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Information Infrastructure for Cooperative Research in Neuroscience  [PDF]
P. J. Durka,G. J. Blinowski,H. Klekowicz,U. Malinowska,R. Ku ,K. J. Blinowska
Computational Intelligence and Neuroscience , 2009, DOI: 10.1155/2009/409624
Abstract: The paper describes a framework for efficient sharing of knowledge between research groups, which have been working for several years without flaws. The obstacles in cooperation are connected primarily with the lack of platforms for effective exchange of experimental data, models, and algorithms. The solution to these problems is proposed by construction of the platform (EEG.pl) with the semantic aware search scheme between portals. The above approach implanted in the international cooperative projects like NEUROMATH may bring the significant progress in designing efficient methods for neuroscience research.
Integrating neuroinformatics tools in TheVirtualBrain  [PDF]
M. Marmaduke Woodman,Lia Domide,Stuart A. Knock,Paula Sanz-Leon,Jochen Mersmann,Anthony R. McIntosh,Viktor Jirsa
Frontiers in Neuroinformatics , 2014, DOI: 10.3389/fninf.2014.00036
Abstract: TheVirtualBrain (TVB) is a neuroinformatics Python package representing the convergence of clinical, systems, and theoretical neuroscience in the analysis, visualization and modeling of neural and neuroimaging dynamics. TVB is composed of a flexible simulator for neural dynamics measured across scales from local populations to large-scale dynamics measured by electroencephalography (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), and core analytic and visualization functions, all accessible through a web browser user interface. A datatype system modeling neuroscientific data ties together these pieces with persistent data storage, based on a combination of SQL and HDF5. These datatypes combine with adapters allowing TVB to integrate other algorithms or computational systems. TVB provides infrastructure for multiple projects and multiple users, possibly participating under multiple roles. For example, a clinician might import patient data to identify several potential lesion points in the patient's connectome. A modeler, working on the same project, tests these points for viability through whole brain simulation, based on the patient's connectome, and subsequent analysis of dynamical features. TVB also drives research forward: the simulator itself represents the culmination of several simulation frameworks in the modeling literature. The availability of the numerical methods, set of neural mass models and forward solutions allows for the construction of a wide range of brain-scale simulation scenarios. This paper briefly outlines the history and motivation for TVB, describing the framework and simulator, giving usage examples in the web UI and Python scripting.
Combining Neuroinformatics Databases for Multi-Level Analysis of Brain Disorders
Hasun Yu,Joon Bang,Yousang Jo,Doheon Lee
Interdisciplinary Bio Central , 2012,
Abstract: With the development of many methods of studying the brain, the field of neuroscience has generated large amounts of information obtained from various techniques: imaging techniques, electrophysiological techniques, techniques for analyzing brain connectivity, techniques for getting molecular information of the brain, etc. A plenty of neuroinformatics databases have been made for storing and sharing this useful information and those databases can be publicly accessed by researchers as needed. However, since there are too many neuroinformatics databases, it is difficult to find the appropriate database depending on the needs of researcher. Moreover, many researchers in neuroscience fields are unfamiliar with using neuroinformatics databases for their studies because data is too diverse for neuroscientists to handle this and there is little precedent for using neuroinformatics databases for their research. Therefore, in this article, we review databases in the field of neuroscience according to both their methods for obtaining data and their objectives to help researchers to use databases properly. We also introduce major neuroinformatics databases for each type of information. In addition, to show examples of novel uses of neuroinformatics databases, we represent several studies that combine neuroinformatics databases of different information types and discover new findings. Finally, we conclude our paper with the discussion of potential applications of neuroinformatics databases

SHEN Jun-xian,

生物物理学报 , 2001,
Abstract: Understanding the brain and all of its functions is one of the great challenges of the 21st Century. Neuroinformatics is defined as a new field combining neuroscience and informatics research to develop and apply advanced tools and approaches needed for understanding the brain. A current initiative is the Human Brain Project aimed at augmenting basic research in the field and developing capacities for analysis, integration, synthesis, modeling, simulation and data presentation. China will make a greater contribution to neuroinformatics.
