Hi Drew,

I'd apply this bias field correction first, on the raw fMRI data.

Best,
Guillaume.


On 13/03/14 16:13, Sevel,Landrew S wrote:
> Hi Guillaume,
> 
> I can give this a try. I have the bias field saved from that step. Should
> this be applied before the functional images are normalized or would it be
> possible to normalize the bias field and then apply before modeling?
> 
> Thanks
> 
> -Drew
> 
> On 3/13/14 12:07 PM, "Guillaume Flandin" <[log in to unmask]> wrote:
> 
>> Dear Drew,
>>
>> what about using New Segment (Segment in SPM12) to estimate and save the
>> bias field from one of your fMRI images and then apply it to all of your
>> images (eg with i1.*i2 in ImCalc)?
>>
>> All the best,
>> Guillaume.
>>
>>
>> On 13/03/14 15:35, Chris Watson wrote:
>>> I don't see how % signal change would "deal with" inhomogeneity.
>>> What does an example image look like?
>>>
>>> On 03/13/2014 11:02 AM, Sevel,Landrew S wrote:
>>>> I wanted to follow up on this briefly. In terms of the subjects with
>>>> data
>>>> removed from the first level mask image, this seems to be due to
>>>> inhomogeneity in almost every case.
>>>>
>>>> I've also noticed that using SPM's brainmask image as an explicit mask
>>>> and
>>>> setting the mask threshold to -inf seems to result in hugely intense
>>>> voxels (F=6000, in one case) in the cerebellum that must be error.
>>>>
>>>> It was suggested, and I noticed previous mention of converting images
>>>> to
>>>> percent signal change to deal with inhomogeneity. Is this a sensible
>>>> alternative? If so, when in processing should this be done and what are
>>>> acceptable calculations to do so?
>>>>
>>>> Many thanks,
>>>>
>>>> Drew
>>>>
>>>> On 3/11/14 2:33 PM, "H. Nebl" <[log in to unmask]> wrote:
>>>>
>>>>> The preprocessing sounds alright (but make sure you used correct
>>>>> parameters during slice timing). You could slightly increase the
>>>>> smoothing kernel, for example 8 mm, but I don't think this will have
>>>>> much
>>>>> influence. See below for some ideas:
>>>>>
>>>>> Some issues related to motion:
>>>>> ------------------------------------------
>>>>> - Slice timing will result in spreading of artefacts from volumes with
>>>>> large motion into adjacent, originally unaffected volumes to some
>>>>> extent.
>>>>> Make sure the correction with ART toolbox accounts for that aspect
>>>>> (e.g.
>>>>> correcting/replacing/deweighting the adjacent volumes as well if
>>>>> necessary)
>>>>> - As far as I remember the default threshold in ART toolbox is pretty
>>>>> liberal. I would look at the data again and check how many volumes
>>>>> show
>>>>> "fast head motion" defined as say >0.5 mm/TR.
>>>>> - If a larger portion of the volumes is affected exclude the entire
>>>>> run.
>>>>> What about the beta/contrast estimates based on the other two runs
>>>>> then?
>>>>> - Are there any correlations between stimulus presentation and motion?
>>>>> Some subjects might move every time a stimulus is delivered.
>>>>>
>>>>> Some issues related to design matrix:
>>>>> -------------------------------------------------
>>>>> - Make sure you use an appropriate high-pass filter
>>>>> - Make sure the derivates of condition A don't correlate with
>>>>> regressor
>>>>> for condition B (this might be the case in a fast-event related
>>>>> design)
>>>>> - You might want to turn to FIR models (as suggested by Chris,
>>>>> canonical
>>>>> HRF might not be appropriate)
>>>>>
>>>>> About the mask:
>>>>> ----------------------
>>>>> Find out why some of the voxels were missing in the second-level mask.
>>>>> Independent of the issue with your coil, these regions might not have
>>>>> been covered at all in some of the subjects or there might be large
>>>>> susceptibility aretefacts (amygdala, OFC). If these voxels have a low
>>>>> intensity they are excluded from individual first-level masks
>>>>> automatically. Adjusting the masking threshold won't necessarily help,
>>>>> the voxels become part of the analysis but you might well obtain beta
>>>>> estimates close to zero, as there is still hardly any signal.
>>>>>
>>>>> About the between-session variability:
>>>>> --------------------------------------------------
>>>>> - Is it related to motion or signal loss in some of the session (see
>>>>> above)? One "bad" session might destroy your effects.
>>>>> - Do the behavioral scores vary as well?
>>>
>>
>> -- 
>> Guillaume Flandin, PhD
>> Wellcome Trust Centre for Neuroimaging
>> University College London
>> 12 Queen Square
>> London WC1N 3BG
> 

-- 
Guillaume Flandin, PhD
Wellcome Trust Centre for Neuroimaging
University College London
12 Queen Square
London WC1N 3BG