fMRI: spatial smoothing

Theoretical Background

Spatial smoothing means that data points are averaged with their neighbours. This has the effect of a low pass filter meaning that high frequencies of the signal are removed from the data while enhancing low frequencies. The result is that sharp "edges" of the images are blurred and spatial correlation within the data is more pronounced (see figure below).

Effect Of Smoothing

The approach of spatial smoothing is commonly used in fMRI studies and is justified by the fact that fMRI data inherently show spatial correlations due to functional similarities of adjacent brain regions and the blurring of the vascular system.

The standard procedure of spatial smoothing is employed by convolving the fMRI signal with a Gaussian function of a specific width.This so called Gaussian kernel is a kernel with the shape of a normal distribution curve. In the figure below you can see a standard Gaussian with a mean of 0 and a standard deviation of 1.

Standard Gaussian

The size of the Gaussian kernel defines the "width" of the curve which determines in turn how much the data is smoothed. The width is not expressed in terms of the standard deviation σ, as customary in statistics, but with the Full Width at Half Maximum (FWHM). In this case the FWHM would be 2.35: The maximum of this curve is y = 0.4 at x = 0. The half maximum is y = 0.2 at x = -1.175 and at x = 1.175. Therefore, the full width of the curve at the point of the half maximum is about 2.35. Nevertheless, the FWHM is also related to the standard deviation σ as follows: FWHM = σ √(8 ln(2)).

Benefits

Improvement of the signal to noise ratio (SNR) => Increasing sensitivity

According to the matched filter theorem, the SNR reaches its maximum when the filter width matches the expected signal width. This, in turn, is of course dependent on the experimental design and the functional brain areas under investigation, e.g. Do you expect a narrow signal in the thalamus versus more extensive activations in the occipital lobe? Therefore, if a signal with a FWHM of 8 mm is expected the applied kernel size should be 8 mm as well.
Improving validity of the statistical tests by making the error distribution more normal

Most parametric tests assume normal error distributions and according to the central limit theorem the distribution of an average tends to be normal with a sufficiently large number of independent observations being averaged.
Accommodation of anatomical and functional variations between subjects

In multi-subject studies, individual brains are coregistered to each other to establish spatial correspondence between the different brains. Still, because of the substantial variation in individual brains, activated areas are rarely represented in exactly the same voxels. To increase the overlap of activated brain regions across subjects smoothing can be applied.

Drawbacks

Reduction of spatial resolution of the data

Spatial smoothing results always in reduced spatial resolution of the data. Therefore, it is important to decide whether a precise localization of the activations is important. However, even worse, if the filter width is set too small, there is practically no positive effect on the SNR while the spatial resolution is reduced.
Edge Artifacts

Along the edges of the brain, brain voxels are smoothed with non-brain voxels, resulting in a dark ring around the brain which might be mistaken for hypoactivity.
Merging

If activation peaks are less than twice the FWHM apart they are detected as a single activation rather than two separated ones.
Extinction

If the filter width is set too large, especially small meaningful activations might be attenuated below the significance threshold.
Mislocalization of activation peaks

As presented by Mikl and colleagues (2008) spatial smoothing almost unavoidably results in shifts of activation peaks. Therefore, as already mentioned above, it is crucial to decide what amount of spatial accuracy is required.