I am starting to use the "optimize" option in DCM version 10 to find the best A-matrix to describe the set of areas I found activated during execution or the imagined execution of movements.
After estimating a fully connected A-matrix of 6 areas (leaving empty matrices B and C), I run the optimize option, which starts with the gready algorithm to reduce the parameters and finally computes the best model which fit the data.
To verify that the optimized model is the best fit of the data, I usually specify and estimate a new model selecting only those connections in the A-matrix which were associated with high strength (Hz) and high probability in the optimized results. In many cases, however, if I compute a BMS using FFX and compare the fully connected model against the new model I created based on the optimization results, I find that the log evidence and the posterior probability of the fully connected one are significantly greater than the optimized one. I would expect the opposite. Sometime I am able to discover the optimal network selecting some additional (or sometime fewer) connections respect to those revealed by the otimization routine, which suggests that I am interpreting partially in the wrong way the optimization results.
What am I doing wrong? Which is the correct way to specify a model using the output from the optimization routine?
Thanks in advance for any help with this issue.
Gianpaolo Basso
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Gianpaolo Basso
Prof. Associato di Neuroradiologia
Università degli Studi di Trento
Dipartimento di Psicologia e Scienze Cognitive
Corso Bettini 31, Rovereto (TN)
Fondazione Salvatore Maugeri
Istituto Scientifico di Pavia
Servizio di Radiologia
Via Maugeri 10 - Pavia
Telefono ufficio Rovereto: +39 0464 808714
email: [log in to unmask]
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