Hi Saad,
Thank you for your quick and detailed response. The information here has
been very helpful for planning our analysis. I do have a couple remaining
questions:
1,2,3) It is possible (although not always necessarily true) that the
probability of a connection ending at a certain target location increases
with the volume of the target location. However, I don't see this as a
problem! As long as the target locations are "comparable between subjects",
meaning that they refer to the same anatomical or functional location across
subjects, this property of increased probability with size is not undesirable.
Thanks for clearing that up - makes sense!
I concede that in some cases, it is not easy to match cortical regions
across subjects, but this is the core of the problem - not the dependence of
tractography on roi size. Dividing by roi size will result in a quantity
that is not very interpretable. A more proper way to do it would be to have
a model for the probability of connection between A and B given that B has a
certain size, which is very hard to formalise. (there is a similar issue
with the effect of distance between A and B). One thing you can probably do
is to include the target roi size into your correlation analyses as
co-regressors. Note that neither the result nor the interpretation of such
analysis would be the same as just dividing by roi size. You will instead
ask the question of whether a behavioural measure can be explained by roi
size alone, or whether adding connectivity measures to those rois gives you
a better model. etc.
If you are working with segmented brain data (e.g., regional anatomical
masks, generated using a T1 in freesurfer), matching cortical regions across
subjects should not be an issue, correct? This would leave only ROI size and
distance as concerns. Your suggestion of including ROI size in a
multivariate assessment seems like a good approach. Is use of the distance
correction parameter in FDT a complete solution to the distance issue, or
should it also be included in multivariate analysis?
1) The connectivity scores that fdt_paths stores are the probability that
the connection through a seed region ends up anywhere in the brain. Such
quantity is not straightforward to interpret in terms of connection
"strength", but rather influenced by many factors, some of them purely
related to the morphology of the connection along its course. So in general,
small values in fdt_paths do not always mean small connectivity strength. We
prefer to use values from the seed_to_target-type analysis where target rois
are chosen carefully (i.e. represent meaningful brain locations that are
comparable across subjects).
Are you referring to tight corners, crossed bundles, and other difficult
features to model when you mention morphology factors?
I suppose when you are using a comparable seed + target mask across
subjects, you are likely to be working with the same tract across subjects -
with the same morphology effects acting upon it. Is this why it is safe to
assess connection scores in this context, unlike when no target mask is
used, and unlike when scores from different seed regions are compared?
Would these concerns about morphology effects not rule out any applications
of the single-point function or single mask function (without a target) in FDT?
Cheers,
Jordan
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