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Applying technologies of additive manufacturing to the field of tissue engineering created a pioneering new approach to model complex cell systems artificially. Regarding its huge potential, bioprinting is still in its infancies and many questions are still unanswered. To address this issue, an extrusion-based bioprinting (EBB) process was used to deposit human embryonic kidney (HEK) cells in a defined pattern. It was shown that the bioprinted construct featured a high degree in viability reaching up to 77% 10 days after printing (DAP). This work displays a proof of principle for a controlled cell formation which shall later be applied to in vitro drug screening tests using various types of cells.
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.
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
Mental disorders are among the leading causes of disability worldwide. The first step in treating
these conditions is to obtain an accurate diagnosis. Machine learning algorithms can provide a
possible solution to this problem, as we describe in this work. We present a method for the
automatic diagnosis of mental disorders based on the matrix of connections obtained from EEG
time series and deep learning. We show that our approach can classify patients with Alzheimer’s
disease and schizophrenia with a high level of accuracy. The comparison with the traditional cases,
that use raw EEG time series, shows that our method provides the highest precision. Therefore, the
application of deep neural networks on data from brain connections is a very promising method
for the diagnosis of neurological disorders.
Diagnosis of autism spectrum disorder based on functional brain networks and machine learning
(2023)
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.
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.
Functional connectivity and convolutional neural networks for automatic classification of EEG data
(2022)
Microelectrode arrays serve as an indispensable tool in electro-physiological research to study the electrical activity of neural cells, enabling measurements of single cell as well as network communication analysis. Recent experimental studies have reported that the neuronal geometry has an influence on electrical signaling and extracellular recordings. However, the corresponding mechanisms are not yet fully understood and require further investigation. Allowing systematic parameter studies, computational modeling provides the opportunity to examine the underlying effects that influence extracellular potentials. In this letter, we present an in silico single cell model to analyze the effect of geometrical variability on the extracellular electric potentials. We describe finite element models of a single neuron with varying geometric complexity in three-dimensional space. The electric potential generation of the neuron is modeled using Hodgkin-Huxley equations. The signal propagation is described with electro-quasi-static equations, and results are compared with corresponding cable equation descriptions. Our results show that both the geometric dimensions and the distribution of ion channels of a neuron are critical factors that significantly influence both the amplitude and shape of extracellular potentials.
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.
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.
Spike-contrast: A novel time scale independent and multivariate measure of spike train synchrony
(2017)
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.
Spike Detection, Refinement And Interpretation Of Recordings In Three-Dimensional In Vitro Systems
(2011)
Using three-dimensional cell culture systems on Microelectrode arrays for biosensing applications
(2010)
A spheroid-based biosensor for the label-free detection of drug-induced field potential alterations
(2012)
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.
Multilayers of silicon oxide/silicon nitride/silicon oxide (ONO) are known for their good
electret properties due to deep energy traps near the material interfaces, facilitating charge
storage. However, measurement of the space charge distribution in such multilayers is a
challenge for conventional methods if layer thickness dimensions shrink below 1 μm. In this
paper, we propose an atomic force microscope based method to determine charge distributions in
ONO layers with spatial resolution below 100 nm. By applying Kelvin probe force microscopy
(KPFM) on freshly cleaved, corona-charged multilayers, the surface potential is measured
directly along the z-axis and across the interfaces. This new method gives insights into charge
distribution and charge movement in inorganic electrets with a high spatial resolution.
In this work we present a new micro-system-technology based production process for
unipolar ferroelectrets. Theoretical analysis of the influence of the air-gap size on the electric
field distribution as well as on the induced charge on the electrodes shows superior performance
of unipolar piezoelectrets with small air-gaps. For the production of these small air-gaps we
developed a new design using a photoresist thermoforming master, an integrated micro-heater
and shadow masks for metallization. Unipolar piezoelectrets produced with this technology
exhibit increased d33-coefficients compared to designs in previous publications. These
piezoelectrets are highly preferable for energy-harvesting applications, as they promise high
electric power output.
