Re: [Freesurfer] few questions regarding qdec

2013-08-19 Thread Douglas N Greve

On 08/16/2013 05:02 PM, Binod Thapa-Chhetry wrote:
 HI all,

 We had submitted a paper based on cortical thickness analysis in Qdec. 
 The reviewer's had couple concerns regarding the QDec analysis. We 
 would appreciate your help to address/resolve those issues. Here are 
 the concerns:

 (1) What was the rationale of using 10mm smoothing? The typical fMRI 
 values for volumteric work is 2-3 x the original voxel size. What's 
 the reason for this value?

 Our response so far: A smoothing kernel of 10 mm was chosen because 
 this setting had been published recently in articles that performed 
 similar analyses.  10 mm therefore was a typical parameter for this 
 type of analysis.  This setting was also the default in the QDEC GUI, 
 also suggesting that it was a typical setting.  We only performed 
 analyses of the data for this project using this setting and did not 
 iteratively test multiple kernel widths.  Also, the FWHM for cortical 
 thickness analyses is somewhat larger than that used in fMRI analyses 
 because it is applied to surface, as opposed to volume-based data.  
 Surface based data are not prone to the same artifacts of including 
 white matter, csf or brain tissue on the other side of a sulcus as 
 volume-based data are.
I think this is a good answer for a question that is not entirely fair. 
Aside from a few arguments about making GRF work, no one has ever had 
much of a justification for the particular choice of FWHM. It is just 
traditional at this point. You might point out that more smoothing is 
generally considered worse and that 10mm on the surface is much less 
than 10mm in the volume and people use 10mm in the volume is not unusual.

 (2) What's the p-value to get in for a cluster-wise analysis and what 
 size is a significant cluster? It's important to know these non 
 cluster-wise values to get a sense of the parameters that determine 
 significance.

 Our response so far: QDEC does not have a parameter that can be 
 adjusted for cluster size or extent as other whole brain analyses such 
 as Statistical Parametric Mapping do.  The researcher can only 
 indicate a statistical cutoff.  We used a p value cutoff of 0.05 after 
 a correction for multiple comparisons that was based on the Monte 
 Carlo permutation cluster analysis with 10,000 iterations. 
 Additionally, our findings comprise of pretty large clusters. This 
 obviates the reason for cluster-thresholding  based on size.
I'm not sure what the reviewer is asking. Is he/she asking for the 
cluster-forming, ie, voxel-wise, threshold? Or the p-value threshold for 
the cluster that you used to report clusters? You should report both. 
Both can be set in the QDEC GUI. I think the defaults are .01 and .05 
respectively. Given the clusterwise p-value, there is a corresponding 
critical cluster size. You can get this from a file in the QDEC output 
folder (in the contrast subfolder). The file will be called something 
like cache.th20.abs.pdf.dat. The 2nd column gives you the cluster size 
(in mm2) and the 4th column gives you the clusterwise p-value. Find the 
size of the cluster that corresponds to .05. In one data set I looked 
at, the size was about 328mm^2

doug





 Thanks in advance for the response.
 Binod





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Re: [Freesurfer] few questions regarding qdec

2013-08-19 Thread Alex Hanganu
Hi

Du A.T. Brain 2007 ”Different regional patterns of cortical thinning in 
AD - used the smoothing level of 10-mm. This article can be added as 
reference.

Additionally higher smoothing levels, for ex. at 20 mm, would bring 
partially inflated patterns due to excessive smoothing and due to 
failure of inference methods to control the proportion of false 
discoveries - Bernal-Rusiel, Neuroimage 2010 
(www.ncibi.nlm.nih.gov/pubmed/20362677)
while lower smoothing levels have an increased potential of preserving 
clusters with low significance.

alex.


Le 19/8 12:58, Douglas N Greve a écrit :
 On 08/16/2013 05:02 PM, Binod Thapa-Chhetry wrote:
 HI all,

 We had submitted a paper based on cortical thickness analysis in Qdec.
 The reviewer's had couple concerns regarding the QDec analysis. We
 would appreciate your help to address/resolve those issues. Here are
 the concerns:

 (1) What was the rationale of using 10mm smoothing? The typical fMRI
 values for volumteric work is 2-3 x the original voxel size. What's
 the reason for this value?

 Our response so far: A smoothing kernel of 10 mm was chosen because
 this setting had been published recently in articles that performed
 similar analyses.  10 mm therefore was a typical parameter for this
 type of analysis.  This setting was also the default in the QDEC GUI,
 also suggesting that it was a typical setting.  We only performed
 analyses of the data for this project using this setting and did not
 iteratively test multiple kernel widths.  Also, the FWHM for cortical
 thickness analyses is somewhat larger than that used in fMRI analyses
 because it is applied to surface, as opposed to volume-based data.
 Surface based data are not prone to the same artifacts of including
 white matter, csf or brain tissue on the other side of a sulcus as
 volume-based data are.
 I think this is a good answer for a question that is not entirely fair.
 Aside from a few arguments about making GRF work, no one has ever had
 much of a justification for the particular choice of FWHM. It is just
 traditional at this point. You might point out that more smoothing is
 generally considered worse and that 10mm on the surface is much less
 than 10mm in the volume and people use 10mm in the volume is not unusual.
 (2) What's the p-value to get in for a cluster-wise analysis and what
 size is a significant cluster? It's important to know these non
 cluster-wise values to get a sense of the parameters that determine
 significance.

 Our response so far: QDEC does not have a parameter that can be
 adjusted for cluster size or extent as other whole brain analyses such
 as Statistical Parametric Mapping do.  The researcher can only
 indicate a statistical cutoff.  We used a p value cutoff of 0.05 after
 a correction for multiple comparisons that was based on the Monte
 Carlo permutation cluster analysis with 10,000 iterations.
 Additionally, our findings comprise of pretty large clusters. This
 obviates the reason for cluster-thresholding  based on size.
 I'm not sure what the reviewer is asking. Is he/she asking for the
 cluster-forming, ie, voxel-wise, threshold? Or the p-value threshold for
 the cluster that you used to report clusters? You should report both.
 Both can be set in the QDEC GUI. I think the defaults are .01 and .05
 respectively. Given the clusterwise p-value, there is a corresponding
 critical cluster size. You can get this from a file in the QDEC output
 folder (in the contrast subfolder). The file will be called something
 like cache.th20.abs.pdf.dat. The 2nd column gives you the cluster size
 (in mm2) and the 4th column gives you the clusterwise p-value. Find the
 size of the cluster that corresponds to .05. In one data set I looked
 at, the size was about 328mm^2

 doug




 Thanks in advance for the response.
 Binod





 ___
 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
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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.