help
help > RE: combining sources
Jun 16, 2014 09:06 PM | Alfonso Nieto-Castanon - Boston University
RE: combining sources
Hi Mary,
Your analyses are perfectly correct. If you select two source ROIs (contrast eye(2)) and your two subject groups (contrast 1 -1), the results will show you those regions that show between-group differences in connectivity with any of the two source ROIs. This will be an F-test so the results will be two-sided by nature (you will notice that the explorer shows you an F-stats threshold and the directionality menu is locked to 'two-sided'), so the results will be identical whether you choose a 1 -1 or a -1 1 contrast across groups.
If, after performing these analyses, you want to know the directionality of the found effects (connectivity differences across groups between the significant clusters and each of your source ROIs), you may do so using the 'explore clusters' button. These are typically reported as post-hoc analyses, and the results may be only significant for one seed ROI or for both, and may show positive or negative effects independently with each seed ROI (the effect sizes displayed here do depend on the sign of your between-subject contrast so if you used a 1 -1 contrast then positive values here will correspond to higher connectivity values in the first group compared to the second).
Another typically useful set of post-hoc analyses involve first importing the connectivity values with each seed ROI using the 'import values' button in your current results explorer, and then using 'tools-> calculator' to further explore the resulting measures (for example, if you find a significant positive between-group difference in connectivity with one seed, you know that that the connectivity values are greater in the first group, but you might still want to know whether that means increased positive connectivity or decreased negative (anti-) correlations in the first group compared to the second; importing the actual connectivity values and displaying them using the calculator allows you to answer these questions and better interpret your results).
Let me know if this clarifies (or if you would prefer to restrict you analyses to only those regions that show positive or negative between-group differences with any of your seed ROIs -but only if they show the same sign with each seed ROI-; this is a bit more convoluted to test but perfectly possible)
Best
Alfonso
Originally posted by Mary Newsome:
Your analyses are perfectly correct. If you select two source ROIs (contrast eye(2)) and your two subject groups (contrast 1 -1), the results will show you those regions that show between-group differences in connectivity with any of the two source ROIs. This will be an F-test so the results will be two-sided by nature (you will notice that the explorer shows you an F-stats threshold and the directionality menu is locked to 'two-sided'), so the results will be identical whether you choose a 1 -1 or a -1 1 contrast across groups.
If, after performing these analyses, you want to know the directionality of the found effects (connectivity differences across groups between the significant clusters and each of your source ROIs), you may do so using the 'explore clusters' button. These are typically reported as post-hoc analyses, and the results may be only significant for one seed ROI or for both, and may show positive or negative effects independently with each seed ROI (the effect sizes displayed here do depend on the sign of your between-subject contrast so if you used a 1 -1 contrast then positive values here will correspond to higher connectivity values in the first group compared to the second).
Another typically useful set of post-hoc analyses involve first importing the connectivity values with each seed ROI using the 'import values' button in your current results explorer, and then using 'tools-> calculator' to further explore the resulting measures (for example, if you find a significant positive between-group difference in connectivity with one seed, you know that that the connectivity values are greater in the first group, but you might still want to know whether that means increased positive connectivity or decreased negative (anti-) correlations in the first group compared to the second; importing the actual connectivity values and displaying them using the calculator allows you to answer these questions and better interpret your results).
Let me know if this clarifies (or if you would prefer to restrict you analyses to only those regions that show positive or negative between-group differences with any of your seed ROIs -but only if they show the same sign with each seed ROI-; this is a bit more convoluted to test but perfectly possible)
Best
Alfonso
Originally posted by Mary Newsome:
Hi Alfonso,
Thanks so much, as usual!
May I clarify something? You mentioned selecting between-subjects contrasts while looking at between sources contrasts. We have two groups in our analysis. When performing the [1 0; 0 1] between-sources contrast, the same results occur if we have [1 -1] or [-1 1] specified for groups, which made me think the analysis was perhaps collapse collapsing across group (but when All was selected, a different result was obtained).
Could you say what the best way of looking at between groups differences when taking an OR approach?
Thanks and best,
Mary
Originally posted by Alfonso Nieto-Castanon:
Thanks so much, as usual!
May I clarify something? You mentioned selecting between-subjects contrasts while looking at between sources contrasts. We have two groups in our analysis. When performing the [1 0; 0 1] between-sources contrast, the same results occur if we have [1 -1] or [-1 1] specified for groups, which made me think the analysis was perhaps collapse collapsing across group (but when All was selected, a different result was obtained).
Could you say what the best way of looking at between groups differences when taking an OR approach?
