Improving the Detection of Activation in High Resolution fMRI using Multivariate

Information

  • Research Project
  • 8438968
  • ApplicationId
    8438968
  • Core Project Number
    R01EB014284
  • Full Project Number
    1R01EB014284-01A1
  • Serial Number
    014284
  • FOA Number
    PA-11-260
  • Sub Project Id
  • Project Start Date
    5/1/2013 - 11 years ago
  • Project End Date
    4/30/2016 - 8 years ago
  • Program Officer Name
    PAI, VINAY MANJUNATH
  • Budget Start Date
    5/1/2013 - 11 years ago
  • Budget End Date
    4/30/2014 - 10 years ago
  • Fiscal Year
    2013
  • Support Year
    01
  • Suffix
    A1
  • Award Notice Date
    4/26/2013 - 11 years ago
Organizations

Improving the Detection of Activation in High Resolution fMRI using Multivariate

DESCRIPTION (provided by applicant): The overall goal of this project is to develop a local multivariate analysis software package for fMRI data analysis. It will provide psychologists and neuroscientists a more powerful tool to analyze their fMRI data using advanced multivariate methods. This project will lead to better brain activation maps and thus promote the discovery of currently unknown aspects of brain function. Mass-univariate analysis, such as the general linear model (GLM), is the prevailing fMRI data analysis method. However, it suffers from blurring of edges of activation and potential elimination of the detection of weak activated regions due to routinely applied fixed isotropic spatial Gaussian smoothing. Local multivariate methods such as canonical correlation analysis (CCA) and its variants have been shown to significantly increase the detection power of fMRI activations and improve activation maps. As an advantage, CCA uses adaptive spatial filtering kernels to accurately extract the signal better in a noisy environment. However, there are several drawbacks, particularly low spatial specificity, long computational time, and single-factor experimental design limitation. Furthermore, a parametric estimation method does not exist to determine the family-wise error rate, no extension to group analysis has been investigated, and no studies extending local CCA to nonlinear CCA for fMRI data using kernel methods have been systematically carried out. All these drawbacks prevent local CCA methods from being widely accepted in neuroscience research in fMRI. In this proposal, our goals are to eliminate these drawbacks using novel local multivariate analysis methods (based on CCA) and to develop a software tool to widen its broader application in the neuroscience research community. We expect this software tool to be particularly valuable for neuroscience research where detections of weak activations or spatially localized patterns of activations are desired. As high resolution imaging and computer power advance, we expect an increase in demand for this software tool, thus advancing new discoveries of brain function and more precise spatial localization of activations. As a particular application, we will focus on studying memory actions using a novel event-related recognition paradigm to investigate the effects of familiarity and recollection in subregions of the medial temporal lobes (MTL) for high resolution fMRI. This research will advance our understanding of hippocampal/MTL contributions to memory, which can substantially advance our understanding of the memory deficits associated with a number of debilitating neurological and psychiatric conditions that show abnormalities in these regions, including mild cognitive impairment (MCI), Alzheimer¿s disease, schizophrenia, and major depression. More generally, it will provide psychologists and neuroscientists a more powerful tool to analyze their fMRI data using advanced multivariate methods.

IC Name
NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING
  • Activity
    R01
  • Administering IC
    EB
  • Application Type
    1
  • Direct Cost Amount
    264854
  • Indirect Cost Amount
    14336
  • Total Cost
    279190
  • Sub Project Total Cost
  • ARRA Funded
    False
  • CFDA Code
    286
  • Ed Inst. Type
  • Funding ICs
    NIBIB:279190\
  • Funding Mechanism
    Non-SBIR/STTR RPGs
  • Study Section
    NOIT
  • Study Section Name
    Neuroscience and Ophthalmic Imaging Technologies Study Section
  • Organization Name
    RYERSON UNIVERSITY
  • Organization Department
  • Organization DUNS
    207723123
  • Organization City
    TORONTO
  • Organization State
    ON
  • Organization Country
    CANADA
  • Organization Zip Code
    M5B 2K3
  • Organization District
    CANADA