[color=#000000]Dear Alfonso,[/color]
[color=#000000]In this post you mentioned that it is possible to constrain the between group analysis to only look into within-network effects.[/color]
[color=#000000]I was wondering how I can do that? I guess I can use the masks generated with ICA as ROIs (using 'Add ICA-network-ROIs' in [i]Setup.ROIs?)[/i]. But what is then the best way to analyse the group differences? And is this the correct way to create the ROIs?[/color]
Thank you!
Best wishes,
Julia
[i]Originally posted by Alfonso Nieto-Castanon:[/i][quote][color=#000000]Dear Shady[/color]
[color=#000000]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).[/color]
[color=#000000]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 [i]ICA.SpatialComponents[/i] tab in the [i]second-level results[/i] 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 [i]Setup.ROIs [/i]and click on the '[i]ROI tools. Add network-ICA ROIs'[/i] 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).[/color]
Regarding your question about Z-value thresholds, in CONN this approach can be performed simply by going to the [i]ICA.SpatialComponents[/i] tab in the [i]second-level results[/i] 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 [i]just[/i] 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 [i]only [/i]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
[i]Originally posted by Shady El Damaty:[/i][quote]Dear fellow CONN-artists,
I've recently begun diving in ICA based methods for extracting functional networks in a modestly large data set (110 subjects, 3 time points). One major question when running ICA is the choice of parameters for data reduction and # of components to be estimated.
The Calhoun paper presents an AIC and MDL approach for performing these estimates. Is this algorithm implemented in CONN? If not, how would one go about extracting the parameters to compute the AIC and MDL? The major parameters of interest would be the log of the maximum likelihood estimate of the model parameters as estimated from the fMRI data, ML (the number of time points following individual subject data reduction) and the number of sources N.
Also, is there an easy way to reconstruct individual subject ICA maps? How would one go about reconstructing individual subject time courses for time course ICA decomposition?
Additionally, how should one pick a Z value threshold? Calhoun suggests calculating the mean and variance of each component across the subjects and then perform random effects inference for a one-sample t-test. Using a standard 0.001 threshold often results in over-saturated maps for some components, whereas other components might tolerate only low thresholds. How does one go about picking a consistent threshold across components?
And lastly, what exactly happens when you contrast an ICA component at the group level? I've performed a contrast between two subject groups (drug users vs non users, [1 -1]) with a single ICA component selected. However the difference between those two subject groups is a cluster that falls outside of the spatial extent of that ICA component (after thresholding). Shouldn't the difference between these two subjects be constrained to only the spatial extent of the chosen ICA component?[/quote][/quote]
[color=#000000]In this post you mentioned that it is possible to constrain the between group analysis to only look into within-network effects.[/color]
[color=#000000]I was wondering how I can do that? I guess I can use the masks generated with ICA as ROIs (using 'Add ICA-network-ROIs' in [i]Setup.ROIs?)[/i]. But what is then the best way to analyse the group differences? And is this the correct way to create the ROIs?[/color]
Thank you!
Best wishes,
Julia
[i]Originally posted by Alfonso Nieto-Castanon:[/i][quote][color=#000000]Dear Shady[/color]
[color=#000000]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).[/color]
[color=#000000]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 [i]ICA.SpatialComponents[/i] tab in the [i]second-level results[/i] 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 [i]Setup.ROIs [/i]and click on the '[i]ROI tools. Add network-ICA ROIs'[/i] 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).[/color]
Regarding your question about Z-value thresholds, in CONN this approach can be performed simply by going to the [i]ICA.SpatialComponents[/i] tab in the [i]second-level results[/i] 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 [i]just[/i] 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 [i]only [/i]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
[i]Originally posted by Shady El Damaty:[/i][quote]Dear fellow CONN-artists,
I've recently begun diving in ICA based methods for extracting functional networks in a modestly large data set (110 subjects, 3 time points). One major question when running ICA is the choice of parameters for data reduction and # of components to be estimated.
The Calhoun paper presents an AIC and MDL approach for performing these estimates. Is this algorithm implemented in CONN? If not, how would one go about extracting the parameters to compute the AIC and MDL? The major parameters of interest would be the log of the maximum likelihood estimate of the model parameters as estimated from the fMRI data, ML (the number of time points following individual subject data reduction) and the number of sources N.
Also, is there an easy way to reconstruct individual subject ICA maps? How would one go about reconstructing individual subject time courses for time course ICA decomposition?
Additionally, how should one pick a Z value threshold? Calhoun suggests calculating the mean and variance of each component across the subjects and then perform random effects inference for a one-sample t-test. Using a standard 0.001 threshold often results in over-saturated maps for some components, whereas other components might tolerate only low thresholds. How does one go about picking a consistent threshold across components?
And lastly, what exactly happens when you contrast an ICA component at the group level? I've performed a contrast between two subject groups (drug users vs non users, [1 -1]) with a single ICA component selected. However the difference between those two subject groups is a cluster that falls outside of the spatial extent of that ICA component (after thresholding). Shouldn't the difference between these two subjects be constrained to only the spatial extent of the chosen ICA component?[/quote][/quote]