Dear Shady

The current ICA implementation in CONN does not include any model-selection approach to help determine the "optimal" number of components in ICA (we will likely end up offering some form of cross-validation approach to help determine the relative significance of different components, but that is still in the works). If you need to compute any of the AIC or MDL model-selection measures, you can probably do that from the group-level spatial maps stored in ICA.ROIs.nii and the associated timeseries stored in ICA.Timeseries.mat (but this is not really straightforward to do).

Regarding your question about reconstructing individual-subject ICA maps (back-projection), those are already computed by CONN, they are stored in the files named BETA_Subject*_Condition*_Measure*.nii, and these are the volumes that are entered into your second-level analyses when looking at the ICA.SpatialComponents tab in the second-level results window. If, on the other hand, you mean that you would like to reconstruct individual-subject ICA maps for other subjects (other than those included in your original ICA analysis), then the way to do this would be to go to Setup.ROIs and click on the 'ROI tools. Add network-ICA ROIs' button. This will add a new weighted-ROI that represents the group-level spatial maps computed in the group-ICA step. Using these network-ROIs as seeds in a new first-level analysis that uses multivariate-regression measures will produce the same individual-subject ICA maps as those computed in the back-projection step, and this can be applied to the same or other subjects (not necessarily those included in the original ICA analyses).

Regarding your question about Z-value thresholds, in CONN this approach can be performed simply by going to the ICA.SpatialComponents tab in the second-level results window, and defining a new one-sample t-test second-level analysis (i.e. simply select the 'AllSubjects' effect, contrast 1). The choice of threshold in those one-sample t-test results is somewhat arbitrary. Because of the nature of ICA analyses, these tests are actually post-hoc tests which will tend to produce strongly significant results. This is fine because these results are mainly used to help identify/represent the network associated with each component, so one is relatively free to choose the threshold values that results in the "cleaner" interpretation separately for each component (there is no confirmatory hypothesis testing here regarding these spatial maps, you just to want to characterize which aspect of the connectivity this component is capturing/representing, so one often uses relatively high/conservative thresholds in order to emphasize each specific network).

Last, regarding your question about between-group comparisons of spatial components, this is related to the above question. The average spatial map within a group does not just represents the network associated with this component but rather the connectivity pattern with this network (i.e. the connectivity between this network and the rest of the brain). Of course, when thresholding these single-group maps using very conservative thresholds you just get the network itself (in the same way that if you look at seed-to-voxel connectivity and use very conservative thresholds you will just get the seed itself). When comparing the spatial map across two groups you are effectively comparing the connectivity with this network across the two groups, so it is perfectly fine to find "out-of-network" areas that show differences in connectivity between the groups (that simply indicates that the connectivity between this network and those areas differs between your two groups). If you are only interested in within-network connectivity, then yes, you may constrain the between-group comparison to only include "within-network" areas, but otherwise (when interested in both within- and between- network connectivity) it is fine to perform these between-group comparisons across the entire brain.  

Hope this helps
Alfonso




Originally posted by Shady El Damaty: