BIOMEMS Lab
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- Neuronales Netz (9)
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- Mikroelektrode (5)
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- Ayahuasca (1)
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- Biologisch abbaubarer Kunststoff (1)
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- Bioprinting (1)
- Bioprinting, FRET, Enzyme-free glucose sensor, PVA hydrogel (1)
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- In Vitro, Ultrasound, Neuromodulation, Neuron (1)
- In vitro cell systems (1)
- Ionisierende Strahlung (1)
- Kardiotoxizität (1)
- MEMS (1)
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- Micro-Heater (1)
- Microelectrode arrayNeurosphereSpHuman embryonic stem cell-derived neurons (1)
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- SU-8 (1)
- Schnittpräparat (1)
- Teflon-FEP (1)
- Tissue Engineering (1)
- Ultraschall (1)
- Weltraumforschung (1)
- biological neural network (1)
- complex networks (1)
- electroplating (1)
- evaluation method (1)
- in silico (1)
- magnetic nanowires (1)
- spike trains (1)
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- Article (18)
- Doctoral Thesis (9)
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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.
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.
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.
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.
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.
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.
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
(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.
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.
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
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.
Functional connectivity and convolutional neural networks for automatic classification of EEG data
(2022)
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.
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.
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.
Our drinking water contains residues of pharmaceuticals. A sub-group of these contaminants are neuro-active
substances, the antiepileptic carbamazepine being one of the most relevant. For assessment of the neurotoxicity of this
drug at a sub-therapeutic level, a cell-based sensor chip platform has been realized and characterized. For this
purpose, a microelectrode array chip was designed and processed in a clean room and optimized in terms of low
processing costs and good recording properties. For characterization of the system neuronal cells were plated on
microelectrode array chips and electrical activity was measured as a function of applied carbamazepine concentration.
We found that the relative spike rate decreased with increasing drug concentration resulted in IC50 values of around 36 μM. This value is five orders of magnitude higher than the maximal dose found in drinking water. IC50 values for
burst rate, burst duration and synchrony were slightly higher, suggesting spike rate being a more sensitive parameter to
carbamazepine.
MEMS-based Micro-Heaters, in combination with thin-film temperature sensors, are often used for providing the necessary amount of thermal energy for sensor-applications. In this work, we propose an integrated micro-heater as actuator for fusion bonding of polymers, which can optimize the production process of electret-based micro-energy-harvesters. By adjusting the design parameters of thermoforming-molds, we can implement thin-film micro-heaters that are capable of generating temperatures of above 300°C for numerous cycles. Utilizing the integrated micro-heater allows local fusion bonding of Fluoroethylenepropylene (Teflon-FEP) foils on a micrometer-scale while reducing unnecessary thermal stress. This is beneficial for the longevity of the micro-energy-harvesters, while simultaneously improving its performance.
Understanding the complexity of biological neural
networks like the human brain is one of the scientific
challenges of our century. The organization of the brain
can be described at different levels, ranging from small neural
networks to entire brain regions. Existing methods for
the description of functionally or effective connectivity are
based on the analysis of relations between the activities of
different neural units by detecting correlations or information
flow. This is a crucial step in understanding neural disorders
like Alzheimer’s disease and their causative factors.
To evaluate these estimation methods, it is necessary to refer
to a neural network with known connectivity, which is
typically unknown for natural biological neural networks.
Therefore, network simulations, also in silico, are available.
In this work, the in silico simulation of large scale neural
networks is established and the influence of different topologies
on the generated patterns of neuronal signals is investigated.
The goal is to develop standard evaluation methods
for neurocomputational algorithms with a realistic large
scale model to enable benchmarking and comparability of
different studies.
Applying technologies of additive manufacturing to the field of tissue engineering created a pioneering new approach tomodelcomplex cell systemsartificially. Regarding its huge potential, bioprinting is still in its infancies and many questions are still unanswered. To address this issue, an extrusion-based bioprinting process was used to deposit human embryonic kidney (HEK) cellsin a defined pattern.It wasshownthat cells remained viable in the bioprinted construct for up to 8 days after printing (DAP). This work displays a proof of principle for a controlled cell formation which shall later be appliedtoin vitro drug screening tests.
Nickel-iron (NiFe) nanowires with a high contentof nickel and large aspect-ratio show promising characteris-tics for applications in modern micro-systems. In this study,nanowires with well-controlled diameter, height and compo-sition are fabricated using nanoporous poly-carbonate (PC)and anodized aluminum-oxide (AAO) membranes. Differ-ences between potentiostatic and pulsed deposition methodswere studied with regard to their flexibility and a recommen-dation for the best use of the individual types is given. Withthe application of the presented processes, future technolo-gies as magnetic energy-harvesting or nano-fluxgate sensorscan be improved.
In Vitro Platform for Acoustic and Electrophysiological Investigations of Ultrasound Neuromodulation
(2017)
The quality of life has increased dramatically over the past decades. Approximately one billion individuals worldwide will be aged 65 years or older by 2030. With the increasing age of human population, prevalence of neurodegenerative ailments, such as Parkinson’s disease or Alzheimer’s disease, increases. Brain pacemakers are proposed to treat their symptoms. They provide the electrical stimulation of neuronal regions of the central or peripheral nervous system. But for the implantation of brain pacemakers, surgery is needed. Those surgical interventions can be avoided by using transcranial stimulation methods such as Transcranial Magnetic Stimulation (TMS). However, magnetic fields lack the possibility of focusing. Ultrasound (US) offers transcranial characteristics as well and allows focusing on a specific target area in the brain. Over 60 years ago, the Hodgkin-Huxley (H-H) model was presented to simulate the electrophysiological activity of neurons but it lacks to describe the impact of mechanical soundwaves on action potentials in a neuron.
Mit der Entwicklung hybrider Strukturen aus Mikroelektroden Arrays und dreidimensionalen
Sphäroiden wurde in dieser Arbeit ein funktionaler, deskriptiver Ansatz der Biosensorik
adressiert. Während biochemische Sensoren ausschließlich analytische Informationen über
die Wechselwirkung von Liganden und Rezeptoren liefern, erlauben zellbasierte Biosensoren
eine physiologische Beschreibung biologischer Systeme in nativen bzw. chemisch oder
physikalisch provozierten Zuständen. Dies impliziert für Anwendungen der Pharmakologie,
Toxikologie und Grundlagenforschung hohes Potenzial, evoziert jedoch eine multidisziplinäre
Aufgabe. Es werden zum einen in vitro Systeme benötigt, die den Ansprüchen
eines physiologisch repräsentativen Abbilds eines Organismus genügen. Zum anderen sind
technische Systeme erforderlich, die Zellreaktionen qualitativ und quantitativ verwertbar
machen.
Einfluss ionisierender Strahlung auf die elektrophysiologischen Eigenschaften kardialer Zellen
(2016)
Die zentrale Fragestellung der Strahlenbiologie behandelt die Wirkung von ionisierender Strahlung auf biologische Systeme. Zwar sind die bei Exposition mit ionisierender Strahlung entstehenden Gesundheitsrisiken bekannt, dennoch sind die Strahlungseffekte auf das Herz immer noch nicht sehr gut verstanden. Dabei ist die Exposition mit hohen Dosen ionisierender Strahlung ein anerkannter Risikofaktor in Bezug auf kardiovaskuläre Erkrankungen.