¥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