Data from: Online spatial normalization for real-time fMRI

Real-time functional magnetic resonance imaging (rtfMRI) is a recently
emerged technique that demands fast data processing within a single
repetition time (TR), such as a TR of 2 seconds. Data preprocessing in
rtfMRI has rarely involved spatial normalization, which can not be
accomplished in a short time period. However, spatial normalization may be
critical for accurate functional localization in a stereotactic space and
is an essential procedure for some emerging applications of rtfMRI. In
this study, we introduced an online spatial normalization method that
adopts a novel affine registration (AFR) procedure based on principal axes
registration (PA) and Gauss-Newton optimization (GN) using the
self-adaptive β parameter, termed PA-GN(β) AFR and nonlinear registration
(NLR) based on discrete cosine transform (DCT). In AFR, PA provides an
appropriate initial estimate of GN to induce the rapid convergence of GN.
In addition, the β parameter, which relies on the change rate of cost
function, is employed to self-adaptively adjust the iteration step of GN.
The accuracy and performance of PA-GN(β) AFR were confirmed using both
simulation and real data and compared with the traditional AFR. The
appropriate cutoff frequency of the DCT basis function in NLR was
determined to balance the accuracy and calculation load of the online
spatial normalization. Finally, the validity of the online spatial
normalization method was further demonstrated by brain activation in the
rtfMRI data.