Helen Barron Wellcome Trust Centre for Neuroimaging 12 Queen Square Overview Eventrelated design vs block design Modelling events Optimising the design Overview Eventrelated design vs block ID: 932681
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Slide1
Event-related fMRI
SPM course May 2015Helen BarronWellcome Trust Centre for Neuroimaging12 Queen Square
Slide2Overview
Event-related design vs block designModelling eventsOptimising the design
Slide3Overview
Event-related design vs block designModelling eventsOptimising the design
Slide4Center for Vital Longevity Face Database
Berkeley Segmentation Datasettime
faces
scenes
faces
scenes
S
cenes vs face processing
How should we order the presentation of the stimuli?
What timing should we use between presentations?
Slide5Brief
StimulusUndershoot
Initial
Undershoot
Peak
BOLD
r
esponse
Initial undershoot
Peak 4-6s post-stimulus
Undershoot before returning to baseline
This is SLOW.
How should we present our stimuli?
Slide6Experimental Designs
time
scene
face
Intermixed / Event-Related Design
time
Block / Epoch Design
activity in face area
activity in scene area
Blocked designs have high statistical power so why would we want to use event-related design?
The order is random
Slide7Event-Related Designs
Advantages over block designs:Post-hoc classification of trials by the experimentere.g. by subsequent memory, Wagner et al., 1998750ms
cheese
+
1250ms
+
2000ms
time
Word trial (2
secs
)
Fixation trial (2
secs
)
“Null event”
Slide8Event-Related Designs
Advantages over block designs:Events which can only be indicated by the participante.g. decision making, perceptual changes, Kleinschmidt et al., 1998
Slide9Event-Related Designs
Advantages over block designs:Paradigms which cannot be blockedwhere surprise is important, oddball designs
time
Slide10Event-Related Designs
Advantages over block designs:Post-hoc classification of trials by the experimentere.g. by subsequent memoryEvents which can only be indicated by the participante.g. decision-making , perceptual changesParadigms that cannot be blocked
e.g. oddball designs
Slide11Overview
Event-related design vs block designModelling eventsOptimising the design
Slide12Modelling events
Model
Block / Epoch
X
Design
time
time
Model
Design
Event related
Slide13ITI(Inter-Trial Interval)
TrialTrialTrial
SOA
(Stimulus Offset Asynchrony)
Trial + ITI
ITI
(Inter-Trial Interval)
time
Terminology
for consistency with previous literature
Event: brief stimulus presentation thought to lead to a brief burst in neural activity
Epoch: sustained stimulus presentation thought to lead to sustained neural activity
Impulse response: BOLD response to an event
Slide14To infer the contribution of a given voxel to house or scene processing we need to model the events in a design matrix
=
+
X
The GLM
Slide15The design matrix
Regressor
1: Face
Regressor
2: Scene
Regressor
3: Constant
X=
Brief
Stimulus
Undershoot
Initial
Undershoot
Peak
We need to model the impulse response function
Slide16time
Design matrix
convolution
down-sample for each scan
Temporal basis
functions
Events across time
The design matrix
Slide17Finite Impulse Response (FIR)
Fourier
Temporal basis function
Gamma function
Slide18Canonical
Haemodynamic Response Function (HRF) used in SPM2 gamma functionsAssumed to be the same everywhere in the brain
Canonical
Temporal basis functions
the standard HRF
Slide19Canonical
Haemodynamic Response Function (HRF) used in SPM2 gamma functions+Multivariate Taylor expansion in time (Temporal Derivative)Canonical
Temporal
Negatively weight
temporal
Positively weight temporal
Temporal basis functions
the standard HRF and derivatives
Slide20Canonical
Haemodynamic Response Function (HRF) used in SPM2 gamma functions+Multivariate Taylor expansion in time (Temporal Derivative)+ Multivariate Taylor expansion in width (Dispersion Derivative)
Canonical
Temporal
Dispersion
Temporal basis functions
the standard HRF and derivatives
Now it is possible to account for variation between brain regions
Slide21Simple convolution
Which design is more efficient?