The need for manipulating matter at its smallest scale has been a hot topic in research for the last decades. As the final goal of arranging single atoms has been achieved, research focuses on two targets: firstly, improving the capabilities of existing technologies towards nanometer resolution and secondly, finding new methods for producing nanostructures in a fast and easy way. Here, we present a new maskless method for sub-micro-patterning of poly(methyl methacrylate) (PMMA) thin films. By applying atomic-force-microscope (AFM) assisted nano-xerography, electric charges were locally injected with an AFM-tip into PMMA. The resulting electrostatic patterns attracted charged gold-nanoparticles, which were selectively deposited onto the PMMA layers with lateral dimensions below 200 nm. In a second step, heat treatment at 275 °C initiated a selective decomposition of the PMMA layer, only observed in PMMA-areas covered with nanoparticles, whereas uncovered areas were not modified by the heat treatment. Analyzing the grooves with the AFM, we found that lines with a width in the sub-micrometer range to several micrometers have successfully been realized. We propose this new and promising method to manufacture nano-grooves used as masking for lift-off processes, for functionalization of underlying areas, or for micro-contact-printing.
This paper presents an optimized laser-bonding process for piezoelectric energy-harvesters based on thin fluorinated-ethylene-propylene (FEP) foils, using an ultra-short-pulse(USP) laser. Due to the minimized thermal stress in the material during bonding, achieved bypulse durations of few picoseconds, we created seams down to 40μm width without generatingholes in the 12.5μm thick FEP-foils. Using a galvanometer scanning system allowed for fastbonding-speed up to several centimeters per second, making the process also suitable for largestructures and areas. The achieved bond strength of the seams under influence of shearingstress was examined using tensile testing, which showed a sufficient strength of about 25 % of the maximum strength of an unbonded, single layer of FEP.
Piezoelectrets fabricated from fluoroethylenepropylene (FEP)-foils have shown drastic increase of their piezoelectric
properties during the last decade. This led to the development of FEP-based energy harvesters, which are about to evolve
into a technology with a power-generation-capacity of milliwatt per square-centimeter at their resonance frequency. Recent
studies focus on piezoelectrets with solely negative charges, as they have a better charge stability and a better suitability for
implementation in rising technologies, like the internet of things (IOT) or portable electronics. With these developments
heading towards applications of piezoelectrets in the near future, there is an urgent need to also address the fabrication
process in terms of scalability, reproducibility and miniaturization. In this study, we firstly present a comprehensive review
of the literature for a deep insight into the research that has been done in the field of FEP-based piezoelectrets. For the first
time, we propose the employment of microsystem-technology and present a process for the fabrication of thermoformed
FEP piezoelectrets based on thermoforming SU-8 templates. Following this process, unipolar piezoelectrets were fabricated with air void dimensions in the range of 300–1000 lm in width and approx. 90 lm in height. For samples with a void
size of 1000 lm, a d33-coefficient up to 26,508 pC/N has been achieved, depending on the applied seismic mass. Finally,
the properties as energy harvester were characterized. At the best, an electrical power output of 0.51 mW was achieved for
an acceleration of 1 g with a seismic mass of 101 g. Such piezoelectrets with highly defined dimensions show good
energy output in relation to volume, with high potential for widespread applications.
In recent years, there has been an increasing interest in electrically conductive hydrogels for a wide range of biomedical applications, like tissue engineering or biosensors. In this study, we present a cost-effective conductive hydrogel based on alginate and graphene nanoplatelets for extrusion-based bioprinters. The hydrogel is prepared under ambient conditions avoiding high temperatures detrimental for cell culture environments. Investigation of the hydrogel revealed a conductivity of up to 7.5 S/cm, depending on the ratio of platelets. Furthermore, in vitro tests with human embyronic kidney cells - as an example cell type - showed good adhesion of the cells to the surface of the conductive hydrogel. Electrochemical measurements revealed a low electrode impedance which is desirable for the extracellular recording, but also low electrode capacitance, which is unfavorable for electrical stimulation purposes. Therefore, future experiments with the graphene nanoplatelets-based hydrogels will focus on electrodes for biosensors and extracellular recordings of neurons or cardiac myocytes.
