Hi Doug,
I am carrying out some correlations between cortical thickness and cognitive ability, and had a question about the use of DODS vs DOSS for my analysis with Qdec. From reading some of the forums, it seems as though DOSS should be used when there are no interactions present. I am including gender as a covariate (fixed factor) and age as a nuisance variable in my model. When I run this with DODS, it shows that the correlation between thickness and cognition does not differ between males and females - does this mean that I should run the correlation using DOSS? I only seem to find significant correlations using DOSS, however I do not want to report these if the analysis is not correct.
Thanks,
Cassie
Cassandra Wannan
*PhD Candidate/Research Assistant/Psychology Tutor*
Melbourne Neuropsychiatry Centre
--------------------------------------------
0433 339 839
cwannan@student.unimelb.edu.au
Hi Cassie, DOSS has a bug in QDEC so please do not use it. I thought we disabled it. What version are you using? If you want to compare DODS vs DOSS, then you'll have to use the command-line stream (mris_preproc, mri_surf2surf to smooth, mri_glmfit, and mri_glmfit-sim) doug
On 03/28/2016 08:10 PM, Cassandra Wannan wrote:
Hi Doug,
I am carrying out some correlations between cortical thickness and cognitive ability, and had a question about the use of DODS vs DOSS for my analysis with Qdec. From reading some of the forums, it seems as though DOSS should be used when there are no interactions present. I am including gender as a covariate (fixed factor) and age as a nuisance variable in my model. When I run this with DODS, it shows that the correlation between thickness and cognition does not differ between males and females - does this mean that I should run the correlation using DOSS? I only seem to find significant correlations using DOSS, however I do not want to report these if the analysis is not correct.
Thanks,
Cassie
Cassandra Wannan
*PhD Candidate/Research Assistant/Psychology Tutor*
Melbourne Neuropsychiatry Centre
0433 339 839
cwannan@student.unimelb.edu.au mailto:cwannan@student.unimelb.edu.au
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Dear FreeSurfer experts,
I am trying to analyze some single time points of my longitudinal data in QDEC. I have 3 groups and created a file for the discrete factor "group" with three levels (1,2 and 3). I have done the same for gender (with 2 levels). The analysis with gender works just fine but when I try to use group as a factor I get an error message that factor 1 must have 2 levels:
SUBJECTS_DIR is '/scr/etsch2/kids/ct' ERROR: QdecGlmDesign::GenerateContrasts: factor 1 must have 2 levels ninputs = 72 Checking inputs nframestot = 72 Allocing output Done allocing nframes = 72 Writing to /scr/etsch2/kids/ct/qdec/lh.thick10_groupdiffsc1/y.mgh gdfReadHeader: reading /scr/etsch2/kids/ct/qdec/lh.thick10_groupdiffsc1/qdec.fsgd INFO: gd2mtx_method is dods Reading source surface /scr/etsch2/kids/ct/87kids_template/surf/lh.white ERROR: no contrasts specified. Error in Analyze: command failed: mri_glmfit --y /scr/etsch2/kids/ct/qdec/lh.thick10_groupdiffsc1/y.mgh --fsgd /scr/etsch2/kids/ct/qdec/lh.thick10_groupdiffsc1/qdec.fsgd dods --glmdir /scr/etsch2/kids/ct/qdec/lh.thick10_groupdiffsc1 --surf 87kids_template lh
In the tutorial it doesn't state that QDEC has to have discrete factors with 2 levels only. Am I doing something wrong or is there a work-around for that maybe?
