The Effect of Spatial Smoothing on Representational Similarity in a Simple Motor Paradigm

Abstract

Multi-voxel pattern analyses (MVPA) are often performed on unsmoothed data, which is very different from the general practice of large smoothing extents in standard voxel-based analyses. In this report, we studied the effect of smoothing on MVPA results in a motor paradigm. Subjects pressed four buttons with two different fingers of the two hands in response to auditory commands. Overall, independent of the degree of smoothing, correlational MVPA showed distinctive patterns for the different hands in all studied regions of interest (motor cortex, prefrontal cortex, and auditory cortices). With regard to the effect of smoothing, our findings suggest that results from correlational MVPA show a minor sensitivity to smoothing. Moderate amounts of smoothing (in this case, 1−4 times the voxel size) improved MVPA correlations, from a slight improvement to large improvements depending on the region involved. None of the regions showed signs of a detrimental effect of moderate levels of smoothing. Even higher amounts of smoothing sometimes had a positive effect, most clearly in low-level auditory cortex. We conclude that smoothing seems to have a minor positive effect on MVPA results, thus researchers should be mindful about the choices they make regarding the level of smoothing.

Default mode network can support the level of detail in experience during active task states

“…Consistent with prior studies (e.g., ref. 46 ) we found lower RSA fit correlations with the nonsmoothed data. We report the results of the nonsmoothed analysis in SI Appendix , Fig.…”

Congruency and reactivation aid memory integration through reinstatement of prior knowledge

“…For the MultiVariate Pattern Analyses (MVPA), all scans were spatially smoothed with a FWHM of 5 mm 24 . Subsequently, because raw scans were used instead of beta values, the scans were manually temporally filtered with a high-pass filter (to remove scanner drift) and demeaned by computing a deviation score of the mean for each non-zero voxel in the brain and dividing by the standard deviation (feature scarling 25 ).…”

“…these outcomes provide insights into the neural mechanisms underlying memory enhancement, which has value for educational learning.The opposite congruency contrast showed significant effects in Left Supplementary Motor Area (MNI [−6, 18, 48], 354 voxels, p < 0.05 whole-brain corrected) and Inferior Triangular Frontal Gyrus 18,26], 257 voxels, p < 0.05 whole-brain corrected). The opposite reactivation contrast showed a significant effect in Right Middle Cingulate Gyrus (MNI [8,24,36], 269 voxels, p < 0.05 whole-brain corrected). No significant effects were found in the opposite contrast for memory and for the interaction analyses.…”

A Comparison of Functional Networks Derived From Representational Similarity, Functional Connectivity, and Univariate Analyses

“…The preprocessing pipeline for this analysis differed slightly from the one used for univariate analysis: data was smoothed at 5 mm FWHM. As such, we optimized the preprocessing parameters to fit the requirements of each analysis (Hendriks et al, 2017). For each ROI, we created a 39 × 39 neural matrix by correlating (Pearson) the multi-voxel patterns between all possible combinations of pairs of stimuli of the social condition (N = 39) and then averaged this across subjects (first-order RSA, Kriegeskorte et al, 2008).…”