NeuroImage
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Recent studies have demonstrated that spatial patterns of fMRI BOLD activity distribution over the brain may be used to classify different groups or mental states. These studies are based on the application of advanced pattern recognition approaches and multivariate statistical classifiers. Most published articles in this field are focused on improving the accuracy rates and many approaches have been proposed to accomplish this task. ⋯ In this work, we introduce the Maximum Uncertainty Linear Discrimination Analysis (MLDA) and show how it can be applied to infer groups' patterns by discriminant hyperplane navigation. In addition, we show that it naturally defines a behavioral score, i.e., an index quantifying the distance between the states of a subject from predefined groups. We validate and illustrate this approach using a motor block design fMRI experiment data with 35 subjects.
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Chronic pediatric traumatic brain injury (TBI) is associated with significant and persistent neurobehavioral deficits. Using diffusion tensor imaging (DTI), we examined area, fractional anisotropy (FA), radial diffusion, and axial diffusion from six regions of the corpus callosum (CC) in 41 children and adolescents with TBI and 31 comparison children. Midsagittal cross-sectional area of the posterior body and isthmus was similar in younger children irrespective of injury status; however, increased area was evident in the older comparison children but was obviated in older children with TBI, suggesting arrested development. ⋯ IQ, working memory, motor, and academic skills were correlated significantly with radial diffusion and/or FA from the isthmus and splenium only in the TBI group. Reduced size and microstructural changes in posterior callosal regions after TBI suggest arrested development, decreased organization, and disrupted myelination. Increased radial diffusivity was the most sensitive DTI-based surrogate marker of the extent of neuronal damage following TBI; FA was most strongly correlated with neuropsychological outcomes.
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The diffusion tensor is a commonly used model for diffusion-weighted MR image data. The parameters are typically estimated by ordinary or weighted least squares on log-transformed data, assuming normal or log-normal distribution of measurement errors respectively. This may not be adequate when using high b-values and or performing high-resolution scans, resulting in poor SNR, in which case the difference between the assumed and the true (Rician) noise model becomes important. ⋯ By pooling the Rician estimates of uncertainty over neighbouring voxel estimates with higher precision, but still not as high as with a Gaussian model, can be obtained. We suggest the use of a Rician estimator when it is important with truly quantitative values and when comparing different predictive models. The higher precision of the Gaussian estimates may be more important when the objective is to compare diffusion related parameters over time or across groups.