• is the xxx.ocn.annot file created by mri_glmfit-sim considered a "label" by freesurfer? or does it need to be converted to a label before I can use mri_segstats to extract the betas from the cluster(s) in the xxx.ocn.annot? I tried using the following command line to attempt to extract the betas from the cluster annotation file created by mri_glmfit-sim, but received an error: 
  

mri_segstats --i beta.mgh --annot fsaverage hemi contrast/xxx.ocn.annot --avgwf avgwf.dat 

• how does one convert an xxx.ocn.annot file to a label? 

• If I have run 3 different glms looking at the relationship between thickness and 3 different behavioral variables, and found 1 (or more) cluster in each glm using mri_glmfit-sim can I create 1 annotation/label with all the clusters from the separate glms and then extract the betas from that one annotation using mri_segstats? Or do I need to treat each glm separately? 
  

• I saw on the listserv some references to matlab functions that can calculate r square and partial correlations for glms with more than one predictor variable (e.g pcc between thickness and behavioral variable 1; pcc between thickness and behavioral variable2; Rsq for overall model) but I could not find the .m scripts for the functions (MRIread or fast_glm_pcc?) - would these functions be appropriate and if so where might I find them? 
  

Thanks in advance for your help!

Laura. 

On Wed, Mar 20, 2013 at 8:29 AM, Douglas N Greve <greve@nmr.mgh.harvard.edu> wrote:

On 03/20/2013 10:17 AM, Laura M. Tully wrote:
> Hi,
>
> I wanted to re-post my questions from a couple of days ago below, but
> with some more specific questions following a search through the
> archives.
>
> I want to be able to extract the beta values from a cluster identified
> using mc-z in a group by behavioral variable interaction so that I can
> 1) plot the relationship of thickness to behavioral variable data by
> group in that cluster, 2) conduct post hoc tests to examine the
> interaction, and 3) calculate the Rsquare and partial correlations for
> each variable in the glm (i.e. how much variation in thickness is
> explained by my behavioral variable).
>
> To extract the beta values from a cluster identified by mc-z would I
> treat the cluster like a label and use mri_segstats to extract the
> beta weights from the cluster? Would I need to make a label of all the
> clusters that I want to do this for first?

You can do it label by label. Or, if the annotation created by
mri_glmfit-sim has all the clusters you want, you can use that (also in
mri_segstats). You can also create an annotation from individual labels
with mris_label2annot.

>
> Is there a way to calculate the basic statistics for the glm and
> extract in table form? i.e. Fs and ps for peaks of each cluster? What
> about Rsquare, or the correlation between thickness and my behavioral
> variable in the clusters? or would I need to compute these outside of
> freesurfer using the extracted betas?

You will need to do it outside of FS. What are Fs and ps?
doug

>
> Thank you!
>
> Laura.
>
>
>
>
>
>
> On Mon, Mar 18, 2013 at 5:05 PM, Laura M. Tully

> <tully.laura@googlemail.com <mailto:tully.laura@googlemail.com>> wrote:
>
>     Hi Freesurfer experts,
>
>     I'm hoping you can help me understand how to interpret
>     interactions in clusters identified in whole brain analysis using
>     glmfit and glmfit-sim. Below I describe what I've done and what
>     I'd like to be able to do. Any suggestions would be most appreciated!
>

>       * I have two groups (patients, controls) and a behavioral

>         variable of interest (social functioning). I am interested in
>         cortical thickness differences between groups (main effect of
>         group), whether cortical thickness relate to social
>         functioning across the group (main effect of social
>         functioning), and whether this relationship differs by group
>         (group x social functioning interaction).

>       * I ran whole brain analysis using mri_glmfit with group and

>         functioning as variables of interest whilst controlling
>         for/regressing out gender, age, and mean thickness. i.e. 4
>         classes (conmale,confemale, ptmale, ptfemale) and 3 continuous
>         variables (age, AvgThickness, Functioning) = 16 regressors.

>       * I tested the group x functioning interaction with the

>         following contrast - is it correct?
>
>     0000000000000.50.5-0.5-0.5
>

>       * I then ran mri_glmfit-sim to identify clusters that survive

>         multiple comparisons. This revealed 4 clusters (3 in LH; 1 in
>         RH) that represent regions showing significant group x
>         functioning interaction.
>       * I visualized the clusters in tksurfer, and by loading the
>         y.fsgd file was able to visualize the plotted data to get a
>         sense of the interaction, but this is as much as I know in
>         terms of how to examine interactions in the cluster data......
>
>     My specifc questions include:
>

>       * I understand that the values in xxx.sig.cluster.mgh overlay

>         reflect log10 p values, the signs of which indicate the
>         direction of the relationship (i.e. -3 = negative correlation
>         between thickness & variable) but I'm not sure how to
>         interpret this in the context of an interaction with group?

>       * I understand that the values in xxx.y.ocn.dat contain the

>         average thickness value for each subject in that cluster and
>         that in a simple between groups test this data could be used
>         to conduct post hoc t-tests to show the direction of the
>         difference, but again I'm not sure how to use this data in the
>         context of the interaction. What do the values represent in a
>         group x variable interaction?
>
>     Ideally, I'd like to extract the contrast estimates for each
>     subject in the group x functioning contrast and plot it in another
>     program and conduct pairwise comparisons (t-tests) in order to get
>     a better understanding of the interaction). I'm not sure how to do
>     this - is it possible? My thinking is that I do something similar
>     in fMRI analysis in spm where I can plot the contrasts in a
>     significant cluster and then extract both the average contrast
>     estimates for each group and the contrast estimates for each
>     individual subject.
>
>     Thanks in advance!
>
>     Laura.
>
>
>     --
>     --
>     Laura M. Tully, MA
>     Social Neuroscience & Psychopathology, Harvard University
>     Center for the Assessment and Prevention of Prodromal States, UCLA
>     Semel Institute of Neuroscience

>     ltully@mednet.ucla.edu <mailto:ltully@mednet.ucla.edu>
>     ltully@fas.harvard.edu <mailto:ltully@fas.harvard.edu>
>     310-267-0170 <tel:310-267-0170>

>     --
>     My musings as a young clinical scientist:
>     http://theclinicalbrain.blogspot.com/
>     Follow me on Twitter: @tully_laura
>
>
>
>
> --
> --
> Laura M. Tully, MA
> Social Neuroscience & Psychopathology, Harvard University
> Center for the Assessment and Prevention of Prodromal States, UCLA
> Semel Institute of Neuroscience

> ltully@mednet.ucla.edu <mailto:ltully@mednet.ucla.edu>
> ltully@fas.harvard.edu <mailto:ltully@fas.harvard.edu>

> 310-267-0170
> --
> My musings as a young clinical scientist:
> http://theclinicalbrain.blogspot.com/
> Follow me on Twitter: @tully_laura
>
>

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--
Douglas N. Greve, Ph.D.
MGH-NMR Center
greve@nmr.mgh.harvard.edu
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--
Laura M. Tully, MA
Social Neuroscience & Psychopathology, Harvard University
Center for the Assessment and Prevention of Prodromal States, UCLA Semel Institute of Neuroscience
ltully@mednet.ucla.edu 
ltully@fas.harvard.edu
310-267-0170
--

My musings as a young clinical scientist: http://theclinicalbrain.blogspot.com/

Follow me on Twitter: @tully_laura