Thanks, I'll try this.
Could these problems be related to the clean up function in segment or is
that only applied to the different tissue class maps?
-Drew
On 3/13/14 12:23 PM, "Guillaume Flandin" <[log in to unmask]> wrote:
>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
|