Dear Freesurfer’s experts,
I would have some questions regarding the LME model to be used in longitudinal stream:
1) Which are the ratio limits or % of missing timepoints accepted ? (according time, I have less and less subjects time points)
2) Is it possible to include patients that would miss the first timepoint but got the others ?
3) Considering a group in longitudinal study, which is the number of subjects minimal of this group accepted for LME modeling ?
4) Finally, concerning quality control and among a big number of total time points, which essential controls are necessary ? (Control of norm.mgz of the base, alignment of longitudinal timepoints on base,… ?)
Best regards, Matthieu
Hi Matthieu,
a few quick answers. Maybe Jorge knows more. Generally number of subjects / time points etc. cannot be specified generally. All depends on how noisy your data is and how large the effect is that you expect to detect. You can do a power analysis in order to figure out how many subject / time points would be needed. There are some tools for that in the LME toolbox: https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels#Poweranal... https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels#Poweranalysis
1. see above 2. yes, also time points can miss from the middle. If you have mainly missing time points at the end, this will bias your analysis to some extend, as the remaining ones may be extremely healthy, as probably the more diseased ones drop out. You may want to do a time-to-event (or survival-analysis) which considers early drop-out. 3. see above (power analysis) 4. GIGO means garbage in, garbage out, so the less you QC, the more likely will your results be junk. The more you QC the less likely will it be junk, but could still be. The FS wiki has lots of tutorial information on checking freesurfer recons. For longitudinal, you should additionally check the surfaces in the base, the brain mask in the base, and the alignment of the time points (although there is some wiggle space for the alignment, as most things are allowed to evolve further for each time point).
Best, Martin
On Nov 21, 2016, at 7:07 PM, Matthieu Vanhoutte matthieuvanhoutte@gmail.com wrote:
Dear Freesurfer’s experts,
I would have some questions regarding the LME model to be used in longitudinal stream:
Which are the ratio limits or % of missing timepoints accepted ? (according time, I have less and less subjects time points)
Is it possible to include patients that would miss the first timepoint but got the others ?
Considering a group in longitudinal study, which is the number of subjects minimal of this group accepted for LME modeling ?
Finally, concerning quality control and among a big number of total time points, which essential controls are necessary ? (Control of norm.mgz of the base, alignment of longitudinal timepoints on base,… ?)
Best regards, Matthieu
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Hi Martin,
Thanks for replying. Please see inline below:
Le 21 nov. 2016 à 20:26, Martin Reuter mreuter@nmr.mgh.harvard.edu a écrit :
Hi Matthieu,
a few quick answers. Maybe Jorge knows more. Generally number of subjects / time points etc. cannot be specified generally. All depends on how noisy your data is and how large the effect is that you expect to detect. You can do a power analysis in order to figure out how many subject / time points would be needed. There are some tools for that in the LME toolbox: https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels#Poweranal... https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels#Poweranalysis
- see above
- yes, also time points can miss from the middle. If you have mainly missing time points at the end, this will bias your analysis to some extend, as the remaining ones may be extremely healthy, as probably the more diseased ones drop out. You may want to do a time-to-event (or survival-analysis) which considers early drop-out.
Is there any way to do with Freesurfer this kind of analysis ?
- see above (power analysis)
- GIGO means garbage in, garbage out, so the less you QC, the more likely will your results be junk. The more you QC the less likely will it be junk, but could still be. The FS wiki has lots of tutorial information on checking freesurfer recons. For longitudinal, you should additionally check the surfaces in the base, the brain mask in the base, and the alignment of the time points (although there is some wiggle space for the alignment, as most things are allowed to evolve further for each time point).
For the alignment of the time points, should I better comparing brainmask or norm.mgz ?
In order to avoid bias by adding further time points in the model by the -add recon all command, is this better for each subject to take into account all the time points existing for it or only the ones that I will include in the model (three time points / subject ; if existing 6 time points for any subject ?)
Best regards, Matthieu
Best, Martin
On Nov 21, 2016, at 7:07 PM, Matthieu Vanhoutte <matthieuvanhoutte@gmail.com mailto:matthieuvanhoutte@gmail.com> wrote:
Dear Freesurfer’s experts,
I would have some questions regarding the LME model to be used in longitudinal stream:
Which are the ratio limits or % of missing timepoints accepted ? (according time, I have less and less subjects time points)
Is it possible to include patients that would miss the first timepoint but got the others ?
Considering a group in longitudinal study, which is the number of subjects minimal of this group accepted for LME modeling ?
Finally, concerning quality control and among a big number of total time points, which essential controls are necessary ? (Control of norm.mgz of the base, alignment of longitudinal timepoints on base,… ?)
Best regards, Matthieu
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu mailto: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.
Hi Matthieu, (also inline)
On Nov 21, 2016, at 10:28 PM, Matthieu Vanhoutte matthieuvanhoutte@gmail.com wrote:
Hi Martin,
Thanks for replying. Please see inline below:
Le 21 nov. 2016 à 20:26, Martin Reuter <mreuter@nmr.mgh.harvard.edu mailto:mreuter@nmr.mgh.harvard.edu> a écrit :
Hi Matthieu,
a few quick answers. Maybe Jorge knows more. Generally number of subjects / time points etc. cannot be specified generally. All depends on how noisy your data is and how large the effect is that you expect to detect. You can do a power analysis in order to figure out how many subject / time points would be needed. There are some tools for that in the LME toolbox: https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels#Poweranal... https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels#Poweranalysis
- see above
- yes, also time points can miss from the middle. If you have mainly missing time points at the end, this will bias your analysis to some extend, as the remaining ones may be extremely healthy, as probably the more diseased ones drop out. You may want to do a time-to-event (or survival-analysis) which considers early drop-out.
Is there any way to do with Freesurfer this kind of analysis ?
https://surfer.nmr.mgh.harvard.edu/fswiki/SurvivalAnalysis https://surfer.nmr.mgh.harvard.edu/fswiki/SurvivalAnalysis Yes, there is also a paper where we do this. It is a combination of LME and Survival Analysis (as for the SA you need to have measurements of all subjects at all time points, so you estimate that from the LME model).
- see above (power analysis)
- GIGO means garbage in, garbage out, so the less you QC, the more likely will your results be junk. The more you QC the less likely will it be junk, but could still be. The FS wiki has lots of tutorial information on checking freesurfer recons. For longitudinal, you should additionally check the surfaces in the base, the brain mask in the base, and the alignment of the time points (although there is some wiggle space for the alignment, as most things are allowed to evolve further for each time point).
For the alignment of the time points, should I better comparing brainmask or norm.mgz ?
It does not really matter, I would use norm.mgz. I would load images on top of each other and then use the opacity slider in Freeview to blend between them (that way the eye can pick up small motions). I would not worry too much about local deformations which could be caused by non-linearity (gradient). But if you see global misalignment (rotation, translation) it is a cause for concern) .
In order to avoid bias by adding further time points in the model by the -add recon all command, is this better for each subject to take into account all the time points existing for it or only the ones that I will include in the model (three time points / subject ; if existing 6 time points for any subject ?)
Usually it is recommended to run all time points in the model (so a base with 6 time points) and not use the - - add flag. Also, Linear Mixed Effects models deal well with missing time points. It is perfectly OK to have differently many time points per subject for that. You should still check if there is a bias (e.g. one group always has 3 time points the other 6) that would not be good. Maybe also consult with a local biostatistician if you are not comfortable with the stats. The LME tools are matlab, and so are the survival-analysis scripts.
Best, Martin
Best regards, Matthieu
Best, Martin
On Nov 21, 2016, at 7:07 PM, Matthieu Vanhoutte <matthieuvanhoutte@gmail.com mailto:matthieuvanhoutte@gmail.com> wrote:
Dear Freesurfer’s experts,
I would have some questions regarding the LME model to be used in longitudinal stream:
Which are the ratio limits or % of missing timepoints accepted ? (according time, I have less and less subjects time points)
Is it possible to include patients that would miss the first timepoint but got the others ?
Considering a group in longitudinal study, which is the number of subjects minimal of this group accepted for LME modeling ?
Finally, concerning quality control and among a big number of total time points, which essential controls are necessary ? (Control of norm.mgz of the base, alignment of longitudinal timepoints on base,… ?)
Best regards, Matthieu
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu mailto:Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu mailto: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 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 mailing list Freesurfer@nmr.mgh.harvard.edu mailto:Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Hi Martin,
Please see inline below:
Le 22 nov. 2016 à 17:04, Martin Reuter mreuter@nmr.mgh.harvard.edu a écrit :
Hi Matthieu, (also inline)
On Nov 21, 2016, at 10:28 PM, Matthieu Vanhoutte <matthieuvanhoutte@gmail.com mailto:matthieuvanhoutte@gmail.com> wrote:
Hi Martin,
Thanks for replying. Please see inline below:
Le 21 nov. 2016 à 20:26, Martin Reuter <mreuter@nmr.mgh.harvard.edu mailto:mreuter@nmr.mgh.harvard.edu> a écrit :
Hi Matthieu,
a few quick answers. Maybe Jorge knows more. Generally number of subjects / time points etc. cannot be specified generally. All depends on how noisy your data is and how large the effect is that you expect to detect. You can do a power analysis in order to figure out how many subject / time points would be needed. There are some tools for that in the LME toolbox: https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels#Poweranal... https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels#Poweranalysis
- see above
- yes, also time points can miss from the middle. If you have mainly missing time points at the end, this will bias your analysis to some extend, as the remaining ones may be extremely healthy, as probably the more diseased ones drop out. You may want to do a time-to-event (or survival-analysis) which considers early drop-out.
