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SUMMARY:A wavelet method for modelling and despiking motion artifacts in f
 MRI time series - Ameera Patel\, Department of Psychiatry\, University of 
 Cambridge
DTSTART:20130827T100000Z
DTEND:20130827T110000Z
UID:TALK46493@talks.cam.ac.uk
CONTACT:Mikail Rubinov
DESCRIPTION:The impact of in-scanner head movement on functional magnetic 
 resonance imaging (fMRI) signal has long been established as undesirable. 
 These effects have been traditionally corrected by methods such as linear 
 regression of head movement parameters. However\, a number of recent indep
 endent studies have demonstrated that these techniques are insufficient to
  remove motion confounds\, and that even small movements can spuriously in
 crease estimates of short-range connectivity. Here I propose a new data-dr
 iven\, spatially-adaptive\, wavelet-based method for identifying\, modelli
 ng\, and removing non-stationary events in fMRI time series\, caused by he
 ad movement\, without the need for data scrubbing. This method involves th
 e addition of just one extra step\, the Wavelet Despike\, in standard imag
 e processing pipelines. With this method\, I demonstrate robust removal of
  a range of different motion artifacts and motion-related biases including
  distance-dependent connectivity artifacts\, at a group and single-subject
  level\, using a range of previously published\, and new diagnostic measur
 es. The Wavelet Despike is able to accommodate the substantial spatial and
  temporal heterogeneity of motion artifacts\, and can consequently remove 
 a range of linear and non-linear\, high and low frequency artifacts from f
 MRI time series. In conclusion\, there is a real risk of motion-related bi
 as in connectivity analysis of fMRI data\, but this risk is generally mana
 geable\, by effective pre-processing strategies designed to attenuate sync
 hronized signal transients induced by ``spiky'' head movements.
LOCATION:BCNI seminar room\, Sir William Hardy Building\, Downing Site
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