help > RE: Statistical analysis: implications of the paper of Eklund et al?
Jul 11, 2019  11:07 AM | Stephen L. - Coma Science Group, GIGA-Consciousness, Hospital & University of Liege
RE: Statistical analysis: implications of the paper of Eklund et al?
Dear Jeff,

Glad my reply could be helpful, if it can save some time and help getting more robust results, that's great!

About what CONN is doing internally to calculate non-parametric results, what CONN is doing has changed since v18a, here are my personal note comparing the various approaches:

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Permutation-based options can be implemented on any NIfTI file using SnPM in SPM, randomise in FSL, or FSL PALM (also affiliated with FSL but standalone script), Eklund's BROCCOLI package, mri_glmfit-sim in FreeSurfer (or future in QDEC, see attached PPT), CONN for fmri (similar approach to PALM, state-of-the-art and generic), VBM/CAT for T1 voxel-based morphometry. In practice, there are two main approaches for non-parametric permutation-based correction: permutation of residuals (FSL PALM, CONN) which is more generic, or "whole-data" permutation (SnPM, BROCCOLI), which a permutation approach requires different permutation schemes for different sorts of analyses and it does not apply to certain scenarios (e.g. one-sample t-test). However, even permutation of residuals can have shortcomings in practice, such as the inability of the "sign-flipping" procedure to build the proper null distribution for the group that contains a single sample/subject (in the case of a single-case study, one patient vs a group). The "fix" that Alfonso introduced in CONN v18a, instead of flipping the signs (or permuting) the residuals, does a full multiplication by a random orthogonal matrix (since both permutation and sign-flip operations can be considered special-cases of an orthogonal transformation, and orthogonal transformations are the most general class of transformations guaranteeing that the randomized data has exactly the same spatial covariance structure as your original data).
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For more infos on this implementation in CONN since v18a: https://www.nitrc.org/forum/forum.php?th...

So essentially, CONN has always been using permutation by sign flipping of the residuals, which is considered the best approach, but for single case studies it yielded some issues, so CONN devised a new approach by applying a full multiplication by a random orthogonal matrix, which is essentially the same thing but the latter seems to be more robust and flexible than the former approach (and I guess also faster since processors and in particular MATLAB is optimized for matricial operations thanks to to BLAS).

In practice, on a few studies I did, I can confirm the results were the same for group analyses, but better for single case studies (whereas before they were nonsensical). This is no proof in the general case, and it would be awesome if Alfonso could publish a paper about it, but as of now there is no reference for this particular approach to my knowledge (but maybe Alfonso can give more infos?).

Hope this helps,
Best regards,
Stephen

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TitleAuthorDate
Mickael Tordjman Jul 10, 2019
Stephen L. Jul 10, 2019
Jeff Browndyke Jul 11, 2019
RE: Statistical analysis: implications of the paper of Eklund et al?
Stephen L. Jul 11, 2019
Mickael Tordjman Jul 11, 2019