• MeSH
• Neurosciences MeSH

• Conspectus
• Fyziologie člověka a srovnávací fyziologie
• NML Fields
• neurovědy
• NML Publication type
• elektronické časopisy

• MeSH
• Models, Neurological MeSH
• Neural Networks, Computer MeSH
• Neurosciences * MeSH
• Computer Simulation MeSH
• Publication type
• Periodical MeSH

• Conspectus
• Lékařské vědy. Lékařství
• NML Fields
• neurovědy
• neurologie
• lékařská informatika

There is great need for coordination around standards and best practices in neuroscience to support efforts to make neuroscience a data-centric discipline. Major brain initiatives launched around the world are poised to generate huge stores of neuroscience data. At the same time, neuroscience, like many domains in biomedicine, is confronting the issues of transparency, rigor, and reproducibility. Widely used, validated standards and best practices are key to addressing the challenges in both big and small data science, as they are essential for integrating diverse data and for developing a robust, effective, and sustainable infrastructure to support open and reproducible neuroscience. However, developing community standards and gaining their adoption is difficult. The current landscape is characterized both by a lack of robust, validated standards and a plethora of overlapping, underdeveloped, untested and underutilized standards and best practices. The International Neuroinformatics Coordinating Facility (INCF), an independent organization dedicated to promoting data sharing through the coordination of infrastructure and standards, has recently implemented a formal procedure for evaluating and endorsing community standards and best practices in support of the FAIR principles. By formally serving as a standards organization dedicated to open and FAIR neuroscience, INCF helps evaluate, promulgate, and coordinate standards and best practices across neuroscience. Here, we provide an overview of the process and discuss how neuroscience can benefit from having a dedicated standards body.

• MeSH
• Neurosciences * MeSH
• Reproducibility of Results MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Review MeSH
• Research Support, N.I.H., Extramural MeSH

Background: INeuroinformatics is a rapidly developing interdisciplinary field which provides an enormous amount of data to be classified, evaluated and interpreted. Usage of exploratory data analysis (EDA) methods is essential in evaluating clinical data in medicine and this analysis remains a big challenge because each new system has specific requirements. Visualizations, models and illustrations of dependency can help in better understanding of measurements in diagnostics and in decision making. The number of available modern EDA packages for developers is increasing as well as the development in the Data Science field. The development of modern methods of data analysis must also be incorporated in university education. Objective: The aim of the study is to design and develop software, which implements current EDA packages and model making procedures for neurological data analyses which could be easily modified. The second objective is to evaluate the possibility of supporting the education of biomedical engineering students at the undergraduate level in order to provide effective support in biomedical data analysis. Methods: An application has been created under the reactive Shiny framework in the R language. Data in .csv or .tsv format are processed on the server side of the application. Results: We have developed a new easy-to-use software named NeuroEDA for interactive web-based biomedical data assessment. This application covers basic descriptive statistics, exploratory graphs and cluster analysis, which is also suitable for big data examination. Furthermore, this application offers methods for robust and non-parametric analysis. These are particularly useful in neuroinformatics from our long-term experience. The application was practically deployed in the evaluation of clinical neurological data and in teaching the subject Biomedical Statistics. Conclusion: We have introduced the possibility of creating biomedical software for clinical use and demonstration in teaching. Among the advantages of the application, is that it is easily expandability with new R packages and quick processing in web browsers. The interactive user interface allows one to work with R’s functions without needing scripting/programming knowledge. Students can acquire practical experience in processing and transformation of heterogeneous medical data not only in biomedical engineering fields, but also at the medical faculties for Medical Informatics. This application is actively used for neuroinformatics data assessment and in discovering some potentially useable hypotheses.

• Keywords
• neuroEDA, neuroinformatika,
• MeSH
• Biomedical Engineering MeSH
• Data Interpretation, Statistical MeSH
• Medical Informatics * MeSH
• Software Design MeSH
• Neurology MeSH
• Software * MeSH
• Statistics as Topic * MeSH
• Teaching MeSH
• Education, Professional MeSH
• Publication type
• Research Support, Non-U.S. Gov't MeSH

Manual and semi-automatic identification of artifacts and unwanted physiological signals in large intracerebral electroencephalographic (iEEG) recordings is time consuming and inaccurate. To date, unsupervised methods to accurately detect iEEG artifacts are not available. This study introduces a novel machine-learning approach for detection of artifacts in iEEG signals in clinically controlled conditions using convolutional neural networks (CNN) and benchmarks the method's performance against expert annotations. The method was trained and tested on data obtained from St Anne's University Hospital (Brno, Czech Republic) and validated on data from Mayo Clinic (Rochester, Minnesota, U.S.A). We show that the proposed technique can be used as a generalized model for iEEG artifact detection. Moreover, a transfer learning process might be used for retraining of the generalized version to form a data-specific model. The generalized model can be efficiently retrained for use with different EEG acquisition systems and noise environments. The generalized and specialized model F1 scores on the testing dataset were 0.81 and 0.96, respectively. The CNN model provides faster, more objective, and more reproducible iEEG artifact detection compared to manual approaches.