Is there any way to do with Freesurfer this kind of analysis ?
https://surfer.nmr.mgh.harvard.edu/fswiki/SurvivalAnalysis https://surfer.nmr.mgh.harvard.edu/fswiki/SurvivalAnalysis Yes, there is also a paper where we do this. It is a combination of LME and Survival Analysis (as for the SA you need to have measurements of all subjects at all time points, so you estimate that from the LME model).
Thank you for the link, I will take a look at. So if understand, this analysis has to be done after LME statistical analysis ? Thereafter since SA need all time points, LME model will allow me to estimate missing time points ?
- see above (power analysis)
- GIGO means garbage in, garbage out, so the less you QC, the more likely will your results be junk. The more you QC the less likely will it be junk, but could still be. The FS wiki has lots of tutorial information on checking freesurfer recons. For longitudinal, you should additionally check the surfaces in the base, the brain mask in the base, and the alignment of the time points (although there is some wiggle space for the alignment, as most things are allowed to evolve further for each time point).
For the alignment of the time points, should I better comparing brainmask or norm.mgz ?
It does not really matter, I would use norm.mgz. I would load images on top of each other and then use the opacity slider in Freeview to blend between them (that way the eye can pick up small motions). I would not worry too much about local deformations which could be caused by non-linearity (gradient). But if you see global misalignment (rotation, translation) it is a cause for concern) .
Ok thank you. The non-linearity you are talking about are well provoked by MRI system and not non-linear registration between time points and template base, aren’t they ?
Best regards, Matthieu
In order to avoid bias by adding further time points in the model by the -add recon all command, is this better for each subject to take into account all the time points existing for it or only the ones that I will include in the model (three time points / subject ; if existing 6 time points for any subject ?)
Usually it is recommended to run all time points in the model (so a base with 6 time points) and not use the - - add flag. Also, Linear Mixed Effects models deal well with missing time points. It is perfectly OK to have differently many time points per subject for that. You should still check if there is a bias (e.g. one group always has 3 time points the other 6) that would not be good. Maybe also consult with a local biostatistician if you are not comfortable with the stats. The LME tools are matlab, and so are the survival-analysis scripts.
Best, Martin
Best regards, Matthieu
Best, Martin
On Nov 21, 2016, at 7:07 PM, Matthieu Vanhoutte <matthieuvanhoutte@gmail.com mailto:matthieuvanhoutte@gmail.com> wrote:
Dear Freesurfer’s experts,
I would have some questions regarding the LME model to be used in longitudinal stream:
Which are the ratio limits or % of missing timepoints accepted ? (according time, I have less and less subjects time points)
Is it possible to include patients that would miss the first timepoint but got the others ?
Considering a group in longitudinal study, which is the number of subjects minimal of this group accepted for LME modeling ?
Finally, concerning quality control and among a big number of total time points, which essential controls are necessary ? (Control of norm.mgz of the base, alignment of longitudinal timepoints on base,… ?)
Best regards, Matthieu
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu mailto:Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu mailto:Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer 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 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 mailing list Freesurfer@nmr.mgh.harvard.edu mailto:Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu mailto:Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer 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 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.
Dear FS's experts,
Could you answer me about questions from below inline precedent mail ?
Best regards, Matthieu
2016-11-22 21:31 GMT+01:00 Matthieu Vanhoutte matthieuvanhoutte@gmail.com:
Hi Martin,
Please see inline below:
Le 22 nov. 2016 à 17:04, Martin Reuter mreuter@nmr.mgh.harvard.edu a écrit :
Hi Matthieu, (also inline)
On Nov 21, 2016, at 10:28 PM, Matthieu Vanhoutte < matthieuvanhoutte@gmail.com> wrote:
Hi Martin,
Thanks for replying. Please see inline below:
Le 21 nov. 2016 à 20:26, Martin Reuter mreuter@nmr.mgh.harvard.edu a écrit :
Hi Matthieu,
a few quick answers. Maybe Jorge knows more. Generally number of subjects / time points etc. cannot be specified generally. All depends on how noisy your data is and how large the effect is that you expect to detect. You can do a power analysis in order to figure out how many subject / time points would be needed. There are some tools for that in the LME toolbox: https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels# Poweranalysis
- see above
- yes, also time points can miss from the middle. If you have mainly
missing time points at the end, this will bias your analysis to some extend, as the remaining ones may be extremely healthy, as probably the more diseased ones drop out. You may want to do a time-to-event (or survival-analysis) which considers early drop-out.
Is there any way to do with Freesurfer this kind of analysis ?
https://surfer.nmr.mgh.harvard.edu/fswiki/SurvivalAnalysis Yes, there is also a paper where we do this. It is a combination of LME and Survival Analysis (as for the SA you need to have measurements of all subjects at all time points, so you estimate that from the LME model).
Thank you for the link, I will take a look at. So if understand, this analysis has to be done after LME statistical analysis ? Thereafter since SA need all time points, LME model will allow me to estimate missing time points ?
- see above (power analysis)
- GIGO means garbage in, garbage out, so the less you QC, the more likely
will your results be junk. The more you QC the less likely will it be junk, but could still be. The FS wiki has lots of tutorial information on checking freesurfer recons. For longitudinal, you should additionally check the surfaces in the base, the brain mask in the base, and the alignment of the time points (although there is some wiggle space for the alignment, as most things are allowed to evolve further for each time point).
For the alignment of the time points, should I better comparing brainmask or norm.mgz ?
It does not really matter, I would use norm.mgz. I would load images on top of each other and then use the opacity slider in Freeview to blend between them (that way the eye can pick up small motions). I would not worry too much about local deformations which could be caused by non-linearity (gradient). But if you see global misalignment (rotation, translation) it is a cause for concern) .
Ok thank you. The non-linearity you are talking about are well provoked by MRI system and not non-linear registration between time points and template base, aren’t they ?
In order to avoid bias by adding further time points in the model by the -add recon all command, is this better for each subject to take into account all the time points existing for it or only the ones that I will include in the model (three time points / subject ; if existing 6 time points for any subject ?)
Usually it is recommended to run all time points in the model (so a base with 6 time points) and not use the - - add flag. Also, Linear Mixed Effects models deal well with missing time points. It is perfectly OK to have differently many time points per subject for that. You should still check if there is a bias (e.g. one group always has 3 time points the other 6) that would not be good. Maybe also consult with a local biostatistician if you are not comfortable with the stats. The LME tools are matlab, and so are the survival-analysis scripts.
Best, Martin
Best regards, Matthieu
Best, Martin
On Nov 21, 2016, at 7:07 PM, Matthieu Vanhoutte < matthieuvanhoutte@gmail.com> wrote:
Dear Freesurfer’s experts,
I would have some questions regarding the LME model to be used in longitudinal stream:
- Which are the ratio limits or % of missing timepoints accepted ?
(according time, I have less and less subjects time points)
- Is it possible to include patients that would miss the first timepoint
but got the others ?
- Considering a group in longitudinal study, which is the number of
subjects minimal of this group accepted for LME modeling ?
- Finally, concerning quality control and among a big number of total
time points, which essential controls are necessary ? (Control of norm.mgz of the base, alignment of longitudinal timepoints on base,… ?)
Best regards, Matthieu
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 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.
Hi Matthieu,
1. yes, LME needs to be done first so that values can be sampled from the fitted model for the SA.
2. yes, I was talking about gradient non-linearities etc that could be in the image from the acquisition. We currently don’t use non-linear registration across time points (only rigid).
Best, Martin
On Nov 22, 2016, at 9:31 PM, Matthieu Vanhoutte matthieuvanhoutte@gmail.com wrote:
Hi Martin,
Please see inline below:
Le 22 nov. 2016 à 17:04, Martin Reuter <mreuter@nmr.mgh.harvard.edu mailto:mreuter@nmr.mgh.harvard.edu> a écrit :
Hi Matthieu, (also inline)
On Nov 21, 2016, at 10:28 PM, Matthieu Vanhoutte <matthieuvanhoutte@gmail.com mailto:matthieuvanhoutte@gmail.com> wrote:
Hi Martin,
Thanks for replying. Please see inline below:
Le 21 nov. 2016 à 20:26, Martin Reuter <mreuter@nmr.mgh.harvard.edu mailto:mreuter@nmr.mgh.harvard.edu> a écrit :
Hi Matthieu,
a few quick answers. Maybe Jorge knows more. Generally number of subjects / time points etc. cannot be specified generally. All depends on how noisy your data is and how large the effect is that you expect to detect. You can do a power analysis in order to figure out how many subject / time points would be needed. There are some tools for that in the LME toolbox: https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels#Poweranal... https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels#Poweranalysis
- see above
- yes, also time points can miss from the middle. If you have mainly missing time points at the end, this will bias your analysis to some extend, as the remaining ones may be extremely healthy, as probably the more diseased ones drop out. You may want to do a time-to-event (or survival-analysis) which considers early drop-out.
Is there any way to do with Freesurfer this kind of analysis ?
https://surfer.nmr.mgh.harvard.edu/fswiki/SurvivalAnalysis https://surfer.nmr.mgh.harvard.edu/fswiki/SurvivalAnalysis Yes, there is also a paper where we do this. It is a combination of LME and Survival Analysis (as for the SA you need to have measurements of all subjects at all time points, so you estimate that from the LME model).
Thank you for the link, I will take a look at. So if understand, this analysis has to be done after LME statistical analysis ? Thereafter since SA need all time points, LME model will allow me to estimate missing time points ?
- see above (power analysis)
- GIGO means garbage in, garbage out, so the less you QC, the more likely will your results be junk. The more you QC the less likely will it be junk, but could still be. The FS wiki has lots of tutorial information on checking freesurfer recons. For longitudinal, you should additionally check the surfaces in the base, the brain mask in the base, and the alignment of the time points (although there is some wiggle space for the alignment, as most things are allowed to evolve further for each time point).
