Functional connectivity analysis using wavelets

Abstract

Functional connectivity is defined as a functionally significant temporal correlation between distinct brain voxels' fMRI time series. While searching for functional connectivity we need to take into account background correlation that inevitably exists in fMRI data. Background (intrinsic) correlation can be present in the data because of a host of factors and some of them include: scanner induced correlations, head movement during the scanning, data processing (smoothing) or biological signals like heart beat or breathing. I have used 4D spatiotemporal wavelet packet resampling technique which allowed me to test hypothesis that the correlation present in the data is due only to background correlation. Thanks to it I was able to find brain regions that were functionally connected with a prechosen seed region. In my experiments I, among others, chose the fusiform face area (FFA) as a seed region to find which other brain areas functionally correlate with it.

Fig. 1. Axial slices of correlation volumes coming from realigned smoothed original functional volumes.