Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia.

Objective. Schizophrenia(SCZ) is a severe mental disorder associated with persistent or recurrent psychosis, hallucinations, delusions, and thought disorders that affect approximately 26 million people worldwide, according to the World Health Organization. Several studies encompass machine learning (ML) and deep learning algorithms to automate the diagnosis of this mental disorder. Others study SCZ brain networks to get new insights into the dynamics of information processing in individuals suffering from the condition. In this paper, we offer a rigorous approach with ML and deep learning techniques for evaluating connectivity matrices and measures of complex networks to establish an automated diagnosis and comprehend the topology and dynamics of brain networks in SCZ individuals.Approach.For this purpose, we employed an functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) dataset. In addition, we combined EEG measures, i.e. Hjorth mobility and complexity, with complex network measurements to be analyzed in our model for the first time in the literature.Main results.When comparing the SCZ group to the control group, we found a high positive correlation between the left superior parietal lobe and the left motor cortex and a positive correlation between the left dorsal posterior cingulate cortex and the left primary motor. Regarding complex network measures, the diameter, which corresponds to the longest shortest path length in a network, may be regarded as a biomarker because it is the most crucial measure in different data modalities. Furthermore, the SCZ brain networks exhibit less segregation and a lower distribution of information. As a result, EEG measures outperformed complex networks in capturing the brain alterations associated with SCZ.Significance. Our model achieved an area under receiver operating characteristic curve (AUC) of 100% and an accuracy of 98.5% for the fMRI, an AUC of 95%, and an accuracy of 95.4% for the EEG data set. These are excellent classification results. Furthermore, we investigated the impact of specific brain connections and network measures on these results, which helped us better describe changes in the diseased brain.

Diagnosis of autism spectrum disorder based on functional brain networks and machine learning.

Autism is a multifaceted neurodevelopmental condition whose accurate diagnosis may be challenging because the associated symptoms and severity vary considerably. The wrong diagnosis can affect families and the educational system, raising the risk of depression, eating disorders, and self-harm. Recently, many works have proposed new methods for the diagnosis of autism based on machine learning and brain data. However, these works focus on only one pairwise statistical metric, ignoring the brain network organization. In this paper, we propose a method for the automatic diagnosis of autism based on functional brain imaging data recorded from 500 subjects, where 242 present autism spectrum disorder considering the regions of interest throughout Bootstrap Analysis of Stable Cluster map. Our method can distinguish the control group from autism spectrum disorder patients with high accuracy. Indeed the best performance provides an AUC near 1.0, which is higher than that found in the literature. We verify that the left ventral posterior cingulate cortex region is less connected to an area in the cerebellum of patients with this neurodevelopment disorder, which agrees with previous studies. The functional brain networks of autism spectrum disorder patients show more segregation, less distribution of information across the network, and less connectivity compared to the control cases. Our workflow provides medical interpretability and can be used on other fMRI and EEG data, including small data sets.

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.

Influence of Neuronal Morphology on the Shape of Extracellular Recordings With Microelectrode Arrays: A Finite Element Analysis.

OBJECTIVE: Measuring neuronal cell activity using microelectrode arrays reveals a great variety of derived signal shapes within extracellular recordings. However, possible mechanisms responsible for this variety have not yet been entirely determined, which might hamper any subsequent analysis of the recorded neuronal data. METHODS: To investigate this issue, we propose a computational model based on the finite element method describing the electrical coupling between an electrically active neuron and an extracellular recording electrode in detail. This allows for a systematic study of possible parameters that may play an essential role in defining or altering the shape of the measured electrode potential. RESULTS: Our results indicate that neuronal geometry, neurite structure, as well as the actual pathways of input potentials that evoke action potential generation, have a significant impact on the shape of the resulting extracellular electrode recording and explain most of the known variations of signal shapes. CONCLUSION: The presented models offer a comprehensive insight into the effect of geometrical and morphological factors on the resulting electrode signal. SIGNIFICANCE: Computational modeling complemented with experimental measurements shows much promise to yield meaningful insights into the electrical activity of a neuronal network.

Modelling and Measurement of Magnetically Soft Nanowire Arrays for Sensor Applications.

Soft magnetic wires and microwires are currently used for the cores of magnetic sensors. Due to their low demagnetization, they contribute to the high sensitivity and the high spatial resolution of fluxgates, Giant Magnetoimpedance (GMI), and inductive sensors. The arrays of nanowires can be prepared by electrodeposition into predefined pores of a nanoporous polycarbonate membrane. While high coercivity arrays with square loops are convenient for information storage and for bistable sensors such as proximity switches, low coercivity cores are needed for linear sensors. We show that coercivity can be controlled by the geometry of the array: increasing the diameter of nanowires (20 µm in length) from 30 nm to 200 nm reduced the coercivity by a factor of 10, while the corresponding decrease in the apparent permeability was only 5-fold. Finite element simulation of nanowire arrays is important for sensor development, but it is computationally demanding. While an array of 2000 wires can be still modelled in 3D, this is impossible for real arrays containing millions of wires. We have developed an equivalent 2D model, which allows us to solve these large arrays with acceptable accuracy. Using this tool, we have shown that as a core of magnetic sensors, nanowires are efficiently employed only together with microcoils with diameter comparable to the nanowire length.

