Neuroinformatics (NEUROINFORMATICS)

Publications in this journal

• Article: XCEDE: An Extensible Schema for Biomedical Data

  Syam Gadde, Nicole Aucoin, Jeffrey S. Grethe, David B. Keator, Daniel S. Marcus, Steve Pieper, FBIRN, MBIRN, BIRN-CC
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  ABSTRACT: The XCEDE (XML-based Clinical and Experimental Data Exchange) XML schema, developed by members of the BIRN (Biomedical Informatics Research Network), provides an extensive metadata hierarchy for storing, describing and documenting the data generated by scientific studies. Currently at version 2.0, the XCEDE schema serves as a specification for the exchange of scientific data between databases, analysis tools, and web services. It provides a structured metadata hierarchy, storing information relevant to various aspects of an experiment (project, subject, protocol, etc.). Each hierarchy level also provides for the storage of data provenance information allowing for a traceable record of processing and/or changes to the underlying data. The schema is extensible to support the needs of various data modalities and to express types of data not originally envisioned by the developers. The latest version of the XCEDE schema and manual are available from http://www.xcede.org/. KeywordsXML–Schema–Database–Biomedical technology
  Neuroinformatics 04/2012; 10(1):19-32.
• Article: A cross-platform freeware tool for digital reconstruction of neuronal arborizations from image stacks

  Kerry M. Brown, Duncan E. Donohue, Giampaolo D’Alessandro, Giorgio A. Ascoli
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  ABSTRACT: Digital reconstruction of neuronal arborizations is an important step in the quantitative investigation of cellular neuroanatomy. In this process, neurites imaged by microscopy are semi-manually traced through the use of specialized computer software and represented as binary trees of branching cylinders (or truncated cones). Such form of the reconstruction files is efficient and parsimonious, and allows extensive morphometric analysis as well as the implementation of biophysical models of electrophysiology. Here, we describe Neuron_Morpho, a plugin for the popular Java application ImageJ that mediates the digital reconstruction of neurons from image stacks. Both the executable and code of Neuron_Morpho are freely distributed (www.maths.soton.ac.uk/staff/D’Alessandro/morpho or www.krasnow.gmu.edu/L-Neuron), and are compatible with all major computer platforms (including Windows, Mac, and Linux). We tested Neuron_Morpho by reconstructing two neurons from each of the two preparations representing different brain areas (hippocampus and cerebellum), neuritic type (pyramidal cell dendrites and olivar axonal projection terminals), and labeling method (rapid Golgi impregnation and anterograde dextran amine), and quantitatively comparing the resulting morphologies to those of the same cells reconstructed with the standard commercial system, Neurolucida. None of the numerous morphometric measures that were analyzed displayed any significant or systematic difference between the two reconstructing systems.
  Neuroinformatics 04/2012; 3(4):343-359.
• Article: “Dynamic” connectivity in neural systems

  Michael Breakspear
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  ABSTRACT: The study of functional interdependences between brain regions is a rapidly growing focus of neuroscience research. This endeavor has been greatly facilitated by the appearance of a number of innovative methodologies for the examination of neurophysiological and neuroimaging data. The aim of this article is to present an overview of dynamical measures of interdependence and contrast these with statistical measures that have been more widely employed. We first review the motivation, conceptual basis, and experimental approach of dynamical measures of interdependence and their application to the study of neural systems. A consideration of boot-strap “surrogate data” techniques, which facilitate hypothesis testing of dynamical measures, is then used to clarify the difference between dynamical and statistical measures of interdependence. An overview of some of the most active research areas—such as the study of the “synchronization manifold,” dynamical interdependence in neurophysiology data and the putative role of nonlinear desynchronization—is then given. We conclude by suggesting that techniques based on dynamical interdependence—or “dynamical connectivity”—show significant potential for extracting meaningful information from functional neuroimaging data.
  Neuroinformatics 04/2012; 2(2):205-224.
• Article: Database Analysis of Simulated and Recorded Electrophysiological Datasets with PANDORA’s Toolbox

