Hi, Peeta-
That kind of trade-off is to be expected: high thresholds bias towards the super significant (and potentially smaller) and low thresholds bias towards very large (and potentially much less significant). That is one of the reasons that Bob developed the ETAC strategy, to incorporate cluster results from a range of significance cut-offs, while still controlling overall false positive rates. Please see this article for more details:
It is an unfortunate (but inherent) side effect of standard clustering strategies that the mostly arbitrary p-value threshold affects results so strongly. The ETAC strategy tries to reduce the influence of the arbitrariness: you can use a range of blur values (say, 4, 6, 8 and 10 mm) and p-values (say, 0.01, 0.005, 0.001, 0.0001) and combine the results.
Note that moving in a very different direction, if you were willing to use ROI-based analysis, you could use something Gang has worked on recently, that reallly removes the need for arbitrary thresholding:
- Chen G, Xiao Y, Taylor PA, Rajendra JK, Riggins T, Geng F, Redcay E, Cox RW (2019). Handling Multiplicity in Neuroimaging Through Bayesian Lenses with Multilevel Modeling. Neuroinformatics. 17(4):515-545. doi:10.1007/s12021-018-9409-6
https://pubmed.ncbi.nlm.nih.gov/30649677/ - Chen G, Padmala S, Chen Y, Taylor PA, Cox RW, Pessoa L (2020). To pool or not to pool: Can we ignore cross-trial variability in FMRI? NeuroImage:
https://www.sciencedirect.com/science/article/pii/S1053811920309812?via%3Dihub
This style of analysis is very appealing for modeling at the group level more consistently, without the need for clusterwise correction.
–pt