HI all, We had submitted a paper based on cortical thickness analysis in Qdec. The reviewer's had couple concerns regarding the QDec analysis. We would appreciate your help to address/resolve those issues. Here are the concerns:
(1) What was the rationale of using 10mm smoothing? The typical fMRI values for volumteric work is 2-3 x the original voxel size. What's the reason for this value? Our response so far: A smoothing kernel of 10 mm was chosen because this setting had been published recently in articles that performed similar analyses. 10 mm therefore was a typical parameter for this type of analysis. This setting was also the default in the QDEC GUI, also suggesting that it was a typical setting. We only performed analyses of the data for this project using this setting and did not iteratively test multiple kernel widths. Also, the FWHM for cortical thickness analyses is somewhat larger than that used in fMRI analyses because it is applied to surface, as opposed to volume-based data. Surface based data are not prone to the same artifacts of including white matter, csf or brain tissue on the other side of a sulcus as volume-based data are. (2) What's the p-value to get in for a cluster-wise analysis and what size is a significant cluster? It's important to know these non cluster-wise values to get a sense of the parameters that determine significance. Our response so far: QDEC does not have a parameter that can be adjusted for cluster size or extent as other whole brain analyses such as Statistical Parametric Mapping do. The researcher can only indicate a statistical cutoff. We used a p value cutoff of 0.05 after a correction for multiple comparisons that was based on the Monte Carlo permutation cluster analysis with 10,000 iterations. Additionally, our findings comprise of pretty large clusters. This obviates the reason for cluster-thresholding based on size. Thanks in advance for the response. Binod
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