  Cengiz Günay, Jeremy R. Edgerton, Su Li, Thomas Sangrey, Astrid A. Prinz, Dieter Jaeger
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  ABSTRACT: Neuronal recordings and computer simulations produce ever growing amounts of data, impeding conventional analysis methods from keeping pace. Such large datasets can be automatically analyzed by taking advantage of the well-established relational database paradigm. Raw electrophysiology data can be entered into a database by extracting its interesting characteristics (e.g., firing rate). Compared to storing the raw data directly, this database representation is several orders of magnitude higher efficient in storage space and processing time. Using two large electrophysiology recording and simulation datasets, we demonstrate that the database can be queried, transformed and analyzed. This process is relatively simple and easy to learn because it takes place entirely in Matlab, using our database analysis toolbox, PANDORA. It is capable of acquiring data from common recording and simulation platforms and exchanging data with external database engines and other analysis toolboxes, which make analysis simpler and highly interoperable. PANDORA is available to be freely used and modified because it is open-source (http://software.incf.org/software/pandora/home).
  Neuroinformatics 04/2012; 7(2):93-111.
• Article: Probable epitopes

  Lindsay Klee, Robert Zand
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  ABSTRACT: Nature holds numerous viral and bacterial proteins with regions of similarity to myelin basic protein antigenic determinants. Bioinformatic technology, including sequence similarity searches, may allow for the detection of biochemical and biophysical relationships between these peptides. Understanding these relationships is essential to understanding immune-mediated disease and, consequently, may be used to elucidate the etiology of pathological demyelinating diseases such as multiple sclerosis. Studies of experimental autoimmune encephalomyelitis have been used to identify antigenic determinants. We have used these determinants to search available databases of viral and bacterial proteins. Our results indicate numerous viral and bacterial protein segments with probabilistic sequence similarity to myelin basic protein antigenic determinants.
  Neuroinformatics 04/2012; 2(1):59-70.
• Source

  Available from: citeseerx.ist.psu.edu

  Article: The extensible neuroimaging archive toolkit

  Daniel S. Marcus, Timothy R. Olsen, Mohana Ramaratnam, Randy L. Buckner
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  ABSTRACT: The Extensible Neuroimaging Archive Toolkit (XNAT) is a software platform designed to facilitate common management and productivity tasks for neuroimaging and associated data. In particular, XNAT enables qualitycontrol procedures and provides secure access to and storage of data. XNAT follows a threetiered architecture that includes a data archive, user interface, and middleware engine. Data can be entered into the archive as XML or through data entry forms. Newly added data are stored in a virtual quarantine until an authorized user has validated it. XNAT subsequently maintains a history profile to track all changes made to the managed data. User access to the archive is provided by a secure web application. The web application provides a number of quality control and productivity features, including data entry forms, data-type-specific searches, searches that combine across data types, detailed reports, and listings of experimental data, upload/download tools, access to standard laboratory workflows, and administration and security tools. XNAT also includes an online image viewer that supports a number of common neuroimaging formats, including DICOM and Analyze. The viewer can be extended to support additional formats and to generate custom displays. By managing data with XNAT, laboratories are prepared to better maintain the long-term integrity of their data, to explore emergent relations across data types, and to share their data with the broader neuroimaging community.
  Neuroinformatics 04/2012; 5(1):11-33.
• Source

