ODVBA: Optimally-Discriminative Voxel-Based Analysis

Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
IEEE transactions on medical imaging 02/2011; 30(8):1441-54. DOI: 10.1109/TMI.2011.2114362
Source: PubMed


Gaussian smoothing of images prior to applying voxel-based statistics is an important step in voxel-based analysis and statistical parametric mapping (VBA-SPM) and is used to account for registration errors, to Gaussianize the data and to integrate imaging signals from a region around each voxel. However, it has also become a limitation of VBA-SPM based methods, since it is often chosen empirically and lacks spatial adaptivity to the shape and spatial extent of the region of interest, such as a region of atrophy or functional activity. In this paper, we propose a new framework, named optimally-discriminative voxel-based analysis (ODVBA), for determining the optimal spatially adaptive smoothing of images, followed by applying voxel-based group analysis. In ODVBA, nonnegative discriminative projection is applied regionally to get the direction that best discriminates between two groups, e.g., patients and controls; this direction is equivalent to local filtering by an optimal kernel whose coefficients define the optimally discriminative direction. By considering all the neighborhoods that contain a given voxel, we then compose this information to produce the statistic for each voxel. Finally, permutation tests are used to obtain a statistical parametric map of group differences. ODVBA has been evaluated using simulated data in which the ground truth is known and with data from an Alzheimer's disease (AD) study. The experimental results have shown that the proposed ODVBA can precisely describe the shape and location of structural abnormality.

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Available from: Tianhao Zhang, Oct 06, 2014
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    • "While the searchlight mapping approach is very attractive , it only explores local relationships and does not account for long distance spatially distributed patterns. A more recent method that has some similarities with searchlight is the optimally-discriminative voxel-based analysis (ODVBA) [20]. ODVBA is a framework proposed to determine the optimal spatially adaptive smoothing. "
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    • "denotes that x is the i th element in N, and δ N denotes the discrimination degree [9]. "
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    ABSTRACT: This significantly extends Multi-Voxel Pattern Analysis (MVPA) methods, such as the Searchlight and related methods, by building on an approach that was recently proposed for structural brain images, and was named Optimally-Discriminative Voxel-Based Analysis (ODVBA), which uses machine learning models to determine the optimal anisotropic filtering of images that enhances group differences. Precise spatial maps of activation are computed by tallying the weights of each voxel to all of the neighborhood in which it belongs, and significance maps are obtained via permutation testing. We adapt this idea to both single and multi-subject fMRI analysis. Both simulated data and real data from 12 adolescent subjects who completed a standard working memory task demonstrated the use of ODVBA in fMRI improves accuracy and spatial specificity of activation detection over Searchlight.
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