm.

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.

Terrestrial Trunked Radio (TETRA) exposure of neuronal in vitro networks.

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 17.64Hz, 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 395MHz, a pulse frequency of 17.64Hz and a differential quaternary phase-shift keying (π/4 DQPSK) modulation. Specific absorption rates (SAR) of 1.17W/kg and 2.21W/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.