JiscMail Logo
Email discussion lists for the UK Education and Research communities

Help for SPM Archives


SPM Archives

SPM Archives


SPM@JISCMAIL.AC.UK


View:

Message:

[

First

|

Previous

|

Next

|

Last

]

By Topic:

[

First

|

Previous

|

Next

|

Last

]

By Author:

[

First

|

Previous

|

Next

|

Last

]

Font:

Proportional Font

LISTSERV Archives

LISTSERV Archives

SPM Home

SPM Home

SPM  April 2008

SPM April 2008

Options

Subscribe or Unsubscribe

Subscribe or Unsubscribe

Log In

Log In

Get Password

Get Password

Subject:

Re: RAVENS maps vs SPM5 modulated data in VBM: Question on smoothness

From:

Christian Gaser <[log in to unmask]>

Reply-To:

Christian Gaser <[log in to unmask]>

Date:

Tue, 1 Apr 2008 07:24:55 +0100

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (97 lines)

Hi John and Nikos,

the modulated images of the RAVENS and the DARTEL methods seem to be very similiar, which is 
not surprising because both approaches are based on a diffeomorphic high-dimensional spatial 
registration. However, DARTEL is using a more tricky approach to create a population template 
based on the given sample.
The erroneous smoothness estimation of the RAVENS maps was caused by an undefined scaling 
factor in the RAVENS maps (and SPM5 is then using the default value of 1). This caused rounding 
errors for the uint8 data, because the maximum values were around 30-50 and after smoothing 
there were only a few different (integer) values existent.

Regards,

Christian


____________________________________________________________________________

Christian Gaser, Ph.D.
Assistant Professor of Computational Neuroscience
Department of Psychiatry
Friedrich-Schiller-University of Jena
Jahnstrasse 3, D-07743 Jena, Germany
Tel: ++49-3641-934752	Fax:   ++49-3641-934755
e-mail: [log in to unmask]
http://dbm.neuro.uni-jena.de

On Mon, 31 Mar 2008 13:40:56 +0000, John Ashburner <[log in to unmask]> wrote:

>The RAVENS registration is more precise than the older spatial normalisation
>routines of SPM.  In SPM, there are only about 1000 parameters to describe
>the warps, whereas RAVENS uses millions.  Generally, if the residuals are
>very smooth, then it could imply that the model is underfitting the data. The
>original concept behind VBM was to treat the spatial normalisation a bit like
>high pass filtering in an SPM analysis (ie to remove "macroscopic"
>differences) so that "mesoscopic" differences could be detected.
>
>Although the introduction of "modulation" (Jacobian transformation) confused a
>lot of people, it meant that the values actually had a more meaningful
>interpretation, and that a VBM analysis was not simply an examination of
>registration error.  "Morphometry" is the study of variation and change in
>the form (size and shape) of organisms. With the introduction of the Jacobian
>transform, the use of the term became slightly closer to its dictionary
>definition, as it involved comparing the regional volumes of grey matter.
>This also meant that more accurate spatial normalisation could be used.
>RAVENS maps have always implicitly preserved the tissue volumes in the warped
>images.
>
>I eventually got around to improving the inter-subject registration model in
>SPM.  In the latest updates of SPM5, there is a DARTEL toolbox, which can be
>used to obtain more precise inter-subject alignment for VBM studies.
>Experience of DARTEL in the FIL is generally positive.  VBM results produce
>higher t stats for the difference that are detected than for the other SPM
>spatial normalisation approaches.  I would expect the RAVENS results to be
>similar.
>
>Best regards,
>-John
>
>On Sunday 30 March 2008 09:54, Nikolaos Koutsouleris wrote:
>> Dear SPMers,
>>
>> recently, I tried the HAMMER normalization algorithm of Davatzikos et al.
>> on data that had been segmented with SPM5. I observed that the smoothness
>> of the obtained RAVENS maps is significantly lower compared the modulated,
>> low-dimensionally normalized volumes of SPM5 (see example of the same
>> subject in the attachments). Correct me if I am wrong, but this may be due
>> to the fact that the HAMMER algorithm uses high-dimensional elastic warping
>> which retains a higher degree of anatomical information compared to the SPM
>> normalization.
>>
>> When I did cluster-level statistical inference on the 10mm-smoothed RAVENS
>> maps (using Satoru's non-stationarity correction toolbox), I noticed that
>> the resulting smoothness estimate was about 5 mm, compared to the
>> SPM-normalized data which was about 11 mm. In the RAVENS analysis a primary
>> threshold of p<0.05 together with FWE-corrected extent threshold of p<0.05
>> produced a minimum number of 1500 voxels, whereas the extent threshold for
>> the SPM -normalized data was around 20000 voxels. This is a dramatic
>> difference which  make clusters significant at a much lower spatial
>> threshold. So, I wonder if this difference is really due to the "roughness"
>> of the RAVENS maps or if the smoothness estimation in SPM is not valid for
>> RAVENS data. Does anybody have similar experiences or possible
>> explanations?
>>
>> Thanks in advance and sorry for this lengthy email!
>>
>> Cheers,
>>
>> Nikos Koutsouleris
>>
>> Imaging Workgroup
>> Department of Psychiatry and Psychotherapy,
>> Ludwig-Maximilians-University,
>> Munich
>===========================================================
==============