Increasing quality and managing complexity in neuroinformatics software development with continuous integration  [PDF]
Yury V. Zaytsev,Abigail Morrison
Frontiers in Neuroinformatics , 2013, DOI: 10.3389/fninf.2012.00031
Abstract: High quality neuroscience research requires accurate, reliable and well maintained neuroinformatics applications. As software projects become larger, offering more functionality and developing a denser web of interdependence between their component parts, we need more sophisticated methods to manage their complexity. If complexity is allowed to get out of hand, either the quality of the software or the speed of development suffer, and in many cases both. To address this issue, here we develop a scalable, low-cost and open source solution for continuous integration (CI), a technique which ensures the quality of changes to the code base during the development procedure, rather than relying on a pre-release integration phase. We demonstrate that a CI-based workflow, due to rapid feedback about code integration problems and tracking of code health measures, enabled substantial increases in productivity for a major neuroinformatics project and additional benefits for three further projects. Beyond the scope of the current study, we identify multiple areas in which CI can be employed to further increase the quality of neuroinformatics projects by improving development practices and incorporating appropriate development tools. Finally, we discuss what measures can be taken to lower the barrier for developers of neuroinformatics applications to adopt this useful technique.

CHEN Wei-chang,WANG Zi-qiang,CHEN Zhi-hua,AN Rong-shu,

生物物理学报 , 2001,
Abstract: Neuroinformatics is a frontier and multi-discipline which combines the brain science, the information science and computer science together to investigate the form of neural information carrier, the mechanisms of generation, transmission, processing, coding, storage and retrieving of the neural information, and to construct the data bank system of neuroscience. Neuroinformatics can be subdivided into two parts: the molecular neuroinformatics and the systematic neuroinformatics. A dual coding theory of neuroinformatics was suggested that the neuroinformation coding includes two forms: the digital coding of neuronic impulse sequence and the weighting coding of the synaptic connection. A perspective was made on the tendency and new advancements of neuroinformatics in the 21 century and the Human Neurome Project (HuNP) was also suggested. Human Neurome Project is aimed to measure the position, size, neuron number and density, neuronic branches, connections and synapses, neurotransmitters and receptors, ion channels, function, etc, of the complex structures in the human brain. The necessity and feasibility of the Human Neurome Project and the construction of neural data bank were discussed. Relationship between Human Neurome Project and the Human Brain Project was also analyzed and discussed.
Neuroinformatics: From Bioinformatics to Databasing the Brain
Thomas M . Morse
Bioinformatics and Biology Insights , 2008,
Abstract: Neuroinformatics seeks to create and maintain web-accessible databases of experimental and computational data, together with innovative software tools, essential for understanding the nervous system in its normal function and in neurological disorders. Neuroinformatics includes traditional bioinformatics of gene and protein sequences in the brain; atlases of brain anatomy and localization of genes and proteins; imaging of brain cells; brain imaging by positron emission tomography (PET), functional magnetic resonance imaging (fMRI), electroencephalography (EEG), magnetoencephalography (MEG) and other methods; many electrophysiological recording methods; and clinical neurological data, among others. Building neuroinformatics databases and tools presents difficult challenges because they span a wide range of spatial scales and types of data stored and analyzed. Traditional bioinformatics, by comparison, focuses primarily on genomic and proteomic data (which of course also presents difficult challenges). Much of bioinformatics analysis focus on sequences (DNA, RNA, and protein molecules), as the type of data that are stored, compared, and sometimes modeled. Bioinformatics is undergoing explosive growth with the addition, for example, of databases that catalog interactions between proteins, of databases that track the evolution of genes, and of systems biology databases which contain models of all aspects of organisms. This commentary briefly reviews neuroinformatics with clarification of its relationship to traditional and modern bioinformatics.
