[color=#000000]Hi Nicole,[/color]
The contrast [1 -1 1 -1 0 0 0 0 0 0 0 0] (selecting 2-back_Ba, 0-back_Ba, 2-Back_Da, and 0-Back_Da in this contrast) is looking at the differences in connectivity between 2-back and 0-back conditions, averaged/summed across both the 'Pre-active' and 'During-active' conditions. Positive values there indicate that, on average across pre- and during- stimulation) functional connectivity was higher during 2-back compared to 0-back tasks.
[color=#000000]If you want, instead, to look at 2-back vs. 0-back differences in connectivity, and evaluate whether those differences are constant (or not) before- and during- stimulation, the contrast for that would be: [1 -1 -1 1 0 0 0 0 0 0 0 0][/color]
[color=#000000]The same applies to the contrast [0 0 1 -1 0 0 0 0 1 -1 0 0], which is now looking at the differences in connectivity between 2-back compared to 0-back tasks, averaged/summed across both the 'during-active stimulation' condition and the 'during-sham stimulation' conditions. If you want, instead to look at whether those 2-back vs. 0-back differences in connectivity are themselves different during active simulation compared to during sham stimulation, the contrast for that would be: [0 0 1 -1 0 0 0 0 -1 1 0 0][/color]
[color=#000000]In general, since you have a full factorial design, you can break-down the contrast that you care about into three parts:[/color]
[color=#000000]A) [b]memory-load contrast[/b] (two values: across 2-back and 0-back levels): e.g. [1 0] to look at 2-back connectivity only, [0 1] to look at 0-back connectivity only, [1 -1] to look at differences between 2-back vs. 0-back connectivity, or [.5 .5] to look at average 2-back and 0-back connectivity[/color]
[color=#000000]B) [b]time contrast[/b] (three values: across pre- during- and post- stimulation levels): e.g. [1 0 0] to look at pre-stimulation connectivity only, [-1 1 0] to look at differences between during- and pre- stimulation, [-1 0 1] to look at differences in connectivity between post- vs. pre- stimulation, etc.[/color]
[color=#000000]and C) [b]stimulation contrast[/b] (two values: across the active and sham stimulation levels): e.g. [1 0] to look at connectivity in the active stimulation condition only, [1 -1] to look at connectivity differences between the active compared to sham stimulation conditions[/color]
[color=#000000]Once you have defined these three contrast A,B, and C, use the syntax in Matlab command-line (or in the 'between-conditions contrast' GUI field):[/color]
[color=#000000]kron(C, kron(B, A))[/color]
[color=#000000]to get the actual contrast vector. For example, in your second example:[/color]
[color=#000000]A) [1 -1] (to look at differences in connectivity between 2-back vs. 0-back conditions)[/color]
[color=#000000]B) [0 1 0] (to look only at the effects during-stimulation (disregarding pre- and post- stimulation)[/color]
[color=#000000]C) [1 -1] (to look at differences in connectivity between the active vs. sham stimulation conditions)[/color]
[color=#000000]could be typed as the contrast:[/color]
[color=#000000]kron( [1 -1] , kron( [0 1 0] , [1 -1] ))[/color]
which would result in the contrast vector:
[0 0 1 -1 0 0 0 0 -1 1 0 0]
Hope this helps
Alfonso
[i]Originally posted by Nicole Nissim:[/i][quote]Hello CONN Users,
I have a question about my second level results and want to be sure I am interpreting the significant ROI-to-ROI results correctly. I have included a screenshot of my results.
Background on the study design: 16 subjects, 6 runs per subject (341 volumes each), 12 conditions, in a 2 (2-back, 0-back) by 3 (pre-, during, post-stimulation) by 2 (active stim vs sham) design (2x2x3). gPPI analysis for ROI-to-ROI results. I added my own ROIs which are labeled 1-15 and correspond to the working memory network.
Brief description of the study: within group design, participants came in for two separate visits (one active stimulation, one sham stimulation inside the scanner during 3 runs of an N-back task, baseline, during, and post-stimulation, thus they have 6 conditions, 3 for active and 3 for sham)
To understand my labeling:
Ba = pre active
Da = during active
Pa = post active
Bs = pre sham
Ds = during sham
Ps = post sham
In the image attached, I have defined my contrast as 1 -1 1 -1 0 0 0 0 0 0 0 0 (selecting 2-back_Ba, 0-back_Ba, 2-Back_Da, and 0-Back_Da in this contrast).
My question: seed 5 has significantly increased connectivity to targets 13, 7, and 6. With this contrast, I am interpreting this to mean: connectivity is increased during active stimulation for 2>0 relative to baseline active 2>0. Is this correct? Or does this result mean: Increased connectivity is occurring in the baseline active condition relative to during active stimulation?
If I select another contrast instead, for example: 0 0 1 -1 0 0 0 0 1 -1 0 0 , and had significant connectivity results, would this mean for 2>0 during active is increased relative to during sham?
I am unclear about the ordering of the different conditions in terms of interpreting the directionality of significant results.
Any help with this would be greatly appreciated!
