Dear FSL experts,
I am wondering whether there is a way to assess "goodness of fit" for each voxel in FSL? What I have done is calculating the temporal variance of the residual and dividing it by the temporal vaiance of the filtered_func_data to look at how much variances are left over:
fslmaths res4d.nii.gz -Tstd res4d_Tstd.nii.gz
fslmaths filtered_func_data.nii.gz -Tstd filtered_func_Tstd.nii.gz
fslmaths res4d_Tstd.nii.gz -div filtered_func_Tstd.nii.gz percentage_unexplained_variance.nii.gz
As far as it makes sense, I have been getting high values from this method in voxels that had high z scores, indicating a large portion of unexplained variance. I am now looking at result from one run in which we compared BOLD signal when the subject was moving his hand versus no movement, which should have very significant signal. However, for the voxel in motor cortex that has the largest z value (18+), the ratio of temporal std of residuals to temporal std of filtered_func_data time series is as high as 0.38. In data from a stroke patient, the ratio was 0.55 for a voxel that has z value of 6, and the average ratio within a 5mm spherical roi centered at the voxel was over 0.7. Is it normal?
I originally asked this question as one of the replies to a different question and got no responses, so I am asking it again here. Thank you!
Best,
Yuqi
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