@yichao Quoting prior text may be easier & faster than screenshots; see relevant thread.
Regarding significance, this is a pure double-dipping / p-hacking problem, and is not anything specific to MRtrix3.
My presumption is that you commenced your experiment with the hypothesis that one or more fixel-wise metrics have a non-zero association with age and/or sex, and a whole-brain FBA with FWE error correction and an alpha of 0.05 was your analysis technique to test that hypothesis. You perform that analysis, and report whatever was or was not significant. Any interrogation of data over and above that is precisely that: a post hoc interrogation of the data. Any particular post hoc numerical analysis may report p<0.05, but the problem is that the test that achieved significance does not align with your original hypothesis.
If, at the commencement of your experiment, you had stated that your hypothesis was of a non-zero relationship between specifically FD and specifically age specifically in the anterior commissure, then sure, you could report that result as statistically significant. But I’m presuming from your description that that was not the case: you used the non-significant fixel-wise FBA results to construct that hypothesis, and then tested that hypothesis using the same data. The problem here is that for basically any experiment, you can go digging through your data, fine-tuning data and parameter selection in order to produce p<0.05, and then report that as a statistically significant result; but that would be fundamentally misleading.
If these data were to be not significant in a whole-brain analysis, but showed suggestion of an association between FD and age in the anterior commissure, so you then collected and analysed a new dataset for testing that hypothesis and those data were shown to achieve p<0.05, then that’s something you could report as statistically significant.