For the alignment of the time points, should I better comparing brainmask or norm.mgz ?
It does not really matter, I would use norm.mgz. I would load images on top of each other and then use the opacity slider in Freeview to blend between them (that way the eye can pick up small motions). I would not worry too much about local deformations which could be caused by non-linearity (gradient). But if you see global misalignment (rotation, translation) it is a cause for concern) .
Ok thank you. The non-linearity you are talking about are well provoked by MRI system and not non-linear registration between time points and template base, aren’t they ?
Best regards, Matthieu
In order to avoid bias by adding further time points in the model by the -add recon all command, is this better for each subject to take into account all the time points existing for it or only the ones that I will include in the model (three time points / subject ; if existing 6 time points for any subject ?)
Usually it is recommended to run all time points in the model (so a base with 6 time points) and not use the - - add flag. Also, Linear Mixed Effects models deal well with missing time points. It is perfectly OK to have differently many time points per subject for that. You should still check if there is a bias (e.g. one group always has 3 time points the other 6) that would not be good. Maybe also consult with a local biostatistician if you are not comfortable with the stats. The LME tools are matlab, and so are the survival-analysis scripts.
Best, Martin
Best regards, Matthieu
Best, Martin
On Nov 21, 2016, at 7:07 PM, Matthieu Vanhoutte <matthieuvanhoutte@gmail.com mailto:matthieuvanhoutte@gmail.com> wrote:
Dear Freesurfer’s experts,
I would have some questions regarding the LME model to be used in longitudinal stream:
Which are the ratio limits or % of missing timepoints accepted ? (according time, I have less and less subjects time points)
Is it possible to include patients that would miss the first timepoint but got the others ?
Considering a group in longitudinal study, which is the number of subjects minimal of this group accepted for LME modeling ?
Finally, concerning quality control and among a big number of total time points, which essential controls are necessary ? (Control of norm.mgz of the base, alignment of longitudinal timepoints on base,… ?)
Best regards, Matthieu
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu mailto:Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
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External Email - Use Caution
Hi Martin,
It's been a long time since this discussion but I return on this from now... The problem is that I followed longitudinal images of two groups where I had mainly missing time points at the end. Than you suggested: *If you have mainly missing time points at the end, this will bias your analysis to some extend, as the remaining ones may be extremely healthy, as probably the more diseased ones drop out. You may want to do a time-to-event (or survival-analysis) which considers early drop-out.*
1) I know the survival analysis toolbox on matlab, but now I would like to know what information will this survival analysis give to me ? 2) Will this analysis tell me if there is a bias ? 3) How to consider early drop-out with this type of analysis based on mass-univariate LME analysis of longitudinal neuroimaging data ?
Thanks in advance for helping.
Best, Matthieu
Le mer. 14 déc. 2016 à 22:14, Martin Reuter mreuter@nmr.mgh.harvard.edu a écrit :
Hi Matthieu,
- yes, LME needs to be done first so that values can be sampled from the
fitted model for the SA.
- yes, I was talking about gradient non-linearities etc that could be in
the image from the acquisition. We currently don’t use non-linear registration across time points (only rigid).
Best, Martin
On Nov 22, 2016, at 9:31 PM, Matthieu Vanhoutte < matthieuvanhoutte@gmail.com> wrote:
Hi Martin,
Please see inline below:
Le 22 nov. 2016 à 17:04, Martin Reuter mreuter@nmr.mgh.harvard.edu a écrit :
Hi Matthieu, (also inline)
On Nov 21, 2016, at 10:28 PM, Matthieu Vanhoutte < matthieuvanhoutte@gmail.com> wrote:
Hi Martin,
Thanks for replying. Please see inline below:
Le 21 nov. 2016 à 20:26, Martin Reuter mreuter@nmr.mgh.harvard.edu a écrit :
Hi Matthieu,
a few quick answers. Maybe Jorge knows more. Generally number of subjects / time points etc. cannot be specified generally. All depends on how noisy your data is and how large the effect is that you expect to detect. You can do a power analysis in order to figure out how many subject / time points would be needed. There are some tools for that in the LME toolbox:
https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels#Poweranal...
- see above
- yes, also time points can miss from the middle. If you have mainly
missing time points at the end, this will bias your analysis to some extend, as the remaining ones may be extremely healthy, as probably the more diseased ones drop out. You may want to do a time-to-event (or survival-analysis) which considers early drop-out.
Is there any way to do with Freesurfer this kind of analysis ?
https://surfer.nmr.mgh.harvard.edu/fswiki/SurvivalAnalysis Yes, there is also a paper where we do this. It is a combination of LME and Survival Analysis (as for the SA you need to have measurements of all subjects at all time points, so you estimate that from the LME model).
Thank you for the link, I will take a look at. So if understand, this analysis has to be done after LME statistical analysis ? Thereafter since SA need all time points, LME model will allow me to estimate missing time points ?
- see above (power analysis)
- GIGO means garbage in, garbage out, so the less you QC, the more likely
will your results be junk. The more you QC the less likely will it be junk, but could still be. The FS wiki has lots of tutorial information on checking freesurfer recons. For longitudinal, you should additionally check the surfaces in the base, the brain mask in the base, and the alignment of the time points (although there is some wiggle space for the alignment, as most things are allowed to evolve further for each time point).
For the alignment of the time points, should I better comparing brainmask or norm.mgz ?
It does not really matter, I would use norm.mgz. I would load images on top of each other and then use the opacity slider in Freeview to blend between them (that way the eye can pick up small motions). I would not worry too much about local deformations which could be caused by non-linearity (gradient). But if you see global misalignment (rotation, translation) it is a cause for concern) .
Ok thank you. The non-linearity you are talking about are well provoked by MRI system and not non-linear registration between time points and template base, aren’t they ?
Best regards, Matthieu
In order to avoid bias by adding further time points in the model by the -add recon all command, is this better for each subject to take into account all the time points existing for it or only the ones that I will include in the model (three time points / subject ; if existing 6 time points for any subject ?)
Usually it is recommended to run all time points in the model (so a base with 6 time points) and not use the - - add flag. Also, Linear Mixed Effects models deal well with missing time points. It is perfectly OK to have differently many time points per subject for that. You should still check if there is a bias (e.g. one group always has 3 time points the other 6) that would not be good. Maybe also consult with a local biostatistician if you are not comfortable with the stats. The LME tools are matlab, and so are the survival-analysis scripts.
Best, Martin
Best regards, Matthieu
Best, Martin
On Nov 21, 2016, at 7:07 PM, Matthieu Vanhoutte < matthieuvanhoutte@gmail.com> wrote:
Dear Freesurfer’s experts,
I would have some questions regarding the LME model to be used in longitudinal stream:
- Which are the ratio limits or % of missing timepoints accepted ?
(according time, I have less and less subjects time points)
- Is it possible to include patients that would miss the first timepoint
but got the others ?
- Considering a group in longitudinal study, which is the number of
subjects minimal of this group accepted for LME modeling ?
- Finally, concerning quality control and among a big number of total
time points, which essential controls are necessary ? (Control of norm.mgz of the base, alignment of longitudinal timepoints on base,… ?)
Best regards, Matthieu
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Hi Matthieu,
1) survival analysis is typically used if you want to detect if the time to an event is longer in one group vs the other (e.g. one group gets placebo the other drug and we want to know if recurrence is later in the drug group). Not sure this is what you need. The nice thing is, it can deal with drop-outs
2) No, you can directly test that (e.g. do more dieseased drop out than healthy, or are the dropouts on average more advanced (test-scores, hippo-volume etc) than the diseased at baseline... many options. you could also test interactions with age , gender etc. However, not finding an interaction may not mean there is no bias, it is just small enough to go undetected with your data size.
3) Survival analysis is a different analysis than LME.
Best, Martin
On Tue, 2018-10-16 at 16:15 +0000, Matthieu Vanhoutte wrote:
External Email - Use Caution Hi Martin,
It's been a long time since this discussion but I return on this from now... The problem is that I followed longitudinal images of two groups where I had mainly missing time points at the end. Than you suggested: If you have mainly missing time points at the end, this will bias your analysis to some extend, as the remaining ones may be extremely healthy, as probably the more diseased ones drop out. You may want to do a time-to-event (or survival-analysis) which considers early drop- out.
- I know the survival analysis toolbox on matlab, but now I would
like to know what information will this survival analysis give to me ? 2) Will this analysis tell me if there is a bias ? 3) How to consider early drop-out with this type of analysis based on mass-univariate LME analysis of longitudinal neuroimaging data ?
Thanks in advance for helping.
Best, Matthieu
Le mer. 14 déc. 2016 à 22:14, Martin Reuter <mreuter@nmr.mgh.harvard. edu> a écrit :
Hi Matthieu,
- yes, LME needs to be done first so that values can be sampled
from the fitted model for the SA.
- yes, I was talking about gradient non-linearities etc that could
be in the image from the acquisition. We currently don’t use non- linear registration across time points (only rigid).
