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help > RE: calculating cohen's d from rZ values
Dec 24, 2014 06:12 AM | Alfonso Nieto-Castanon - Boston University
RE: calculating cohen's d from rZ values
Hi Patrick,
I am not sure I am understanding correctly, could you please clarify the following?
a) when you say "fed those seeds back into the conn model to explore potential connectivity paths", I am assuming you entered the mask of significant clusters from your original seed-to-voxel analyses (looking at between-group differences in connectivity with a R&L seed) as new ROIs into CONN, and then performed ROI-to-ROI first-level analyses using bivariate correlations and only these new ROIs as sources. In your second-level analyses you then looked at average (across both groups) ROI-to-ROI effects to determine significant connections between these ROIs (roi1-roi2, roi1-roi3, and roi2-roi4). Am I interpreting correctly?
b) not sure what you mean by "extracted single subject eigenvariates across each cluster to determine pattern of correlation in each group", are you referring to the step above (but now using single-group contrasts in your second-level analyses) or are you talking about a different set of analyses?
c) the second paragraph is clearer I believe, just rephrasing to make sure I am interpreting correctly: you then run a new set of first-level ROI-to-ROI analyses using your roi1 to roi4 ROIs as sources, and now using bivariate regression measures, and then looked at second-level within-group effects, limiting your results to only those ROI-to-ROI connections of interest (those where you found in the step (a) above significant across-group connectivity effects; i.e. roi1-roi2, roi1-roi3, etc.). In addition, for the subset of connections where you found bi-directional significant effects in this newer (bivariate-regression) set of analyses you then extracted the connectivity strengths for each subject and performed paired t-tests to explore directionality effects (e.g. higher roi1-roi2 vs. roi2-roi1 effects). Let me know if I am misinterpreting/misrepresenting anything here.
Thanks!
Alfonso
Originally posted by Patrick McConnell:
I am not sure I am understanding correctly, could you please clarify the following?
a) when you say "fed those seeds back into the conn model to explore potential connectivity paths", I am assuming you entered the mask of significant clusters from your original seed-to-voxel analyses (looking at between-group differences in connectivity with a R&L seed) as new ROIs into CONN, and then performed ROI-to-ROI first-level analyses using bivariate correlations and only these new ROIs as sources. In your second-level analyses you then looked at average (across both groups) ROI-to-ROI effects to determine significant connections between these ROIs (roi1-roi2, roi1-roi3, and roi2-roi4). Am I interpreting correctly?
b) not sure what you mean by "extracted single subject eigenvariates across each cluster to determine pattern of correlation in each group", are you referring to the step above (but now using single-group contrasts in your second-level analyses) or are you talking about a different set of analyses?
c) the second paragraph is clearer I believe, just rephrasing to make sure I am interpreting correctly: you then run a new set of first-level ROI-to-ROI analyses using your roi1 to roi4 ROIs as sources, and now using bivariate regression measures, and then looked at second-level within-group effects, limiting your results to only those ROI-to-ROI connections of interest (those where you found in the step (a) above significant across-group connectivity effects; i.e. roi1-roi2, roi1-roi3, etc.). In addition, for the subset of connections where you found bi-directional significant effects in this newer (bivariate-regression) set of analyses you then extracted the connectivity strengths for each subject and performed paired t-tests to explore directionality effects (e.g. higher roi1-roi2 vs. roi2-roi1 effects). Let me know if I am misinterpreting/misrepresenting anything here.
Thanks!
Alfonso
Originally posted by Patrick McConnell:
Thanks, Alfonso!
The approach I took was initially a hypothesis driven bivariate correlation (between-groups), seed-voxel analysis with a a R & L seed. I thresholded results in SPM at p<.001 and p<.05 cluster FWE and made functional ROIs from significant results and fed those seeds back into the conn model to explore potential connectivity paths. I ended up with a path from roi1 to roi2 and roi3, and from roi2 to roi4. I extracted single subject eigenvariates across each cluster to determine the pattern of correlation in each group (e.g., +/-, +/+, -/-) and to get an indication of effect size.
To explore potential effective connectivity between these functionally defined regions, I ran a bivariate regression (within-group), ROI-ROI analysis using those paths shown to be significant, finding all of them to be bidirectionally significant in one group but not the other (p <.001, p< .05 FDR), although the magnitude of t-stat varied by direction. To explore this further, I went in and extracted regression coefficients for each subject (only where significant bidirectional effects were observed) and ran paired-samples t-tests to see if the magnitude of t-stat was significantly larger for one direction (e.g., roi1-->roi2 vs. roi2-roi1) than the other.
So,
1) Is my approach statistically valid, or "double-dipping"?
2) Is it appropriate to infer directionality of effective connectivity based on the above approach?
Thanks!!!
-Patrick
The approach I took was initially a hypothesis driven bivariate correlation (between-groups), seed-voxel analysis with a a R & L seed. I thresholded results in SPM at p<.001 and p<.05 cluster FWE and made functional ROIs from significant results and fed those seeds back into the conn model to explore potential connectivity paths. I ended up with a path from roi1 to roi2 and roi3, and from roi2 to roi4. I extracted single subject eigenvariates across each cluster to determine the pattern of correlation in each group (e.g., +/-, +/+, -/-) and to get an indication of effect size.
To explore potential effective connectivity between these functionally defined regions, I ran a bivariate regression (within-group), ROI-ROI analysis using those paths shown to be significant, finding all of them to be bidirectionally significant in one group but not the other (p <.001, p< .05 FDR), although the magnitude of t-stat varied by direction. To explore this further, I went in and extracted regression coefficients for each subject (only where significant bidirectional effects were observed) and ran paired-samples t-tests to see if the magnitude of t-stat was significantly larger for one direction (e.g., roi1-->roi2 vs. roi2-roi1) than the other.
So,
1) Is my approach statistically valid, or "double-dipping"?
2) Is it appropriate to infer directionality of effective connectivity based on the above approach?
Thanks!!!
-Patrick
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Title | Author | Date |
---|---|---|
Crystal Goh | Jun 2, 2012 | |
Alfonso Nieto-Castanon | Jul 8, 2012 | |
Patrick McConnell | Dec 20, 2014 | |
Alfonso Nieto-Castanon | Dec 21, 2014 | |
Patrick McConnell | Dec 21, 2014 | |
Alfonso Nieto-Castanon | Dec 24, 2014 | |
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