  Brain atlases are a ubiquitous tool used for analyzing and interpreting brain
imaging datasets. Traditionally, brain atlases divided the brain into regions
separated by anatomical landmarks. In the last decade, several attempts have
been made to parcellate the brain into regions with distinct functional
activity using fMRI. To construct a brain atlas using fMRI, data driven
algorithms are used to group voxels with similar functional activity together
to form regions. Hierarchical clustering is one parcellation method that has
been used for functional parcellation of the brain, resulting in parcellations
that align well with cytoarchitectonic divisions of the brain. However, few
rigorous data driven evaluations of the method have been performed. Moreover,
the effect of removing autocorrelation trends from fMRI time series
(prewhitening) on the structure of the resultant atlas has not been previously
explored. In this paper, we use hierarchical clustering to produce functional
parcellations of the brain using hierarchical clustering. We use both
prewhitened and raw fMRI time series to construct the atlas. The resultant
atlases were then evaluated for their homogeneity, separation between regions,
reproducibility across subjects, and reproducibility across scans.
