*conn toolbox* Frequently Asked Questions

Contents **1**Setup: How do I start a new project? What data do I need?**2**Setup: What are first-level and second-level covariates?**3**Preprocessing: What variables should I add to the ‘Confounds’ list? How is the aCompCor method implemented in the toolbox?**4**Preprocessing: What are the histogram-looking plots in the Preprocessing step? What they should look like?**5**Analyses: What are the sources in the first-level analysis step?**6**Analyses: What are the different connectivity measures in the first-level analysis step? Should I use correlation or regression measures?**7**Analyses: I ran bivariate correlations, what are my 2nd level beta values?**8**Analyses: What are voxel-to-voxel analyses? What do the measures listed in the first-level analyses->voxel-to-voxel tab represent?**9**Results: How to specify an AN(C)OVA model across subjects?**10**Results: How to specify a regression model across subjects?**11**Results: Can I test mixed within- between- subject models?**12**Results: Can I evaluate F-contrast within the conn toolbox second-level analyses?**13**Results: How do I correct for 'multiple-comparisons' the second-level results?**14**Results: I want to enter the first-level connectivity maps into SPM or other toolbox for additional second-level analyses. Where can I find the appropriate first-level maps?**15**Results: Can I get from the toolbox the correlation ROI-to-ROI matrix for a single subject?**16**Results: What are NBS, connection-, seed-, and network- level statistics in ROI-to-ROI analyses, and how to use them**17**Results: What do the ‘graph-theory’ measures (local/global efficiency and cost) represent? Could you recommend a paper or tutorial?**18**Other: Do any of the files output by the toolbox contain the filtered fMRI data (i.e. voxel-wise time series data after removal of confounds and bandpass filtering)?**19**Other: Are coordinates reported in Talairach or MNI space?**20**Other: Are the FDR corrections in the 'seed-to-voxel explorer' done the older SPM5 way? i.e. not "peak" or "cluster"?**21**Other: I have to move my imaging data files from one drive to another; is there a way to edit the CONN toolbox to change the directory paths for subject image files and/or already computed analysis files?**22**Troubleshooting: Error Index exceeds matrix dimensions. Error in ==> conn_process … CONN_x.Setup.rois.files{nsub}{nroi}{1}; end**23**Troubleshooting: Error using ==> fwrite. Invalid byte count to skip.**24**Troubleshooting: CONN: Error using ==> spm_create_vol>create_vol. "unknown" is an unrecognised datatype (NaN).**25**Troubleshooting: Error using ==> save. Unable to write file permission denied.**26**Troubleshooting: Error in ==> conn_menumanager**27**Troubleshooting: I have found an error on one subject, fixed it, and want to continue the analyses without redoing the analyses already-performed for the previous subjects. How can I do this?**28**General: How to reference the conn toolbox?**29**General: How to get more help?
## Setup: How do I start a new project? What data do I need?If you have If you have If you have You can also entirely skip the gui and define all of the necessary information through scripts. See the conn toolbox batch manual for additional information. ## Setup: What are first-level and second-level covariates?First-level covariates are ## Preprocessing: What variables should I add to the ‘Confounds’ list? How is the aCompCor method implemented in the toolbox?By default the toolbox implements the aCompCor strategy (Behzadi et al. 2007) for removal of confounding effects from the BOLD timeseries before computing connectivity measures. This is implemented by populating the Confound list in the ‘Preprocessing’ tab with: 1) White matter and CSF effects (characterized by 3 dimensions each, representing the variability of BOLD signal timeseries observed within those areas); 2) Main session- or task- effects (for task-related analyses), and their first temporal derivatives; and 3) realignment parameters and other first-level covariates. You can modify the entries in this list through the GUI or through the batch commands if you wish to include a different set of potential confounder variables. The toolbox will regress out from the BOLD timeseries (for each ROI and/or for each voxel) all of the effects listed in the ‘Confounds’ list, before computation of any connectivity measures.
## Preprocessing: What are the histogram-looking plots in the Preprocessing step? What they should look like?These plots represent the (sample) distribution of voxel-to-voxel measures
## Analyses: What are the sources in the first-level analysis step?
While typically sources are defined using the default ‘derivatives order’=0 and ‘dimensions=1’ values, you can also modify these values to create multiple sources from each ROI (higher
## Analyses: What are the different connectivity measures in the first-level analysis step? Should I use correlation or regression measures?There are four connectivity measures that can be computed by the toolbox: bivariate correlation, semipartial correlation, bivariate regression, and multiple regression measures. Most people seem to be using the simpler bivariate correlations (as a measure of 'total' functional connectivity between two areas). Semi-partial correlations are used when you want to obtain instead the 'unique' contribution of a given source on a target area (controlling for the contributions of other additional source areas), this is useful for example when studying in more detail potential paths underlying the functional connectivity between two areas. Bivariate and multiple regression measures are equivalent to bivariate and semi-partial correlation measures, but their units instead represent 'effective change' (percent signal change in target area associated with each percent signal change in source area; something closer to 'effective' connectivity). These measures are useful for example when one is concerned about potential differences in BOLD signal variance driving the connectivity/correlation results (regression measures are not biased by differences in variance between conditions/populations, while correlation measures can be (e.g. Friston, 2011).
