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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.
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.
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.
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.
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.
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.
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