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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
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.
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.
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.
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.
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.
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.