Dear Michal:
There are two ways
One is to do DCM model comparison, if for example the groups are based on
different network models. However, this too implements a fixed effects type
of analysis.
A second was is to do a random effects analysis on the relevant DCM
parameters from the A, B or C matrices. Depending on the number of
comparisons you may have to correct for multiple comparisons.
Darren
Darren R. Gitelman, M.D.
Cognitive Neurology and Alzheimer¹s Disease Center
Northwestern Univ., 320 E. Superior St., Searle 11-470, Chicago, IL 60611
Voice: (312) 908-9023 Fax: (312) 908-8789
| -----Original Message-----
| From: SPM (Statistical Parametric Mapping)
| [mailto:[log in to unmask]] On Behalf Of Ing. Michal Mikl
| Sent: Tuesday, April 11, 2006 2:52 AM
| To: [log in to unmask]
| Subject: [SPM] DCM comparisons at gorup level
|
| Dear DCM experts,
|
| I have question about right algorithm/procedure to compare
| DCMs at group level. In our study we have 20 subjects.
| Several DCMs were constructed for each subject. Our goal is
| to find the most appropriate model for population inferences
| about effective connectivity.
|
| What algorithm/procedure is the best solution to reach our goal?
|
| We tried to use averaging of DCMs across individual subjects
| and subsequently comparing these group-DCMs (various
| structures of intrinsic connections). But I doubt about this
| procedure. First, it is only fixed-effect analysis, and
| second, I don't know if comparing averaged DCMs is appripriate.
|
| Thanks for some help
|
| Best regards,
| Michal Mikl
|
|
|
| ********************************************
| Ing. Michal Mikl
| functional Magnetic Resonance Imaging
| 1st Department of Neurology
| St. Anne´s University Hospital
| 656 91, Brno, Czech Republic
| Phone: +420-543-182-685
| e-mail: [log in to unmask]
| ********************************************
|
|