New: a case for group analysis that doesn't need multiple comparison correction

AFNI Message Board
1

Here is some new work by Gang (again??) on analyzing data sets without the need for pesky multiple comparisons corrections:

How to Deal with Multiplicity in Neuroimaging? A Case for Global Calibration
https://www.biorxiv.org/content/10.1101/706747v1

And definitely worth checking out for anyone with a matrix appearing in their analyses/results.

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Is this implemented in AFNI?

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Yes. The programs RBA, MBA and 3dISC use Bayesian multilevel (BML) modeling.

For the MBA program for matrix-based analysis:
Chen G, Burkner P-C, Taylor PA, Li Z, Yin L, Glen DR, Kinnison J, Cox RW, Pessoa L (2019). An Integrative Approach to Matrix-Based Analyses in Neuroimaging. Human Brain Mapping (in press) doi:10.1101/459545
https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.24686

For a Bayesian Multilevel (BML) modeling approach with the RBA program:
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/

For an ROI-based approach through Bayesian multilevel (BML) modeling to ISC (inter-subject correlation) and naturalistic FMRI:
Chen G, PA Taylor, Qu X, Molfese PJ, Bandettini PA, Cox RW, Finn ES (2020). Untangling the Relatedness among Correlations, Part III: Inter-Subject Correlation Analysis through Bayesian Multilevel Modeling for Naturalistic Scanning. NeuroImage 216:116474. doi:10.1016/j.neuroimage.2019.116474
https://pubmed.ncbi.nlm.nih.gov/31884057/

And you can see a broader list of papers and specific tools here:
https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/published/citations.html

–pt