Paper Info
Title
The effect of spatial resolution on decoding accuracy in fMRI multivariate pattern analysis.
Abstract
Multivariate pattern analysis (MVPA) in fMRI has been used to extract information from distributed cortical activation patterns, which may go undetected in conventional univariate analysis. However, little is known about the physical and physiological underpinnings of MVPA in fMRI as well as about the effect of spatial smoothing on its performance. Several studies have addressed these issues, but their investigation was limited to the visual cortex at 3T with conflicting results. Here, we used ultra-high field (7T) fMRI to investigate the effect of spatial resolution and smoothing on decoding of speech content (vowels) and speaker identity from auditory cortical responses. To that end, we acquired high-resolution (1.1mm isotropic) fMRI data and additionally reconstructed them at 2.2 and 3.3mm in-plane spatial resolutions from the original k-space data. Furthermore, the data at each resolution were spatially smoothed with different 3D Gaussian kernel sizes (i.e. no smoothing or 1.1, 2.2, 3.3, 4.4, or 8.8mm kernels). For all spatial resolutions and smoothing kernels, we demonstrate the feasibility of decoding speech content (vowel) and speaker identity at 7T using support vector machine (SVM) MVPA. In addition, we found that high spatial frequencies are informative for vowel decoding and that the relative contribution of high and low spatial frequencies is different across the two decoding tasks. Moderate smoothing (up to 2.2mm) improved the accuracies for both decoding of vowels and speakers, possibly due to reduction of noise (e.g. residual motion artifacts or instrument noise) while still preserving information at high spatial frequency. In summary, our results show that – even with the same stimuli and within the same brain areas – the optimal spatial resolution for MVPA in fMRI depends on the specific decoding task of interest.
Year
DOI
Venue
2016
10.1016/j.neuroimage.2016.02.033
NeuroImage
Keywords
Field
DocType
Multivariate pattern analysis,fMRI,7T,Spatial resolution,Spatial smoothing,Auditory cortex
Visual cortex,Pattern recognition,Computer science,Support vector machine,Speech recognition,Smoothing,Artificial intelligence,Vowel,Decoding methods,Speech perception,Gaussian function,Spatial frequency
Journal
Volume
ISSN
Citations 
132
1053-8119
9
PageRank 
References 
Authors
0.51
14
6
Name
Order
Citations
PageRank
Anna Gardumi1422.05
Dimo Ivanov218110.15
Lars Hausfeld3264.53
Giancarlo Valente412710.62
Elia Formisano577858.91
Kâmil Uludağ61689.18