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help > Non-parametric cluster correction single subj
Dec 11, 2017 07:12 PM | Alfonso Nieto-Castanon - Boston University
Non-parametric cluster correction single subj
Dear Stephen,
Thank you very much for this thread, I have been looking at this for the past few days and I believe you are absolutely right and there is in fact an issue with the standard randomization procedure in these group-vs-one-sample comparison scenarios (e.g. a two-sample t-test where one group has a single subject). I am attaching a potential patch, this is still an ongoing work since I am still working on running simulations to more fully validate the new procedure but if you would like to give it a try and let me know if you run into any issues that would be fantastic (I implemented this patch on the development version, which also includes things like the ability to parallelize the permutation/randomization procedure; I believe I have sent you all necessary files to have this patch working with version 17f but please let me know if you run into any installation issues).
To elaborate a bit, the procedure CONN uses (same as FSL randomise) for permutation/randomization analyses is not a simple permutation approach, but rather a "randomization of residuals" approach. The procedure consists of, first, computing the second-level model residuals for each subject, and then, many times, building a new dataset by randomly flipping the signs of these residuals and running the second-level analyses on these new data, using the results of these randomized datasets to build a distribution of your statistic of interest (e.g. cluster sizes) under the null hypothesis. The main advantage of this "randomization of residuals" approach (e.g. CONN, FSL) compared to the "permutations of residuals" or purely "permutation" approaches (e.g. SnPM, BROCCOLI) is that it works exactly in the same way for all GLM analyses, while 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). Having said that, you are absolutely right that in this group-vs-one-sample comparison scenarios the standard randomization of residuals approach does not offer the correct statistics. The reason for this seems to be related to the inability of the "sign-flipping" procedure to build the proper null distribution for the group that contains a single sample/subject. The "fix" that I introduced in the attached file is to use, instead of flipping the signs (or permuting) the residuals, a full multiplication by a random orthogonal matrix (since both permutation and sign-flip operations can be considered special-cases of a 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).
Let me know your thoughts/comments
Alfonso
Originally posted by Stephen L.:
Thank you very much for this thread, I have been looking at this for the past few days and I believe you are absolutely right and there is in fact an issue with the standard randomization procedure in these group-vs-one-sample comparison scenarios (e.g. a two-sample t-test where one group has a single subject). I am attaching a potential patch, this is still an ongoing work since I am still working on running simulations to more fully validate the new procedure but if you would like to give it a try and let me know if you run into any issues that would be fantastic (I implemented this patch on the development version, which also includes things like the ability to parallelize the permutation/randomization procedure; I believe I have sent you all necessary files to have this patch working with version 17f but please let me know if you run into any installation issues).
To elaborate a bit, the procedure CONN uses (same as FSL randomise) for permutation/randomization analyses is not a simple permutation approach, but rather a "randomization of residuals" approach. The procedure consists of, first, computing the second-level model residuals for each subject, and then, many times, building a new dataset by randomly flipping the signs of these residuals and running the second-level analyses on these new data, using the results of these randomized datasets to build a distribution of your statistic of interest (e.g. cluster sizes) under the null hypothesis. The main advantage of this "randomization of residuals" approach (e.g. CONN, FSL) compared to the "permutations of residuals" or purely "permutation" approaches (e.g. SnPM, BROCCOLI) is that it works exactly in the same way for all GLM analyses, while 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). Having said that, you are absolutely right that in this group-vs-one-sample comparison scenarios the standard randomization of residuals approach does not offer the correct statistics. The reason for this seems to be related to the inability of the "sign-flipping" procedure to build the proper null distribution for the group that contains a single sample/subject. The "fix" that I introduced in the attached file is to use, instead of flipping the signs (or permuting) the residuals, a full multiplication by a random orthogonal matrix (since both permutation and sign-flip operations can be considered special-cases of a 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).
Let me know your thoughts/comments
Alfonso
Originally posted by Stephen L.:
But still I don't believe the results I got were
correct in my specific case, as the non-parametric cluster-wise
correction was equivalent to no correction :-/
Threaded View
| Title | Author | Date |
|---|---|---|
| Stephen L. | Nov 23, 2017 | |
| Stephen L. | Nov 27, 2017 | |
| Alfonso Nieto-Castanon | Nov 29, 2017 | |
| Stephen L. | Nov 30, 2017 | |
| Stephen L. | Nov 30, 2017 | |
| Stephen L. | Dec 10, 2017 | |
| Stephen L. | Dec 10, 2017 | |
| Alfonso Nieto-Castanon | Dec 11, 2017 | |
| Stephen L. | Feb 8, 2018 | |
| Stephen L. | Feb 8, 2018 | |
| Stephen L. | Dec 11, 2017 | |