sorry, you lost me there ...
On 8/31/2022 12:39 PM, Angela Fang wrote:
External Email - Use Caution
That makes sense! And if we wanted to identify brain regions
associated with valence (across self/non-self) that is unique from
condition 2, can we add a 5^th condition? Maybe we can code self as 2
and non-self as 1 so that condition 2 (self*valence) would be
different from condition 5 (valence ratings only)?
Incredibly helpful, Doug- thank you so much!
*From: *<freesurfer-boun...@nmr.mgh.harvard.edu> on behalf of "Douglas
N. Greve" <dgr...@mgh.harvard.edu>
*Reply-To: *Freesurfer support list <freesurfer@nmr.mgh.harvard.edu>
*Date: *Tuesday, August 30, 2022 at 7:38 AM
*To: *"freesurfer@nmr.mgh.harvard.edu" <freesurfer@nmr.mgh.harvard.edu>
*Subject: *Re: [Freesurfer] FSFAST first level covariates
Oh, I think I see now. In realty, each trial is either self or not
self and then you have a separate valence measure, so your coding
should be
1. Self-offset
2. SelfValence
3. NoSelf-offset
4. NoSelfValence
Where 1+2 are used for a self event and 3+4 are used for a noself
event. If you want to test for the difference between self and noself
valence slope, then you would just use -a 2 -c 4
On 8/29/2022 12:08 PM, Angela Fang wrote:
* External Email - Use Caution *
Great, thanks so much Doug- this setup makes sense to me. However,
isn’t it a problem that the brain regions associated with the
contrast for -a 3 and the contrast for -a 4 would be the same for
trials that subjects rate as 1 (self-relevant) vs 0 (not
self-relevant)? Could we code it as 2 (self-relevant) and 1 (not
self-relevant)?
Many thanks!!
*From: *<freesurfer-boun...@nmr.mgh.harvard.edu>
<mailto:freesurfer-boun...@nmr.mgh.harvard.edu> on behalf of
"Douglas N. Greve" <dgr...@mgh.harvard.edu>
<mailto:dgr...@mgh.harvard.edu>
*Reply-To: *Freesurfer support list
<freesurfer@nmr.mgh.harvard.edu>
<mailto:freesurfer@nmr.mgh.harvard.edu>
*Date: *Sunday, August 28, 2022 at 1:07 PM
*To: *"freesurfer@nmr.mgh.harvard.edu"
<mailto:freesurfer@nmr.mgh.harvard.edu>
<freesurfer@nmr.mgh.harvard.edu>
<mailto:freesurfer@nmr.mgh.harvard.edu>
*Subject: *Re: [Freesurfer] FSFAST first level covariates
I was under the impression that Self and Valence were ratings from
the same event (in that mail archive, they were different events
and so needed different offsets). If Self and Valence are from the
same event, then you would have something like
1. Offset
2. Self
3. Valence
4. Self*Valence
I've never tried the interaction (self*valence). You might have to
demean before computing the product
On 8/14/2022 4:58 PM, Angela Fang wrote:
* External Email - Use Caution *
The two coding schemes are different because the second one
does include the self*valence variable you’re talking about,
whereas the first one doesn’t. I only included the 2^nd offset
because you suggested to someone else to include it (see
*MailScanner has detected a possible fraud attempt from
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If we don’t need it, would it just be 2 conditions, as follows?
1. SelfOffset
2. Self*ValenceSlope
But then I’m not clear how to get the main effect of valence
(brain regions that scale with increasing emotion valence,
while holding self-relevance constant)?
*From: *<freesurfer-boun...@nmr.mgh.harvard.edu>
<mailto:freesurfer-boun...@nmr.mgh.harvard.edu> on behalf of
"Douglas N. Greve" <dgr...@mgh.harvard.edu>
<mailto:dgr...@mgh.harvard.edu>
*Reply-To: *Freesurfer support list
<freesurfer@nmr.mgh.harvard.edu>
<mailto:freesurfer@nmr.mgh.harvard.edu>
*Date: *Sunday, August 14, 2022 at 1:37 PM
*To: *"freesurfer@nmr.mgh.harvard.edu"
<mailto:freesurfer@nmr.mgh.harvard.edu>
<freesurfer@nmr.mgh.harvard.edu>
<mailto:freesurfer@nmr.mgh.harvard.edu>
*Subject: *Re: [Freesurfer] FSFAST first level covariates
Those look like they are the same coding scheme. What is
different? You can only have one offset. The Self vs Valence
-a 2 -a 4 is not testing for an interaction. If you want an
interaction you have to create a new variable which is
SelfRating*ValenceRating.