Privacy for Personal Neuroinformatics  [PDF]
Arkadiusz Stopczynski,Dazza Greenwood,Lars Kai Hansen,Alex Pentland
Computer Science , 2014,
Abstract: Human brain activity collected in the form of Electroencephalography (EEG), even with low number of sensors, is an extremely rich signal. Traces collected from multiple channels and with high sampling rates capture many important aspects of participants' brain activity and can be used as a unique personal identifier. The motivation for sharing EEG signals is significant, as a mean to understand the relation between brain activity and well-being, or for communication with medical services. As the equipment for such data collection becomes more available and widely used, the opportunities for using the data are growing; at the same time however inherent privacy risks are mounting. The same raw EEG signal can be used for example to diagnose mental diseases, find traces of epilepsy, and decode personality traits. The current practice of the informed consent of the participants for the use of the data either prevents reuse of the raw signal or does not truly respect participants' right to privacy by reusing the same raw data for purposes much different than originally consented to. Here we propose an integration of a personal neuroinformatics system, Smartphone Brain Scanner, with a general privacy framework openPDS. We show how raw high-dimensionality data can be collected on a mobile device, uploaded to a server, and subsequently operated on and accessed by applications or researchers, without disclosing the raw signal. Those extracted features of the raw signal, called answers, are of significantly lower-dimensionality, and provide the full utility of the data in given context, without the risk of disclosing sensitive raw signal. Such architecture significantly mitigates a very serious privacy risk related to raw EEG recordings floating around and being used and reused for various purposes.
Neuroinformatics Literature System Oriented to Knowledge Discovery: Design and Preliminary Implementation  [PDF]
Journal of Neurological Sciences , 2007,
Abstract: Neuroscience research has brought on enormous amounts of diverse data, this has led to the difficulty for the neuroscientists to grasp all userful information and conduct the comprehensive brain research independently. In addition, many barriers exist to the querying of multiple databases, including mismatches in query language, access mechanisms, data models and semantic deduction. In order to solve the neuroscientific information integration and database interoperability, we have been developing an internet-accessible Neuroinformatics Literature System(NILS) for neuroimaging and brain function research. NILS is designed for knowledge discovery and NILS consists of 8 main function modules: System Maintenance, Literature Renewal, Literature Retrieval, Result Display/Output, Literature Information Analysis, Neuroimaging Integration, Neuroinformation Text Mining and Nerve Ontology Prototype Construction. It makes possible to semiautomatic gather, coordinate, analyze and store data from multiple underlying sources into a single high-performance environment operating from a local server, provides multi-disciplinary and multi-level integrative retrieval service, helps us to establish a network of comprehensive neuroscientific knowledge for fully understanding the brain principle of work. NILS will not only serve as a foundation platform for the future study pattern driven by hypothesis, but also reveal a great variety of implication relations in the literature, which was previously rarely-known.
Neuroinformatics: What are us, where are we going, how to measure our way?  [PDF]
A. N. Gorban
Physics , 2003,
Abstract: What is neuroinformatics? We can define it as a direction of science and information technology, dealing with development and study of the methods for solution of problems by means of neural networks. A field of science cannot be determined only by fixing what it is "dealing with". The main component, actually constituting a scientific direction, is "THE GREAT PROBLEM", around which the efforts are concentrated. One may state even categorically: if there is no a great problem, there is no a field of science, but only more or less skilful imitation. What is "THE GREAT PROBLEM" for neuroinformatics? The problem of effective parallelism, the study of brain (solution of mysteries of thinking), etc are discussed. The neuroinformatics was considered not only as a science, but as a services sector too. The main ideas of generalized technology of extraction of explicit knowledge from data are presented. The mathematical achievements generated by neuroinformatics, the problem of provability of neurocomputations, and benefits of neural network realization of solution of a problem are discussed.
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