Dear Chris,
> Brilliant, thank you Dr Rowe, that's really helpful.
>
> I take it then that decisions about how many/which volumes to model
> uniquely would simply be made on a participant-by-participant basis,
> where you'd exclude participants with too much movement and
> arbitrarily select a cutoff for the others... it's funny that I can't
> find a mention of standards people typically use.
>
My recommendation is to apply the same threshold to every subject in a
given study. Although arbitrary, it is consistent throughout the group
and not biased by inspection of individual data or SPMs, although it
does mean that the number of 'bad volumes' may differ slightly from
person to person. If spikes are the problem, the threshold for tsdiffana
outputs can be anywhere within quite a wide range, because the variance
due to a spike is very large compared to any 'physiological' variance.
For movement artefacts, it is more complex, especially if you have two
groups, one of which moves more than the other eg children or patients
with PD. You have a choice: do you prefer a systematic differences
between groups in terms of 1st level model DMs, or a systematic
difference between groups in terms of unmodeled (error) variance
resulting from more movement? Others may comment on this. For single
group studies on healthy young adults, I carry the same threshold over
from study to study to prevent cycling between preprocessing steps and
statistical review (the absolute value will depend on your scanner set
up) .
As Volkmar emphasised, one should also keep working on whether design,
experiment setup and subject instructions can be improved to reduce
these problems in raw data, rather than rely too much on post-processing
or modeling solutions.
Best wishes,
James
> With thanks
>
> Christopher
>
>
> On 17/12/2008, at 4:36 AM, James Rowe wrote:
>
>> Dear Chris and Volkmar,
>>
>>> the simple answer is: you should use ArtifactRepair/TSDiffAna/some
>>> other
>>> tool to assess the quality of your data, but you should never
>>> attempt to
>>> "repair" your time series by removing/replacing slices/scans.
>> I broadly agree, but there is another approach that may help. If you
>> have an objective criterion (predefined), applicable to the tsdiffana
>> output, for an artefact (eg spike) then this can be used to create a
>> nuisance regressor identifying an affected volume, effectively
>> removing that volume from estimation of the parameters for
>> experimental effects. This is not removing or repairing data, but
>> accommodating the artefact within the GLM.
>>
>> This is similar to what happens when including movement parameters
>> and their temporal derivatives: a sudden large displacement gives a 1
>> or 2 volume 'peak' in the rp' regressor, against a low value for
>> other volumes. However, with an objective threshold, this can be
>> binarised, to 'ok scans = 0' and 'this bad volume =1'. If the
>> threshold is too low, you will have too many nuisance regressors (one
>> for each bad volume) burning up degrees of freedom and adversely
>> affecting the normal distribution of residuals. However, a handful of
>> such regressors for a long study would seem a reasonable way to
>> remove the effects artefacts like occasional spikes.
>>
>> Best wishes,
>>
>> James
>>
>>
>>> If there
>>> are technical reasons for bad image quality, you should improve your
>>> setup and scan again. If there is head movement in some subject that
>>> severely distorts your data, you should scan another subject. If head
>>> movement is due to your experimental design, you should check whether
>>> the design, experiment setup and subject instructions can be improved.
>>>
>>> The only acceptable reason for "repairing" bad data would be that your
>>> individual subject has very special abilities that can not be found in
>>> any other subject. There is a tutorial for ArtifactRepair at
>>>
>>> http://cibsr.stanford.edu/tools/ArtRepair/ArtRepair.htm
>>>
>>> which discusses the available options and their pros and cons.
>>>
>>> Volkmar
>>>
>>>
>>> Am Mittwoch, den 17.12.2008, 01:22 +0000 schrieb Christopher Benjamin:
>>>
>>>> Hi,
>>>>
>>>> I didn't receive a response to this post; apologies for re-posting
>>>> if this is quite basic but I'm having trouble finding a practical
>>>> discussion of these issues. Any help would be invaluable,
>>>>
>>>> With thanks,
>>>>
>>>> Chris
>>>>
>>>> ---------------------------
>>>>
>>>> Hi Spmmers,
>>>>
>>>> I'm removing artefact from event-related fMRI data using Sue
>>>> Whitfield Gabrieli's automated artifact detection toolbox
>>>> (http://web.mit.edu/swg/software.htm).
>>>>
>>>> I'm wondering if people are using any standard criteria for removal
>>>> of images in terms of -
>>>>
>>>> 1. Image signal variation (i.e., deviation of signal from the
>>>> series mean);
>>>> 2. Participant movement, and
>>>> 3. Participant rotation.
>>>>
>>>> In terms of criteria I've seen, 1. often seems to be used to
>>>> exclude images that deviate from the mean by more than two or three
>>>> standard deviations; (which I think makes sense);
>>>> 2. to exclude images in which movement beyond .5 or 1mm occurred,
>>>> and for
>>>> 3. exclusion of iamges where rotation of more than .05 radians
>>>> occurred.
>>>>
>>>> I realise there are unlikely to be rigid criteria, especially given
>>>> the limitations inherent in scanning some populations, but I'd
>>>> greatly appreciate anyone point me to a stand-out
>>>> reference/discussion...
>>>>
>>>> With thanks
>>>>
>>>> Christopher
>>>>
>>>>
>>
>>
>> --
>>
>> --------------------------------------
>> Dr James Rowe
>> Senior Clinical Research Associate and
>> Consultant Neurologist, Cambridge University Department of Clinical
>> Neurosciences,
>> Box 83, R3 Neurosciences,
>> Addenbrooke's Hospital, Cambridge, CB2 2QQ
>> UK
>>
>> Tel: +44 (0)1223 273630
>
--
--------------------------------------
Dr James Rowe
Senior Clinical Research Associate and
Consultant Neurologist,
Cambridge University Department of Clinical Neurosciences,
Box 83, R3 Neurosciences,
Addenbrooke's Hospital,
Cambridge,
CB2 2QQ
UK
Tel: +44 (0)1223 273630
|
