People here have been helpful in the past with questions like this, and I have spent all day trying to figure out what to do but could find no answer.

I have a matrix of pearson's R data that I wished to smooth using a gaussian kernal and use to generate Z-scores from which I will then determine a region of interest. The smoothing does improve the signal to noise ratio but I think the z-scores are being suppressed due to the smoothing process. When I perform the fisher transform (ie R->Z conversion) on the smoothed data the standard deviation is less than what would be expected from a set of pearson's R values, which I think may be invalidating the Z-scores. My solution was to "adjust" the Z scores of the smoothed data by multiplying every value of the matrix by the ratio of stdev(Data)/stdev(SmoothedData)... where stdev=standard deviation.

The result can be seen in the lower left panel, blue curves are just there for reference:

Am I doing something wrong here?