## Analyses: I ran bivariate correlations, what are my 2nd level beta values?They represent Fisher-transformed correlation coefficient values, i.e. atanh(r), where r is the correlation coefficient between the source area and target area (voxels or regions)
## Analyses: What are voxel-to-voxel analyses? What do the measures listed in the first-level analyses->voxel-to-voxel tab represent?Voxel-to-voxel analyses are a new addition available from conn toolbox v.13. They represent subject-level measures that are derived from the full matrix of voxel-to-voxel correlation values. Currently the analyses include
## Results: How to specify an AN(C)OVA model across subjects?In the conn toolbox second-level analyses are implemented using a general linear model (GLM), which encompasses anova as well as linear regression models. In the Setup->second-level covariates tab you can define as many subject-effects as you wish. Then in the ‘between-subjects effect’ list in the Results tab you can simply select a subset of these effects to be included in each general linear model. For example, for a study with two subject groups (e.g. patients and controls) you could define two effects by dummy coding these subject groups (e.g. the 1) To compare the connectivity results of patients vs. controls (disregarding the effect of other covariates) select ‘patients’ and ‘controls’ in the 2) To perform the same comparison, but now controlling for potential between-group differences in performance, select ‘patients’, ‘controls’, and ‘performance’ in the 3) To perform the same comparison, but now additionally controlling for potential group*performance interactions (e.g. differences between groups in the association between performance on connectivity), select ‘patients’, ‘controls’, ‘performance_patients’, and ‘performance_controls’ in the 4) In the same interaction model above, if you wish to test group*performance interactions you would select the same subject effects, and enter [0,0,1,-1] in the As an additional note, when defining a second-level general linear model the conn toolbox will evaluate whether you wish to include all of the analyzed subjects or not in each particular analysis by exploring the selected ## Results: How to specify a regression model across subjects?Following the same experiment example above (Ancova model), you could define the following models/contrasts: 1) To evaluate the association between performance and connectivity across 2) To evaluate the association between performance and connectivity 3) To evaluate between-group differences in this association see the example (4) in the Ancova model (group*performance interaction) You could also have additional regressors (e.g. IQ) and wish to evaluate the unique contribution of each of these effects. For this you could, for example, define: 4) To evaluate the unique association between performance and connectivity when controlling by IQ across all subjects, you would select the ‘all’, ‘performance’, and ‘IQ’ effects in the
## Results: Can I test mixed within- between- subject models?Yes. You could have two within-subject conditions (e.g. task and rest in a block design), and (if these conditions are defined in the 1) To evaluate the difference in connectivity between task and rest across all subjects, select ‘all’ in the 2) To evaluate possible condition*group interactions (e.g. modulation of task vs. rest connectivity differences across groups), select ‘patients’ and ‘controls’ in the Another potential source of within-subject effects is multiple seeds/rois. For example, you might wish to evaluate how similar/different the connectivity patterns of two different seeds are. For this you would simply select the two ROIs in the
## Results: Can I evaluate F-contrast within the conn toolbox second-level analyses?This feature has been implemented in version 13f. F-contrasts in voxel-level analyses are implemented as repeated-measures analyses using ReML estimation of covariance components and evaluated through F-statistical parameter maps. F-contrasts in ROI-level analyses are implemented as multivariate analyses and evaluated through F- or Wilks lambda statistics depending on the dimensionality of the within- and between- subjects contrasts.
## Results: How do I correct for 'multiple-comparisons' the second-level results?For Similarly, for Note, nevertheless, that there is some discussion as to what constitutes an 'appropriate' correction. Many of the discussion boils down to making sure that your statistical inferences always pertain to the analysis units corresponding to the one threshold that is using an analysis-wise false positive control method (e.g. do not make inferences about individual clusters of activation if you are correcting using voxel-level FDR correction; do not make inferences about individual ROI-to-ROI connections if you are using seed-level FWE correction; etc.) ## Results: I want to enter the first-level connectivity maps into SPM or other toolbox for additional second-level analyses. Where can I find the appropriate first-level maps?The files conn_*/results/firstlevel/ANALYSIS_01/BETA_Subject*_Condition*_Source*.nii contain the connectivity maps for each subject/condition/source (e.g. fisher transformed correlation values if using ‘bivariate correlation’ connectivity measures). The file _list_conditions.txt in the same folder will tell you the association between condition numbers (in the filenames) and condition names (as defined in the conn project), and the file _list_sources.txt in the same folder will tell you the association between source numbers and source names.
## Results: Can I get from the toolbox the correlation ROI-to-ROI matrix for a single subject?You can find that information in the files: conn_*/results/firstlevel/ANALYSIS_01/resultsROI_Subject###_Condition###.mat This file will contain a matrix Z with the ROI-to-ROI connectivity values (Fisher-transformed correlation coefficients). In particular the value Z(i,j) will contain the connectivity between source ROI 'i' and target ROI 'j'. The names of these ROIs can be read from the variables 'names' and 'names2', respectively, from the same .mat file. In other words, names{i} is the source ROI and names{j} is the target ROI corresponding to the Z(i,j) value. Note that source ROIs are all of the ROIs that you entered as sources in the first-level analysis step (which are typically a subset of all of the ROIs entered in the original Setup step). In contrast target ROIs are all of the ROIs entered in the Setup step. The ROIs are sorted so that the square matrix In addition, the file
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