Wavelet packet based clustering for the study of functional connectivity in the rat brain

This paper presents a data-driven clustering method based on the use of wavelet packet features for the study of functionally connected regions in the brain. In particular, wavelet packets are used because of their optimal whitening properties for 1/f-like processes, in association with a uniform segmentation of the frequency axis. Features obtained by the wavelet packet transform are grouped together using agglomerative clustering with standardize Euclidian distance. The results obtained are compared with known atlas of the rat brain and with results obtained from a technique based on the standard wavelet transform previously presented by the same authors. The presented approach produces clusters that are well matched to classical anatomical regions in the sensorimotor cortex of the rat brain and also produces clusters that roughly correspond to primary motor, primary somatosensory and secondary somatosensory areas, with the subcortical clusters typically comprising one or two groups in the caudate putamen area.