Best, Martin
On Nov 22, 2016, at 9:31 PM, Matthieu Vanhoutte <matthieuvanhoutt e@gmail.com> wrote:
Hi Martin,
Please see inline below:
Le 22 nov. 2016 à 17:04, Martin Reuter <mreuter@nmr.mgh.harvard .edu> a écrit :
Hi Matthieu, (also inline)
On Nov 21, 2016, at 10:28 PM, Matthieu Vanhoutte <matthieuvan houtte@gmail.com> wrote:
Hi Martin,
Thanks for replying. Please see inline below:
Le 21 nov. 2016 à 20:26, Martin Reuter <mreuter@nmr.mgh.har vard.edu> a écrit :
Hi Matthieu,
a few quick answers. Maybe Jorge knows more. Generally number of subjects / time points etc. cannot be specified generally. All depends on how noisy your data is and how large the effect is that you expect to detect. You can do a power analysis in order to figure out how many subject / time points would be needed. There are some tools for that in the LME toolbox: https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffect sModels#Poweranalysis
- see above
- yes, also time points can miss from the middle. If you
have mainly missing time points at the end, this will bias your analysis to some extend, as the remaining ones may be extremely healthy, as probably the more diseased ones drop out. You may want to do a time-to-event (or survival- analysis) which considers early drop-out.
Is there any way to do with Freesurfer this kind of analysis ?
https://surfer.nmr.mgh.harvard.edu/fswiki/SurvivalAnalysis%C2%A0 Yes, there is also a paper where we do this. It is a combination of LME and Survival Analysis (as for the SA you need to have measurements of all subjects at all time points, so you estimate that from the LME model).
Thank you for the link, I will take a look at. So if understand, this analysis has to be done after LME statistical analysis ? Thereafter since SA need all time points, LME model will allow me to estimate missing time points ?
- see above (power analysis)
- GIGO means garbage in, garbage out, so the less you QC,
the more likely will your results be junk. The more you QC the less likely will it be junk, but could still be. The FS wiki has lots of tutorial information on checking freesurfer recons. For longitudinal, you should additionally check the surfaces in the base, the brain mask in the base, and the alignment of the time points (although there is some wiggle space for the alignment, as most things are allowed to evolve further for each time point).
For the alignment of the time points, should I better comparing brainmask or norm.mgz ?
It does not really matter, I would use norm.mgz. I would load images on top of each other and then use the opacity slider in Freeview to blend between them (that way the eye can pick up small motions). I would not worry too much about local deformations which could be caused by non-linearity (gradient). But if you see global misalignment (rotation, translation) it is a cause for concern) .
Ok thank you. The non-linearity you are talking about are well provoked by MRI system and not non-linear registration between time points and template base, aren’t they ?
Best regards, Matthieu
In order to avoid bias by adding further time points in the model by the -add recon all command, is this better for each subject to take into account all the time points existing for it or only the ones that I will include in the model (three time points / subject ; if existing 6 time points for any subject ?)
Usually it is recommended to run all time points in the model (so a base with 6 time points) and not use the - - add flag. Also, Linear Mixed Effects models deal well with missing time points. It is perfectly OK to have differently many time points per subject for that. You should still check if there is a bias (e.g. one group always has 3 time points the other 6) that would not be good. Maybe also consult with a local biostatistician if you are not comfortable with the stats. The LME tools are matlab, and so are the survival-analysis scripts.
Best, Martin
Best regards, Matthieu
Best, Martin
> On Nov 21, 2016, at 7:07 PM, Matthieu Vanhoutte <matthieu > vanhoutte@gmail.com> wrote: > > Dear Freesurfer’s experts, > > I would have some questions regarding the LME model to be > used in longitudinal stream: > > 1) Which are the ratio limits or % of missing timepoints > accepted ? (according time, I have less and less subjects > time points) > > 2) Is it possible to include patients that would miss the > first timepoint but got the others ? > > 3) Considering a group in longitudinal study, which is > the number of subjects minimal of this group accepted for > LME modeling ? > > 4) Finally, concerning quality control and among a big > number of total time points, which essential controls are > necessary ? (Control of norm.mgz of the base, alignment > of longitudinal timepoints on base,… ?) > > Best regards, > Matthieu > > > _______________________________________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesur > fer > >
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Hi Martin,
Thanks for your answer.
I actually compare neurospychological scores at baseline between drop-out subjects and subjects with full time-points. If I ever find that drop-out subjects are more severely affected than the subjects with full time-points, then there might be a bias in the results of my LME study ?
How could I argue that significant patterns found in my LME study between both groups are still valid accounting for this bias ? Is LME method robust enough for compensating this kind of drop-out ?
Best, Matthieu
Le mer. 17 oct. 2018 à 18:33, Martin Reuter mreuter@nmr.mgh.harvard.edu a écrit :
Hi Matthieu,
- survival analysis is typically used if you want to detect if the
time to an event is longer in one group vs the other (e.g. one group gets placebo the other drug and we want to know if recurrence is later in the drug group). Not sure this is what you need. The nice thing is, it can deal with drop-outs
- No, you can directly test that (e.g. do more dieseased drop out than
healthy, or are the dropouts on average more advanced (test-scores, hippo-volume etc) than the diseased at baseline... many options. you could also test interactions with age , gender etc. However, not finding an interaction may not mean there is no bias, it is just small enough to go undetected with your data size.
- Survival analysis is a different analysis than LME.
Best, Martin
On Tue, 2018-10-16 at 16:15 +0000, Matthieu Vanhoutte wrote:
External Email - Use CautionHi Martin,
It's been a long time since this discussion but I return on this from now... The problem is that I followed longitudinal images of two groups where I had mainly missing time points at the end. Than you suggested: If you have mainly missing time points at the end, this will bias your analysis to some extend, as the remaining ones may be extremely healthy, as probably the more diseased ones drop out. You may want to do a time-to-event (or survival-analysis) which considers early drop- out.
- I know the survival analysis toolbox on matlab, but now I would
like to know what information will this survival analysis give to me ? 2) Will this analysis tell me if there is a bias ? 3) How to consider early drop-out with this type of analysis based on mass-univariate LME analysis of longitudinal neuroimaging data ?
Thanks in advance for helping.
Best, Matthieu
Le mer. 14 déc. 2016 à 22:14, Martin Reuter <mreuter@nmr.mgh.harvard. edu> a écrit :
Hi Matthieu,
- yes, LME needs to be done first so that values can be sampled
from the fitted model for the SA.
- yes, I was talking about gradient non-linearities etc that could
be in the image from the acquisition. We currently don’t use non- linear registration across time points (only rigid).
Best, Martin
On Nov 22, 2016, at 9:31 PM, Matthieu Vanhoutte <matthieuvanhoutt e@gmail.com> wrote:
Hi Martin,
Please see inline below:
Le 22 nov. 2016 à 17:04, Martin Reuter <mreuter@nmr.mgh.harvard .edu> a écrit :
Hi Matthieu, (also inline)
On Nov 21, 2016, at 10:28 PM, Matthieu Vanhoutte <matthieuvan houtte@gmail.com> wrote:
Hi Martin,
Thanks for replying. Please see inline below:
> Le 21 nov. 2016 à 20:26, Martin Reuter <mreuter@nmr.mgh.har > vard.edu> a écrit : > > Hi Matthieu, > > a few quick answers. Maybe Jorge knows more. > Generally number of subjects / time points etc. cannot be > specified generally. All depends on how noisy your data is > and how large the effect is that you expect to detect. You > can do a power analysis in order to figure out how many > subject / time points would be needed. There are some tools > for that in the LME toolbox: > https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffect > sModels#Poweranalysis > > 1. see above > 2. yes, also time points can miss from the middle. If you > have mainly missing time points at the end, this will bias > your analysis to some extend, as the remaining ones may be > extremely healthy, as probably the more diseased ones drop > out. You may want to do a time-to-event (or survival- > analysis) which considers early drop-out.
Is there any way to do with Freesurfer this kind of analysis ?
https://surfer.nmr.mgh.harvard.edu/fswiki/SurvivalAnalysis Yes, there is also a paper where we do this. It is a combination of LME and Survival Analysis (as for the SA you need to have measurements of all subjects at all time points, so you estimate that from the LME model).
Thank you for the link, I will take a look at. So if understand, this analysis has to be done after LME statistical analysis ? Thereafter since SA need all time points, LME model will allow me to estimate missing time points ?
> 3. see above (power analysis) > 4. GIGO means garbage in, garbage out, so the less you QC, > the more likely will your results be junk. The more you QC > the less likely will it be junk, but could still be. The FS > wiki has lots of tutorial information on checking > freesurfer recons. For longitudinal, you should > additionally check the surfaces in the base, the brain mask > in the base, and the alignment of the time points (although > there is some wiggle space for the alignment, as most > things are allowed to evolve further for each time point).
For the alignment of the time points, should I better comparing brainmask or norm.mgz ?
It does not really matter, I would use norm.mgz. I would load images on top of each other and then use the opacity slider in Freeview to blend between them (that way the eye can pick up small motions). I would not worry too much about local deformations which could be caused by non-linearity (gradient). But if you see global misalignment (rotation, translation) it is a cause for concern) .
Ok thank you. The non-linearity you are talking about are well provoked by MRI system and not non-linear registration between time points and template base, aren’t they ?
Best regards, Matthieu
In order to avoid bias by adding further time points in the model by the -add recon all command, is this better for each subject to take into account all the time points existing for it or only the ones that I will include in the model (three time points / subject ; if existing 6 time points for any subject ?)
Usually it is recommended to run all time points in the model (so a base with 6 time points) and not use the - - add flag. Also, Linear Mixed Effects models deal well with missing time points. It is perfectly OK to have differently many time points per subject for that. You should still check if there is a bias (e.g. one group always has 3 time points the other 6) that would not be good. Maybe also consult with a local biostatistician if you are not comfortable with the stats. The LME tools are matlab, and so are the survival-analysis scripts.