Top of Message | Previous Page | Permalink

JiscMail Tools


RSS Feeds and Sharing


Advanced Options


Archives

March 2024
February 2024
January 2024
December 2023
November 2023
October 2023
September 2023
August 2023
July 2023
June 2023
May 2023
April 2023
March 2023
February 2023
January 2023
December 2022
November 2022
October 2022
September 2022
August 2022
July 2022
June 2022
May 2022
April 2022
March 2022
February 2022
January 2022
December 2021
November 2021
October 2021
September 2021
August 2021
July 2021
June 2021
May 2021
April 2021
March 2021
February 2021
January 2021
December 2020
November 2020
October 2020
September 2020
August 2020
July 2020
June 2020
May 2020
April 2020
March 2020
February 2020
January 2020
December 2019
November 2019
October 2019
September 2019
August 2019
July 2019
June 2019
May 2019
April 2019
March 2019
February 2019
January 2019
December 2018
November 2018
October 2018
September 2018
August 2018
July 2018
June 2018
May 2018
April 2018
March 2018
February 2018
January 2018
December 2017
November 2017
October 2017
September 2017
August 2017
July 2017
June 2017
May 2017
April 2017
March 2017
February 2017
January 2017
December 2016
November 2016
October 2016
September 2016
August 2016
July 2016
June 2016
May 2016
April 2016
March 2016
February 2016
January 2016
December 2015
November 2015
October 2015
September 2015
August 2015
July 2015
June 2015
May 2015
April 2015
March 2015
February 2015
January 2015
December 2014
November 2014
October 2014
September 2014
August 2014
July 2014
June 2014
May 2014
April 2014
March 2014
February 2014
January 2014
December 2013
November 2013
October 2013
September 2013
August 2013
July 2013
June 2013
May 2013
April 2013
March 2013
February 2013
January 2013
December 2012
November 2012
October 2012
September 2012
August 2012
July 2012
June 2012
May 2012
April 2012
March 2012
February 2012
January 2012
December 2011
November 2011
October 2011
September 2011
August 2011
July 2011
June 2011
May 2011
April 2011
March 2011
February 2011
January 2011
December 2010
November 2010
October 2010
September 2010
August 2010
July 2010
June 2010
May 2010
April 2010
March 2010
February 2010
January 2010
December 2009
November 2009
October 2009
September 2009
August 2009
July 2009
June 2009
May 2009
April 2009
March 2009
February 2009
January 2009
December 2008
November 2008
October 2008
September 2008
August 2008
July 2008
June 2008
May 2008
April 2008
March 2008
February 2008
January 2008
December 2007
November 2007
October 2007
September 2007
August 2007
July 2007
June 2007
May 2007
April 2007
March 2007
February 2007
January 2007
2006
2005
2004
2003
2002
2001
2000
1999
1998


JiscMail is a Jisc service.

View our service policies at https://www.jiscmail.ac.uk/policyandsecurity/ and Jisc's privacy policy at https://www.jisc.ac.uk/website/privacy-notice

For help and support help@jisc.ac.uk

Secured by F-Secure Anti-Virus CataList Email List Search Powered by the LISTSERV Email List Manager