help
help > RE: Global signal
Jul 3, 2013 03:07 AM | Alfonso Nieto-Castanon - Boston University
RE: Global signal
Thanks Vincent, I could not have said
it better. Global signal regression was first proposed as a method
to counter the effects of motion-related and other physiological
artifacts in fcMRI analyses. When unaccounted for, these
effects tend to act as confounding effects typically
inflating/biasing the resulting connectivity measures. You
can observe this effect if you look at the distribution of
voxel-to-voxel connectivity values labeled as 'original' in the
conn toolbox 'Preprocessing' tab, which typically appears heavily
shifted towards the right (positive bias). So a first natural
correction was to compute the global effects (from the average BOLD
signal across the entire brain) and regress-out these effects at
every voxel to somewhat 'center' this distribution. The problem
with this approach, as pointed out by Murphy, was that it has the
potential to introduce artifactual negative correlations between
brain regions, mainly stemming from the fact that the average BOLD
signal contains a mixture of movement/physiologial effects but also
signals of neural origin. As a response to this critique, several
labs started regressing-out instead average signals extracted only
from CSF and white-matter areas in order to minimize neural sources
from the resulting mixture, as it was shown that many
motion-related as well as physiological effects tend to also be
similarly present in these 'noise' areas (although in different
proportions). The CompCor approach extends this basic idea by
extracting multiple signals from each of these areas (instead of
simply the average signal from each area), which serves as a richer
representation of the range of subject-motion and physiological
effects on the BOLD signal, offering better protection against the
potential biases that these confounding effects could otherwise
introduce, while still avoiding the inclusion of potential signals
of neural origin in order not to artifactually introduce negative
correlations into the resulting connectivity measures (Chai et al.
goes into a lot of more detail and examples about these issues if
you are interested).
Hope this helps
Alfonso
Originally posted by Vincent Beliveau:
Hope this helps
Alfonso
Originally posted by Vincent Beliveau:
Hi Dan,
I've looked into this when I started using conn and it does not use global signal regression. A paper emphasizing this is Chai, Xiaoqian J., et al., 2012, "Anticorrelations in resting state networks without global signal regression.". Conn uses eroded white matter and csf masks and regresses out the principal component of the signal from those. This method in theory does not suffer from systematic introduction of negative correlation as pointed out by Murphy 2009 (which is the main concern with global signal regression) but it also retains some of the advantages of global signal regression by removing noise from white matter and csf. Maybe Alfonso can enlighten us more on this topic?
Kind regards,
Vincent.
I've looked into this when I started using conn and it does not use global signal regression. A paper emphasizing this is Chai, Xiaoqian J., et al., 2012, "Anticorrelations in resting state networks without global signal regression.". Conn uses eroded white matter and csf masks and regresses out the principal component of the signal from those. This method in theory does not suffer from systematic introduction of negative correlation as pointed out by Murphy 2009 (which is the main concern with global signal regression) but it also retains some of the advantages of global signal regression by removing noise from white matter and csf. Maybe Alfonso can enlighten us more on this topic?
Kind regards,
Vincent.
Threaded View
| Title | Author | Date |
|---|---|---|
| Camilla Borgsted Larsen | Jan 7, 2013 | |
| Daniel Fitzgerald | Jun 19, 2013 | |
| Vincent Beliveau | Jun 20, 2013 | |
| Alfonso Nieto-Castanon | Jul 3, 2013 | |