YEARS

2006-2013

AUTHORS

Steven L Small

TITLE

Bioinformatics Infrastructure for Large Scale Studies of Aphasia Recovery

ABSTRACT

DESCRIPTION (provided by applicant): Large prospective studies of aphasia recovery that incorporate anatomical, physiological, and behavioral data are virtually non-existent. This has a significant impact on virtually all research into the diagnosis, prognosis, and treatment of aphasia, since we do not know the natural course of the disease, and thus cannot adequately inform patients and families or assess the effects of therapeutic interventions. We believe that the complexities of data management, particularly regarding anatomical and physiological data, represent a major stumbling block to the design and execution of such studies. With such diverse sources of information as demographic and medical data, cognitive and linguistic test results, electrophysiological recordings, and many types of brain images, it is hard enough to perform single case studies that attempt to relate these data to each other, let alone studies that include statistically meaningful numbers of participants. Even when the problem is restricted to a single data type, such as functional MRI data, we do not have the ability to scale up the methods used in individual subjects to larger groups. Both the large volume of data and the complexity of data processing cause difficulties. We thus propose to build computational infrastructure (R21 phase) to facilitate the prospective investigation of aphasia recovery (R33 phase). The infrastructure is based on the use of (a) database technology to represent diverse data types within a single representational framework;and (b) "grid" computing to distribute data and data processing over many storage devices and computers, using software developed in federally (NSF) funded basic computational research that allows investigators to express complex data processing algorithms in a convenient manner. The longitudinal aphasia study will use structural and functional MRI and diffusion tensor imaging, along with language and cognitive measures, to characterize the natural course of physiological and behavioral recovery from aphasia. The physiology of recovery will be quantified in neural network models of individual patient imaging data and their mathematical "fit" to normative templates derived from imaging data on healthy age-matched adults. The changes in these models over time will be related to the behavioral changes to construct a theory of recovery. The computational infrastructure will provide the means to encode the diverse types of data needed for aphasia recovery research in such a way that complex queries involving multiple data types (e.g., brain activation and language performance) can be retrieved easily, and that queries requiring significant computer processing (e.g., peak detection in imaging time series) can be answered quickly due to grid computing. Finally, this infrastructure and data will be shared, and a user of the system from virtually anywhere could pose such questions using the relational database query interface.

FUNDED PUBLICATIONS

  • Biological approaches to aphasia treatment
  • Biological approaches to aphasia treatment.
  • Functional and structural aging of the speech sensorimotor neural system: functional magnetic resonance imaging evidence.
  • Functional restoration for the stroke survivor: informing the efforts of engineers.
  • On the context-dependent nature of the contribution of the ventral premotor cortex to speech perception.
  • Database-managed grid-enabled analysis of neuroimaging data: the CNARI framework.
  • How to use: Click on a object to move its position. Double click to open its homepage. Right click to preview its contents.

    Download the RDF metadata as:   json-ld nt turtle xml License info


    24 TRIPLES      17 PREDICATES      25 URIs      9 LITERALS

    Subject Predicate Object
    1 grants:250d09b9c4d313da8ae597cf9b3ff6f1 sg:abstract DESCRIPTION (provided by applicant): Large prospective studies of aphasia recovery that incorporate anatomical, physiological, and behavioral data are virtually non-existent. This has a significant impact on virtually all research into the diagnosis, prognosis, and treatment of aphasia, since we do not know the natural course of the disease, and thus cannot adequately inform patients and families or assess the effects of therapeutic interventions. We believe that the complexities of data management, particularly regarding anatomical and physiological data, represent a major stumbling block to the design and execution of such studies. With such diverse sources of information as demographic and medical data, cognitive and linguistic test results, electrophysiological recordings, and many types of brain images, it is hard enough to perform single case studies that attempt to relate these data to each other, let alone studies that include statistically meaningful numbers of participants. Even when the problem is restricted to a single data type, such as functional MRI data, we do not have the ability to scale up the methods used in individual subjects to larger groups. Both the large volume of data and the complexity of data processing cause difficulties. We thus propose to build computational infrastructure (R21 phase) to facilitate the prospective investigation of aphasia recovery (R33 phase). The infrastructure is based on the use of (a) database technology to represent diverse data types within a single representational framework;and (b) "grid" computing to distribute data and data processing over many storage devices and computers, using software developed in federally (NSF) funded basic computational research that allows investigators to express complex data processing algorithms in a convenient manner. The longitudinal aphasia study will use structural and functional MRI and diffusion tensor imaging, along with language and cognitive measures, to characterize the natural course of physiological and behavioral recovery from aphasia. The physiology of recovery will be quantified in neural network models of individual patient imaging data and their mathematical "fit" to normative templates derived from imaging data on healthy age-matched adults. The changes in these models over time will be related to the behavioral changes to construct a theory of recovery. The computational infrastructure will provide the means to encode the diverse types of data needed for aphasia recovery research in such a way that complex queries involving multiple data types (e.g., brain activation and language performance) can be retrieved easily, and that queries requiring significant computer processing (e.g., peak detection in imaging time series) can be answered quickly due to grid computing. Finally, this infrastructure and data will be shared, and a user of the system from virtually anywhere could pose such questions using the relational database query interface.
    2 sg:endYear 2013
    3 sg:fundingAmount 1921605.0
    4 sg:fundingCurrency USD
    5 sg:hasContribution contributions:d1217b5bf6f2d7431731866f92a121ab
    6 sg:hasFieldOfResearchCode anzsrc-for:08
    7 anzsrc-for:0801
    8 anzsrc-for:0806
    9 sg:hasFundedPublication articles:18da3637fe677727fbce1ae70b5fb107
    10 articles:48b90da552e47472559fccfdb12df4c5
    11 articles:832b44b3e4e027671f62ee149e72ba6a
    12 articles:b0d81b22e29374d0634406c0fdca7fbf
    13 articles:c31ab293e31ee42b3e3ca358222d0143
    14 articles:e710d0987d976bd6534039c3b210a83c
    15 sg:hasFundingOrganization grid-institutes:grid.214431.1
    16 sg:hasRecipientOrganization grid-institutes:grid.266093.8
    17 sg:language English
    18 sg:license http://scigraph.springernature.com/explorer/license/
    19 sg:scigraphId 250d09b9c4d313da8ae597cf9b3ff6f1
    20 sg:startYear 2006
    21 sg:title Bioinformatics Infrastructure for Large Scale Studies of Aphasia Recovery
    22 sg:webpage http://projectreporter.nih.gov/project_info_description.cfm?aid=8221187
    23 rdf:type sg:Grant
    24 rdfs:label Grant: Bioinformatics Infrastructure for Large Scale Studies of Aphasia Recovery
    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular JSON format for linked data.

    curl -H 'Accept: application/ld+json' 'http://scigraph.springernature.com/things/grants/250d09b9c4d313da8ae597cf9b3ff6f1'

    N-Triples is a line-based linked data format ideal for batch operations .

    curl -H 'Accept: application/n-triples' 'http://scigraph.springernature.com/things/grants/250d09b9c4d313da8ae597cf9b3ff6f1'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'http://scigraph.springernature.com/things/grants/250d09b9c4d313da8ae597cf9b3ff6f1'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'http://scigraph.springernature.com/things/grants/250d09b9c4d313da8ae597cf9b3ff6f1'






    Preview window. Press ESC to close (or click here)


    ...