Comparison of Energy Signals from the 4D DWT of Resting State FMRI Data Obtained from a Study on Schizophrenia

In this paper, we explore the use of the 4D discrete wavelet transform (DWT) on fMRI data. The data set comes from a study on schizophrenia. The compact support of the wavelet transform means that it keeps phenomenon localized in all 4 dimensions. We examine 16 sub-signals (frequency banded components of the signal) resulting from the 4D DWT, repre-senting the sub-signals as energy fluctuations over time. Next, we correlate these, and examine the variance. We find that grouping the variance data shows a small but clear trend of the control group versus those with a schizophrenia diagnosis. The DWT analyzes a signal into approximations and details, which we expect reflect the similarities and differences between brain activity, respectively. We want to know how the regions of brain activity vary with time, and the high-pass data encodes these changes over time.