Transparent poly(3,4-ethylenedioxythiophene)-based microelectrodes for extracellular recording
(2018)
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 NO−3 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.
In recent years, the demand for sustainable and degradable materials and electronic devices has increased significantly.
Among a range of biodegradable polymers, poly(lactic acid) (PLA) is a good alternative to conventional petrol-based polymers because of its attractive mechanical properties and its easy processability. Recently, PLA has also been described as a promising dielectric material with piezoelectric and electret properties. We expect that PLA—after further optimization— will play an important role as a material for environmentally friendly sensors in the future, where first applications such as air filters and pressure sensors have already been proposed. However, degradation under normal ambient conditions is very slow, and an accelerated and controllable degradation process is highly desirable for any type of PLA-based sensors.
Enzymatic hydrolysis with embedded enzymes has been proposed as an approach to accelerate and control degradation. In this work, we investigate the properties of PLA in terms of dielectric and mechanical properties with a focus on its ability to store charges after the enzyme proteinase K (Trit. album) has been incorporated. Results reveal that proteinase K has a positive effect on the charge stability of solvent-cast PLA electrets after complete evaporation of the solvent. Furthermore, we observed a concentration-dependent acceleration of mass loss in a Tris-HCl buffer. A fast degradation within only one day occurred at a concentration of 6 wt% proteinase K.
Combined effects of ionizing radiation and cardio-active drugs on human iPSC-derived cardiomyocytes
(2014)
High LET radiation shows no major cellular and functional effects on primary cardiomyocytes in vitro
(2018)
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.
In vitro platform for acoustic and electrophysiological investigations of ultrasound stimulation
(2017)
Since the first time discovered that ultrasound can influence neuronal activity - more than half a century ago - a lot of progress has been made in this field. The possibilities of ultrasound for neuromodulation have been demonstrated in many experiments such as in vivo stimulation of rodent brain or of human cochlear, Further, in vitro experiments with hippocampal slices and other cell types have been performed as well.
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.
Graphene is a promising material both as a coating for existing neural electrodes as well as for transparent electrodes made exclusively from graphene. We studied graphene‐based microelectrodes by investigating their recording and stimulation properties in order to evaluate their suitability for neuronal implants. In this work, we compare three different electrode material compositions. Microelectrode arrays (MEA) with an electrode size of about 700 μm2 were prepared of gold, graphene on gold, and plain graphene on glass substrate. In order to reduce polymer contamination during graphene transfer, we employed a polymer‐free transfer and lift‐off process. Impedance studies revealed a value of 2.3 MΩ at 1 kHz for plain, and 0.88 MΩ for graphene on gold. Neuronal recording experiments showed a sufficient SNR for both graphene‐based materials and a stable impedance, unaffected by surface degradation metal electrodes are known for. Stimulation measurements yielded a charge injection capacity of 0.15 mC/cm2 using biphasic pulses of 1 ms and 1 μA transparent graphene electrodes. Cyclic voltammetry revealed a large voltage range of −1.4 V to +1.6 V before water electrolysis occurs. Graphene‐coated gold microelectrodes show enhanced recording properties, whereas plain graphene electrodes might be better suited for stimulation applications.
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.
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.
