1. Signal fluctuations in 2D and 3D fMRI at 7 Tesla
J. Jorge, J. Marques, W. van der Zwaag, P. Figueiredo
Institute for Systems and Robotics / Instituto Superior Técnico; Lisboa, Portugal
Introduction
In the last decades, functional magnetic resonance imaging (fMRI) techniques have been widely used for the study of human brain function.
The development of high-field MRI systems has allowed for significant improvements in image signal-to-noise ratio (SNR), potentially leading to higher sensitivity and spatial resolution.
Recent studies have shown that these potential advantages become compromised by increased signal fluctuations from correlated noise sources, which include subject motion, physiological processes, and spontaneous neural activity.
It is thus important to explore methods capable of minimizing these problematic tendencies, such as improved acquisition techniques (3D vs. 2D) and/or adequate correction methods.
Results
Methods
FMRI data were acquired from healthy subjects at rest, with eyes closed, and later submitted to a visual Localizer (Loc) paradigm; acquisitions were performed with a 7 Tesla Siemens system, utilizing a multi-slice (2D) EPI method and a segmented (3D) EVI method (Zwaag et al., ISMRM 2009).
Functional data were motion-corrected and submitted to FSL FEAT pre-stats, stats and post-stats routines; design matrices built for general linear model (GLM) analyses primarily included a base set DM0 containing Loc paradigm and slow drift regressors.
A principal component analysis (PCA)-based approach was adopted for correlated-noise correction, following the basic principles of previously described methodology (Bianciardi et al., MRM 2009), with a few modifications.
For results extraction, an active region of interest (ROIAct) was defined as the binary intersection between a manually selected visual area mask and a cluster activation mask from a basic stats + post-stats Loc data analysis, utilizing a general visual activation contrast from the Loc paradigm (Faces + Houses + Objects + Scrambled Objects vs. background).
Average results obtained for both acquisition techniques in terms of uncorrected, RND-corrected, and PCA-corrected data.
Axial slices from a representative example of ROIAct (in red) and an additional ROI designated as ROIRef (in blue), necessary for the PCA-based correction approach.
Results were averaged across subjects and compared between different acquisition types (2D and 3D) and different GLM models (base regressor sets DM0, PCA-derived sets, and randomized PCA sets (RND)).
The evolution of spatial and temporal SNR with fMRI voxel width in white matter ROIs were estimated by means of digital image resizing in Matlab.
PCA-based models achieved better results than both DM0 and RND models for all outcome measures in general.
Results from base and RND models were usually poorer for segmented EVI data relatively to EPI data.
For most measures, improvements in results from RND to PCA models were more significant in 3D data; PCA-corrected 3D results became similar to or better than results from either uncorrected or PCA-corrected 2D data.
Conclusions
The PCA correction method allowed for significant improvements in all activation sensitivity measures. Furthermore, data from 3D acquisitions, which were found to be more affected by correlated noise, did show superior improvements in general as compared to 2D data. Hence, from this perspective, this new acquisition technique has the potential to become a useful tool for high-field imaging, if adequate corrections are considered.
RecPad th edition of the Portuguese Conference on Pattern Recognition, UTAD University, Vila Real city, October 29th