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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