Cheers, Clara
Dear FreeSurfer experts,
I am trying to analyze some single time points of my longitudinal data in QDEC. I have 3 groups and created a file for the discrete factor "group" with three levels (1,2 and 3). I have done the same for gender (with 2 levels). The analysis with gender works just fine but when I try to use group as a factor I get an error message that factor 1 must have 2 levels:
SUBJECTS_DIR is '/scr/etsch2/kids/ct' ERROR: QdecGlmDesign::GenerateContrasts: factor 1 must have 2 levels ninputs = 72 Checking inputs nframestot = 72 Allocing output Done allocing nframes = 72 Writing to /scr/etsch2/kids/ct/qdec/lh.thick10_groupdiffsc1/y.mgh gdfReadHeader: reading /scr/etsch2/kids/ct/qdec/lh.thick10_groupdiffsc1/qdec.fsgd INFO: gd2mtx_method is dods Reading source surface /scr/etsch2/kids/ct/87kids_template/surf/lh.white ERROR: no contrasts specified. Error in Analyze: command failed: mri_glmfit --y /scr/etsch2/kids/ct/qdec/lh.thick10_groupdiffsc1/y.mgh --fsgd /scr/etsch2/kids/ct/qdec/lh.thick10_groupdiffsc1/qdec.fsgd dods --glmdir /scr/etsch2/kids/ct/qdec/lh.thick10_groupdiffsc1 --surf 87kids_template lh
In the tutorial it doesn't state that QDEC has to have discrete factors with 2 levels only. Am I doing something wrong or is there a work-around for that maybe?
Cheers, Clara
Dear FreeSurfer experts,
I am trying to analyze some single time points of my longitudinal data in QDEC. I have 3 groups and created a file for the discrete factor "group" with three levels (1,2 and 3). I have done the same for gender (with 2 levels). The analysis with gender works just fine but when I try to use group as a factor I get an error message that factor 1 must have 2 levels:
SUBJECTS_DIR is '/scr/etsch2/kids/ct' ERROR: QdecGlmDesign::GenerateContrasts: factor 1 must have 2 levels ninputs = 72 Checking inputs nframestot = 72 Allocing output Done allocing nframes = 72 Writing to /scr/etsch2/kids/ct/qdec/lh.thick10_groupdiffsc1/y.mgh gdfReadHeader: reading /scr/etsch2/kids/ct/qdec/lh.thick10_groupdiffsc1/qdec.fsgd INFO: gd2mtx_method is dods Reading source surface /scr/etsch2/kids/ct/87kids_template/surf/lh.white ERROR: no contrasts specified. Error in Analyze: command failed: mri_glmfit --y /scr/etsch2/kids/ct/qdec/lh.thick10_groupdiffsc1/y.mgh --fsgd /scr/etsch2/kids/ct/qdec/lh.thick10_groupdiffsc1/qdec.fsgd dods --glmdir /scr/etsch2/kids/ct/qdec/lh.thick10_groupdiffsc1 --surf 87kids_template lh
In the tutorial it doesn't state that QDEC has to have discrete factors with 2 levels only. Am I doing something wrong or is there a work-around for that?
Cheers, Clara
Hi Clara,
I think the message is correct and Qdec can only do 2 levels. Qdec is rather limited. You need to use mri_glmfit for that.
Best, Martin
On 04/08/2016 08:14 AM, Clara Kühn wrote:
Dear FreeSurfer experts,
I am trying to analyze some single time points of my longitudinal data in QDEC. I have 3 groups and created a file for the discrete factor "group" with three levels (1,2 and 3). I have done the same for gender (with 2 levels). The analysis with gender works just fine but when I try to use group as a factor I get an error message that factor 1 must have 2 levels:
SUBJECTS_DIR is '/scr/etsch2/kids/ct' ERROR: QdecGlmDesign::GenerateContrasts: factor 1 must have 2 levels ninputs = 72 Checking inputs nframestot = 72 Allocing output Done allocing nframes = 72 Writing to /scr/etsch2/kids/ct/qdec/lh.thick10_groupdiffsc1/y.mgh gdfReadHeader: reading /scr/etsch2/kids/ct/qdec/lh.thick10_groupdiffsc1/qdec.fsgd INFO: gd2mtx_method is dods Reading source surface /scr/etsch2/kids/ct/87kids_template/surf/lh.white ERROR: no contrasts specified. Error in Analyze: command failed: mri_glmfit --y /scr/etsch2/kids/ct/qdec/lh.thick10_groupdiffsc1/y.mgh --fsgd /scr/etsch2/kids/ct/qdec/lh.thick10_groupdiffsc1/qdec.fsgd dods --glmdir /scr/etsch2/kids/ct/qdec/lh.thick10_groupdiffsc1 --surf 87kids_template lh
In the tutorial it doesn't state that QDEC has to have discrete factors with 2 levels only. Am I doing something wrong or is there a work-around for that?