Best, Martin
Best regards, Matthieu
> Best, Martin > > > On Nov 21, 2016, at 7:07 PM, Matthieu Vanhoutte <matthieu > > vanhoutte@gmail.com> wrote: > > > > Dear Freesurfer’s experts, > > > > I would have some questions regarding the LME model to be > > used in longitudinal stream: > > > > 1) Which are the ratio limits or % of missing timepoints > > accepted ? (according time, I have less and less subjects > > time points) > > > > 2) Is it possible to include patients that would miss the > > first timepoint but got the others ? > > > > 3) Considering a group in longitudinal study, which is > > the number of subjects minimal of this group accepted for > > LME modeling ? > > > > 4) Finally, concerning quality control and among a big > > number of total time points, which essential controls are > > necessary ? (Control of norm.mgz of the base, alignment > > of longitudinal timepoints on base,… ?) > > > > Best regards, > > Matthieu > > > > > > _______________________________________________ > > Freesurfer mailing list > > Freesurfer@nmr.mgh.harvard.edu > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesur > > fer > > > > > > _______________________________________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfe > r > > > 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.
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Hi Matthieu,
in LME it is OK if subjects have differently many time points in general. You need to ask a statistician about your specific setup, but I think it might be OK (basically less time points, means more variance on the slope estimates, but that should be considered in LME). But I am not a statistician.
Best, Martin
On Wed, 2018-10-17 at 18:46 +0200, Matthieu Vanhoutte wrote:
External Email - Use Caution Hi Martin,
Thanks for your answer.
I actually compare neurospychological scores at baseline between drop-out subjects and subjects with full time-points. If I ever find that drop-out subjects are more severely affected than the subjects with full time-points, then there might be a bias in the results of my LME study ?
How could I argue that significant patterns found in my LME study between both groups are still valid accounting for this bias ? Is LME method robust enough for compensating this kind of drop-out ?
Best, Matthieu
Le mer. 17 oct. 2018 à 18:33, Martin Reuter <mreuter@nmr.mgh.harvard. edu> a écrit :
Hi Matthieu,
- survival analysis is typically used if you want to detect if the
time to an event is longer in one group vs the other (e.g. one group gets placebo the other drug and we want to know if recurrence is later in the drug group). Not sure this is what you need. The nice thing is, it can deal with drop-outs
- No, you can directly test that (e.g. do more dieseased drop out
than healthy, or are the dropouts on average more advanced (test-scores, hippo-volume etc) than the diseased at baseline... many options. you could also test interactions with age , gender etc. However, not finding an interaction may not mean there is no bias, it is just small enough to go undetected with your data size.
- Survival analysis is a different analysis than LME.
Best, Martin
On Tue, 2018-10-16 at 16:15 +0000, Matthieu Vanhoutte wrote:
External Email - Use Caution Hi Martin, It's been a long time since this discussion but I return on this
from
now... The problem is that I followed longitudinal images of two groups where I had mainly missing time points at the end. Than
you
suggested: If you have mainly missing time points at the end, this will bias your analysis to some extend, as the remaining ones may be
extremely
healthy, as probably the more diseased ones drop out. You may
want to
do a time-to-event (or survival-analysis) which considers early
drop-
out.
- I know the survival analysis toolbox on matlab, but now I
would
like to know what information will this survival analysis give to
me
? 2) Will this analysis tell me if there is a bias ? 3) How to consider early drop-out with this type of analysis
based on
mass-univariate LME analysis of longitudinal neuroimaging data ? Thanks in advance for helping. Best, Matthieu Le mer. 14 déc. 2016 à 22:14, Martin Reuter <mreuter@nmr.mgh.harv
ard.
edu> a écrit :
Hi Matthieu,
- yes, LME needs to be done first so that values can be
sampled
from the fitted model for the SA. 2. yes, I was talking about gradient non-linearities etc that
could
be in the image from the acquisition. We currently don’t use
non-
linear registration across time points (only rigid). Best, Martin
On Nov 22, 2016, at 9:31 PM, Matthieu Vanhoutte
<matthieuvanhoutt
e@gmail.com> wrote: Hi Martin, Please see inline below:
Le 22 nov. 2016 à 17:04, Martin Reuter <mreuter@nmr.mgh.har
vard
.edu> a écrit : Hi Matthieu, (also inline) > On Nov 21, 2016, at 10:28 PM, Matthieu Vanhoutte
<matthieuvan
> houtte@gmail.com> wrote: > > Hi Martin, > > Thanks for replying. Please see inline below: > > > Le 21 nov. 2016 à 20:26, Martin Reuter <mreuter@nmr.mgh
.har
> > vard.edu> a écrit : > > > > Hi Matthieu, > > > > a few quick answers. Maybe Jorge knows more. > > Generally number of subjects / time points etc. cannot
be
> > specified generally. All depends on how noisy your data
is
> > and how large the effect is that you expect to detect.
You
> > can do a power analysis in order to figure out how many > > subject / time points would be needed. There are some
tools
> > for that in the LME toolbox: > > https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEf
fect
> > sModels#Poweranalysis > > > > 1. see above > > 2. yes, also time points can miss from the middle. If
you
> > have mainly missing time points at the end, this will
bias
> > your analysis to some extend, as the remaining ones may
be
> > extremely healthy, as probably the more diseased ones
drop
> > out. You may want to do a time-to-event (or survival- > > analysis) which considers early drop-out. > > Is there any way to do with Freesurfer this kind of
analysis
> ? https://surfer.nmr.mgh.harvard.edu/fswiki/SurvivalAnalysis%C2%A0 Yes, there is also a paper where we do this. It is a combination of LME and Survival Analysis (as for the SA you need to have measurements of all subjects at all time
points,
so you estimate that from the LME model).
Thank you for the link, I will take a look at. So if
understand,
this analysis has to be done after LME statistical analysis ? Thereafter since SA need all time points, LME model will
allow me
to estimate missing time points ?
> > 3. see above (power analysis) > > 4. GIGO means garbage in, garbage out, so the less you
QC,
> > the more likely will your results be junk. The more you
QC
> > the less likely will it be junk, but could still be.
The FS
> > wiki has lots of tutorial information on checking > > freesurfer recons. For longitudinal, you should > > additionally check the surfaces in the base, the brain
mask
> > in the base, and the alignment of the time points
(although
> > there is some wiggle space for the alignment, as most > > things are allowed to evolve further for each time
point).
> > For the alignment of the time points, should I better > comparing brainmask or norm.mgz ? It does not really matter, I would use norm.mgz. I would
load
images on top of each other and then use the opacity slider
in
Freeview to blend between them (that way the eye can pick
up
small motions). I would not worry too much about local deformations which could be caused by non-linearity
(gradient).
But if you see global misalignment (rotation, translation)
it
is a cause for concern) .
Ok thank you. The non-linearity you are talking about are
well
provoked by MRI system and not non-linear registration
between
time points and template base, aren’t they ? Best regards, Matthieu
> In order to avoid bias by adding further time points in
the
> model by the -add recon all command, is this better for
each
> subject to take into account all the time points existing
for
> it or only the ones that I will include in the model
(three
> time points / subject ; if existing 6 time points for any > subject ?) > Usually it is recommended to run all time points in the
model
(so a base with 6 time points) and not use the - - add
flag.
Also, Linear Mixed Effects models deal well with missing
time
points. It is perfectly OK to have differently many time
points
per subject for that. You should still check if there is a
bias
(e.g. one group always has 3 time points the other 6) that would not be good. Maybe also consult with a local biostatistician if you are not comfortable with the stats.
The
LME tools are matlab, and so are the survival-analysis scripts. Best, Martin > Best regards, > Matthieu > > > Best, Martin > > > > > On Nov 21, 2016, at 7:07 PM, Matthieu Vanhoutte
<matthieu
> > > vanhoutte@gmail.com> wrote: > > > > > > Dear Freesurfer’s experts, > > > > > > I would have some questions regarding the LME model
to be
> > > used in longitudinal stream: > > > > > > 1) Which are the ratio limits or % of missing
timepoints
> > > accepted ? (according time, I have less and less
subjects
> > > time points) > > > > > > 2) Is it possible to include patients that would miss
the
> > > first timepoint but got the others ? > > > > > > 3) Considering a group in longitudinal study, which
is
> > > the number of subjects minimal of this group accepted
for
> > > LME modeling ? > > > > > > 4) Finally, concerning quality control and among a
big
> > > number of total time points, which essential controls
are
> > > necessary ? (Control of norm.mgz of the base,
alignment
> > > of longitudinal timepoints on base,… ?) > > > > > > Best regards, > > > Matthieu > > > > > > > > > _______________________________________________ > > > Freesurfer mailing list > > > Freesurfer@nmr.mgh.harvard.edu > > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/fre
esur
> > > fer > > > > > > > > > > _______________________________________________ > > Freesurfer mailing list > > Freesurfer@nmr.mgh.harvard.edu > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/frees
urfe
> > r > > > > > > 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%C2%A0. 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 mailing list > Freesurfer@nmr.mgh.harvard.edu > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesur
fer
_______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfe
r
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%C2%A0. 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 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 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
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External Email - Use Caution
Hi Martin,
Do you mean that high attrition (less time points with time) would induce more variance on the slope estimates but this effect would be compensated by LME modeling ?
Would there be proportion of early drop outs to respect in order to compensate bias with LME ?
Thanks, Matthieu
Le ven. 19 oct. 2018 à 16:19, Martin Reuter mreuter@nmr.mgh.harvard.edu a écrit :
Hi Matthieu,
in LME it is OK if subjects have differently many time points in general. You need to ask a statistician about your specific setup, but I think it might be OK (basically less time points, means more variance on the slope estimates, but that should be considered in LME). But I am not a statistician.
Best, Martin
On Wed, 2018-10-17 at 18:46 +0200, Matthieu Vanhoutte wrote:
External Email - Use CautionHi Martin,
Thanks for your answer.
