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**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:

------------

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

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:

------------

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).

-------------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

## Threaded View

Title | Author | Date |
---|---|---|

Mickael Tordjman |
Jul 10, 2019 | |

Stephen L. |
Jul 10, 2019 | |

Jeff Browndyke |
Jul 11, 2019 | |

Stephen L. |
Jul 11, 2019 | |

Mickael Tordjman |
Jul 11, 2019 | |