At present, most neurotoxicological studies in the field of microelectrode array (MEA) technology are based on in vivo or in vitro animal models, particularly in mice or rats. These models contributed much to our knowledge about molecular and cellular mechanisms of neurotoxins affecting the central nervous system. Yet, animal models are not necessarily capable to forecasting the effectiveness of treatment in clinical trials. Therefore the development and characterization of suitable neuronal cell models derived from human cells is emerging in the field of toxicity testing. In this study, we present neurospheres (NS) as a new three dimensional (3D) neuronal cell model based on human embryonic stem cells (hESC), coupled onto microelectrode arrays (MEA). This cell-based assay serves as platform to investigate effects of neuroactive substances on network communication. In order to characterize the neurospheres’ reaction, the well-known GABA receptor antagonist bicuculline and the sodium channel blocker carbamazepine are applied. Preliminary results reveal anticipated and physiologically reasonable alterations in network activity. Thus, we have the first evidence that hESC derived NS are a promising 3D cell model for neurotoxicity testing. In further studies, this model will be used to investigate effects of ionizing radiation on network signaling.
Human embryonic stem cell derived neurospheres form functional networks on microelectrode arrays
(2015)
Various studies have shown that two dimensional (2D) neuronal cell cultures does not recapitulate structure and physiology of three-dimensional (3D) in vivo tissues. These findings are of paramount importance for drug screening, since the response to neurotoxicological substances may differ for 2D und 3D cell culture models. To address this topic, we present human embryonic stem cell (hESC) derived neurospheres (NS) coupled onto microelectrode array (MEA) chips as a model system that includes a 3D NS as well as an outgrowing 2D monolayer allowing direct comparison of functionality within one culture. Preliminary results revealed an enhanced functional reactions of 3D NS to GABA receptor antagonist bicuculline compared to the 2D domain. Thus we have the first evidence that hESC derived NS are a promising model system for neurotoxicity testing enabling a direct comparison between functionality of neurons grown in 2D or 3D.
The present white paper concerns the indications and recommendations of the SciSpacE Science Community to make progress in filling the gaps of knowledge that prevent us from answering the question: “How Do Gravity Alterations Affect Animal and Human Systems at a Cellular/Tissue Level?” This is one of the five major scientific issues of the ESA roadmap “Biology in Space and Analogue Environments”. Despite the many studies conducted so far on spaceflight adaptation mechanisms and related pathophysiological alterations observed in astronauts, we are not yet able to elaborate a synthetic integrated model of the many changes occurring at different system and functional levels. Consequently, it is difficult to develop credible models for predicting long-term consequences of human adaptation to the space environment, as well as to implement medical support plans for long-term missions and a strategy for preventing the possible health risks due to prolonged exposure to spaceflight beyond the low Earth orbit (LEO). The research activities suggested by the scientific community have the aim to overcome these problems by striving to connect biological and physiological aspects in a more holistic view of space adaptation effects.
Due to the increasing number of diabetes patients worldwide there is an enormous need for accurate, fast and someday also continuous or even closed loop monitoring of blood glucose level. More than 50 years after Clark and Lyons proposed the first glucose enzyme electrodes this concept is still widely in use today. Most concepts use the enzyme glucose oxidase (GOx) that reacts with glucose. These reactions cause a current that is proportional to the amount of glucose present at the sensor. Thus, if the sample volume is known, the blood sugar level can be measured. Although these electrodes have been in use for so long they have the disadvantage of a limited shelf time. In this work we present an enzyme free approach for glucose detection applying ultrathin gold films. According to the basic Fuchs-Sondheimer-theory and other more sophisticated models the resistivity of ultrathin metal films is dominated by scattering effects at their surface. Chemical reactions at the metallic surface are expected to change the conductivity properties and thus these changes can be used to detect molecules. This can be done by creating a self-assembled monolayer at the gold surface. When molecules such as glucose bind to the end groups of this layer the electron scattering and thus the conductivity of the film is expected to change. Ultrathin gold films with a thickness of 6 nm show the largest relative change in resistivity and are thus the preferred film thickness for this application. These gold films show a significant change in resistance when model molecules sodium sulfide and dextran are present, whereas the resistance of a platinum reference electrode does not change significantly.
