Bioinformatics Infrastructure for Large Scale Studies of Aphasia Recovery View Homepage


Ontology type: schema:MonetaryGrant     


Grant Info

YEARS

2006-2008

FUNDING AMOUNT

380465 USD

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. More... »

URL

http://projectreporter.nih.gov/project_info_description.cfm?aid=7289307

Related SciGraph Publications

  • 2009-11. Biological approaches to aphasia treatment in CURRENT NEUROLOGY AND NEUROSCIENCE REPORTS
  • JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/2208", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/2208", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "type": "DefinedTerm"
          }
        ], 
        "amount": {
          "currency": "USD", 
          "type": "MonetaryAmount", 
          "value": "380465"
        }, 
        "description": "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.", 
        "endDate": "2008-08-31T00:00:00Z", 
        "funder": {
          "id": "https://www.grid.ac/institutes/grid.214431.1", 
          "type": "Organization"
        }, 
        "id": "sg:grant.2607768", 
        "identifier": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "2607768"
            ]
          }, 
          {
            "name": "nih_id", 
            "type": "PropertyValue", 
            "value": [
              "R21DC008638"
            ]
          }
        ], 
        "inLanguage": [
          "en"
        ], 
        "keywords": [
          "data management", 
          "algorithms", 
          "multiple data types", 
          "normative template", 
          "behavioral recovery", 
          "linguistic test results", 
          "adults", 
          "physiology", 
          "system", 
          "grid computing", 
          "NSF", 
          "single representational framework", 
          "electrophysiological recordings", 
          "significant impact", 
          "diverse types", 
          "R33 phase", 
          "grid", 
          "aphasia recovery research", 
          "healthy age", 
          "prospective investigation", 
          "diagnosis", 
          "brain images", 
          "therapeutic intervention", 
          "theory", 
          "relational database query interface", 
          "individual subjects", 
          "means", 
          "single case study", 
          "language", 
          "such questions", 
          "Large Scale Studies", 
          "model", 
          "such studies", 
          "such diverse sources", 
          "ability", 
          "information", 
          "alone study", 
          "behavioral data", 
          "large volumes", 
          "major stumbling block", 
          "basic computational research", 
          "queries", 
          "brain activation", 
          "family", 
          "data processing", 
          "applicants", 
          "investigators", 
          "aphasia recovery", 
          "computer", 
          "large group", 
          "diffusion tensor imaging", 
          "cognitive measures", 
          "functional MRI data", 
          "bioinformatics infrastructure", 
          "individual patients", 
          "changes", 
          "participants", 
          "prognosis", 
          "imaging time series", 
          "use", 
          "behavioral changes", 
          "meaningful number", 
          "physiological data", 
          "natural course", 
          "way", 
          "single data type", 
          "description", 
          "research", 
          "execution", 
          "aphasia", 
          "peak detection", 
          "database technology", 
          "patients", 
          "disease", 
          "language performance", 
          "design", 
          "data", 
          "convenient manner", 
          "effect", 
          "diverse data types", 
          "infrastructure", 
          "medical data", 
          "neural network model", 
          "many storage devices", 
          "software", 
          "users", 
          "longitudinal aphasia study", 
          "functional MRI", 
          "recovery", 
          "complex queries", 
          "computational infrastructure", 
          "complexity", 
          "significant computer processing", 
          "time", 
          "data processing cause difficulties", 
          "treatment", 
          "R21 phase", 
          "problem", 
          "many types", 
          "complex data", 
          "METHODS", 
          "large prospective study"
        ], 
        "name": "Bioinformatics Infrastructure for Large Scale Studies of Aphasia Recovery", 
        "recipient": [
          {
            "id": "https://www.grid.ac/institutes/grid.170205.1", 
            "type": "Organization"
          }, 
          {
            "affiliation": {
              "id": "https://www.grid.ac/institutes/grid.170205.1", 
              "name": "UNIVERSITY OF CHICAGO", 
              "type": "Organization"
            }, 
            "familyName": "SMALL", 
            "givenName": "STEVEN L", 
            "id": "sg:person.0620416267.28", 
            "type": "Person"
          }, 
          {
            "member": "sg:person.0620416267.28", 
            "roleName": "PI", 
            "type": "Role"
          }
        ], 
        "sameAs": [
          "https://app.dimensions.ai/details/grant/grant.2607768"
        ], 
        "sdDataset": "grants", 
        "sdDatePublished": "2019-03-07T12:03", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com.uberresearch.data.processor/core_data/20181219_192338/projects/base/nih_projects_15.xml.gz", 
        "startDate": "2006-09-20T00:00:00Z", 
        "type": "MonetaryGrant", 
        "url": "http://projectreporter.nih.gov/project_info_description.cfm?aid=7289307"
      }
    ]
     

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

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/grant.2607768'

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

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/grant.2607768'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/grant.2607768'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/grant.2607768'


     

    This table displays all metadata directly associated to this object as RDF triples.

