Correlator beware: correlation has limited selectivity for fMRI data analysis.

Groups of time-courses created from fMRI data by the frequently used correlation analysis are often highly heterogeneous. This heterogeneity is due to the limited selectivity of correlation when trying to match brain time-courses to an externally imposed activation paradigm. Thus, this process unnecessarily generates many type I errors (false positives). Furthermore, as a consequence of the heterogeneity, time-courses identified and grouped by correlation may in fact describe different activations. After demonstrating this inadequacy, we give one particular approach to partition such a heterogeneous group into internally more homogeneous subgroups, using Kendall's coefficient of concordance W, and show its applicability and application to both simulated and in vivo data. Such group partition and "purification" will help subsequent inferential methods to deal more efficiently with false positives.