Dear All,
I am puzzled by the meaning of 'color scale bar', which appears in the first
figure of the FsTutorial/Visualization
(https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/Visualization)
Could you please let me know the meaning of colorbar?
Does the 'color scale bar' show the cortical
Hi all
I have run the script make_average_subject and the error message at the end is:
ln: creating symbolic link `rh.sphere' to `rh.sphere.reg': Operation not
permitted
only rh* files have been created.
Can you help?
Best wishes
Carolyn
___
Hi all,
I tried running it again with --no-symlink but this error came up
ERROR: Flag --no-symlink unrecognized.
any advice?
Best wishes
Carolyn
___
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Hi Rhuang,
On that particular page the color bar is showing the sigificance (-log10(p))
for the group difference in that example. However, if you were displaying a
cortical thickness map for an individual subject the color bar would then be
showing the cortical thickness values (in mm).
It is
I have been able to run it with --no-symlink (the flag has to go last),
but I get errors like this:
mris_make_average_surface: could not read surface file
/Volumes/data/NL-GCS/subjects/freesurfer_3.0.1/GCS_adults/--no-symlink/surf/rh.sphere.reg
Thanks.
Anthony
Furlong, Carolyn wrote:
Hi
Hi Jenni
Thanks a lot for your reply.
Following the descriptions on
FsTutorial/Visualization
(https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/Visualization)
the 'color scale bar' indicates
area coded in blue == cortical thickness of Female that of Male
(eg. 1mm 2mm),
area
Thank you very much!
I assume that when doing the first-level analysis with Feat and then project
onto the surface, one shouldn't smooth so much in 3D volume space, right? How
much smoothing should be used?
Thanks a lot,
Christine
Bruce Fischl [EMAIL PROTECTED] wrote: Hi Christine,
we have
Hi Christine,
I'm not certain you'd want to smooth on the volume at all.
See this paper for illustrations
Desai R, Liebenthal E, Possing ET, Waldron E, Binder JR. (2005) Volumetric
vs. surface-based alignment for localization of auditory cortex activation.
*Neuroimage* *26:*4
Hi,
I'm not sure where you are getting the descriptions below. This
statistical map on the surface is actually looking at the correlations of
thickness and age, not gender. Can you be more specific as to what you
are refering to with the following descriptions:
the 'color scale bar'
you may be using a version that does not have this option. Try using these
doug
ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/make_average_subject
ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/make_average_surface
Furlong, Carolyn wrote:
Hi all,
I tried
Hi Jenni,
Many thanks for message.
I tried to go through the Freesurfer Tutorial
(https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial)
There are three sessions in Day-2, including Session_1a and Session_1b.
In Session_1a (FreeSurfer Tutorial: Group Analysis), the FSGD file was
presented as
In general, it is better not so smooth in the volume. However, we often
smooth some ( 5mm) as the epi-anatomical registration is sometimes
inaccurate due to epi distortion.
doug
[EMAIL PROTECTED] wrote:
Hi Christine,
I'm not certain you'd want to smooth on the volume at all.
See this
Hi,
So the contrast used to perform this statistical test kept the two groups
(male and female) constant and only looked for an interaction between
thickness and age. So in this case the blue is where thickness and age
are negatively correlated (as the age goes up the cortex gets thinner)
Rhuang,
The inflated surface figure shows the -log10(p) significance map of
thickness vs. age, for both groups (male and female). When you click on
a vertex to bring-up a plot, it is showing you the actual thickness data
(in mm) for that vertex for each of the subjects. This is intended to
Dave,
Here is some more information on mris_thickness_diff:
mris_thickness_diff uses closest Euclidean distance to
define correspondence across the two surfaces. It does
not assume that the correspondence is given by the vertex IDs.
But if one has two surfaces that are the 'same' but not
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