Cheers, Clara _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
That's what I feared. But ok, I'll try that. Thank you!
----- Ursprüngliche Mail ----- Von: "mreuter" mreuter@nmr.mgh.harvard.edu An: "Freesurfer support list" freesurfer@nmr.mgh.harvard.edu Gesendet: Freitag, 8. April 2016 14:55:42 Betreff: Re: [Freesurfer] REPOST: QDEC analysis
Hi Clara,
I think the message is correct and Qdec can only do 2 levels. Qdec is rather limited. You need to use mri_glmfit for that.
Best, Martin
On 04/08/2016 08:14 AM, Clara Kühn wrote:
Dear FreeSurfer experts,
I am trying to analyze some single time points of my longitudinal data in QDEC. I have 3 groups and created a file for the discrete factor "group" with three levels (1,2 and 3). I have done the same for gender (with 2 levels). The analysis with gender works just fine but when I try to use group as a factor I get an error message that factor 1 must have 2 levels:
SUBJECTS_DIR is '/scr/etsch2/kids/ct' ERROR: QdecGlmDesign::GenerateContrasts: factor 1 must have 2 levels ninputs = 72 Checking inputs nframestot = 72 Allocing output Done allocing nframes = 72 Writing to /scr/etsch2/kids/ct/qdec/lh.thick10_groupdiffsc1/y.mgh gdfReadHeader: reading /scr/etsch2/kids/ct/qdec/lh.thick10_groupdiffsc1/qdec.fsgd INFO: gd2mtx_method is dods Reading source surface /scr/etsch2/kids/ct/87kids_template/surf/lh.white ERROR: no contrasts specified. Error in Analyze: command failed: mri_glmfit --y /scr/etsch2/kids/ct/qdec/lh.thick10_groupdiffsc1/y.mgh --fsgd /scr/etsch2/kids/ct/qdec/lh.thick10_groupdiffsc1/qdec.fsgd dods --glmdir /scr/etsch2/kids/ct/qdec/lh.thick10_groupdiffsc1 --surf 87kids_template lh
In the tutorial it doesn't state that QDEC has to have discrete factors with 2 levels only. Am I doing something wrong or is there a work-around for that?
Cheers, Clara _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Dear FreeSurfer experts,
for the analysis in QDEC I created my own Monte Carlo correction. My questions relate to the threshold option.
1. Would I use neg if I have mostly blue clusters in the QDEC display and pos if I have mostly red clusters?
2. When do I use abs?
3. I compared the neg option at different thresholds (1.3, 2 and 2.3) and I get different clusters: 1.3 (=.05) # ClusterNo Max VtxMax Size(mm^2) MNIX MNIY MNIZ CWP CWPLow CWPHi NVtxs Annot 1 -2.669 76591 1148.00 -6.7 14.5 62.7 0.00880 0.00760 0.01000 1600 superiorfrontal 2 -2.443 91912 2128.78 -10.3 55.3 -23.5 0.00010 0.00000 0.00020 2971 medialorbitofrontal
2.0 (=.01) # ClusterNo Max VtxMax Size(mm^2) MNIX MNIY MNIZ CWP CWPLow CWPHi NVtxs Annot 1 -3.289 92754 420.03 -36.5 57.0 -21.0 0.02140 0.01960 0.02330 571 parsorbitalis 2 -2.669 76591 428.11 -6.7 14.5 62.7 0.01940 0.01760 0.02120 604 superiorfrontal
2.3 (=.005) # ClusterNo Max VtxMax Size(mm^2) MNIX MNIY MNIZ CWP CWPLow CWPHi NVtxs Annot 1 -3.289 92754 314.80 -36.5 57.0 -21.0 0.01900 0.01730 0.02080 410 parsorbitalis
Technically, if parsorbitalis is significant at .01 and .005, shouldn't it also be significant at .05? I've compared the clusters in freeview and the superiorfrontal is the same cluster in both 1.3 and 2.0. The parsorbitalis and the medialorbitofrontal clusters are completely different, not even closely overlapping.