Thanks and best,
Mary
Originally posted by Alfonso Nieto-Castanon:
Hi Mary,
Yes, that is exactly correct, and the exact contrast values depend on what you exactly intent by 'aggregating'.
If by 'aggregating' you mean that you want to look at the connectivity with either left- or right- BA46, then you may select both ROIs in the 'sources' list, and enter the 'between-sources' contrast [1 0;0 1] (or eye(2)). The resulting analysis will be a F-test highlighting those regions that show significant association with either left-BA46 or with right-BA46.
If, on the other hand, by 'aggregating' you want to look at the average connectivity with left- and right- BA46, then you may select both ROIs in the 'sources' list, and enter the 'between-sources' contrast [.5 .5]. The resulting analysis will be a T-test highlighting those regions that show significant association with left/right BA46 (averaging the connectivity values across left-BA46 and right-BA46).
Independent of the above, you may select any arbitrary between-subjects and/or between-conditions contrasts for more complex comparisons. The interpretation of those will be the same as above (a contrast [1 0;0 1] across sources is equivalent to an OR conjunction of the original analyses across the two ROIs; while a contrast [.5 .5] across sources is equivalent to performing the original analyses on the average connectivity with the two ROIs)
Hope this clarifies
Best
Alfonso
Originally posted by Mary Newsome:
Yes, that is exactly correct, and the exact contrast values depend on what you exactly intent by 'aggregating'.
If by 'aggregating' you mean that you want to look at the connectivity with either left- or right- BA46, then you may select both ROIs in the 'sources' list, and enter the 'between-sources' contrast [1 0;0 1] (or eye(2)). The resulting analysis will be a F-test highlighting those regions that show significant association with either left-BA46 or with right-BA46.
If, on the other hand, by 'aggregating' you want to look at the average connectivity with left- and right- BA46, then you may select both ROIs in the 'sources' list, and enter the 'between-sources' contrast [.5 .5]. The resulting analysis will be a T-test highlighting those regions that show significant association with left/right BA46 (averaging the connectivity values across left-BA46 and right-BA46).
Independent of the above, you may select any arbitrary between-subjects and/or between-conditions contrasts for more complex comparisons. The interpretation of those will be the same as above (a contrast [1 0;0 1] across sources is equivalent to an OR conjunction of the original analyses across the two ROIs; while a contrast [.5 .5] across sources is equivalent to performing the original analyses on the average connectivity with the two ROIs)
Hope this clarifies
Best
Alfonso
Originally posted by Mary Newsome:
Hello Alfonso and everyone,
I was recently looking online at some PowerPoint slides that Dr. Whitfield-Gabrieli had written. In the slides. it was suggested that one could select two sources simultaneously from the Sources column to "aggregate or compare the connectivity results across several ROIs (e.g. to compare the connectivity between LLP & RLP select both sources and enter [1,-1] in the 'between-sources contrast' field)". (p. 47 of attached file)
If I wanted to aggregate right and left BA 46, for example, I would select both, but I'm not sure what the weights would be. Would they be be .5 and .5?
Thanks for any help, (and thanks for all of the great help you have already provided for this great program!)
Mary
I was recently looking online at some PowerPoint slides that Dr. Whitfield-Gabrieli had written. In the slides. it was suggested that one could select two sources simultaneously from the Sources column to "aggregate or compare the connectivity results across several ROIs (e.g. to compare the connectivity between LLP & RLP select both sources and enter [1,-1] in the 'between-sources contrast' field)". (p. 47 of attached file)
If I wanted to aggregate right and left BA 46, for example, I would select both, but I'm not sure what the weights would be. Would they be be .5 and .5?
Thanks for any help, (and thanks for all of the great help you have already provided for this great program!)
Mary
Threaded View
| Title | Author | Date |
|---|---|---|
| Mary Newsome | Jun 12, 2014 | |
| Alfonso Nieto-Castanon | Jun 13, 2014 | |
| Mary Newsome | Jun 16, 2014 | |
| Alfonso Nieto-Castanon | Jun 16, 2014 | |
| Laila Franke | Nov 14, 2019 | |
| Mary Newsome | Jun 17, 2014 | |
| Alfonso Nieto-Castanon | Jun 18, 2014 | |
| Mary Newsome | Jun 18, 2014 | |
| Alfonso Nieto-Castanon | Jun 18, 2014 | |
| Mary Newsome | Jun 19, 2014 | |
| Mary Newsome | Jun 19, 2014 | |
| Alfonso Nieto-Castanon | Jun 20, 2014 | |
| Mary Newsome | Jun 21, 2014 | |