A Model-Free De-Drifting Approach for Detecting BOLD Activities in fMRI Data

Abstract

A model-free method for efficiently capturing drifts in functional magnetic resonance imaging (fMRI) data is presented. The proposed algorithm applies a first order differencing to the fMRI time series samples in order to remove the drift effect. Initially, a consistent hemodynamic response function (HRF) of the fMRI voxel is estimated using linear least-squares. An optimal estimate of the drift is then obtained based on a wavelet thresholding technique applied to the generated residuals after eliminating the induced activation response. Finally, the de-drifted fMRI voxel response is acquired by removing the estimated drift from the fMRI time-series. Its performance is assessed using simulated and motor-task real fMRI data sets obtained from both block and event-related designs. The application results reveal that the proposed method, which avoids the selection of a model to remove the drift component unlike traditional methods, is efficient in de-drifting the fMRI time-series and offers blood oxygen level-dependent (BOLD)-fMRI signal improvement and enhanced activation detection.