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Automated and nonbiased regional quantification of functional neuroimaging data
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In the quantification of functional neuroimaging data, region-of-interest (ROI) analysis can be used to assess a variety of properties of the activation signal, but taken alone these properties are susceptible to noise and may fail to accurately describe overall regional involvement. Here, the authors present and evaluate an automated method for quantification and localization of functional neuroimaging data that combines multiple properties of the activation signal to generate rank-order lists of regional activation results.
The proposed automated quantification method, referred to as neuroimaging results decomposition (NIRD), begins by decomposing an activation map into a hierarchical list of ROIs using a digital atlas. In an intermediate step, the ROIs are rank-ordered according to extent, mean intensity, and total intensity. A final rank-order list (NIRD average rank) is created by sorting the ROIs according to the average of their ranks from the intermediate step. The authors hypothesized that NIRD average rank would have improved regional quantification accuracy compared to all other quantitative metrics, including methods based on properties of statistical clusters. To test their hypothesis, NIRD rankings were directly compared to three common cluster-based methods using simulated fMRI data both with and without realistic head motion.
For both the no-motion and motion datasets, an analysis of variance found that significant differences between the quantification methods existed (F = 64.8, p < 0.0001 for no motion; F = 55.2, p < 0.0001 for motion), and a post-hoc test found that NIRD average rank was the most accurate quantification method tested (p < 0.05 for both datasets). Furthermore, all variants of the NIRD method were found to be significantly more accurate than the cluster-based methods in all cases.
These results confirm their hypothesis and demonstrate that the proposed NIRD methodology provides improved regional quantification accuracy compared to cluster-based methods.
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