Low Temperature Substrate Transfer Technique for 3D Vertically Aligned Carbon Nanotube Architectures
(2012)
Improving the interface between electrodes and neurons has been the focus of research for the last decade. Neuroelectrodes should show small geometrical surface area and low impedance for measuring and high charge injection capacities for stimulation. Increasing the electrochemically active surface area by using nanoporous electrode material or by integrating nanostructures onto planar electrodes is a common approach to improve this interface. In this paper a simulation approach for neuro electrodes' characteristics with integrated high aspect ratio nano structures based on a point-contact-model is presented. The results are compared with experimental findings conducted with real nanostructured microelectrodes. In particular, effects of carbon nanotubes and gold nanowires integrated onto microelectrodes are described. Simulated and measured impedance properties are presented and its effects onto the transfer function between the neural membrane potential and the amplifier output signal are studied based on the point-contact-model. Simulations show, in good agreement with experimental results, that electrode impedances can be dramatically reduced by the integration of high aspect ratio nanostructures such as gold nanowires and carbon nanotubes. This lowers thermal noise and improves the signal-to-noise ratio for measuring electrodes. It also may increase the adhesion of cells to the substrate and thus increase measurable signal amplitudes.
Niedrigtemperatur-Transfer von vertikal ausgerichteten Kohlenstoff Nanoröhren auf Mikrostrukturen
(2011)
Vertikal angeordnete Kohlenstoff Nanoröhren (engl. vertically aligned carbon nanotubes; VA-CNT) können seit einigen Jahren mit hoher Reproduzierbarkeit durch CVD-Verfahren bei 600 °C - 800 °C auf Katalysatorschichten aus Aluminium und Eisen hergestellt werden. Sie zeigen sehr gute Eigenschaften im Bereich der Feldemission, aber auch in sub-µm Vias. Allerdings sind die relativ hohen Temperaturen oft nicht prozesskompatibel und müssen vermieden werden um keine Degradation darunterliegender Strukturen in Kauf zu nehmen. Da die Reduzierung der CVD-Temperaturen beim Wachstum der VA-CNTs unter 600 °C zu hohen Defektraten führt, schlagen wir ein neues Verfahren zum Transfer der VA-CNTs bei niedrigen Temperaturen vor. In einer Kombination aus Flip-Chip- und Stempel-Technik werden hoch geordnete, mikrostrukturierte VA-CNTs auf temperaturempfindliche Substrate aus Kalk-Natron-Glas, Gold oder Kupfer bei Temperaturen von 100 °C transferiert. Die vertikale Struktur der Nanoröhren bleibt dabei erhalten. Eine leitende Verbindung kann durch ein leitfähiges, bio-kompatibles Polymer gewährleistet werden, was eine Erweiterung des Einsatzbereichs auf Life-Science Applikationen erlauben würde.
Electrodes based on carbon nanotubes are a promising approach to manufacture highly sensitive sensors with a low limit of signal detection and a high signal-to-noise ratio. This is achieved by dramatically increasing the electrochemical active surface area without increasing the overall geometrical dimensions. Typically, carbon nanotube electrodes are nearly planar and composed of randomly distributed carbon nanotube networks having a limited surface gain for a specific geometrical surface area. To overcome this limitation, we have introduced vertically aligned carbon nanotube (VACNT) networks as electrodes, which are arranged in a microelectrode pattern of 60 single electrodes. Each microelectrode features a very high aspect ratio of more than 300 and thus a dramatically increased surface area. These microelectrodes composed of VACNT networks display dramatically decreased impedance over the entire frequency range compared to planar microelectrodes caused by the enormous capacity increase. This is experimentally verified by electrochemical impedance spectroscopy and cyclic voltammetry.
Long‐term electromagnetic exposure of developing neuronal networks: A flexible experimental setup
(2016)
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.
