Abstract
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.
Highlights
Real-time functional magnetic resonance imaging is a recently emerged technique that permits the online observation of brain activity during recording
The maximum distance (Dmax(r,e)) between the referenced matrix (Mr), which consisted of the given parameters, and the matrix (Me), which consisted of the parameters estimated using traditional affine registration (AFR) or principal axes registration (PA)-Gauss-Newton optimization (GN)(b) AFR, was much less than a voxel size (3.163.164.8 mm3) for both AFR methods
The proposed online spatial normalization significantly improved the performance of traditional AFR using PA and the selfadaptive b parameter, while the accuracy was maintained the same as with the traditional AFR
Summary
Real-time functional magnetic resonance imaging (rtfMRI) is a recently emerged technique that permits the online observation of brain activity during recording This technique is essential for a variety of applications, such as neurofeedback, in which subjects are trained to self-regulate the local blood oxygen level dependent (BOLD) response in specific brain areas to improve their behavioral performance [1,2]. Application of the rtfMRI technique on the self-regulation of brain connectivity and network activities has become an attractive topic [18,19,20,21] In these applications, spatial normalization may be an essential procedure prior to network analysis, such as semiblind independent component analysis (ICA) [22,23], when template masks are used or when the image needs to be analyzed in a stereotactic space. The default mode network (DMN) [24] of the individual may be automatically measured using a DMN template from MNI space [22]
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