Dear Andrew,

Thanks for your reply. But I still can’t understand. In my rs-fmri study, I just have one sample. I want to obtain significant edges and nodes that can predict aggression behavior. Currently, I have chosen t-test. The first column of the design matrix is the value of the aggrrssion variable, the next three columns are the values of the covariates, the contrst is [1 0 0 0], and the threshold is 3.1. Is it ok to set it up like this?  If this method is not right, what should I do？

Thank you, in advance, for your time,

Sincerely,

LiYuan

Originally posted by Andrew Zalesky:

Hi Yuan,

I think that you most likely need to select the two-sample t-test. 

The one-sample test is probably not what you need here. 

Andrew

 

Originally posted by li yuan:

Dear Andrew,

First of all, thank you for this very nice tool! I would like to ask something about the underlying process of the one sample t-test. What is written in the manual is: "The one sample test randomly flips the sign of each data point for each permutation". 

In my study, I chose the t-test button. In resting state fmri analysis, using the NBS, find the edges and nodes where FC is related to behavior.  However, the subjects were not grouped. The first column of the design matrix is the value of the behavioral variable, the next three columns are the values of the covariates, the contrst is [1 0 0 0], and the threshold is 3.1. Is it ok to set it up like this?

References

[1] 

Vatansever, D., Karapanagiotidis, T., Margulies, D. S., Jefferies, E., & Smallwood, J. (2020). Distinct patterns of thought mediate the link between brain functional connectomes and well-being. Network Neuroscience, 4(3), 637–657. https://doi.org/10.1162/netn_a_00137

[2]

Wang, X., Zhuang, K., Li, Z., & Qiu, J. (2022). The functional connectivity basis of creative achievement linked with openness to experience and divergent thinking. Biological Psychology, 168, 108260. https://doi.org/10.1016/j.biopsycho.2021.108260

 

Thank you, in advance, for your time,
Sincerely,

 

Yuan