  Available from: papers.cnl.salk.edu

  Article: Development of a model for microphysiological simulations

  Gina E. Sosinsky, Thomas J. Deerinck, Rocco Greco, Casey H. Buitenhuys, Thomas M. Bartol, Mark H. Ellisman
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  ABSTRACT: The node of Ranvier is a complex structure found along myelinated nerves of vertebrate animals. Specific membrane, cytoskeletal, junctional, extracellular matrix proteins and organelles interact to maintain and regulate associated ion movements between spaces in the nodal complex, potentially influencing response variation during repetitive activations or metabolic stress. Understanding and building high resolution three dimensional (3D) structures of the node of Ranvier, including localization of specific macromolecules, is crucial to a better understanding of the relationship between its structure and function and the macromolecular basis for impaired conduction in disease. Using serial section electron tomographic methods, we have constructed accurate 3D models of the nodal complex from mouse spinal roots with resolution better than 7.5nm. These reconstructed volumes contain 75–80% of the thickness of the nodal region. We also directly imaged the glial axonal junctions that serve to anchor the terminal loops of the myelin lamellae to the axolemma. We created a model of an intact node of Ranvier by truncating the volume at its mid-point in Z, duplicating the remaining volume and then merging the new half volume with mirror symmetry about the Z-axis. We added to this model the distribution and number of Na+ channels on this reconstruction using tools associated with the MCell simulation program environment. The model created provides accurate structural descriptions of the membrane compartments, external spaces, and formed structures enabling more realistic simulations of the role of the node in modulation of impulse propagation than have been conducted on myelinated nerve previously.
  Neuroinformatics 04/2012; 3(2):133-162.
• Article: Tools and approaches for the construction of knowledge models from the neuroscientific literature

  Gully A. P. C. Burns, Arshad M. Khan, Shahram Ghandeharizadeh, Mark A. O’Neill, Yi-Shin Chen
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  ABSTRACT: Within this paper, we describe a neuroinformatics project (called “NeuroScholar,” http://www.neuroscholar.org/) that enables researchers to examine, manage, manipulate, and use the information contained within the published neuroscientific literature. The project is built within a multi-level, multi-component framework constructed with the use of software engineering methods that themselves provide code-building functionality for neuroinformaticians. We describe the different software layers of the system. First, we present a hypothetical usage scenario illustrating how NeuroScholar permits users to address largescale questions in a way that would otherwise be impossible. We do this by applying NeuroScholar to a “real-world” neuroscience question: How is stress-related information processed in the brain? We then explain how the overall design of NeuroScholar enables the system to work and illustrate different components of the user interface. We then describe the knowledge management strategy we use to store interpretations. Finally, we describe the software engineering framework we have devised (called the “View-Primitive-Data Model framework,” [VPDMf]) to provide an open-source, accelerated software development environment for the project. We believe that NeuroScholar will be useful to experimental neuroscientists by helping them interact with the primary neuroscientific literature in a meaningful way, and to neuroinformaticians by providing them with useful, affordable software engineering tools.
  Neuroinformatics 04/2012; 1(1):81-109.
• Article: The Neuroscience Information Framework: A Data and Knowledge Environment for Neuroscience

  Daniel Gardner, Huda Akil, Giorgio A. Ascoli, Douglas M. Bowden, William Bug, Duncan E. Donohue, David H. Goldberg, Bernice Grafstein, Jeffrey S. Grethe, Amarnath Gupta, [......], David N. Kennedy, Luis Marenco, Maryann E. Martone, Perry L. Miller, Hans-Michael Müller, Adrian Robert, Gordon M. Shepherd, Paul W. Sternberg, David C. Van Essen, Robert W. Williams
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  ABSTRACT: With support from the Institutes and Centers forming the NIH Blueprint for Neuroscience Research, we have designed and implemented a new initiative for integrating access to and use of Web-based neuroscience resources: the Neuroscience Information Framework. The Framework arises from the expressed need of the neuroscience community for neuroinformatic tools and resources to aid scientific inquiry, builds upon prior development of neuroinformatics by the Human Brain Project and others, and directly derives from the Society for Neuroscience’s Neuroscience Database Gateway. Partnered with the Society, its Neuroinformatics Committee, and volunteer consultant-collaborators, our multi-site consortium has developed: (1) a comprehensive, dynamic, inventory of Web-accessible neuroscience resources, (2) an extended and integrated terminology describing resources and contents, and (3) a framework accepting and aiding concept-based queries. Evolving instantiations of the Framework may be viewed at http://nif.nih.gov, http://neurogateway.org, and other sites as they come on line.
  Neuroinformatics 01/2008; 6(3):149-160.
• Article: Inverse Current-Source Density Method in 3D: Reconstruction Fidelity, Boundary Effects, and Influence of Distant Sources

