statistic problem

I am trying to extract statistical information from multiple regions 
from a random effects model I created.  The model was created from 
subtracting con files which resulted in img files.  How can I extract 
the statistical information without the files being contrasts?

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Stat Problem

I am trying to extract statistical information from multiple regions 
from a random effects model I created.  The model was created from 
subtracting con files which resulted in img files.  Normally to get stat 
info I have to use con files already in the model.  How can i extract 
regions of interest from the model and get the information without them 
being con files?

Holly Greenwald

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comparison of 2 ROIs

We're interested in the following analysis:

(SUB - SUPRA) - (ROI_1 - ROI_2)

SUB = subliminal duration stimuli
SUPRA = supraliminal duration stimuli
ROI_1 = amygdala
ROI_2 = pre-frontal cortex

Creating the first half of the equation is straightforward in the SPM contrast manager, but how do we do a
subtraction of two ROIs? Surely volume alone is an issue.

Many thanks for your suggestions!

Carolyn

Carolyn L. Fort
Research Specialist, Sr.
Department of Psychiatry
University of Arizona
PO Box 245002
Tucson, AZ 85724-5002
v: 520-626-8568
f: 520-626-6050

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Re: comparison of 2 ROIs

Hi,

> (SUB - SUPRA) - (ROI_1 - ROI_2)
>
> SUB = subliminal duration stimuli
> SUPRA = supraliminal duration stimuli
> ROI_1 = amygdala
> ROI_2 = pre-frontal cortex
>
> Creating the first half of the equation is straightforward in the SPM contrast manager, but how do we do a
subtraction of two ROIs? Surely volume alone is an issue.

Do you mean:

(SUB-SUPRA) for ROI1 - (SUB-SUPRA) for ROI2?

I think you'd have to do that outside marsbar, by generating sub-supra
values for each subject, and each ROI, and entering them into a
repeated measures analysis...

Best,

Matthew

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fitted event responses

Hi,

I am trying to create fitted event responses using the following code: 

--------------------------------------------------------------------
% extract data
Y = get_marsy(roi, D, 'mean');

% MarsBaR estimation
E = estimate(D, Y);

% create fitted time courses
[this_tc dt] = event_fitted(E, event_spec, event_duration);
--------------------------------------------------------------------

This works great. However, when I extract raw time-courses with MarsBar and
average the raw response across events and subjects, the raw and fitted
waveforms seem to differ by quite a bit. 

I was wondering if it would it be possible for me to access/see the "raw"
waveform/timeseries that is being fitted (using the event_fitted function)?
I tried the following: 

------------------------
K>> SPM=des_struct(E) 

SPM = 

         nscan: [289 262]

Re: fitted event responses

Hi,

> This works great. However, when I extract raw time-courses with MarsBar and
> average the raw response across events and subjects, the raw and fitted
> waveforms seem to differ by quite a bit.

Sorry, I am not quite sure what you mean.  If the events overlap, then
the SPM model will attempt to estimate the effect of this overlap and
adjust for it, in which case you'd expect some difference between the
fitted events and the raw averaging.  Also, if you model for the event
(such as HRF) is different from the actual response, SPM can't pick
this up and the averaging and fitted responses will look different.

Is that what you meant?

Matthew

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Re: error with maroi

Hi,

Very sorry to be so slow to get back to this.

> ---------------------------------------
> ??? Undefined function or method 'maroi' for input arguments of type 'char'.
>
> Error in ==> beta_script at 23
> roi_array{1} = maroi(Kug_ROI_1);
> ----------------------------------------------

I've run into problems like this on matlab 7.1 - there seems to be a
strange and nasty bug in matlab to do with finding the right paths for
objects.  In the end, I found that doing this:

marsbar on
maroi:

would make it behave as expected.  Does that work for you?

Thanks a lot,

Matthew

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Re: fitted event responses

Sorry for the confusion. Here are some clarifications: 
a) The events are highly non-overlapping (separated by ~60s), so I don't
think that is a problem
b) Again, I am fitting the response with a Fourier model (6 basis
functions). So this makes no apriori assumptions about the shape of the HRF.

I think the problem is that I am using the 'Scaling' option (global
normalization), which creates a different fitted time-course compared to the
raw-event averaged BOLD response. I was wondering where I could find the
"scaled" time-course in the marsbar design object, D.

Also, I was wondering if the ROI time-series that MarsBar uses to fit the
model would be accessible from anywhere within the marsBar design object D
(this is the time-course that should be available inside D to create the fit
when the event_fitted function is called). That way I could just sweep all
adjustments SPM makes to the raw timecourse under the rug!

Many thanks,
Sridhar

Matthew Brett wrote:
> 
> Hi,
> 
>> This works great. However, when I extract raw time-courses with MarsBar
>> and
>> average the raw response across events and subjects, the raw and fitted
>> waveforms seem to differ by quite a bit.
> 

problem with marsbar

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event-related raw data time series

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