Major depressive disorder (MDD) is a multifaceted condition that affects millions of people worldwide and is a leading cause of disability. There is an urgent need for an automated and objective method to detect MDD due to the limitations of traditional diagnostic approaches. In this paper, we propose a methodology based on machine and deep learning to classify patients with MDD and identify altered functional connectivity patterns from EEG data. We compare several connectivity metrics and machine learning algorithms. Complex network measures are used to identify structural brain abnormalities in MDD. Using Spearman correlation for network construction and the SVM classifier, we verify that it is possible to identify MDD patients with high accuracy, exceeding literature results. The SHAP (SHAPley Additive Explanations) summary plot highlights the importance of C4-F8 connections and also reveals dysfunction in certain brain areas and hyperconnectivity in others. Despite the lower performance of the complex network measures for the classification problem, assortativity was found to be a promising biomarker. Our findings suggest that understanding and diagnosing MDD may be aided by the use of machine learning methods and complex networks.
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.
Analysis of electrophysiological characteristics of cardiomyocytes following radiation exposure
(2012)
Analysis of electrophysiological characteristics of cardiomyocytes following radiation exposure
(2011)
Periodically, the European Space Agency (ESA) updates scientific roadmaps in consultation with the scientific community. The ESA SciSpacE Science Community White Paper (SSCWP) 9, “Biology in Space and Analogue Environments”, focusses in 5 main topic areas, aiming to address key community-identified knowledge gaps in Space Biology. Here we present one of the identified topic areas, which is also an unanswered question of life science research in Space: “How to Obtain an Integrated Picture of the Molecular Networks Involved in Adaptation to Microgravity in Different Biological Systems?” The manuscript reports the main gaps of knowledge which have been identified by the community in the above topic area as well as the approach the community indicates to address the gaps not yet bridged. Moreover, the relevance that these research activities might have for the space exploration programs and also for application in industrial and technological fields on Earth is briefly discussed.
A novel 3D-printed glucose sensor is presented for cell culture application. Glucose sensing was performed using a fluorescence resonance energy transfer (FRET)-based assay principle based on ConA and dextran. Both molecules are encapsulated in alginate microspheres and embedded in the UV-curable, stable hydrogel polyvinyl alcohol (PVA). The rheology of the formulation was adapted to obtain good properties for an extrusion-based printing process. The printed sensor structures were tested for their ability to detect glucose in vitro. A proportional increase in fluorescence intensity was observed in a concentration range of 0 - 2 g/L glucose. Tests with HEK cell cultures also showed good cell compatibility and excellent adhesion properties on plasma-treated Petri dishes. The printed sensors were able to detect the glucose decay associated with the metabolic activities of the fast-growing HEK cells in the cell culture medium over ten days. The proof-of-principle study shows that metabolic processes in cell cultures can be monitored with the new printed sensor using a standard fluorescence wide-field microscope.
Progress in mechanobiology allowed us to better understand the important role of mechanical forces in the regulation of biological processes. Space research in the field of life sciences clearly showed that gravity plays a crucial role in biological processes. The space environment offers the unique opportunity to carry out experiments without gravity, helping us not only to understand the effects of gravitational alterations on biological systems but also the mechanisms underlying mechanoperception and cell/tissue response to mechanical and gravitational stresses. Despite the progress made so far, for future space exploration programs it is necessary to increase our knowledge on the mechanotransduction processes as well as on the molecular mechanisms underlying microgravity-induced cell and tissue alterations. This white paper reports the suggestions and recommendations of the SciSpacE Science Community for the elaboration of the section of the European Space Agency roadmap “Biology in Space and Analogue Environments” focusing on “How are cells and tissues influenced by gravity and what are the gravity perception mechanisms?” The knowledge gaps that prevent the Science Community from fully answering this question and the activities proposed to fill them are discussed.
A high-frequency photocathode based on carbon nanotube (CNT) blocks on semiisolating GaAs or low-temperature grown GaAs was fabricated and used for electron emission in a diode configuration. The CNT blocks can achieve high currents and current densities of I=0.5 mA and J=28 mA/cm 2 at E=2.1 V/μm, respectively. The photomodulation was achieved with a GaAs photoswitch electrically connected to the bottom of the CNT block emitter. This photocathode can find applications in high frequency tubes and optically driven X-ray sources.