  Szymon Łęski, Daniel K. Wójcik, Joanna Tereszczuk, Daniel A. Świejkowski, Ewa Kublik, Andrzej Wróbel
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  ABSTRACT: Estimation of the continuous current-source density in bulk tissue from a finite set of electrode measurements is a daunting task. Here we present a methodology which allows such a reconstruction by generalizing the one-dimensional inverse CSD method. The idea is to assume a particular plausible form of CSD within a class described by a number of parameters which can be estimated from available data, for example a set of cubic splines in 3D spanned on a fixed grid of the same size as the set of measurements. To avoid specificity of particular choice of reconstruction grid we add random jitter to the points positions and show that it leads to a correct reconstruction. We propose different ways of improving the quality of reconstruction which take into account the sources located outside the recording region through appropriate boundary treatment. The efficiency of the traditional CSD and variants of inverse CSD methods is compared using several fidelity measures on different test data to investigate when one of the methods is superior to the others. The methods are illustrated with reconstructions of CSD from potentials evoked by stimulation of a bunch of whiskers recorded in a slab of the rat forebrain on a grid of 4×5×7 positions.
  Neuroinformatics 11/2007; 5(4):207-222.
• Source

  Available from: nitrc.org

  Article: Value Added by Data Sharing: Long-Term Potentiation of Neuroscience Research

  Yuan Liu, Giorgio A. Ascoli
  Neuroinformatics 08/2007; 5(3):143-145.
• Article: Sharing and reusing gene expression profiling data in neuroscience.

  Xiang Wan, Paul Pavlidis
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  ABSTRACT: As public availability of gene expression profiling data increases, it is natural to ask how these data can be used by neuroscientists. Here we review the public availability of high-throughput expression data in neuroscience and how it has been reused, and tools that have been developed to facilitate reuse. There is increasing interest in making expression data reuse a routine part of the neuroscience tool-kit, but there are a number of challenges. Data must become more readily available in public databases; efforts to encourage investigators to make data available are important, as is education on the benefits of public data release. Once released, data must be better-annotated. Techniques and tools for data reuse are also in need of improvement. Integration of expression profiling data with neuroscience-specific resources such as anatomical atlases will further increase the value of expression data.
  Neuroinformatics 02/2007; 5(3):161-75.
• Source

  Available from: nitrc.org

  Article: REX: response exploration for neuroimaging datasets.

  Eugene P Duff, Ross Cunnington, Gary F Egan
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  ABSTRACT: Neuroimaging technologies produce large and complex datasets. The challenge of comprehensively analysing the recorded dynamics remains an important field of research. The whole-brain linear modelling of hypothesised response dynamics and experimental effects must utilise simple basis sets, which may not detect unexpected or complex signal effects. These unmodelled effects can influence statistical mapping results, and provide important additional clues to the underlying neural dynamics. They can be detected via exploration of the raw signal, however this can be difficult. Specialised visualisation tools are required to manage the huge number of voxels, events and scans. Many effects can be occluded by noise in individual voxel time-series. This paper describes a visualisation framework developed for the assessment of entire neuroimaging datasets. While currently available tools tend to be tied to a specific model of experimental effects, this framework includes a novel metadata schema that enables the rapid selection and processing of responses based on easily-adjusted classifications of scans, brain regions, and events. Flexible event-related averaging and process pipelining capabilities enable users to investigate the effects of preprocessing algorithms and to visualise power spectra and other transformations of the data. The framework has been implemented as a MATLAB package, REX (Response Exploration), which has been utilised within our lab and is now publicly available for download. Its interface enables the real-time control of data selection and processing, for very rapid visualisation. The concepts outlined in this paper have general applicability, and could provide significant further functionality to neuroimaging databasing and process pipeline environments.
  Neuroinformatics 02/2007; 5(4):223-34.