Although there are many potential design offered in the 2nd-level analysis I recommend to use the “Full factorial” design because it covers most statistical designs. For cross-sectional VBM data you have usually 1..n samples and optionally covariates and nuisance parameters:
Number of factor levels | Number of covariates | Statistical Model |
1 | 0 | one-sample t-test |
1 | 1 | single regression |
1 | >1 | multiple regression |
2 | 0 | two-sample t-test |
>2 | 0 | Anova |
>1 | >0 | Ancova (for nuisance parameters) or Interaction (for covariates) |
Example for cross-sectional design
The following suggestions are only valid if you have unmodulated data or data modulated for non-linear effects only (indicated by ‘m0’) and you would like remove the confounding effects due to different brain size. Please check the section on modulation for more information.
SPM interface | Comment |
Also covers regression and one/two-sample t-test | |
For VBM usually one factor to define your samples | |
Give name like “sample” | |
Enter number of samples | |
Always ‘yes’ for cross-sectional designs | |
Is variance equal/unequal between samples? | |
Do not use | |
Do not use | |
Level=sample (start with 1) | |
Select scans for sample 1 (swc* or sm0wc*) | |
Define for each sample | |
n=number of samples | |
Select scans for sample n (swc* or sm0wc*) | |
Define covariates and nuisance parameters | |
Enter values for each scan/subject | |
Interaction sample x covariate ? | |
You can also choose “Overall mean” | |
Always use abs. threshold to restrict analysis to one tissue type | |
Start with 0.1 and increase up to 0.25 to decrease search volume | |
Redundant if you are using absolute thresholds | |
Usually not neccessary if you are using absolute thresholds | |
Do not use | |
Do not use | |
Do not use | |
Do not use |