Hello, Our group is trying to decide if, and to what extent, using ICA-FIX improves GLM results in our data analysis. We analyzed the same data with the same models once using FIX prior to calculating stats and again without using FIX.
We want to know how we might compare the two. One idea we had was to determine how well the model fit, as a reduction in noise in the data should hopefully produce a more consistent model fit. To measure this, I tried to use the VARCOPEs associated with each COPE as a measure of variability of the model fit. However, because the scale of these images varied with the scale of the input data, I decided to divide each VARCOPE image by the variance-across-time of the input data, producing something like an R-squared image. Another method would be to calculate the variance of the res4d image and divide that by the total variance and then subtract from an image of all 1's. That should hopefully produce an R-squared for the entire GLM model, as opposed to an individual COPE. I'm wondering if I'm fundamentally thinking about these images wrong, and especially if there is a better way to compare the two analysis streams. Without ground truth, is it possible to say whether FIX improved the analysis in a GLM framework? Thanks, Andrew Poppe, PhD Olin Center Institute of Living Hartford Hospital This e-mail message, including any attachments, is for the sole use of the intended recipient(s) and may contain confidential and privileged information. Any unauthorized review, use, disclosure, or distribution is prohibited. If you are not the intended recipient, or an employee or agent responsible for delivering the message to the intended recipient, please contact the sender by reply e-mail and destroy all copies of the original message, including any attachments. _______________________________________________ HCP-Users mailing list HCP-Users@humanconnectome.org http://lists.humanconnectome.org/mailman/listinfo/hcp-users