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:

Monospaced Font

LISTSERV Archives

LISTSERV Archives

SPM Home

SPM Home

SPM  February 2011

SPM February 2011

Options

Subscribe or Unsubscribe

Subscribe or Unsubscribe

Log In

Log In

Get Password

Get Password

Subject:

Re: Can't find driving effects for PPI analysis

From:

"MCLAREN, Donald" <[log in to unmask]>

Reply-To:

MCLAREN, Donald

Date:

Fri, 25 Feb 2011 10:13:26 -0500

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (144 lines)

As you can see the *PSY* negative columns do line up well together,
but the positives do not. Samething with the PPI, where you have the
control condition, the curves line up. Now, if we think about the
concept of the the general linear model (Y=BX)...

Trying to fit the all-control vector to the data is much different
than fitting the condition1-control vector. I think this reiterates
the point I'm making in a paper I'm about to submit on PPI. That is
PPI as it is currently implemented fits these vectors above, rather
than fitting a vector for condition1, condition2, .., and control. In
splitting them up, one is then estimating the relationship of each
component, rather than the joint relationship. My hunch is that if you
were to separate them conditions, you would either eliminate the
average effect OR more likely find out which one is driving the
effect. When you only model 2 conditions, there is a chance that you
attribute the variance of the data to the wrong factor or it ends up
in the error term. Also, when you only model 2 conditions, you are
only modelling the activitation effect of those 2 conditions.
Modelling has shown that the individual model fit is improved when you
separate the conditions.

I'm adding some comments to the code for splitting the vectors and
have termed the approach "a generalizable form of PPI (gPPI)" and
hopefully can provide you with the code later today.

Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General
Hospital and Harvard Medical School
Office: (773) 406-2464
=====================
This e-mail contains CONFIDENTIAL INFORMATION which may contain
PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED
and which is intended only for the use of the individual or entity
named above. If the reader of the e-mail is not the intended recipient
or the employee or agent responsible for delivering it to the intended
recipient, you are hereby notified that you are in possession of
confidential and privileged information. Any unauthorized use,
disclosure, copying or the taking of any action in reliance on the
contents of this information is strictly prohibited and may be
unlawful. If you have received this e-mail unintentionally, please
immediately notify the sender via telephone at (773) 406-2464 or
email.



On Thu, Feb 24, 2011 at 6:50 PM, J S Lee <[log in to unmask]> wrote:
> Hi Donald,
> Thank you for your reply!
> I am using SPM8.
> I looked at the SPM.xX.X values for each subject. The values in the SPM.xX.X
> *PSY* columns for each of my models line up perfectly across subjects. For
> the different models within a subject, there also seems to be correspondence
> for the PSY negative values (although there are slight baseline shifts
> between different models). The values in the PPI column, however, do not
> correspond so well. I don't think I would have expected the PPI values to
> correspond across subjects, however, because the VOI values differ from
> subject to subject, so the PPI should also differ as it represents an
> interaction? The PPI values for different models in the same subject also do
> not show perfect correspondence (png attached: It shows 2 subjects' SPM.xX.X
> PPI values over the first 80 scans. The All conds - control model and Cond 1
> - control PPI values are plotted for each subject. I didn't include the Cond
> 2 - control, Cond 3 - control, etc. for clarity).
>
> All models are coming from the same VOI, so I think the adjustment has to be
> the same for all models (all extractions were adjusted for an F contrast of
> the effects of interest at the first-level model). Is this what you meant?
> The voxels are also identical (again, coming from the same VOI I think they
> have to be?).
>
>
> Thank you very much for taking the time to consider my question--even
> looking at the PSY and PPI columns has been useful!
>
> Jamie
>
>>
>>Are you using SPM8? The issue of summing was fixed in one of the later
>>releases of SPM5, so if you have an older version, that could explain
>>some of the issue. You could check to make sure that negative aspects
>>of the SPM.xX.X for the PPI term are the same for all subjects. You
>>could plot them.
>>Are you using the same adjustment for all models?
>>Are you using exactly the same voxels for all models?
>
>
>>
>>On Tue, Feb 22, 2011 at 6:21 PM, J S Lee <[log in to unmask]> wrote:
>>> Dear list,
>>>
>>> I conducted a PPI analysis in an experiment with 6 conditions. To
>>> replicate
>>> a previous study's PPI analysis, I was interested in connectivity
>>> differences between 5 of the conditions compared to the control (6th)
>>> condition, so extracted my VOI (using an all effects of interest
>>> contrast),
>>> then created a PPI model with a [1 1 1 1 1 -1] weighting for the
>>> psychological context regressor. I get a reasonable replication of the
>>> same
>>> PPI effects from the previous study, so the results are sensible.
>>>
>>> However, in that previous study, there were not enough trials of each of
>>> the
>>> 5 conditions to realistically analyze them separately, which is why I
>>> collapsed across them. In this study, there are many more trials, so I
>>> was
>>> hoping to look at which of the 5 conditions were driving the original PPI
>>> results. I was given hope when the initial PPI replicated in this new
>>> study.
>>> However, when I create separate PPI models for each condition versus
>>> control
>>> (e.g., context regressors using [1 0 0 0 0 -1] for model 1, [0 1 0 0 0
>>> -1]
>>> for model 2, etc.), NONE of these analyses show the same pattern as the 1
>>> 1
>>> 1 1 1 -1 model does. Mostly there are no significant (or anywhere near
>>> significant) results, and those random speckles that do show up at low
>>> threshold are not in the same places.
>>>
>>> Is it theoretically possible that 5 conditions vs 1 other can produce a
>>> PPI,
>>> but that none of those conditions singly vs the 1 other can do that? Or
>>> must
>>> there be an error? I have checked the microtime onset files to make the
>>> context is specified correctly, and made sure everything matches up in
>>> terms
>>> of specifying the conditions. Everything about the models looks fine to
>>> me.
>>> I know the 5 conditions vs 1 is a bit unbalanced, but it replicates the
>>> previous study (in which the 5 vs 1 were equal in terms of number of
>>> trials), and I understand that when creating the context variable one
>>> does
>>> NOT sum the vector to zero the way one would in defining a contrast for a
>>> regional activation analysis.
>>>
>>> Many thanks in advance for any thoughts,
>>> Jamie Lee
>
> _______________________________________________________________
> Get the Free email that has everyone talking at http://www.mail2world.com
> Unlimited Email Storage POP3 Calendar SMS Translator Much More!

Top of Message | Previous Page | Permalink

JiscMail Tools


RSS Feeds and Sharing


Advanced Options


Archives

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

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