On 8/10/2022 2:38 PM, Angela Fang wrote:
* External Email - Use Caution *
Hello,
Just re-sending my question below. If I have a variable
with 2 levels (yes/no) and another variable that is
continuous, based on this post (*MailScanner has detected
a possible fraud attempt from "secure-web.cisco.com"
claiming to be*
https://www.mail-archive.com/freesurfer@nmr.mgh.harvard.edu/msg19957.html
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it sounds like I should code as follows:
1. SelfOffset
2. Self-ValenceSlope (would the weight in the 4^th column
reflect the value of self multiplied by the value of
valence for this participant?)
3. NonSelfOffset
4. NonSelf-ValenceSlope
If the other way of modifying the paradigm file is also
acceptable to test the interaction (as I describe below),
that would also be helpful to know.
Thanks!
Angela
*From: *<freesurfer-boun...@nmr.mgh.harvard.edu>
<mailto:freesurfer-boun...@nmr.mgh.harvard.edu> on behalf
of Angela Fang <angf...@uw.edu> <mailto:angf...@uw.edu>
*Reply-To: *Freesurfer support list
<freesurfer@nmr.mgh.harvard.edu>
<mailto:freesurfer@nmr.mgh.harvard.edu>
*Date: *Monday, August 1, 2022 at 4:35 PM
*To: *Freesurfer support list
<freesurfer@nmr.mgh.harvard.edu>
<mailto:freesurfer@nmr.mgh.harvard.edu>
*Subject: *Re: [Freesurfer] FSFAST first level covariates
* External Email - Use Caution *
Hi Doug,
Nevermind to my first question! I read this post
(*MailScanner has detected a possible fraud attempt from
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https://www.mail-archive.com/freesurfer@nmr.mgh.harvard.edu/msg32235.html
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and realized that we always include a subject-specific par
file in each run for first-level analyses.
However, I’m still confused about how to modify my
paradigm file. I also need to model the trials of
non-interest, so would it be as follows?
0 1 2.5 1.0 SelfOffset
0 2 2.5 1.0 SelfSlope
(equal to subject’s rating of self-relevance)
0 3 2.5 1.0 ValenceOffset
0 4 2.5 3.0 ValenceSlope (equal to
subject’s rating of valence)
2.5 0 2.5 1.0 FIXATION
5.0 1 2.5 1.0 SelfOffset
5.0 2 2.5 0 SelfSlope
(equal to subject’s rating of self-relevance, in this case
subject responded 0, or non-relevant)
5.0 3 2.5 1.0 ValenceOffset
5.0 4 2.5 2.0 ValenceSlope
(equal to subject’s rating of valence)
7.5 5 2.5 1.0 OTHER
Do these contrasts look correct to you?
Self vs Fixation -a 1 -c 0 (main effect of self)
Valence vs Fixation -a 3 -c 0 (main effect of valence)
Self vs Valence -a 2 -a 4 (interaction between self x valence)
Thank you so much for your help!
Angela
*From: *<freesurfer-boun...@nmr.mgh.harvard.edu>
<mailto:freesurfer-boun...@nmr.mgh.harvard.edu> on behalf
of Angela Fang <angf...@uw.edu> <mailto:angf...@uw.edu>
*Reply-To: *Freesurfer support list
<freesurfer@nmr.mgh.harvard.edu>
<mailto:freesurfer@nmr.mgh.harvard.edu>
*Date: *Thursday, July 28, 2022 at 1:02 PM
*To: *Freesurfer support list
<freesurfer@nmr.mgh.harvard.edu>
<mailto:freesurfer@nmr.mgh.harvard.edu>
*Subject: *Re: [Freesurfer] FSFAST first level covariates
Thanks Doug. This wiki page is extremely helpful. However,
my question is about individual subject responses. I could
see how you could include a summary (e.g., average) value
of the parametric variable across subjects in your
“weight” column but it’s not clear to me how you could
integrate individual subject responses to each word in the
parametric modulation paradigm file? I’m imagining
something like the FSGD file where a value is given for
each subject, but for first-level analysis.
