Note why we don't implement TFCE in NiMARE (currently)

docs/cbma.rst

@@ -138,3 +138,20 @@ The Monte Carlo FWE correction approach implemented in NiMARE produces three new
     the voxel's summary statistic value lands on this null distribution.
     **Voxel-level correction is generally more conservative than cluster-level correction,
     so it is only recommended for very large meta-analyses (i.e., hundreds of studies).**
+
+.. admonition:: What about threshold-free cluster enhancement?
+
+    TFCE :footcite:p:`smith2009threshold` is a voxel-level metric that combines signal magnitude and
+    cluster extent to enhance the importance of clusters that are large, have high magnitude, or both.
+
+    It can be applied to coordinate-based meta-analyses as an alternate metric to the
+    maximum summary statistic (``level-voxel``), cluster mass (``desc-mass``), or cluster size (``desc-size``).
+    However, recent work by Frahm et al. :footcite:p:`frahm2022evaluation` has indicated that the costs of performing
+    TFCE-based inference (e.g., massively increased computation time) outweigh any observable benefits.
+    As such, we have chosen not to implement TFCE-based correction within NiMARE,
+    although there is a closed pull request with an implementation that worked at the time it was closed
+    (see `#655 <https://github.com/neurostuff/NiMARE/pull/655>`_).
+
+References
+----------
+.. footbibliography::

docs/references.bib

@@ -105,6 +105,20 @@ @article{fisher1946statistical
   publisher={Oliver and Boyd}
 }
 
+@article{frahm2022evaluation,
+  author={Frahm, Lennart and Cieslik, Edna C. and Hoffstaedter, Felix and Satterthwaite, Theodore D. and Fox, Peter T. and Langner, Robert and Eickhoff, Simon B.},
+  title={Evaluation of thresholding methods for activation likelihood estimation meta-analysis via large-scale simulations},
+  journal={Human Brain Mapping},
+  volume={n/a},
+  number={n/a},
+  pages={},
+  year={2022},
+  keywords={family-wise error, FWE, multiple comparison correction, neuroimaging meta-analysis, significance thresholding, threshold-free cluster enhancement cluster extent},
+  doi={10.1002/hbm.25898},
+  url={https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.25898},
+  abstract={In recent neuroimaging studies, threshold-free cluster enhancement (TFCE) gained popularity as a sophisticated thresholding method for statistical inference. It was shown to feature higher sensitivity than the frequently used approach of controlling the cluster-level family-wise error (cFWE) and it does not require setting a cluster-forming threshold at voxel level. Here, we examined the applicability of TFCE to a widely used method for coordinate-based neuroimaging meta-analysis, Activation Likelihood Estimation (ALE), by means of large-scale simulations. We created over 200,000 artificial meta-analysis datasets by independently varying the total number of experiments included and the amount of spatial convergence across experiments. Next, we applied ALE to all datasets and compared the performance of TFCE to both voxel-level and cluster-level FWE correction approaches. All three multiple-comparison correction methods yielded valid results, with only about 5\% of the significant clusters being based on spurious convergence, which corresponds to the nominal level the methods were controlling for. On average, TFCE's sensitivity was comparable to that of cFWE correction, but it was slightly worse for a subset of parameter combinations, even after TFCE parameter optimization. cFWE yielded the largest significant clusters, closely followed by TFCE, while voxel-level FWE correction yielded substantially smaller clusters, showcasing its high spatial specificity. Given that TFCE does not outperform the standard cFWE correction but is computationally much more expensive, we conclude that employing TFCE for ALE cannot be recommended to the general user.}
+}
+
 @article{freedman1983nonstochastic,
   title={A nonstochastic interpretation of reported significance levels},
   author={Freedman, David and Lane, David},
@@ -230,6 +244,19 @@ @article{rubin2017decoding
   doi={10.1371/journal.pcbi.1005649}
 }
 
+@article{smith2009threshold,
+ 	title={Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference},
+ 	author={Smith, Stephen M and Nichols, Thomas E},
+ 	journal={Neuroimage},
+ 	volume={44},
+ 	number={1},
+ 	pages={83--98},
+ 	year={2009},
+ 	publisher={Elsevier},
+ 	url={https://doi.org/10.1016/j.neuroimage.2008.03.061},
+ 	doi={10.1016/j.neuroimage.2008.03.061}
+ }
+
 @misc{sochat2015ttoz,
   author = {Sochat, Vanessa},
   title = {TtoZ Original Release},