Local Normalization

 Local Normalization

Using our normative data, we explored several aspects of this normal cortical variability, both within and across individual healthy volunteers. Using our metrics of similarity (Euclidean distance and cosine similarity), we first hypothesized that, across healthy volunteers, pairs of patches at the same location (homotopic patches) should be more similar to each other than pairs of randomly selected patches at different locations (heterotopic patches).  Using the same metrics, we also tested the hypothesis that homologous contralateral patches within individual subjects, as are commonly compared when calculating asymmetry indices, would be more similar to each other than randomly selected heterotopic patches.  For each comparison, the mean and standard deviation of the included patches are reported. Welch's t-test was used to compare the distribution of Euclidean distances and cosine similarities between patches because both distributions were approximately normally distributed but often had unequal variances.  Effect size was reported as in \cite{cohen1988statistical}\(d=0.2\) as small\(d=0.5\) as medium, and \(d=0.5\) as large.