• MeSH
• Artifacts * MeSH
• Electroencephalography methods   MeSH
• Humans MeSH
• Brain physiology   MeSH
• Neural Networks, Computer * MeSH
• Retrospective Studies MeSH
• Machine Learning * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Research Support, N.I.H., Extramural MeSH

Annotation of multiple regions of interest across the whole mouse brain is an indispensable process for quantitative evaluation of a multitude of study endpoints in neuroscience digital pathology. Prior experience and domain expert knowledge are the key aspects for image annotation quality and consistency. At present, image annotation is often achieved manually by certified pathologists or trained technicians, limiting the total throughput of studies performed at neuroscience digital pathology labs. It may also mean that simpler and quicker methods of examining tissue samples are used by non-pathologists, especially in the early stages of research and preclinical studies. To address these limitations and to meet the growing demand for image analysis in a pharmaceutical setting, we developed AnNoBrainer, an open-source software tool that leverages deep learning, image registration, and standard cortical brain templates to automatically annotate individual brain regions on 2D pathology slides. Application of AnNoBrainer to a published set of pathology slides from transgenic mice models of synucleinopathy revealed comparable accuracy, increased reproducibility, and a significant reduction (~ 50%) in time spent on brain annotation, quality control and labelling compared to trained scientists in pathology. Taken together, AnNoBrainer offers a rapid, accurate, and reproducible automated annotation of mouse brain images that largely meets the experts' histopathological assessment standards (> 85% of cases) and enables high-throughput image analysis workflows in digital pathology labs.

• MeSH
• Deep Learning * MeSH
• Brain * diagnostic imaging   pathology   MeSH
• Mice, Transgenic MeSH
• Mice MeSH
• Image Processing, Computer-Assisted * methods   MeSH
• Reproducibility of Results MeSH
• Software standards   MeSH
• Animals MeSH
• Check Tag
• Mice MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH

As in other areas of experimental science, operation of electrophysiological laboratory, design and performance of electrophysiological experiments, collection, storage and sharing of experimental data and metadata, analysis and interpretation of these data, and publication of results are time consuming activities. If these activities are well organized and supported by a suitable infrastructure, work efficiency of researchers increases significantly. This article deals with the main concepts, design, and development of software and hardware infrastructure for research in electrophysiology. The described infrastructure has been primarily developed for the needs of neuroinformatics laboratory at the University of West Bohemia, the Czech Republic. However, from the beginning it has been also designed and developed to be open and applicable in laboratories that do similar research. After introducing the laboratory and the whole architectural concept the individual parts of the infrastructure are described. The central element of the software infrastructure is a web-based portal that enables community researchers to store, share, download and search data and metadata from electrophysiological experiments. The data model, domain ontology and usage of semantic web languages and technologies are described. Current data publication policy used in the portal is briefly introduced. The registration of the portal within Neuroscience Information Framework is described. Then the methods used for processing of electrophysiological signals are presented. The specific modifications of these methods introduced by laboratory researches are summarized; the methods are organized into a laboratory workflow. Other parts of the software infrastructure include mobile and offline solutions for data/metadata storing and a hardware stimulator communicating with an EEG amplifier and recording software.

• Publication type
• Journal Article MeSH

• MeSH
• Medical Informatics MeSH
• Computational Biology MeSH
• Publication type
• Congress MeSH
• Collected Work MeSH

• Conspectus
• Informační věda
• NML Fields
• lékařská informatika

• MeSH
• Electroencephalography MeSH
• Brain Mapping MeSH
• Computer Simulation MeSH
• Medical Informatics Applications MeSH

• Conspectus
• Patologie. Klinická medicína
• NML Fields
• neurologie
• NML Publication type
• studie

Smoking, excessive drinking, overeating and physical inactivity are well-established risk factors decreasing human physical performance. Moreover, epidemiological work has identified modifiable lifestyle factors, such as poor diet and physical and cognitive inactivity that are associated with the risk of reduced cognitive performance. Definition, collection and annotation of human reaction times and suitable health related data and metadata provides researchers with a necessary source for further analysis of human physical and cognitive performance. The collection of human reaction times and supporting health related data was obtained from two groups comprising together 349 people of all ages - the visitors of the Days of Science and Technology 2016 held on the Pilsen central square and members of the Mensa Czech Republic visiting the neuroinformatics lab at the University of West Bohemia. Each provided dataset contains a complete or partial set of data obtained from the following measurements: hands and legs reaction times, color vision, spirometry, electrocardiography, blood pressure, blood glucose, body proportions and flexibility. It also provides a sufficient set of metadata (age, gender and summary of the participant's current life style and health) to allow researchers to perform further analysis. This article has two main aims. The first aim is to provide a well annotated collection of human reaction times and health related data that is suitable for further analysis of lifestyle and human cognitive and physical performance. This data collection is complemented with a preliminarily statistical evaluation. The second aim is to present a procedure of efficient acquisition of human reaction times and supporting health related data in non-lab and lab conditions.

• Publication type
• Journal Article MeSH