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.

Transparent poly(3,4-ethylenedioxythiophene)-based microelectrodes for extracellular recording.

It is well known that at the interface between neuronal tissue and recording electrode low electrical impedance is required. However, if simultaneous optical detection or stimulation is an issue, good optical transmittance of the electrode material is desirable as well. State-of-the-art titanium nitride electrodes provide superior low impedance compared to gold or iridium, but are nontransparent. Transparent electrode materials like the transparent conducting oxide, indium tin oxide (ITO), or graphene offer high light transmittance (>80%) but reveal relatively high impedance. In this paper, the authors propose the conducting polymer poly(3,4-ethylenedioxythiophene) with the counter ion NO3- as the electrode material for low impedance and good optical transmittance properties. The polymer is electrochemically deposited onto ITO improving the relatively high impedance of ITO. This multilayer electrode allows not only for electrophysiological recordings of cardiomyocytes but also for monitoring of cell contraction under the microscope. Electrochemical impedance spectroscopy and action potential recordings reveal that the new transparent electrodes are a good compromise in terms of low impedance and transparency if deposition parameters are optimized.

High LET radiation shows no major cellular and functional effects on primary cardiomyocytes in vitro.

It is well known that ionizing radiation causes adverse effects on various mammalian tissues. However, there is little information on the biological effects of heavy ion radiation on the heart. In order to fill this gap, we systematically examined DNA-damage induction and repair, as well as proliferation and apoptosis in avian cardiomyocyte cultures irradiated with heavy ions such as titanium and iron, relevant for manned space-flight, and carbon ions, as used for radiotherapy. Further, and to our knowledge for the first time, we analyzed the effect of heavy ion radiation on the electrophysiology of primary cardiomyocytes derived from chicken embryos using the non-invasive microelectrode array (MEA) technology. As electrophysiological endpoints beat rate and field action potential duration were analyzed. The cultures clearly exhibited the capacity to repair induced DNA damage almost completely within 24 h, even at doses of 7 Gy, and almost completely recovered from radiation-induced changes in proliferative behavior. Interestingly, no significant effects on apoptosis could be detected. Especially the functionality of primary cardiac cells exhibited a surprisingly high robustness against heavy ion radiation, even at doses of up to 7 Gy. In contrast to our previous study with X-rays the beat rate remained more or less unaffected after heavy ion radiation, independently of beam quality. The only change we could observe was an increase of the field action potential duration of up to 30% after titanium irradiation, diminishing within the following three days. This potentially pathological observation may be an indication that heavy ion irradiation at high doses could bear a long-term risk for cardiovascular disease induction.

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.

A stationary wavelet transform and a time-frequency based spike detection algorithm for extracellular recorded data.

OBJECTIVE: Spike detection from extracellular recordings is a crucial preprocessing step when analyzing neuronal activity. The decision whether a specific part of the signal is a spike or not is important for any kind of other subsequent preprocessing steps, like spike sorting or burst detection in order to reduce the classification of erroneously identified spikes. Many spike detection algorithms have already been suggested, all working reasonably well whenever the signal-to-noise ratio is large enough. When the noise level is high, however, these algorithms have a poor performance. APPROACH: In this paper we present two new spike detection algorithms. The first is based on a stationary wavelet energy operator and the second is based on the time-frequency representation of spikes. Both algorithms are more reliable than all of the most commonly used methods. MAIN RESULTS: The performance of the algorithms is confirmed by using simulated data, resembling original data recorded from cortical neurons with multielectrode arrays. In order to demonstrate that the performance of the algorithms is not restricted to only one specific set of data, we also verify the performance using a simulated publicly available data set. We show that both proposed algorithms have the best performance under all tested methods, regardless of the signal-to-noise ratio in both data sets. SIGNIFICANCE: This contribution will redound to the benefit of electrophysiological investigations of human cells. Especially the spatial and temporal analysis of neural network communications is improved by using the proposed spike detection algorithms.

Long-term electromagnetic exposure of developing neuronal networks: A flexible experimental setup.

Neuronal networks in vitro are considered one of the most promising targets of research to assess potential electromagnetic field induced effects on neuronal functionality. A few exposure studies revealed there is currently no evidence of any adverse health effects caused by weak electromagnetic fields. Nevertheless, some published results are inconsistent. Particularly, doubts have been raised regarding possible athermal biological effects in the young brain during neuronal development. Therefore, we developed and characterized a flexible experimental setup based on a transverse electromagnetic waveguide, allowing controlled, reproducible exposure of developing neuronal networks in vitro. Measurement of S-parameters confirmed very good performance of the Stripline in the band of 800-1000 MHz. Simulations suggested a flexible positioning of cell culture dishes throughout a large exposure area, as specific absorption rate values were quite independent of their position (361.7 ± 11.4 mW/kg) at 1 W, 900 MHz. During exposure, thermal drift inside cellular medium did not exceed 0.1 K. Embryonic rat cortical neurons were cultivated on microelectrode array chips to non-invasively assess electrophysiological properties of electrogenic networks. Measurements were taken for several weeks, which attest to the experimental setup being a reliable system for long-term studies on developing neuronal tissue.

