| Standard DCM |
|
|
| DCM.options.analysis |
Data feature to be modeled |
'ERP', 'CSD' |
| DCM.options.model |
Type of neural mass model |
'ERP', 'CMC',
'MMC', 'BGT', 'NFM',
'NMM' |
| DCM.options.spatial |
Type of spatial (forward) model |
'ECD', 'IMG',
'LFP' |
| DCM.options.trials |
Indices of trials (conditions) |
[1 2] |
| DCM.options.Nmodes |
Number of spatial modes to invert |
8 |
| DCM.options.D |
Time bin decimation (down-sampling) |
1 |
| DCM.options.Tdcm |
[start end] Time window in ms |
[0 1000] |
| DCM.options.onset |
Stimulus onset in ms – used in DCM for
ERP |
60 |
| DCM.options.dur |
Stimulus dispersion (standard deviations) in
ms – used in DCM for ERP |
16 |
| DCM.options.Fdcm |
[start end] Frequency window in Hz –
used in DCM for CSD |
[4 48] |
| Generic DCM |
|
|
| DCM.options.model(n).source |
Type of neural mass model for the
n-th source |
'ERP', 'CMC',
'MMC', 'BGT' |
| DCM.options.model(n).B |
Index number of intrinsic connections
exhibiting condition-specific effects (optional) |
[2 3 4 7], [1 4 7 10], [1:10] |
| DCM.options.model(n).J |
Index number of neural states that contribute
to the measured signal. Sets their prior expectation to 1
(optional) |
3 |
| DCM.options.model(n).K |
Index number of neural states for which their
contribution to the measured signal is estimated from the data. Sets
their prior variance to 1/32 (optional) |
[1 7] |
| Other options as listed for the standard DCM
implementation |
|
|
