You would have to get a connectivity matrix (either from someone who has
analyzed the data, or from analysis that you do yourself).
Even still, there won't be a "standard" because:
* there are different ways of measuring connectivity (probabilistic vs.
deterministic tractography; resting-state fMRI seed-based correlations;
structural (thickness) covariance; etc.)
* what kind of variables "should" be adjusted for? Age and sex might be
universally agreed upon; what about head size/TIV? or SES? Should
tractography streamline counts (or whatever you're using) be adjusted
for seed/target ROI size? etc.
* there are different parcellations (Desikan-Killiany, Destrieux, AAL,
voxel-based, random, etc.)
* there are different methods for community detection
However if you chose one method from the above bullet points and did the
same for your data (e.g. if you have a patient group and you want to
compare to a "standard" control group), I think it would be a reasonable
comparison. Another issue would be different scanning sequences in the
HCP vs. your study, and that HCP data hasn't been tested for
validity/reliability (I'm thinking along the lines of IQ testing like
the WISC).
Chris
On 01/20/2015 05:01 AM, Alexander Lebedev wrote:
*_Dear Colleagues,_*
I am sorry if my question is a bit out-of-topic...
I wonder if there is a an opportunity to find a community affiliation
vector estimated using any of the large-scale datasets (like the Human
Connectome Project, for example).. I was just thinking that having
independently estimated community structure (assuming the same age
group) for further extraction of modularity-based metrics makes more
sense than using the exact same sets to estimate both community
structure and related measures like participation and diversity
coefficients.
Thanks in advance!
*===*
*Best Wishes,*
*Alex*
--
Alexander V. Lebedev MD PhD | Postdoctoral Researcher
Department of Neurobiology, Care Sciences and Society | Karolinska
Institutet
Aging Research Center | 113 30 Stockholm, Sweden | Gävlegatan 16
Center for Age-Related Medicine | Researcher
Stavanger University Hospital
4011 Stavanger, Norway | Armauer Hansens vei 20
Mobile (Sweden): +46 7 659 04 649
Skype ID: alexander.vl.lebedev
alexander.vl.lebe...@gmail.com
<mailto:alexander.vl.lebe...@gmail.com> | www.ki-su-arc.se
<http://www.ki-su-arc.se/>
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