Do you have any idea why that is and what it does differently? Thank you! Cheers, Clara
Dear FreeSurfer experts,
for the analysis in QDEC I created my own Monte Carlo correction. My questions relate to the threshold option.
1. Would I use neg if I have mostly blue clusters in the QDEC display and pos if I have mostly red clusters?
2. When do I use abs?
3. I compared the neg option at different thresholds (1.3, 2.0 and 2.3) and I get different clusters: 1.3 (=.05) # ClusterNo Max VtxMax Size(mm^2) MNIX MNIY MNIZ CWP CWPLow CWPHi NVtxs Annot 1 -2.669 76591 1148.00 -6.7 14.5 62.7 0.00880 0.00760 0.01000 1600 superiorfrontal 2 -2.443 91912 2128.78 -10.3 55.3 -23.5 0.00010 0.00000 0.00020 2971 medialorbitofrontal
2.0 (=.01) # ClusterNo Max VtxMax Size(mm^2) MNIX MNIY MNIZ CWP CWPLow CWPHi NVtxs Annot 1 -3.289 92754 420.03 -36.5 57.0 -21.0 0.02140 0.01960 0.02330 571 parsorbitalis 2 -2.669 76591 428.11 -6.7 14.5 62.7 0.01940 0.01760 0.02120 604 superiorfrontal
2.3 (=.005) # ClusterNo Max VtxMax Size(mm^2) MNIX MNIY MNIZ CWP CWPLow CWPHi NVtxs Annot 1 -3.289 92754 314.80 -36.5 57.0 -21.0 0.01900 0.01730 0.02080 410 parsorbitalis
Technically, if parsorbitalis is significant at .01 and .005, shouldn't it also be significant at .05? I've compared the clusters in freeview and the superiorfrontal is the same cluster in both 1.3 and 2.0. The parsorbitalis and the medialorbitofrontal clusters are completely different, not even closely overlapping.
Do you have any idea why that is and what it does differently? Thank you! Cheers, Clara
sorry, I could have sworn that I answered this one
On 04/12/2016 08:57 AM, Clara Kühn wrote:
Dear FreeSurfer experts,
for the analysis in QDEC I created my own Monte Carlo correction. My questions relate to the threshold option.
- Would I use neg if I have mostly blue clusters in the QDEC display and pos if I have mostly red clusters?
No, you would use neg when you have an apriori assumption that your effect is going to be negative. Once you look at the results, it is no long apriori
- When do I use abs?
If you do not have an apriori assumption about the sign of the effect
- I compared the neg option at different thresholds (1.3, 2.0 and 2.3) and I get different clusters:
1.3 (=.05) # ClusterNo Max VtxMax Size(mm^2) MNIX MNIY MNIZ CWP CWPLow CWPHi NVtxs Annot 1 -2.669 76591 1148.00 -6.7 14.5 62.7 0.00880 0.00760 0.01000 1600 superiorfrontal 2 -2.443 91912 2128.78 -10.3 55.3 -23.5 0.00010 0.00000 0.00020 2971 medialorbitofrontal
2.0 (=.01) # ClusterNo Max VtxMax Size(mm^2) MNIX MNIY MNIZ CWP CWPLow CWPHi NVtxs Annot 1 -3.289 92754 420.03 -36.5 57.0 -21.0 0.02140 0.01960 0.02330 571 parsorbitalis 2 -2.669 76591 428.11 -6.7 14.5 62.7 0.01940 0.01760 0.02120 604 superiorfrontal
2.3 (=.005) # ClusterNo Max VtxMax Size(mm^2) MNIX MNIY MNIZ CWP CWPLow CWPHi NVtxs Annot 1 -3.289 92754 314.80 -36.5 57.0 -21.0 0.01900 0.01730 0.02080 410 parsorbitalis
Technically, if parsorbitalis is significant at .01 and .005, shouldn't it also be significant at .05? I've compared the clusters in freeview and the superiorfrontal is the same cluster in both 1.3 and 2.0. The parsorbitalis and the medialorbitofrontal clusters are completely different, not even closely overlapping.
