Experimental Design for fMRI - humanbrainmapping.org · experimental design and assumptions about...
Transcript of Experimental Design for fMRI - humanbrainmapping.org · experimental design and assumptions about...
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Experimental Design for fMRI���OHBM Advanced fMRI Educational Course 2014
Thomas Liu UCSD Center for Func6onal MRI
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Experimental Design
Condition 1 Condition 2 Condition 3
Design 1
Design 2
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Why worry about design?
If your result needs a statistician then you should design a better experiment. --Baron Ernest Rutherford
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Which is the best design?
E
It depends on the experimental question.
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• Statistical Efficiency: maximize contrast of interest versus noise.
• Psychological factors: is the design too boring? Minimize anticipation, habituation, boredom, etc.
What to optimize?
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General Linear Model
y = Xh + Sb + n
Parameters of Interest
Design Matrix
Nuisance Parameters
Nuisance Matrix Data
Additive Gaussian
Noise
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Test Statistic
€
t ∝ parameter estimatevariance of parameter estimate
Thermal noise, physiological noise, low frequency drifts, motion
Stimulus, neural activity, field strength, vascular state
Also depends on Experimental Design!!!
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From Poldrack et al , 2012
Hemodynamic Response Func6on (HRF)
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Efficiency
€
Efficiency∝ 1Variance of Parameter Estimate€
t ∝ parameter estimatevariance of parameter estimate
Amplitude of the response Coefficients of the Hemodynamic Response Coefficients of Basis Func6ons
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Questions and Assumptions
Where is the activation? à Assume we know the shape of the HRF
but not its amplitude. à Or sometimes assume something about
the shape
What does the HRF look like? à Assume we know the shape of the HRF
but not its amplitude. à Or sometimes assume something about
the shape
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Image-based Example
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Image-based Example
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Image-based Example
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Fundamental Trade-off
Detection Power = Estimation Efficiency for overall amplitude
Shape Estimation Efficiency
Block Design Periodic
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Fundamental Trade-off
Detection Power
Estim
atio
n Ef
ficie
ncy
Periodic h1
Efficiency for overall amplitude∝ 1Variance h1( )
Efficiency
∝1
Var h1( )+Var h2( )+!+Var hN( )
Semi-Random
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Basis Functions
€
If we know something about the shape, we can use abasis function expansion : h = Bc
4 basis functions
5 random HDRs using basis functions
5 random HDRs w/o basis functions
Here if we assume basis functions, we only need to estimate 4 parameters as opposed to 20.
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Trade-off w/ basis functions Semi-Random
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f
A
σ1 >> σ2
v1 v2
σ1u1
σ2u2
Geometric View Spectral View (a) Assume total knowledge about HRF
f
A
σ1 > σ2
v1 v2 σ1u1
σ2u2
(b) Assume some knowledge about HRF
f
A
σ1 = σ2
v1 v2 σ1u1 σ2u2
(c) Assume no knowledge about HRF
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Wang et al MRM 2003
Performance as func6on of task frequency
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Arterial Spin Labeling
Wait Tag by Magne6c Inversion
Tag
Image 1
Control
Image 2
Tag by Magne6c Inversion
Tag
Image 3
Control
Image 4
Perfusion Images
-‐0.5 1 -‐0.5 +0.5 +0.5 -‐1
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Arterial Spin Labeling Block Design
Event-related Design
Tag Control Tag Control Tag Control
TCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTC
TCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTC
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Arterial Spin Labeling
Liu et al 2002; Aguirre et al
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Multi-echo BOLD ���and low-frquency drifts
Evans et al ISMRM 2014; p. 4218; See also Evans et al HBM 2014 Poster 2019
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Multiple Trial Types GLM
y = Xh + Sb + n
X = [X1 X2 … XQ] h = [h1
T h2
T … hQT]T
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Multiple Trial Types Overview Efficiency includes individual trials and also contrasts between trials.
€
Rtot =K
average variance of HRF amplitude estimates for all trial types and pairwise contrasts"
# $
%
& '
€
ξtot =1
average variance of HRF estimatesfor all trial types and pairwise contrasts#
$ %
&
' (
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Optimal Frequency Optimal frequency of occurrence depends on weighting of individual trials and contrasts. Example: With Q = 2 trial types, if only contrasts are of interest p = 0.5. If only trials are of interest, p = 0.2929. If both trials and contrasts are of interest p = 1/3.
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Psychological Considerations
Random
Problems with habituation, anticipation, and boredom
Semi-‐Random
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Entropy
H = Entropy = log2(number of possible outcomes)
2H = linear measure of randomness à proportional to efficiency
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Multiple Trial Types Trade-off
Detection Power
Effic
ienc
y
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Design As the number of trial types increases, it becomes more difficult to achieve the theoretical trade-offs. Random search becomes impractical and results in non-optimal designs. For unknown HDR, should use an m-sequence based design when possible. Designs based on block or m-sequences are useful for obtaining intermediate trade-offs or for optimizing with basis functions or correlated noise.
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Optimality of m-sequences
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Clustered m-sequences
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Additional Complexities Ø The impact of low frequency drifts and correlated noise -- this will change the optimal design.
Ø Impact of nonlinearities in the BOLD response.
Ø Designs where the timing is constrained by psychology.
Ø In general, need to search over space of possible solutions, taking into account these practical concerns.
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Genetic Algorithms
Wager and Nichols 2003
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Genetic Algorithms
Wager and Nichols 2003
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Genetic Algorithms
Kao et al, NIMG 2009
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Genetic Algorithms
Kao et al, Appl. Sta6s6cs, 2012
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Robust MaxiMin Designs
Kao et al, Ann. Appl. Stat. , 2013; see also Maus et al NIMG 2010
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Optimization w/ Design Constraints
Probe Distractor Decide
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Optimal Design for DCM
Daunizeau et al, PLOS Comp. Bio 2011
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Optimal Design for DCM
Daunizeau et al, PLOS Comp. Bio 2011
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Optimal Design for MVPA
Coutanche and Thompson-‐Schill, NIMG 2012
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Software Packages • AFNI: Rsfgen and 3dDeconvolve – random generation and
evaluation of designs • http://surfer.nmr.mgh.harvard.edu/optseq/ -- random search over
designs • http://www.mathworks.com/matlabcentrałfileexchange/authors/
3515 -- code for generating m-sequeces • http://cfmriweb.ucsd.edu/ttliu/mttfmri_toolbox.html -- code for
clustered m-sequences and other designs • http://www.nitrc.org/projects/pobe/ -- optimal designs of
multiple-subject block design experiments
• Genetic Algorithms: http://www.columbia.edu/cu/psychology/tor/software.htm AND
http://www.jstatsoft.org/v30/i11/
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Summary • The “optimal” design depends on both
experimental design and assumptions about the hemodynamic response and other factors.
• Theoretical framework provides insight into the fundamental tradeoffs.
• Use search algorithms (such as GA) to find optimal designs under varying assumptions.
• Open questions related to optimization with design constraints.
• Optimization for advanced and emerging analysis methods.