Best,
Nicole[/quote]
The contrast [1 -1 1 -1 0 0 0 0 0 0 0 0] (selecting 2-back_Ba, 0-back_Ba, 2-Back_Da, and 0-Back_Da in this contrast) is looking at the differences in connectivity between 2-back and 0-back conditions, averaged/summed across both the 'Pre-active' and 'During-active' conditions. Positive values there indicate that, on average across pre- and during- stimulation) functional connectivity was higher during 2-back compared to 0-back tasks.
[color=#000000]If you want, instead, to look at 2-back vs. 0-back differences in connectivity, and evaluate whether those differences are constant (or not) before- and during- stimulation, the contrast for that would be: [1 -1 -1 1 0 0 0 0 0 0 0 0][/color]
[color=#000000]The same applies to the contrast [0 0 1 -1 0 0 0 0 1 -1 0 0], which is now looking at the differences in connectivity between 2-back compared to 0-back tasks, averaged/summed across both the 'during-active stimulation' condition and the 'during-sham stimulation' conditions. If you want, instead to look at whether those 2-back vs. 0-back differences in connectivity are themselves different during active simulation compared to during sham stimulation, the contrast for that would be: [0 0 1 -1 0 0 0 0 -1 1 0 0][/color]
[color=#000000]In general, since you have a full factorial design, you can break-down the contrast that you care about into three parts:[/color]
[color=#000000]A) [b]memory-load contrast[/b] (two values: across 2-back and 0-back levels): e.g. [1 0] to look at 2-back connectivity only, [0 1] to look at 0-back connectivity only, [1 -1] to look at differences between 2-back vs. 0-back connectivity, or [.5 .5] to look at average 2-back and 0-back connectivity[/color]
[color=#000000]B) [b]time contrast[/b] (three values: across pre- during- and post- stimulation levels): e.g. [1 0 0] to look at pre-stimulation connectivity only, [-1 1 0] to look at differences between during- and pre- stimulation, [-1 0 1] to look at differences in connectivity between post- vs. pre- stimulation, etc.[/color]
[color=#000000]and C) [b]stimulation contrast[/b] (two values: across the active and sham stimulation levels): e.g. [1 0] to look at connectivity in the active stimulation condition only, [1 -1] to look at connectivity differences between the active compared to sham stimulation conditions[/color]
[color=#000000]Once you have defined these three contrast A,B, and C, use the syntax in Matlab command-line (or in the 'between-conditions contrast' GUI field):[/color]
[color=#000000]kron(C, kron(B, A))[/color]
[color=#000000]to get the actual contrast vector. For example, in your second example:[/color]
[color=#000000]A) [1 -1] (to look at differences in connectivity between 2-back vs. 0-back conditions)[/color]
[color=#000000]B) [0 1 0] (to look only at the effects during-stimulation (disregarding pre- and post- stimulation)[/color]
[color=#000000]C) [1 -1] (to look at differences in connectivity between the active vs. sham stimulation conditions)[/color]
[color=#000000]could be typed as the contrast:[/color]
[color=#000000]kron( [1 -1] , kron( [0 1 0] , [1 -1] ))[/color]
which would result in the contrast vector:
[0 0 1 -1 0 0 0 0 -1 1 0 0]
Hope this helps
Alfonso
[i]Originally posted by Nicole Nissim:[/i][quote]Hello CONN Users,
I have a question about my second level results and want to be sure I am interpreting the significant ROI-to-ROI results correctly. I have included a screenshot of my results.
Background on the study design: 16 subjects, 6 runs per subject (341 volumes each), 12 conditions, in a 2 (2-back, 0-back) by 3 (pre-, during, post-stimulation) by 2 (active stim vs sham) design (2x2x3). gPPI analysis for ROI-to-ROI results. I added my own ROIs which are labeled 1-15 and correspond to the working memory network.
Brief description of the study: within group design, participants came in for two separate visits (one active stimulation, one sham stimulation inside the scanner during 3 runs of an N-back task, baseline, during, and post-stimulation, thus they have 6 conditions, 3 for active and 3 for sham)
To understand my labeling:
Ba = pre active
Da = during active
Pa = post active
Bs = pre sham
Ds = during sham
Ps = post sham
In the image attached, I have defined my contrast as 1 -1 1 -1 0 0 0 0 0 0 0 0 (selecting 2-back_Ba, 0-back_Ba, 2-Back_Da, and 0-Back_Da in this contrast).
My question: seed 5 has significantly increased connectivity to targets 13, 7, and 6. With this contrast, I am interpreting this to mean: connectivity is increased during active stimulation for 2>0 relative to baseline active 2>0. Is this correct? Or does this result mean: Increased connectivity is occurring in the baseline active condition relative to during active stimulation?
If I select another contrast instead, for example: 0 0 1 -1 0 0 0 0 1 -1 0 0 , and had significant connectivity results, would this mean for 2>0 during active is increased relative to during sham?
I am unclear about the ordering of the different conditions in terms of interpreting the directionality of significant results.
Any help with this would be greatly appreciated!
Best,
Nicole[/quote]