Dear AFNI experts,
I have a question in generating group-level maps of the functional correlation maps that is highly positively skewed.
I basically followed the steps of simple correlation analysis from seed region time series during task performance (https://afni.nimh.nih.gov/SimAna)
- original volumes were cleaned by subtracting baseline/nuisance activity included activity from ventricles, white-matter, signal drift, head-motions.
- From the clean data, I ran 3dDeconvolve with with the time seed activity from seed regions with removing signal drift (polort) / head motions.
- Based on the correlations / R^2 values, I transformed into correlation Z-maps.
- I didn’t apply any bandpass filter on the data.
For each individual participant, I have seed-based Fisher’s z-transformed correlation map. The resulting Z-map ranges about [–0.32, 1.02] or so, but my correlation maps are highly positively skewed consistently all participants – except a few regions (around ventricles) showing negative correlations, I see mostly positive correlations across the whole brain. Because mostly all regions exhibit positive correlations in all participants, I get a significant group map from 3dttest++ on z-maps almost all places in the brain (i.e., group map from 3dttest++ against 0).
It is possible the single-subject correlation Z-map can be just wrong. But if this is just the case that they are really positively skewed (mostly showing positive correlations) – I was wondering how I should go about finding a group-level cluster in the dataset that is not normally distributed around 0.
Thanks in advance!