We have a similar design as someone else who posted a
similar question (*MailScanner has detected a possible
fraud attempt from "secure-web.cisco.com" claiming to be*
https://www.mail-archive.com/freesurfer@nmr.mgh.harvard.edu/msg19957.html
<https://secure-web.cisco.com/11nFbIrJYBqRI1W_4wY-HvfdEF3GG6xLL8So8t0i9yKbcElVyl_nJoDI6XedAGY2kKd_eP-dnsWeccOw2qajd375GRCeiUjqaXv3C7vOkrGEOiSiqfcPQ9y73ROdtl0jJIGemdoYQDd3GcX-dKx6qDwBcPE_qNlqxB0ZTcsDfTwK88OkoVtftMo1zKBWSiZBV9p0GO2erUcSoXtVI-AITDr9jULRDzVL_IzxtPdtuSBrYXMASRi7ex2oKftjJjyG_HMgygf_ULhSYIsHviihCwfx4uO5_zrvh8H84AxAsv33zsFjOaYeZ826JkD3E99hxrAKW3jYr3PjfN-zNZjQLJA/https%3A%2F%2Fwww.mail-archive.com%2Ffreesurfer%40nmr.mgh.harvard.edu%2Fmsg19957.html>).
We have an event-related experiment presenting trait
adjectives in terms of whether they describe themselves
(SELF condition) or someone else (OTHER condition). We are
interested in testing a 2x2 ANOVA to examine an
interaction between self-relevance x emotional valence.
Assuming you can’t integrate individual subject responses
to each word in the paradigm file, would we set it up as
follows?
“Usual” paradigm file:
0 1 2.5 1.0 SELF
2.5 0 2.5 1.0 FIXATION
5.0 1 2.5 1.0 SELF
7.5 2 2.5 1.0 OTHER
Parametric modulation paradigm file:
0 1 2.5 1.0 SELFoffset
0 2 2.5 0.8 SELFslope
0 3 2.5 1.0 VALENCEoffset
0 4 2.5 2.0 VALENCEslope
(where 0.8 reflects the percentage of time the word was
endorsed as self-relevant and 2.0 is the average valence
rating given for that word)
And then create a contrast of 2 vs 4 to test the
interaction? Would testing contrast 1 vs 0 be a test of
the main effect of self-relevance and contrast 3 vs 0 the
main effect of valence?
Thanks so much for your help!
Angela
*From: *<freesurfer-boun...@nmr.mgh.harvard.edu>
<mailto:freesurfer-boun...@nmr.mgh.harvard.edu> on behalf
of "Douglas N. Greve" <dgr...@mgh.harvard.edu>
<mailto:dgr...@mgh.harvard.edu>
*Reply-To: *Freesurfer support list
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*Date: *Thursday, July 28, 2022 at 10:25 AM
*To: *"freesurfer@nmr.mgh.harvard.edu"
<mailto:freesurfer@nmr.mgh.harvard.edu>
<freesurfer@nmr.mgh.harvard.edu>
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*Subject: *Re: [Freesurfer] FSFAST first level covariates
Yes, see *MailScanner has detected a possible fraud
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https://surfer.nmr.mgh.harvard.edu/fswiki/FsFastParametricModulation
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On 7/25/2022 6:56 PM, Angela Fang wrote:
* External Email - Use Caution *
Hi Freesurfer community,
I have run participants through an event-related fMRI
task in which subjects rate whether trait adjectives
are descriptive of themselves or not, and afterwards
asked them to rate each trait word on emotional
valence. Is it possible to include these individual
level subjective ratings of emotional valence as
covariates in the first level contrast in FSFAST? If
so, how?
Thanks,
Angela
---
Angela Fang, Ph.D.
Assistant Professor
Department of Psychology
University of Washington
Lab website: *MailScanner has detected a possible
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Pronouns: she, her, hers
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