WHY HEAD MOTION SUCKS AND WHAT YOU MIGHT BE ABLE DO ABOUT IT

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WHY HEAD MOTION SUCKSAND WHAT YOU MIGHT BE
ABLE DO ABOUT IT
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Head Motion Main Artifacts

• Head motion can lead to spurious activations or
  can hinder the ability to find real activations.
• Severity of problem depends on correlation
  between motion and paradigm
• Head motion increases residuals, making
  statistical effects weaker.
• Regions move over time
• ROI analysis ROI may shift
• Voxelwise analyses averages activated and
  nonactivated voxels
• Motion of the head (or any other large mass)
  leads to changes to field map
• Spin history effects
• Voxel may move between excitation pulse and
  readout

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Motion ? Intensity Changes
A
B
C
Slide modified from Duke course
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Motion ? Spurious Activation at Edges
lateral motion in x direction
motion in z direction (e.g., padding sinks)
brain position
stat map
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Spurious Activation at Edges

• spurious activation is a problem for head motion
  during a run but not for motion between runs

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Motion ? Increased Residuals
?1



?2

fMRI Signal
Residuals
Design Matrix

Betas
x
what we CAN explain
what we CANNOT explain
how much of it we CAN explain


x
our data
Statistical significance is basically a ratio of
explained to unexplained variance
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Regions Shift Over Time

• A time course from a selected region will sample
  a different part of the brain over time if the
  head shifts
• For example, if we define a ROI in run 1 but the
  head moves between runs 1 and 2, our defined ROI
  is now sampling less of the area we wanted and
  more of adjacent space
• This is a problem for motion between runs as well
  as within runs

?
time1
time2
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Motion Correction Algorithms
pitch
roll
yaw
z translation
y translation
x translation

• Most algorithms assume a rigid body (i.e., that
  brain doesnt deform with movement)
• Align each volume of the brain to a target volume
  using six parameters three translations and
  three rotations
• Target volume the functional volume that is
  closest in time to the anatomical image

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BVQX Motion Correction Options
Analysis/fMRI 2D data preprocessing menu

• Motion correct .fmr file (2D) before any other
  preprocessing
• Why?
• Align each volume to the volume closest to the
  anatomical
• Why?

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Head Motion Good, Bad,
Slide from Duke course
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and catastrophically bad
Slide from Duke course
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Problems with Motion Correction

• lose information from top and bottom of image
• possible solution prospective motion correction
• calculate motion prior to volume collection and
  change slice plan accordingly

were missing data here
we have extra data here
Time 1
Time 2
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Why Motion Correction Can Be Suboptimal

• Parts of brain (top or bottom slices) may move
  out of scanned volume (with z-direction motion or
  rotations)
• Motion correction requires spatial interpolation,
  leads to blurring
• fast algorithms (trilinear interpolation) arent
  as good as slow ones (sinc interpolation)
• Motion correction

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Why Motion Correction Algorithms Can Fail

• Activation can be misinterpreted as motion
• particularly problematic for least squares
  algorithms (Friere Mangin, 2001)
• Field distortions associated with moving mass
  (including mass of the head) can be
  misinterpreted as motion

Spurious activation created by motion correction
in SPM (least squares)
Mutual information algorithm in SPM has fewer
problems
Friere Mangin, 2001
Simulated activation
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Mass Motion Artifacts

• motion of any mass in the magnetic field,
  including the head, is a problem

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Head Motion Field Map Artifacts
Phantom

• Bag of Saline on a Stick
• experimenter moves saline left and right every
  30 sec without touching subject or phantom

Data from Jody Culham
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A. Pre-corrected Statistical Map 1
B. Time Course 1
7
1.0
Left
Right
Left
Right
Left
.60
Signal Change
0
-.60
-4
-1.0
r value
C. Pre-corrected Statistical Map 2
D. Time Course 2
900
Signal Change
0
0
F. Motion Correction Parameters
E. Post-corrected Statistical Map 1
Data from Jody Culham
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Head Motion Solution to Susceptibility

• Solution
• one trial every 10 or 20 sec
• fMRI signal is delayed 5 sec
• distinguish true activity from artifacts
• IMPORTANT Subject must remain in constant
  configuration between trials

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Different motions different effects
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The Fridge Rule

• When it doubt, throw it out!

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Head Restraint
Vacuum Pack
Head Vise (more comfortable than it sounds!)
Bite Bar
Thermoplastic mask
Often a whack of foam padding works as well as
anything
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Prevention is the Best Remedy

• Tell your subjects how to be good subjects
• Dont move is too vague
• Make sure the subject is comfy going in
• avoid princess and the pea phenomenon
• Emphasize importance of not moving at all during
  beeping
• do not change posture
• if possible, do not swallow
• do not change mouth position
• do not tense up at start of scan
• Discourage any movements that would displace the
  head between scans
• Do not use compressible head support
• For a summary of info to give first-time
  subjects, see
• http//defiant.ssc.uwo.ca/Jody_web/Subject_Info/fi
  rsttime_subjects.htm

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Upcoming Papers in fMRI Journal Club

• Field, A. S., Yen, Y. F., Burdette, J. H.,
  Elster, A. D. (2000). False cerebral activation
  on BOLD functional MR images study of
  low-amplitude motion weakly correlated to
  stimulus. AJNR Am J Neuroradiol, 21(8),
  1388-1396.
• Oakes, T. R., Johnstone, T., Ores Walsh, K. S.,
  Greischar, L. L., Alexander, A. L., Fox, A. S.,
  et al. (2005). Comparison of fMRI motion
  correction software tools. Neuroimage, 28(3),
  529-543.
• Johnstone, T., Ores Walsh, K. S., Greischar, L.
  L., Alexander, A. L., Fox, A. S., Davidson, R.
  J., et al. (2006). Motion correction and the use
  of motion covariates in multiple-subject fMRI
  analysis. Hum Brain Mapp, 27(10), 779-788.

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What Were Working On

• Rob, Jason, Simon, Teresa, Erik, Philippe, and
  Jody are working on testing the efficacy of
  different approaches on different types of data
  from our magnet
• Types of Data
• phantom with mass motion artifacts
• correlated head motion
• sinking head motion
• Types of Solutions
• BV motion correction
• AFNI motion correction
• inclusion of covariates
• Ravis vessel suppression routines