    146 TRIPLES      19 PREDICATES      124 URIs      116 LITERALS      5 BLANK NODES

    Subject Predicate Object
    1 sg:grant.2607768 schema:about anzsrc-for:2208
    2 schema:amount N1134deb089a245a0972c9ec3d7203f4c
    3 schema:description 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.
    4 schema:endDate 2008-08-31T00:00:00Z
    5 schema:funder https://www.grid.ac/institutes/grid.214431.1
    6 schema:identifier N1cd81d975be248ffad2c1a73f0d84c90
    7 N52ac39808d6f4828be8d1f44208310fa
    8 schema:inLanguage en
    9 schema:keywords Large Scale Studies
    10 METHODS
    11 NSF
    12 R21 phase
    13 R33 phase
    14 ability
    15 adults
    16 algorithms
    17 alone study
    18 aphasia
    19 aphasia recovery
    20 aphasia recovery research
    21 applicants
    22 basic computational research
    23 behavioral changes
    24 behavioral data
    25 behavioral recovery
    26 bioinformatics infrastructure
    27 brain activation
    28 brain images
    29 changes
    30 cognitive measures
    31 complex data
    32 complex queries
    33 complexity
    34 computational infrastructure
    35 computer
    36 convenient manner
    37 data
    38 data management
    39 data processing
    40 data processing cause difficulties
    41 database technology
    42 description
    43 design
    44 diagnosis
    45 diffusion tensor imaging
    46 disease
    47 diverse data types
    48 diverse types
    49 effect
    50 electrophysiological recordings
    51 execution
    52 family
    53 functional MRI
    54 functional MRI data
    55 grid
    56 grid computing
    57 healthy age
    58 imaging time series
    59 individual patients
    60 individual subjects
    61 information
    62 infrastructure
    63 investigators
    64 language
    65 language performance
    66 large group
    67 large prospective study
    68 large volumes
    69 linguistic test results
    70 longitudinal aphasia study
    71 major stumbling block
    72 many storage devices
    73 many types
    74 meaningful number
    75 means
    76 medical data
    77 model
    78 multiple data types
    79 natural course
    80 neural network model
    81 normative template
    82 participants
    83 patients
    84 peak detection
    85 physiological data
    86 physiology
    87 problem
    88 prognosis
    89 prospective investigation
    90 queries
    91 recovery
    92 relational database query interface
    93 research
    94 significant computer processing
    95 significant impact
    96 single case study
    97 single data type
    98 single representational framework
    99 software
    100 such diverse sources
    101 such questions
    102 such studies
    103 system
    104 theory
    105 therapeutic intervention
    106 time
    107 treatment
    108 use
    109 users
    110 way
    111 schema:name Bioinformatics Infrastructure for Large Scale Studies of Aphasia Recovery
    112 schema:recipient N0c408655fc2b46d5a2af7b95c714015d
    113 sg:person.0620416267.28
    114 https://www.grid.ac/institutes/grid.170205.1
    115 schema:sameAs https://app.dimensions.ai/details/grant/grant.2607768
    116 schema:sdDatePublished 2019-03-07T12:03
    117 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    118 schema:sdPublisher Nc645a953f82142ae9520aaabf38e6f35
    119 schema:startDate 2006-09-20T00:00:00Z
    120 schema:url http://projectreporter.nih.gov/project_info_description.cfm?aid=7289307
    121 sgo:license sg:explorer/license/
    122 sgo:sdDataset grants
    123 rdf:type schema:MonetaryGrant
    124 N0c408655fc2b46d5a2af7b95c714015d schema:member sg:person.0620416267.28
    125 schema:roleName PI
    126 rdf:type schema:Role
    127 N1134deb089a245a0972c9ec3d7203f4c schema:currency USD
    128 schema:value 380465
    129 rdf:type schema:MonetaryAmount
    130 N1cd81d975be248ffad2c1a73f0d84c90 schema:name dimensions_id
    131 schema:value 2607768
    132 rdf:type schema:PropertyValue
    133 N52ac39808d6f4828be8d1f44208310fa schema:name nih_id
    134 schema:value R21DC008638
    135 rdf:type schema:PropertyValue
    136 Nc645a953f82142ae9520aaabf38e6f35 schema:name Springer Nature - SN SciGraph project
    137 rdf:type schema:Organization
    138 anzsrc-for:2208 schema:inDefinedTermSet anzsrc-for:
    139 rdf:type schema:DefinedTerm
    140 sg:person.0620416267.28 schema:affiliation https://www.grid.ac/institutes/grid.170205.1
    141 schema:familyName SMALL
    142 schema:givenName STEVEN L
    143 rdf:type schema:Person
    144 https://www.grid.ac/institutes/grid.170205.1 schema:name UNIVERSITY OF CHICAGO
    145 rdf:type schema:Organization
    146 https://www.grid.ac/institutes/grid.214431.1 schema:Organization
     




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


    ...