You are confusing the two types of thresholds. The voxel-wise threshold that you are changing defines what is and is not a cluster. As you change it clusters will change size. As you make it more liberal, you make it more likely that you see a cluster of a certain size by chance (ie, the p-value for the cluster gets worse). So as you make the threshold more liberal, there are two competing effects: (1) the cluster gets bigger, and (2) the p-value of a cluster of a given fixed size gets worse. If the cluster size does not increase enough to overcome the second effect, then the cluster p-value will get worse. It is just very complicated.
Do you have any idea why that is and what it does differently? Thank you! Cheers, Clara _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Hi Doug,
thanks for your reply. It made things a lot clearer. I totally understand that you're probably receiving more than one cry for help per day.
What would you say are the conventions for picking a threshold for analyses on structural data?
Cheers, Clara
----- Ursprüngliche Mail ----- Von: "Douglas N Greve" greve@nmr.mgh.harvard.edu An: freesurfer@nmr.mgh.harvard.edu Gesendet: Mittwoch, 13. April 2016 00:15:41 Betreff: Re: [Freesurfer] REPOST: Monte Carlo correction in QDEC
sorry, I could have sworn that I answered this one
On 04/12/2016 08:57 AM, Clara Kühn wrote:
Dear FreeSurfer experts,
for the analysis in QDEC I created my own Monte Carlo correction. My questions relate to the threshold option.
- Would I use neg if I have mostly blue clusters in the QDEC display and pos if I have mostly red clusters?
No, you would use neg when you have an apriori assumption that your effect is going to be negative. Once you look at the results, it is no long apriori
- When do I use abs?
If you do not have an apriori assumption about the sign of the effect
- I compared the neg option at different thresholds (1.3, 2.0 and 2.3) and I get different clusters:
1.3 (=.05) # ClusterNo Max VtxMax Size(mm^2) MNIX MNIY MNIZ CWP CWPLow CWPHi NVtxs Annot 1 -2.669 76591 1148.00 -6.7 14.5 62.7 0.00880 0.00760 0.01000 1600 superiorfrontal 2 -2.443 91912 2128.78 -10.3 55.3 -23.5 0.00010 0.00000 0.00020 2971 medialorbitofrontal
2.0 (=.01) # ClusterNo Max VtxMax Size(mm^2) MNIX MNIY MNIZ CWP CWPLow CWPHi NVtxs Annot 1 -3.289 92754 420.03 -36.5 57.0 -21.0 0.02140 0.01960 0.02330 571 parsorbitalis 2 -2.669 76591 428.11 -6.7 14.5 62.7 0.01940 0.01760 0.02120 604 superiorfrontal
2.3 (=.005) # ClusterNo Max VtxMax Size(mm^2) MNIX MNIY MNIZ CWP CWPLow CWPHi NVtxs Annot 1 -3.289 92754 314.80 -36.5 57.0 -21.0 0.01900 0.01730 0.02080 410 parsorbitalis
Technically, if parsorbitalis is significant at .01 and .005, shouldn't it also be significant at .05? I've compared the clusters in freeview and the superiorfrontal is the same cluster in both 1.3 and 2.0. The parsorbitalis and the medialorbitofrontal clusters are completely different, not even closely overlapping.
You are confusing the two types of thresholds. The voxel-wise threshold that you are changing defines what is and is not a cluster. As you change it clusters will change size. As you make it more liberal, you make it more likely that you see a cluster of a certain size by chance (ie, the p-value for the cluster gets worse). So as you make the threshold more liberal, there are two competing effects: (1) the cluster gets bigger, and (2) the p-value of a cluster of a given fixed size gets worse. If the cluster size does not increase enough to overcome the second effect, then the cluster p-value will get worse. It is just very complicated.