I actually compare neurospychological scores at baseline between drop-out subjects and subjects with full time-points. If I ever find that drop-out subjects are more severely affected than the subjects with full time-points, then there might be a bias in the results of my LME study ?
How could I argue that significant patterns found in my LME study between both groups are still valid accounting for this bias ? Is LME method robust enough for compensating this kind of drop-out ?
Best, Matthieu
Le mer. 17 oct. 2018 à 18:33, Martin Reuter <mreuter@nmr.mgh.harvard. edu> a écrit :
Hi Matthieu,
- survival analysis is typically used if you want to detect if the
time to an event is longer in one group vs the other (e.g. one group gets placebo the other drug and we want to know if recurrence is later in the drug group). Not sure this is what you need. The nice thing is, it can deal with drop-outs
- No, you can directly test that (e.g. do more dieseased drop out
than healthy, or are the dropouts on average more advanced (test-scores, hippo-volume etc) than the diseased at baseline... many options. you could also test interactions with age , gender etc. However, not finding an interaction may not mean there is no bias, it is just small enough to go undetected with your data size.
- Survival analysis is a different analysis than LME.
Best, Martin
On Tue, 2018-10-16 at 16:15 +0000, Matthieu Vanhoutte wrote:
External Email - Use CautionHi Martin,
It's been a long time since this discussion but I return on this
from
now... The problem is that I followed longitudinal images of two groups where I had mainly missing time points at the end. Than
you
suggested: If you have mainly missing time points at the end, this will bias your analysis to some extend, as the remaining ones may be
extremely
healthy, as probably the more diseased ones drop out. You may
want to
do a time-to-event (or survival-analysis) which considers early
drop-
out.
- I know the survival analysis toolbox on matlab, but now I
would
like to know what information will this survival analysis give to
me
? 2) Will this analysis tell me if there is a bias ? 3) How to consider early drop-out with this type of analysis
based on
mass-univariate LME analysis of longitudinal neuroimaging data ?
Thanks in advance for helping.
Best, Matthieu
Le mer. 14 déc. 2016 à 22:14, Martin Reuter <mreuter@nmr.mgh.harv
ard.
edu> a écrit :
Hi Matthieu,
- yes, LME needs to be done first so that values can be
sampled
from the fitted model for the SA.
- yes, I was talking about gradient non-linearities etc that
could
be in the image from the acquisition. We currently don’t use
non-
linear registration across time points (only rigid).
Best, Martin
On Nov 22, 2016, at 9:31 PM, Matthieu Vanhoutte
<matthieuvanhoutt
e@gmail.com> wrote:
Hi Martin,
Please see inline below:
> Le 22 nov. 2016 à 17:04, Martin Reuter <mreuter@nmr.mgh.har
vard
> .edu> a écrit : > > Hi Matthieu, > (also inline) > > > On Nov 21, 2016, at 10:28 PM, Matthieu Vanhoutte
<matthieuvan
> > houtte@gmail.com> wrote: > > > > Hi Martin, > > > > Thanks for replying. Please see inline below: > > > > > Le 21 nov. 2016 à 20:26, Martin Reuter <mreuter@nmr.mgh
.har
> > > vard.edu> a écrit : > > > > > > Hi Matthieu, > > > > > > a few quick answers. Maybe Jorge knows more. > > > Generally number of subjects / time points etc. cannot
be
> > > specified generally. All depends on how noisy your data
is
> > > and how large the effect is that you expect to detect.
You
> > > can do a power analysis in order to figure out how many > > > subject / time points would be needed. There are some
tools
> > > for that in the LME toolbox: > > > https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEf
fect
> > > sModels#Poweranalysis > > > > > > 1. see above > > > 2. yes, also time points can miss from the middle. If
you
> > > have mainly missing time points at the end, this will
bias
> > > your analysis to some extend, as the remaining ones may
be
> > > extremely healthy, as probably the more diseased ones
drop
> > > out. You may want to do a time-to-event (or survival- > > > analysis) which considers early drop-out. > > > > Is there any way to do with Freesurfer this kind of
analysis
> > ? > > https://surfer.nmr.mgh.harvard.edu/fswiki/SurvivalAnalysis > Yes, there is also a paper where we do this. It is a > combination of LME and Survival Analysis (as for the SA you > need to have measurements of all subjects at all time
points,
> so you estimate that from the LME model).
Thank you for the link, I will take a look at. So if
understand,
this analysis has to be done after LME statistical analysis ? Thereafter since SA need all time points, LME model will
allow me
to estimate missing time points ?
> > > 3. see above (power analysis) > > > 4. GIGO means garbage in, garbage out, so the less you
QC,
> > > the more likely will your results be junk. The more you
QC
> > > the less likely will it be junk, but could still be.
The FS
> > > wiki has lots of tutorial information on checking > > > freesurfer recons. For longitudinal, you should > > > additionally check the surfaces in the base, the brain
mask
> > > in the base, and the alignment of the time points
(although
> > > there is some wiggle space for the alignment, as most > > > things are allowed to evolve further for each time
point).
> > > > For the alignment of the time points, should I better > > comparing brainmask or norm.mgz ? > > It does not really matter, I would use norm.mgz. I would
load
> images on top of each other and then use the opacity slider
in
> Freeview to blend between them (that way the eye can pick
up
> small motions). I would not worry too much about local > deformations which could be caused by non-linearity
(gradient).
> But if you see global misalignment (rotation, translation)
it
> is a cause for concern) .
Ok thank you. The non-linearity you are talking about are
well
provoked by MRI system and not non-linear registration
between
time points and template base, aren’t they ?
Best regards, Matthieu
> > In order to avoid bias by adding further time points in
the
> > model by the -add recon all command, is this better for
each
> > subject to take into account all the time points existing
for
> > it or only the ones that I will include in the model
(three
> > time points / subject ; if existing 6 time points for any > > subject ?) > > > > Usually it is recommended to run all time points in the
model
> (so a base with 6 time points) and not use the - - add
flag.
> Also, Linear Mixed Effects models deal well with missing
time
> points. It is perfectly OK to have differently many time
points
> per subject for that. You should still check if there is a
bias
> (e.g. one group always has 3 time points the other 6) that > would not be good. Maybe also consult with a local > biostatistician if you are not comfortable with the stats.
The
> LME tools are matlab, and so are the survival-analysis > scripts. > > Best, Martin > > > > > Best regards, > > Matthieu > > > > > Best, Martin > > > > > > > On Nov 21, 2016, at 7:07 PM, Matthieu Vanhoutte
<matthieu
> > > > vanhoutte@gmail.com> wrote: > > > > > > > > Dear Freesurfer’s experts, > > > > > > > > I would have some questions regarding the LME model
to be
> > > > used in longitudinal stream: > > > > > > > > 1) Which are the ratio limits or % of missing
timepoints
> > > > accepted ? (according time, I have less and less
subjects
> > > > time points) > > > > > > > > 2) Is it possible to include patients that would miss
the
> > > > first timepoint but got the others ? > > > > > > > > 3) Considering a group in longitudinal study, which
is
> > > > the number of subjects minimal of this group accepted
for
> > > > LME modeling ? > > > > > > > > 4) Finally, concerning quality control and among a
big
> > > > number of total time points, which essential controls
are
> > > > necessary ? (Control of norm.mgz of the base,
alignment
> > > > of longitudinal timepoints on base,… ?) > > > > > > > > Best regards, > > > > Matthieu > > > > > > > > > > > > _______________________________________________ > > > > Freesurfer mailing list > > > > Freesurfer@nmr.mgh.harvard.edu > > > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/fre
esur
> > > > fer > > > > > > > > > > > > > > _______________________________________________ > > > Freesurfer mailing list > > > Freesurfer@nmr.mgh.harvard.edu > > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/frees
urfe
> > > r > > > > > > > > > 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 mailing list > > Freesurfer@nmr.mgh.harvard.edu > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesur
fer
> > _______________________________________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfe
r
> > > 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 mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
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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 mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
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External Email - Use Caution
Hi Martin,
I come back on my previous question concerning survival bias in LME.
Do you have an advice ?
Best, Matthieu
Le 19 oct. 2018 à 16:30, Matthieu Vanhoutte matthieuvanhoutte@gmail.com a écrit :
Hi Martin,
Do you mean that high attrition (less time points with time) would induce more variance on the slope estimates but this effect would be compensated by LME modeling ?
Would there be proportion of early drop outs to respect in order to compensate bias with LME ?
Thanks, Matthieu
Le ven. 19 oct. 2018 à 16:19, Martin Reuter <mreuter@nmr.mgh.harvard.edu mailto:mreuter@nmr.mgh.harvard.edu> a écrit : Hi Matthieu,
in LME it is OK if subjects have differently many time points in general. You need to ask a statistician about your specific setup, but I think it might be OK (basically less time points, means more variance on the slope estimates, but that should be considered in LME). But I am not a statistician.
Best, Martin
On Wed, 2018-10-17 at 18:46 +0200, Matthieu Vanhoutte wrote:
External Email - Use CautionHi Martin,
Thanks for your answer.
I actually compare neurospychological scores at baseline between drop-out subjects and subjects with full time-points. If I ever find that drop-out subjects are more severely affected than the subjects with full time-points, then there might be a bias in the results of my LME study ?
How could I argue that significant patterns found in my LME study between both groups are still valid accounting for this bias ? Is LME method robust enough for compensating this kind of drop-out ?
