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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