I am new to tfNBS analysis using connectomstats and though probably a silly question, I am seeking help interpreting the “fwe_1mpvalue.csv” output file from a negative contrast vector.
We had two groups in the two column design matrix and we tested two contrast vectors separately: pos [1, -1] and neg [-1, 1] to see which edges were significantly different.
Both the pos and neg contrast “fwe_1mpvalue.csv” output files contained mostly zeroes. However, the “pos_fwe_1mpvalue” file also had values between 0.001-0.040 (which looked to me like normal p-values) while the “neg_fwe_1mpvalue” file contained values ranging up to 0.99+.
Based on other queries in this forum I see that values in the “fwe_1mpvalue.csv” file are calculated as “fwe_pvalue = 1 - p”.
My questions are:
does the “fwe_pvalue = 1 - p” apply only to a positive contrast, and would the negative contrast be “fwe_pvalue = -1 - p” ?
Or another way of putting it: Would the values in either neg or pos “fwe_pvalue.csv” files need to be above 0.95 to represent a significant difference? And therefore in my example, the significant differences are only observed in the negative contrast since it contains values above 0.95?
would the significant p-values in the neg file mean that the second group has increased connectivity in those edges compared to the first group and are the connectivity scores I should be using in the “enhanced.csv” file?
I guess I’m probably confusing myself, but just want to be sure.
I’m not an expert on this but here’s my understanding -
If you were doing a simple t-test, then yes the sign of the contrast only changes the direction of the t-statistic and your first assumption would hold. I would expect to see this in the uncorrected p-values but there can be some variation from this in the FWE-corrected p-values due to randomness in the permutation distribution.
However, when I look into my own data and try these two contrasts between groups, I don’t see a 1:1 correspondence between the positive and negative contrast uncorrected p-values (they’re related but not exactly 1-the_other_p). I’m not sure where this comes from I would be careful to conclude that the positive contrast p-values are exactly 1-negative contrast p-values.
The significant differences are only those connections where fwe_1mpvalue>0.95 in the negative contrast, but if you didn’t have a pre-specified directional hypothesis, you should correct for looking at both directions, in which case only connections exceeding fwe_1mpvalue>0.975 should be considered.
Interpretation is correct, but I don’t think the enhanced.csv gives you interpretable connectivity values. To do this, I would find connections exceeding your statistical threshold and then grab the raw connectivity values (from the connectomes that were used as input to connectomestats) for those suprathreshold connections.