Slide22Overview
Event-related design vs block designModelling eventsOptimising the design
Slide23Optimising design: The Aim
We want to:Maximize our t-statistic where there’s an effect – i.e. our efficiency or sensitivityWe need to choose a good:Stimulus orderITISOA
Slide24Which SOA is optimal?
Not very efficient…
Very inefficient…
Which design is more efficient?
Neither are very good
16s SOA
4s SOA
Slide25Short randomised SOA
Stimulus (“Neural”)HRF
Predicted Data
More efficient…
=
Null events
Slide26Block design SOA
Stimulus (“Neural”)HRFPredicted Data
Even more efficient…
=
Slide27
=
=
Stimulus (“Neural”)
HRF
Predicted Data
Fourier Transform
Fourier Transform
Block Design, blocks (epochs) = 20s, short ISI
Analysing
efficiency: Fourier transform
Slide28Randomised Design, SOAmin
= 4s, highpass filter = 1/120sFourier TransformFourier Transform
=
=
Stimulus (“Neural”)
HRF
Predicted Data
Analysing
efficiency: Fourier transform
Slide29Sinusoidal modulation, f=1/33s
Fourier TransformFourier Transform=
Stimulus (“Neural”)
HRF
Predicted Data
=
=
The optimal SOA
Slide30X: design matrix
c: contrast vector
β
: beta vector
Maximise t by minimising the squared variance
Assuming
σ
is independent of our design, taking a fixed contrast we can only alter our design matrix
Analysing
efficiency: maximising t value
Slide31Happy (A) vs sad (B) faces: need to know both (A-B) and (A + B)
AB A0.5
0.5
B
0.5
0.5
Transition matrix
Values are probabilities of that condition occurring
Efficiency Example #
1
Two event types, A and B
Randomly intermixed (event-related):
ABBAABABB…
Question: What’s the best SOA to use?
Optimising the SOA
Slide32Contrast for Differential
Effect (A-B)
Contrast for Common
Effect (A+B)
SOA (s)
Efficiency
Efficiency Example #1
Note: the optimal SOA for the two contrasts differ
O
ptimal
efficiency
A+B: 16-20s, A-B:
0s
Given a particular design matrix, the different contrasts have different efficiencies.
Slide33Two event types, A and B
Randomly intermixed (event-related) with null events:AB-BAA--B---ABB…
Question: What’s the best SOA to use?
A
B
A
0.33
0.33
B
0.33
0.33
Transition matrix
Values are probabilities of that condition occurring
Efficiency Example #2
Slide34Efficiency Example #2
(
A+B)
(A-B)
SOA (s)
Efficiency
With the addition of null events the optimal SOA
i
s roughly matched for the two contrasts.
O
ptimal
efficiency
A+B:
0s
, A-B:
0s
Should we just use SOAs of 0s?
Slide35Non-linear effects
If the IRs sum in a linear manner then we are OK! But at short SOAs we get non-linearities in the data (saturation effects). Assume linear summation of BOLD response, up to a certain temporal proximity of event
Linear model
Non linear data
(‘saturation effect’)
Linear model is good until SOAs of <1s-2s
Trade off between packing more events
in and having nonlinear saturation effects
which are not modelled.
Friston
et al., 1999
Slide36Efficiency Summary
Block designs:Generally efficient but often not appropriate.Optimal block length 16s with short SOA (beware of high-pass filter).Event-related designs:Efficiency depends on the contrast of interestWith short SOAs ‘null events’ (jittered ITI) can optimise efficiency across multiple contrasts. Non-linear effects start to become problematic at SOA<2s
Slide37Summary
Choosing whether to use an event-related or block designChoosing how to model the BOLD responseOptimising the timing of the experiment (design efficiency)
Slide38Further ReadingBooks
(http://www.fil.ion.ucl.ac.uk/spm/doc/)Statistical Parametric MappingHuman Brain FunctionOnline lecturesSPM Course http://www.fil.ion.ucl.ac.uk/spm/course/video/ Websiteshttp://mindhive.mit.edu/imaging