Growing neuronal islands on multi-electrode arrays using an accurate positioning-µCP device.

BACKGROUND: Multi-electrode arrays (MEAs) allow non-invasive multi-unit recording in-vitro from cultured neuronal networks. For sufficient neuronal growth and adhesion on such MEAs, substrate preparation is required. Plating of dissociated neurons on a uniformly prepared MEA's surface results in the formation of spatially extended random networks with substantial inter-sample variability. Such cultures are not optimally suited to study the relationship between defined structure and dynamics in neuronal networks. To overcome these shortcomings, neurons can be cultured with pre-defined topology by spatially structured surface modification. Spatially structuring a MEA surface accurately and reproducibly with the equipment of a typical cell-culture laboratory is challenging. NEW METHOD: In this paper, we present a novel approach utilizing micro-contact printing (µCP) combined with a custom-made device to accurately position patterns on MEAs with high precision. We call this technique AP-µCP (accurate positioning micro-contact printing). COMPARISON WITH EXISTING METHODS: Other approaches presented in the literature using µCP for patterning either relied on facilities or techniques not readily available in a standard cell culture laboratory, or they did not specify means of precise pattern positioning. CONCLUSION: Here we present a relatively simple device for reproducible and precise patterning in a standard cell-culture laboratory setting. The patterned neuronal islands on MEAs provide a basis for high throughput electrophysiology to study the dynamics of single neurons and neuronal networks.

Electrophysiologic and cellular characteristics of cardiomyocytes after X-ray irradiation.

The aim of this study was to investigate possible effects of ionizing irradiation on the electrophysiological functionality of cardiac myocytes in vitro. Primary chicken cardiomyocytes with spontaneous beating activity were irradiated with X-rays (dose range of 0.5-7 Gy). Functional alterations of cardiac cell cultures were evaluated up to 7 days after irradiation using microelectrode arrays. As examined endpoints, cell proliferation, apoptosis, reactive oxygen species (ROS) and DNA damage were evaluated. The beat rate of the cardiac networks increased in a dose-dependent manner over one week. The duration of single action potentials was slightly shortened. Additionally, we observed lower numbers of mitotic and S-phase cells at certain time points after irradiation. Also, the number of cells with γH2AX foci increased as a function of the dose. No significant changes in the level of ROS were detected. Induction of apoptosis was generally negligibly low. This is the first report to directly show alterations in cardiac electrophysiology caused by ionizing radiation, which were detectable up to one week after irradiation.

Growth and structural discrimination of cortical neurons on randomly oriented and vertically aligned dense carbon nanotube networks.

The growth of cortical neurons on three dimensional structures of spatially defined (structured) randomly oriented, as well as on vertically aligned, carbon nanotubes (CNT) is studied. Cortical neurons are attracted towards both types of CNT nano-architectures. For both, neurons form clusters in close vicinity to the CNT structures whereupon the randomly oriented CNTs are more closely colonised than the CNT pillars. Neurons develop communication paths via neurites on both nanoarchitectures. These neuron cells attach preferentially on the CNT sidewalls of the vertically aligned CNT architecture instead than onto the tips of the individual CNT pillars.

A novel automated spike sorting algorithm with adaptable feature extraction.

To study the electrophysiological properties of neuronal networks, in vitro studies based on microelectrode arrays have become a viable tool for analysis. Although in constant progress, a challenging task still remains in this area: the development of an efficient spike sorting algorithm that allows an accurate signal analysis at the single-cell level. Most sorting algorithms currently available only extract a specific feature type, such as the principal components or Wavelet coefficients of the measured spike signals in order to separate different spike shapes generated by different neurons. However, due to the great variety in the obtained spike shapes, the derivation of an optimal feature set is still a very complex issue that current algorithms struggle with. To address this problem, we propose a novel algorithm that (i) extracts a variety of geometric, Wavelet and principal component-based features and (ii) automatically derives a feature subset, most suitable for sorting an individual set of spike signals. Thus, there is a new approach that evaluates the probability distribution of the obtained spike features and consequently determines the candidates most suitable for the actual spike sorting. These candidates can be formed into an individually adjusted set of spike features, allowing a separation of the various shapes present in the obtained neuronal signal by a subsequent expectation maximisation clustering algorithm. Test results with simulated data files and data obtained from chick embryonic neurons cultured on microelectrode arrays showed an excellent classification result, indicating the superior performance of the described algorithm approach.