help > RE: NBS one sample-test with more negative than positive values/thresholding of connectivity matrices.
Feb 24, 2022  06:02 AM | Andrew Zalesky
RE: NBS one sample-test with more negative than positive values/thresholding of connectivity matrices.
Hi Ole, 


1. I'm not quite sure what argument is being referred to here. But it seems that the paired t-test is appropriate for your case. Many users use the one-sample test when in fact they should be using the t-test. 

2. I don't really follow your concern. It is perfectly fine for most (or all) permutations to show larger components than an observed component. This would indicate that your observed component is not significant because it is consistent with the null data. It is also perfectly fine for one contrast to show more widespread effects than the reverse contrast.

3. Both thresholding approaches are reasonable and have been used in the literature. I don't think that an imbalance between negative and positive connections precludes use of these methods.    

I am sorry that I cannot be more insightful with my answers. Perhaps you could clarify, since I am not sure what exactly you are asking in 2 and 3.

best,
Andrew
Originally posted by Ole Jonas Böken:
Hi Andrew,

Many thanks for your answers!

1.) Please allow me to clarify:
We compare fMRI-based connectivity from two experimental conditions. We have a within-participant design, hence we'd use a paired t-test. Does the same argument of yours apply to the paired t-test?
In "classic" task activation designs, however, we would first contrast the conditions at the individual level and then combine the contrast images at the group level using a one-sample t-test. Hence our questions on the one sample t-test.
Would you advise to use the paired sample t-test for NBS?

2.) When comparing the two conditions, we noticed that we find differences in both directions (i.e. we have connections where condition A>B, and we have connections with AB or B>A). And the permutations randomly flip the condition labels (A vs. B). And inference is done on the size of the components alone. And not on their topological position.
When looking at the relatively sparse A>B directions, the sheer number of effects in the B>A direction seem to cause a large component in the permuted networks. In other words: We are under the impression that we find a meaningful empirical component at expected locations (which is rather small), but the permutations show even larger components (at different locations). Which of course interferes with the NBS inference.

Does this make any sense to you? And do you think that our concern is justified?

We are aware that this should not present a barrier for statistical inferences per se, but we are suspecting that this might influence our results negatively.
For instance, in the case of the one-sample t-test you stated elsewhere in the forum, that if someone aims to conduct a one-sample t-test, this someone should be cautious about having more negative than positive connectivity values —> https://www.nitrc.org/forum/forum.php?th...

3.) Regarding the thresholding:
Applying light proportional thresholding (i.e. removing the most negatively signed connectivity differences), we do get NBS results that we deem meaningful. Applying an absolute threshold and zeroing all negatively signed connectivity differences, we also find NBS results that we deem meaningful. But such thresholding would only be justified if the misbalance between positive and negative connectivity differences was actually a concern. Hence our questions.
Best,
Ole

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TitleAuthorDate
Ole Jonas Böken Feb 17, 2022
Ole Jonas Böken Feb 28, 2022
Andrew Zalesky Mar 3, 2022
Ole Jonas Böken Feb 23, 2022
RE: NBS one sample-test with more negative than positive values/thresholding of connectivity matrices.
Andrew Zalesky Feb 24, 2022
Andrew Zalesky Feb 17, 2022