Hi,
Ok, thanks. I think the problem is that I'm not totally clear on what the
practical implications of this are. I got the warning at 5 voxels for the
cope I ran... at each voxel, a different subject (or 2) seems to be the
outlier, some of the voxels are in areas I'm interested in. Is this
something that I would throw away subjects based on? or, would that only
happen if there were a certain number of voxels at which the warning
appears? If I flip through the var_filtered_func_data and see people who
look different to my eye, is there some way of determining if they are
enough of an 'outlier' to eliminate?
And, if it is not something to eliminate people on, does it mean I
should be wary of interpreting the results around the locations of those
voxels? Does the warning indicate that flame was unable to do stats
properly, or just that I should be aware that there are a few places with a
lot of variance? Sorry for all the questions, I'm just not completely sure
how to proceed from here.
thanks,
Katie
___________________________________
Katie Karlsgodt
Dept of Psychology/Cognitive Neuroscience
University of California, Los Angeles
[log in to unmask]
phone: (310) 794-9673
fax: (310) 794-9740
> From: Steve Smith <[log in to unmask]>
> Reply-To: FSL - FMRIB's Software Library <[log in to unmask]>
> Date: Sat, 28 Oct 2006 08:02:43 +0100
> To: <[log in to unmask]>
> Subject: Re: [FSL] Flame stage 1+2 error
>
> Hi - the log output will also tell you _where_ those voxels are - so
> you can open a new FSLView, load in the filtered_func_data and
> var_filtered_func_data, and go to this voxel, and turn on the
> timeseries display for both 4D datasets - then this may show you
> where and who are the problems.
>
> Hope this helps?
>
> Cheers, Steve.
>
>
> On 27 Oct 2006, at 20:05, Katie Karlsgodt wrote:
>
>> Hello,
>> I'm running a group of 31 subjects (2 groups of 14 and 17) in
>> Flame and
>> am getting the :"WARNING: FLAME stage 2 has given and abnormally large
>> difference to stage 1" error at 5 different places in the analysis.
>> I see
>> from the responses in the archives that this may be due to
>> variance/outliers:
>>
>> "this normally means that you have strong outliers in the data,
>> particularly as your number of inputs is not small. You should look
>> at the 4D cope and varcope files input to the higher-level analysis
>> and flick through the timepoints to see if you have any strong
>> outliers - that may resolve this."
>>
>> I've flipped through, and I do seem to have some people who look
>> different,
>> I'm just not sure how to actually define an outlier in this case?
>> There was
>> one subject in particular who was really much different, so I
>> removed him
>> and reran it, but still got the error. Do you have a recommended
>> way for
>> checking these images? I'm just not sure what I'm looking for.
>>
>> thanks,
>> Katie
>> ___________________________________
>> Katie Karlsgodt
>> Dept of Psychology/Cognitive Neuroscience
>> University of California, Los Angeles
>>
>> [log in to unmask]
>> phone: (310) 794-9673
>> fax: (310) 794-9740
>
>
> ------------------------------------------------------------------------
> ---
> Stephen M. Smith, Professor of Biomedical Engineering
> Associate Director, Oxford University FMRIB Centre
>
> FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK
> +44 (0) 1865 222726 (fax 222717)
> [log in to unmask] http://www.fmrib.ox.ac.uk/~steve
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>
>
>
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