Best, Matthieu
Le mer. 17 oct. 2018 à 18:33, Martin Reuter <mreuter@nmr.mgh.harvard. edu> a écrit :
Hi Matthieu,
- survival analysis is typically used if you want to detect if the
time to an event is longer in one group vs the other (e.g. one group gets placebo the other drug and we want to know if recurrence is later in the drug group). Not sure this is what you need. The nice thing is, it can deal with drop-outs
- No, you can directly test that (e.g. do more dieseased drop out
than healthy, or are the dropouts on average more advanced (test-scores, hippo-volume etc) than the diseased at baseline... many options. you could also test interactions with age , gender etc. However, not finding an interaction may not mean there is no bias, it is just small enough to go undetected with your data size.
- Survival analysis is a different analysis than LME.
Best, Martin
On Tue, 2018-10-16 at 16:15 +0000, Matthieu Vanhoutte wrote:
External Email - Use CautionHi Martin,
It's been a long time since this discussion but I return on this
from
now... The problem is that I followed longitudinal images of two groups where I had mainly missing time points at the end. Than
you
suggested: If you have mainly missing time points at the end, this will bias your analysis to some extend, as the remaining ones may be
extremely
healthy, as probably the more diseased ones drop out. You may
want to
do a time-to-event (or survival-analysis) which considers early
drop-
out.
- I know the survival analysis toolbox on matlab, but now I
would
like to know what information will this survival analysis give to
me
? 2) Will this analysis tell me if there is a bias ? 3) How to consider early drop-out with this type of analysis
based on
mass-univariate LME analysis of longitudinal neuroimaging data ?
Thanks in advance for helping.
Best, Matthieu
Le mer. 14 déc. 2016 à 22:14, Martin Reuter <mreuter@nmr.mgh.harv
ard.
edu> a écrit :
Hi Matthieu,
- yes, LME needs to be done first so that values can be
sampled
from the fitted model for the SA.
- yes, I was talking about gradient non-linearities etc that
could
be in the image from the acquisition. We currently don’t use
non-
linear registration across time points (only rigid).
Best, Martin
On Nov 22, 2016, at 9:31 PM, Matthieu Vanhoutte
<matthieuvanhoutt
e@gmail.com mailto:e@gmail.com> wrote:
Hi Martin,
Please see inline below:
> Le 22 nov. 2016 à 17:04, Martin Reuter <mreuter@nmr.mgh.har
vard
> .edu> a écrit : > > Hi Matthieu, > (also inline) > > > On Nov 21, 2016, at 10:28 PM, Matthieu Vanhoutte
<matthieuvan
> > houtte@gmail.com mailto:houtte@gmail.com> wrote: > > > > Hi Martin, > > > > Thanks for replying. Please see inline below: > > > > > Le 21 nov. 2016 à 20:26, Martin Reuter <mreuter@nmr.mgh
.har
> > > vard.edu http://vard.edu/> a écrit : > > > > > > Hi Matthieu, > > > > > > a few quick answers. Maybe Jorge knows more. > > > Generally number of subjects / time points etc. cannot
be
> > > specified generally. All depends on how noisy your data
is
> > > and how large the effect is that you expect to detect.
You
> > > can do a power analysis in order to figure out how many > > > subject / time points would be needed. There are some
tools
> > > for that in the LME toolbox: > > > https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEf https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEf
fect
> > > sModels#Poweranalysis > > > > > > 1. see above > > > 2. yes, also time points can miss from the middle. If
you
> > > have mainly missing time points at the end, this will
bias
> > > your analysis to some extend, as the remaining ones may
be
> > > extremely healthy, as probably the more diseased ones
drop
> > > out. You may want to do a time-to-event (or survival- > > > analysis) which considers early drop-out. > > > > Is there any way to do with Freesurfer this kind of
analysis
> > ? > > https://surfer.nmr.mgh.harvard.edu/fswiki/SurvivalAnalysis https://surfer.nmr.mgh.harvard.edu/fswiki/SurvivalAnalysis > Yes, there is also a paper where we do this. It is a > combination of LME and Survival Analysis (as for the SA you > need to have measurements of all subjects at all time
points,
> so you estimate that from the LME model).
Thank you for the link, I will take a look at. So if
understand,
this analysis has to be done after LME statistical analysis ? Thereafter since SA need all time points, LME model will
allow me
to estimate missing time points ?
> > > 3. see above (power analysis) > > > 4. GIGO means garbage in, garbage out, so the less you
QC,
> > > the more likely will your results be junk. The more you
QC
> > > the less likely will it be junk, but could still be.
The FS
> > > wiki has lots of tutorial information on checking > > > freesurfer recons. For longitudinal, you should > > > additionally check the surfaces in the base, the brain
mask
> > > in the base, and the alignment of the time points
(although
> > > there is some wiggle space for the alignment, as most > > > things are allowed to evolve further for each time
point).
> > > > For the alignment of the time points, should I better > > comparing brainmask or norm.mgz ? > > It does not really matter, I would use norm.mgz. I would
load
> images on top of each other and then use the opacity slider
in
> Freeview to blend between them (that way the eye can pick
up
> small motions). I would not worry too much about local > deformations which could be caused by non-linearity
(gradient).
> But if you see global misalignment (rotation, translation)
it
> is a cause for concern) .
Ok thank you. The non-linearity you are talking about are
well
provoked by MRI system and not non-linear registration
between
time points and template base, aren’t they ?
Best regards, Matthieu
> > In order to avoid bias by adding further time points in
the
> > model by the -add recon all command, is this better for
each
> > subject to take into account all the time points existing
for
> > it or only the ones that I will include in the model
(three
> > time points / subject ; if existing 6 time points for any > > subject ?) > > > > Usually it is recommended to run all time points in the
model
> (so a base with 6 time points) and not use the - - add
flag.
> Also, Linear Mixed Effects models deal well with missing
time
> points. It is perfectly OK to have differently many time
points
> per subject for that. You should still check if there is a
bias
> (e.g. one group always has 3 time points the other 6) that > would not be good. Maybe also consult with a local > biostatistician if you are not comfortable with the stats.
The
> LME tools are matlab, and so are the survival-analysis > scripts. > > Best, Martin > > > > > Best regards, > > Matthieu > > > > > Best, Martin > > > > > > > On Nov 21, 2016, at 7:07 PM, Matthieu Vanhoutte
<matthieu
> > > > vanhoutte@gmail.com mailto:vanhoutte@gmail.com> wrote: > > > > > > > > Dear Freesurfer’s experts, > > > > > > > > I would have some questions regarding the LME model
to be
> > > > used in longitudinal stream: > > > > > > > > 1) Which are the ratio limits or % of missing
timepoints
> > > > accepted ? (according time, I have less and less
subjects
> > > > time points) > > > > > > > > 2) Is it possible to include patients that would miss
the
> > > > first timepoint but got the others ? > > > > > > > > 3) Considering a group in longitudinal study, which
is
> > > > the number of subjects minimal of this group accepted
for
> > > > LME modeling ? > > > > > > > > 4) Finally, concerning quality control and among a
big
> > > > number of total time points, which essential controls
are
> > > > necessary ? (Control of norm.mgz of the base,
alignment
> > > > of longitudinal timepoints on base,… ?) > > > > > > > > Best regards, > > > > Matthieu > > > > > > > > > > > > _______________________________________________ > > > > Freesurfer mailing list > > > > Freesurfer@nmr.mgh.harvard.edu mailto:Freesurfer@nmr.mgh.harvard.edu > > > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/fre https://mail.nmr.mgh.harvard.edu/mailman/listinfo/fre
esur
> > > > fer > > > > > > > > > > > > > > _______________________________________________ > > > Freesurfer mailing list > > > Freesurfer@nmr.mgh.harvard.edu mailto:Freesurfer@nmr.mgh.harvard.edu > > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/frees https://mail.nmr.mgh.harvard.edu/mailman/listinfo/frees
urfe
> > > r > > > > > > > > > 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 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 mailing list > > Freesurfer@nmr.mgh.harvard.edu mailto:Freesurfer@nmr.mgh.harvard.edu > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesur https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesur
fer
> > _______________________________________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu mailto:Freesurfer@nmr.mgh.harvard.edu > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfe https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfe
r
> > > 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 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.
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Hi Matthieu,
no, sorry. Maybe you can find a local biostatistician who knows.
A slope estimate with less time points is more uncertain than one with more time points (obviously). Also a slope estimate with time points on a line is more certain than one with time points values all over the place. This information is used in the LME model. But I don't know to what extend any potential biases are removed. For example, if one group has only 2 time points and the other had 5, I would be very suspicious. You can, of course, always reduce the number of time points in the second group to match the first and see if your results remain stable.
Best, Martin
On Fri, 2018-12-07 at 22:33 +0100, Matthieu Vanhoutte wrote:
External Email - Use Caution Hi Martin,
I come back on my previous question concerning survival bias in LME.
Do you have an advice ?
Best, Matthieu
Le 19 oct. 2018 à 16:30, Matthieu Vanhoutte <matthieuvanhoutte@gmai l.com> a écrit :
Hi Martin,
Do you mean that high attrition (less time points with time) would induce more variance on the slope estimates but this effect would be compensated by LME modeling ?
Would there be proportion of early drop outs to respect in order to compensate bias with LME ?
Thanks, Matthieu
Le ven. 19 oct. 2018 à 16:19, Martin Reuter <mreuter@nmr.mgh.harvar d.edu> a écrit :
Hi Matthieu,
in LME it is OK if subjects have differently many time points in general. You need to ask a statistician about your specific setup, but I think it might be OK (basically less time points, means more variance on the slope estimates, but that should be considered in LME). But I am not a statistician.