Do you have any idea why that is and what it does differently? Thank you! Cheers, Clara _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
In most neuroimaging, people use p<.01 (sig threshold > 2). It is more important to have a more stringent threshold when using gaussian random fields because the assumptions built into it. Since we use simulations directly, we don't have problems with those assumptions and so I think you can go down to p<.05 (sig>1.3). Other than that, there are not good guidelines.
On 4/13/16 8:36 AM, Clara Kühn wrote:
Hi Doug,
thanks for your reply. It made things a lot clearer. I totally understand that you're probably receiving more than one cry for help per day.
What would you say are the conventions for picking a threshold for analyses on structural data?
Cheers, Clara
----- Ursprüngliche Mail ----- Von: "Douglas N Greve" greve@nmr.mgh.harvard.edu An: freesurfer@nmr.mgh.harvard.edu Gesendet: Mittwoch, 13. April 2016 00:15:41 Betreff: Re: [Freesurfer] REPOST: Monte Carlo correction in QDEC
sorry, I could have sworn that I answered this one
On 04/12/2016 08:57 AM, Clara Kühn wrote:
Dear FreeSurfer experts,
for the analysis in QDEC I created my own Monte Carlo correction. My questions relate to the threshold option.
- Would I use neg if I have mostly blue clusters in the QDEC display and pos if I have mostly red clusters?
No, you would use neg when you have an apriori assumption that your effect is going to be negative. Once you look at the results, it is no long apriori
- When do I use abs?
If you do not have an apriori assumption about the sign of the effect
- I compared the neg option at different thresholds (1.3, 2.0 and 2.3) and I get different clusters:
1.3 (=.05) # ClusterNo Max VtxMax Size(mm^2) MNIX MNIY MNIZ CWP CWPLow CWPHi NVtxs Annot 1 -2.669 76591 1148.00 -6.7 14.5 62.7 0.00880 0.00760 0.01000 1600 superiorfrontal 2 -2.443 91912 2128.78 -10.3 55.3 -23.5 0.00010 0.00000 0.00020 2971 medialorbitofrontal
2.0 (=.01) # ClusterNo Max VtxMax Size(mm^2) MNIX MNIY MNIZ CWP CWPLow CWPHi NVtxs Annot 1 -3.289 92754 420.03 -36.5 57.0 -21.0 0.02140 0.01960 0.02330 571 parsorbitalis 2 -2.669 76591 428.11 -6.7 14.5 62.7 0.01940 0.01760 0.02120 604 superiorfrontal
2.3 (=.005) # ClusterNo Max VtxMax Size(mm^2) MNIX MNIY MNIZ CWP CWPLow CWPHi NVtxs Annot 1 -3.289 92754 314.80 -36.5 57.0 -21.0 0.01900 0.01730 0.02080 410 parsorbitalis
Technically, if parsorbitalis is significant at .01 and .005, shouldn't it also be significant at .05? I've compared the clusters in freeview and the superiorfrontal is the same cluster in both 1.3 and 2.0. The parsorbitalis and the medialorbitofrontal clusters are completely different, not even closely overlapping.
You are confusing the two types of thresholds. The voxel-wise threshold that you are changing defines what is and is not a cluster. As you change it clusters will change size. As you make it more liberal, you make it more likely that you see a cluster of a certain size by chance (ie, the p-value for the cluster gets worse). So as you make the threshold more liberal, there are two competing effects: (1) the cluster gets bigger, and (2) the p-value of a cluster of a given fixed size gets worse. If the cluster size does not increase enough to overcome the second effect, then the cluster p-value will get worse. It is just very complicated.
Do you have any idea why that is and what it does differently? Thank you! Cheers, Clara _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Thank you!
