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 2009

SPM April 2009

Options

Subscribe or Unsubscribe

Subscribe or Unsubscribe

Log In

Log In

Get Password

Get Password

Subject:

Re: NaNs in con images - second level analysis

From:

Elizabeth Liddle <[log in to unmask]>

Reply-To:

Elizabeth Liddle <[log in to unmask]>

Date:

Fri, 17 Apr 2009 10:14:23 +0100

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (1 lines)

Well, depending on datatype you can have zeros or NaN's to represent voxels in which (I assume) there wasn't enough variance to get a meaningful beta.  Is this correct?



If you have zeros rather than NaNs you can use implicit masking to convert the zeros to NaN's.



What I did was to write a script that takes all the con images (one from each subject), and for each voxel, counts the number of NaNs.  Where this is less than some threshold say 10% of the total), for each NaN, I substitute a random number from a distribution with the same mean and variance as the remaining non NaN values for that voxel, and then saves the con image with a new prefix.  When you look at the new con image there's a kind of lumpy "patch" over any holes (the patch isn't smoothed of course).  And what it means is that when you put the new images into a second level analysis, those voxels will be included, but they shouldn't bias the results (or at least that's what I'm asking).  When the second level analysis is a between groups comparison, I compare groups at second level, I take the mean and variance across both groups, on the principle that under the null the con values will be drawn from the same distribution.



Ideally it would be good to do a Monte Carlo type analysis, but my mind boggles at how one would interpret the results at cluster level, even if one had the computing time, and it's cluster level where there is a potential problem (type II errors arising if real clusters are broken by holes).



Elizabeth





-----Original Message-----

From: Allyson C. Rosen Ph.D. [mailto:[log in to unmask]] 

Sent: 15 April 2009 14:30

To: Elizabeth Liddle

Subject: Re: [SPM] NaNs in con images - second level analysis



Elizabeth,



Thanks for posting this.  I was having trouble finding someone else who had NaN's in their images.  It also looks like MarsBar can't find any data in functionally defined images based on this con images even though there are data there.  I'm stopped dead here.  I got NaN's in my first level analysis.  Have you seen this?



----- Original Message -----

From: "Elizabeth Liddle" <[log in to unmask]>

To: [log in to unmask]

Sent: Wednesday, April 15, 2009 6:20:52 AM GMT -08:00 US/Canada Pacific

Subject: [SPM] NaNs in con images - second level analysis









Dear SPMers 



  



As I understand it, when you do a second level analysis, any voxel in any of the first level con images that contains a NaN in one subject is excluded from the second level analysis.  When the number of subjects is large, this can be problematic – the second level con image starts to look like a swiss cheese, and also tends to lose data from the edges.  So my question is whether anyone has figured out a way of getting a statistically valid t value for voxels in which there is missing data in a smallish number of subjects.   This seems to be of particular importance if you are interested in cluster level significance, because a hole in the wrong place may destroy a valid cluster. 



  



Clearly replacing the voxel value with the mean for the rest of the subjects will understate the variance, and thus result in an inflated t value.  So instead I tried replacing NaNs in voxels in which they occurred in only a small proportion of subjects with a random number drawn from a distribution with the same mean and variance as in the remaining participants’ values for that voxel. 



  



Can anyone see a problem with this approach?  Is there an alternative? 



  



Elizabeth 



  



  



_____________________________________________ 



Dr Elizabeth Liddle 



Developmental Psychiatry 



E Floor, South Block 



QMC 



Nottingham 



NG7 2UH 



  



Tel: +44 (0)115 823 0271 



  





This message has been checked for viruses but the contents of an attachment may still contain software viruses, which could damage your computer system: you are advised to perform your own checks. Email communications with the University of Nottingham may be monitored as permitted by UK legislation.


This message has been checked for viruses but the contents of an attachment
may still contain software viruses, which could damage your computer system:
you are advised to perform your own checks. Email communications with the
University of Nottingham may be monitored as permitted by UK legislation.

Top of Message | Previous Page | Permalink

JiscMail Tools


RSS Feeds and Sharing


Advanced Options


Archives

April 2024
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