¥ STEP
3: Normalizing the Data - Calculating Percent Change
GThis particular step
is a bit more involved, because it is comprised of three parts. Each part will be described in
detail:
G
A.Set
all background values (outside of the volume) to zero with 3dAutomask
B.Do a
voxel-by-voxel calculation of the mean intensity value with 3dTstat
C.Do a
voxel-by-voxel calculation of the percent signal change with 3dcalc
GWhy should we
normalize our data?
åNormalization
becomes an important issue when comparing data across subjects, because
baseline/rest states will vary from subject to subject
åThe amount of
activation in response to a stimulus event will also vary from subject to
subject
åAs a result, the
baseline Ideal Response Function (IRF) and the stimulus IRF will vary from subject
to subject -- we must account for this variability
åBy converting to
percent change, we can compare the activation calibrated with the
relative change of signal, instead of the arbitrary baseline of FMRI signal