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In this section we illustrate the potential benefit of a heteroscedastic variance model (i.e. by allowing for separate first-level variances) compared to the homoscedastic variance model (otherwise known as OLS for random-effects analysis of FMRI data [9]).
Both the heteroscedastic model (equation 16) and the
homoscedastic model (equation 17) provide an unbiased
estimate of the group level parameter of interest
. They
differ, however, in their associated variance,
, and therefore will give different
or
statistic.
The associated variance for the heteroscedastic model is always less
than or equal to that of the homoscedastic model, as can be shown
using Jensen's inequality. This will result in an increase in the
expected statistics values for the model in
equation 16:
As a quantitative example of this increase we have generated a
simulated group FMRI study, with a known set of first-level
variances
, together with a second-level variance
. From these known values we calculate the expected
percentage
increase in
- statistic (see above) and show this versus changes in
the ratio of the second-level variance to
the mean first-level variance (the assumed variance in the homo-
scedastic model). First-level variances were taken from a real FMRI
group study (simple motor paradigm) estimated using the GLS
pre-whitening approach as implemented in FILM (part of
FSL [7,17]). These estimates of first-level
variances are realistic representatives of typical first-level variance
structures and are defined to be ground-truth in the following
simulation.
Figure 1 shows the expected percentage increase in
-scores for three different sets of first-level variances, each
of them containing one or more 'outliers' (see boxplots). As can be
seen from the first three images, the expected percentage increase,
for a given first-level variance structure, only depends on the ratio
of second-level variance to mean first-level variance
(
) and is independent of the
group-level effect size
. As the second-
level variance becomes considerably larger than the mean first-
level variance, the expected increase in
-score tends to zero.
When these variances are approximately equal, the heteroscedastic
model allows for a
increase in
-score,
increasing to much larger values as the second-level variance
decreases relative to the mean first-level variance.
The group-level effect size determines the actual
-level, as shown by the overlaid contour plots (solid lines show
-
score levels for the heteroscedastic model while dash-dotted lines
show the
-score levels for the homoscedastic model). Over a
large range of variance ratios the improvements gained by the
heteroscedastic model have substantial implications on post-
thresholded results, e.g. typical sub-threshold values of
increase to super-threshold values of
.
Figure 2 shows similar plots for cases where the
first-level variances do not contain large outliers, that is, they
are close to the homoscedastic model. In this case, the expected
percentage -score increase is smaller, but still potentially
important.
It is clear from these figures, that the exact configuration of
first-level variances has a major impact on the improvements gained
by using the heteroscedastic model. This dependency is further
investigated in figure 3, where we show the expected
(log-) increase in -score for a set of variance
configurations estimated from
voxels in a real group
study, assuming equal second-level and mean first-level variances.
This histogram shows that approximately
voxels have a
increase in expected
-score.
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