The document discusses different statistical methods for analyzing neuroimaging data: - ALE (Activation Likelihood Estimation) identifies areas of consistent activation across studies and represents activation likelihood as a statistical value. - KDA (Kernel Density Analysis) calculates the density of activation peaks within a radius of each voxel. - MULTI-KDA performs KDA separately on each study and represents the results as binary contrast indicator maps. The document also briefly defines the Wilcoxon signed-rank test as a non-parametric test that can be used as an alternative to paired t-tests when the population is not normally distributed. It assesses whether the mean ranks of two related samples differ.

46. "Correlation coefficient" by Kiatdd - Own work. Licensed under CC BY-SA 3.0 via Wikimedia Commons

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76. column 1: raw data: big black dots
showing the local maxima that are
reported, dotted line is “true” simulated
signal, black thick line is that signal with
added noise.
column 2: shows the results of ALE: the
result is more of a curve because the
“ALE statistic” reflects a probability value
that at least one peak is within r mm of
each voxel, so the highest values of
course correspond to actual peaks.
column 3: kernel density analysis (KDA)
gives us a value at each voxel that
represent the number of peaks within r
mm of that voxel. If we divide by voxel
resolution we can turn that into a
“density”
column 4: is MULTI kernel density
analysis, which is the same as KDA, but
the procedure is done for each
study. The resulting “contrast indicator
maps” are either 1 (yes, there is a peak
within r mm) or 0 (nope).

83. The Wilcoxon signed-rank test is a non-parametric statistical
hypothesis test used when comparing two related samples,
matched samples, or repeated measurements on a single
sample to assess whether their population mean ranks differ
(i.e. it is a paired difference test). It can be used as an
alternative to the paired Student's t-test, t-test for matched
pairs, or the t-test for dependent samples when the
population cannot be assumed to be normally distributed.[1]