Best, Martin
On Wed, 2018-10-17 at 18:46 +0200, Matthieu Vanhoutte wrote:
External Email - Use Caution Hi Martin, Thanks for your answer. I actually compare neurospychological scores at baseline
between
drop-out subjects and subjects with full time-points. If I ever
find
that drop-out subjects are more severely affected than the
subjects
with full time-points, then there might be a bias in the
results of
my LME study ? How could I argue that significant patterns found in my LME
study
between both groups are still valid accounting for this bias ?
Is LME
method robust enough for compensating this kind of drop-out ? Best, Matthieu Le mer. 17 oct. 2018 à 18:33, Martin Reuter <mreuter@nmr.mgh.ha
rvard.
edu> a écrit :
Hi Matthieu,
- survival analysis is typically used if you want to detect
if the
time to an event is longer in one group vs the other (e.g.
one
group gets placebo the other drug and we want to know if recurrence
is
later in the drug group). Not sure this is what you need. The nice
thing
is, it can deal with drop-outs 2) No, you can directly test that (e.g. do more dieseased
drop out
than healthy, or are the dropouts on average more advanced (test-
scores,
hippo-volume etc) than the diseased at baseline... many
options.
you could also test interactions with age , gender etc. However,
not
finding an interaction may not mean there is no bias, it is
just
small enough to go undetected with your data size. 3) Survival analysis is a different analysis than LME. Best, Martin On Tue, 2018-10-16 at 16:15 +0000, Matthieu Vanhoutte wrote:
External Email - Use Caution Hi Martin, It's been a long time since this discussion but I return on
this
from
now... The problem is that I followed longitudinal images
of two
groups where I had mainly missing time points at the end.
Than
you
suggested: If you have mainly missing time points at the end, this
will bias
your analysis to some extend, as the remaining ones may be
extremely
healthy, as probably the more diseased ones drop out. You
may
want to
do a time-to-event (or survival-analysis) which considers
early
drop-
out.
- I know the survival analysis toolbox on matlab, but now
I
would
like to know what information will this survival analysis
give to
me
? 2) Will this analysis tell me if there is a bias ? 3) How to consider early drop-out with this type of
analysis
based on
mass-univariate LME analysis of longitudinal neuroimaging
data ?
Thanks in advance for helping. Best, Matthieu Le mer. 14 déc. 2016 à 22:14, Martin Reuter <mreuter@nmr.mg
h.harv
ard.
edu> a écrit : > Hi Matthieu, > > 1. yes, LME needs to be done first so that values can be
sampled
> from the fitted model for the SA. > > 2. yes, I was talking about gradient non-linearities etc
that
could
> be in the image from the acquisition. We currently don’t
use
non-
> linear registration across time points (only rigid). > > Best, Martin > > > > On Nov 22, 2016, at 9:31 PM, Matthieu Vanhoutte
<matthieuvanhoutt
> > e@gmail.com> wrote: > > > > Hi Martin, > > > > Please see inline below: > > > > > Le 22 nov. 2016 à 17:04, Martin Reuter <mreuter@nmr.m
gh.har
vard
> > > .edu> a écrit : > > > > > > Hi Matthieu, > > > (also inline) > > > > > > > On Nov 21, 2016, at 10:28 PM, Matthieu Vanhoutte
<matthieuvan
> > > > houtte@gmail.com> wrote: > > > > > > > > Hi Martin, > > > > > > > > Thanks for replying. Please see inline below: > > > > > > > > > Le 21 nov. 2016 à 20:26, Martin Reuter <mreuter@n
mr.mgh
.har
> > > > > vard.edu> a écrit : > > > > > > > > > > Hi Matthieu, > > > > > > > > > > a few quick answers. Maybe Jorge knows more. > > > > > Generally number of subjects / time points etc.
cannot
be
> > > > > specified generally. All depends on how noisy
your data
is
> > > > > and how large the effect is that you expect to
detect.
You
> > > > > can do a power analysis in order to figure out
how many
> > > > > subject / time points would be needed. There are
some
tools
> > > > > for that in the LME toolbox: > > > > > https://surfer.nmr.mgh.harvard.edu/fswiki/LinearM
ixedEf
fect
> > > > > sModels#Poweranalysis > > > > > > > > > > 1. see above > > > > > 2. yes, also time points can miss from the
middle. If
you
> > > > > have mainly missing time points at the end, this
will
bias
> > > > > your analysis to some extend, as the remaining
ones may
be
> > > > > extremely healthy, as probably the more diseased
ones
drop
> > > > > out. You may want to do a time-to-event (or
survival-
> > > > > analysis) which considers early drop-out. > > > > > > > > Is there any way to do with Freesurfer this kind of
analysis
> > > > ? > > > > > > https://surfer.nmr.mgh.harvard.edu/fswiki/SurvivalAna
lysis
> > > Yes, there is also a paper where we do this. It is a > > > combination of LME and Survival Analysis (as for the
SA you
> > > need to have measurements of all subjects at all time
points,
> > > so you estimate that from the LME model). > > > > Thank you for the link, I will take a look at. So if
understand,
> > this analysis has to be done after LME statistical
analysis ?
> > Thereafter since SA need all time points, LME model
will
allow me
> > to estimate missing time points ? > > > > > > > 3. see above (power analysis) > > > > > 4. GIGO means garbage in, garbage out, so the
less you
QC,
> > > > > the more likely will your results be junk. The
more you
QC
> > > > > the less likely will it be junk, but could still
be.
The FS
> > > > > wiki has lots of tutorial information on checking > > > > > freesurfer recons. For longitudinal, you should > > > > > additionally check the surfaces in the base, the
brain
mask
> > > > > in the base, and the alignment of the time points
(although
> > > > > there is some wiggle space for the alignment, as
most
> > > > > things are allowed to evolve further for each
time
point).
> > > > > > > > For the alignment of the time points, should I
better
> > > > comparing brainmask or norm.mgz ? > > > > > > It does not really matter, I would use norm.mgz. I
would
load
> > > images on top of each other and then use the opacity
slider
in
> > > Freeview to blend between them (that way the eye can
pick
up
> > > small motions). I would not worry too much about
local
> > > deformations which could be caused by non-linearity
(gradient).
> > > But if you see global misalignment (rotation,
translation)
it
> > > is a cause for concern) . > > > > Ok thank you. The non-linearity you are talking about
are
well
> > provoked by MRI system and not non-linear registration
between
> > time points and template base, aren’t they ? > > > > Best regards, > > Matthieu > > > > > > In order to avoid bias by adding further time
points in
the
> > > > model by the -add recon all command, is this better
for
each
> > > > subject to take into account all the time points
existing
for
> > > > it or only the ones that I will include in the
model
(three
> > > > time points / subject ; if existing 6 time points
for any
> > > > subject ?) > > > > > > > > > > Usually it is recommended to run all time points in
the
model
> > > (so a base with 6 time points) and not use the - -
add
flag.
> > > Also, Linear Mixed Effects models deal well with
missing
time
> > > points. It is perfectly OK to have differently many
time
points
> > > per subject for that. You should still check if there
is a
bias
> > > (e.g. one group always has 3 time points the other 6)
that
> > > would not be good. Maybe also consult with a local > > > biostatistician if you are not comfortable with the
stats.
The
> > > LME tools are matlab, and so are the survival-
analysis
> > > scripts. > > > > > > Best, Martin > > > > > > > > > > > > > Best regards, > > > > Matthieu > > > > > > > > > Best, Martin > > > > > > > > > > > On Nov 21, 2016, at 7:07 PM, Matthieu Vanhoutte
<matthieu
> > > > > > vanhoutte@gmail.com> wrote: > > > > > > > > > > > > Dear Freesurfer’s experts, > > > > > > > > > > > > I would have some questions regarding the LME
model
to be
> > > > > > used in longitudinal stream: > > > > > > > > > > > > 1) Which are the ratio limits or % of missing
timepoints
> > > > > > accepted ? (according time, I have less and
less
subjects
> > > > > > time points) > > > > > > > > > > > > 2) Is it possible to include patients that
would miss
the
> > > > > > first timepoint but got the others ? > > > > > > > > > > > > 3) Considering a group in longitudinal study,
which
is
> > > > > > the number of subjects minimal of this group
accepted
for
> > > > > > LME modeling ? > > > > > > > > > > > > 4) Finally, concerning quality control and
among a
big
> > > > > > number of total time points, which essential
controls
are
> > > > > > necessary ? (Control of norm.mgz of the base,
alignment
> > > > > > of longitudinal timepoints on base,… ?) > > > > > > > > > > > > Best regards, > > > > > > Matthieu > > > > > > > > > > > > > > > > > > _______________________________________________ > > > > > > Freesurfer mailing list > > > > > > Freesurfer@nmr.mgh.harvard.edu > > > > > > https://mail.nmr.mgh.harvard.edu/mailman/listin
fo/fre
esur
> > > > > > fer > > > > > > > > > > > > > > > > > > > > > > _______________________________________________ > > > > > Freesurfer mailing list > > > > > Freesurfer@nmr.mgh.harvard.edu > > > > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo
/frees
urfe
> > > > > r > > > > > > > > > > > > > > > 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%C2%A0. If the
was
> > > > > sent to you in error > > > > > but does not contain patient information, please
contact
> > > > > the sender and properly > > > > > dispose of the e-mail. > > > > > > > > _______________________________________________ > > > > Freesurfer mailing list > > > > Freesurfer@nmr.mgh.harvard.edu > > > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/f
reesur
fer
> > > > > > _______________________________________________ > > > Freesurfer mailing list > > > Freesurfer@nmr.mgh.harvard.edu > > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/fre
esurfe
r
> > > > > > > > > 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%C2%A0. 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 mailing list > > Freesurfer@nmr.mgh.harvard.edu > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/frees
urfer
> > _______________________________________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesur
fer
> > > 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 mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfe
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Matthieu Vanhoutte