----- Ursprüngliche Mail ----- Von: "Douglas Greve" greve@nmr.mgh.harvard.edu An: freesurfer@nmr.mgh.harvard.edu Gesendet: Mittwoch, 13. April 2016 16:46:19 Betreff: Re: [Freesurfer] REPOST: Monte Carlo correction in QDEC
In most neuroimaging, people use p<.01 (sig threshold > 2). It is more important to have a more stringent threshold when using gaussian random fields because the assumptions built into it. Since we use simulations directly, we don't have problems with those assumptions and so I think you can go down to p<.05 (sig>1.3). Other than that, there are not good guidelines.
On 4/13/16 8:36 AM, Clara Kühn wrote:
Hi Doug,
thanks for your reply. It made things a lot clearer. I totally understand that you're probably receiving more than one cry for help per day.
What would you say are the conventions for picking a threshold for analyses on structural data?
Cheers, Clara
----- Ursprüngliche Mail ----- Von: "Douglas N Greve" greve@nmr.mgh.harvard.edu An: freesurfer@nmr.mgh.harvard.edu Gesendet: Mittwoch, 13. April 2016 00:15:41 Betreff: Re: [Freesurfer] REPOST: Monte Carlo correction in QDEC
sorry, I could have sworn that I answered this one
On 04/12/2016 08:57 AM, Clara Kühn wrote:
Dear FreeSurfer experts,
for the analysis in QDEC I created my own Monte Carlo correction. My questions relate to the threshold option.
- Would I use neg if I have mostly blue clusters in the QDEC display and pos if I have mostly red clusters?
No, you would use neg when you have an apriori assumption that your effect is going to be negative. Once you look at the results, it is no long apriori
- When do I use abs?
If you do not have an apriori assumption about the sign of the effect
- I compared the neg option at different thresholds (1.3, 2.0 and 2.3) and I get different clusters:
1.3 (=.05) # ClusterNo Max VtxMax Size(mm^2) MNIX MNIY MNIZ CWP CWPLow CWPHi NVtxs Annot 1 -2.669 76591 1148.00 -6.7 14.5 62.7 0.00880 0.00760 0.01000 1600 superiorfrontal 2 -2.443 91912 2128.78 -10.3 55.3 -23.5 0.00010 0.00000 0.00020 2971 medialorbitofrontal
2.0 (=.01) # ClusterNo Max VtxMax Size(mm^2) MNIX MNIY MNIZ CWP CWPLow CWPHi NVtxs Annot 1 -3.289 92754 420.03 -36.5 57.0 -21.0 0.02140 0.01960 0.02330 571 parsorbitalis 2 -2.669 76591 428.11 -6.7 14.5 62.7 0.01940 0.01760 0.02120 604 superiorfrontal
2.3 (=.005) # ClusterNo Max VtxMax Size(mm^2) MNIX MNIY MNIZ CWP CWPLow CWPHi NVtxs Annot 1 -3.289 92754 314.80 -36.5 57.0 -21.0 0.01900 0.01730 0.02080 410 parsorbitalis
Technically, if parsorbitalis is significant at .01 and .005, shouldn't it also be significant at .05? I've compared the clusters in freeview and the superiorfrontal is the same cluster in both 1.3 and 2.0. The parsorbitalis and the medialorbitofrontal clusters are completely different, not even closely overlapping.
You are confusing the two types of thresholds. The voxel-wise threshold that you are changing defines what is and is not a cluster. As you change it clusters will change size. As you make it more liberal, you make it more likely that you see a cluster of a certain size by chance (ie, the p-value for the cluster gets worse). So as you make the threshold more liberal, there are two competing effects: (1) the cluster gets bigger, and (2) the p-value of a cluster of a given fixed size gets worse. If the cluster size does not increase enough to overcome the second effect, then the cluster p-value will get worse. It is just very complicated.
Do you have any idea why that is and what it does differently? Thank you! Cheers, Clara _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
_______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail.
freesurfer@nmr.mgh.harvard.edu
-
Cassandra Wannan -
Clara Kühn -
Douglas Greve -
Douglas N Greve
-
Martin Reuter