Model-Free Optimal De-Drifting And Enhanced Detection In FMRI Data
Adnan Shah, Abd-Krim Seghouane

Discriminating between active and non-active brain voxels
in noisy functional magnetic resonance imaging (fMRI) data
plays an important role when investigating task-related activations
of the neuronal sites. A novel method for efficiently
capturing drifts in the functionalmagnetic resonance imaging
(fMRI) data is presented that leads to enhanced fMRI activation
detection. The proposed algorithm apply a first order
differencing to the fMRI time series samples in order to remove
the drift effect. Using linear least-squares, a consistent
hemodynamic response function (HRF) of the fMRI voxel is
estimated as a first-step that leads to an optimal estimate of
the drift based on a wavelet thresholding technique. The dedrifted
fMRI voxel response is then obtained by removing the
estimated drift from the fMRI time-series. Its performance is
assessed using a visual task real fMRI data set. The application
results reveal that the proposed method, which avoids the
selection of a model to remove the drift component, leads to
an improved activation detection performance in fMRI data.