¥Data Preparation: Spatial Smoothing
HSpatial variability of both FMRI activation and the Talairach
transform (the common space) can result in little or no overlap of function
between subjects.
åData
smoothing is used to reduce this problem.
íLeads to loss of spatial resolution, but that is a price to be
paid with the Talairach transform (or any current technique that does
inter-subject anatomical alignments)
åIn principle, smoothing should be done on time series data, before
data fitting (i.e., before 3dDeconvolve
or 3dNLfim, etc.)
íOtherwise one has to decide on how to smooth statistical
parameters.
¥In statistical data sets, each voxel has a multitude of
different parameters associated with it like a regression coefficient, t-statistic, F-statistic,
etc.
¥Combining some statistical parameters across voxels would result
in
parameters with unknown distributions
íIt is OK to blur percent signal change values that come out of the
regression
analysis, since these numbers depend linearly on the input data (unlike the F-
and t-statistics)
íBlurring in 3D is done using 3dmerge
with the -1blur_fwhm
option
íBlurring on the surface is done with program SurfSmooth