SPM course May 2015 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: 320899
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Slide1
Event-related fMRI
SPM course May 2015Helen BarronWellcome Trust Centre for Neuroimaging12 Queen SquareSlide2
Overview
Event-related design vs block designModelling eventsOptimising the designSlide3
Overview
Event-related design vs block designModelling eventsOptimising the designSlide4
Center 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? Slide5
Brief
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?Slide6
Experimental 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 randomSlide7
Event-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”Slide8
Event-Related Designs
Advantages over block designs:Events which can only be indicated by the participante.g. decision making, perceptual changes, Kleinschmidt et al., 1998Slide9
Event-Related Designs
Advantages over block designs:Paradigms which cannot be blockedwhere surprise is important, oddball designs
timeSlide10
Event-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 designsSlide11
Overview
Event-related design vs block designModelling eventsOptimising the designSlide12
Modelling events
Model
Block / Epoch
X
Design
time
time
Model
Design
Event relatedSlide13
ITI(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 eventSlide14
To infer the contribution of a given voxel to house or scene processing we need to model the events in a design matrix
=
+
X
The GLMSlide15
The 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 functionSlide16
time
Design matrix
convolution
down-sample for each scan
Temporal basis
functions
Events across time
The design matrixSlide17
Finite Impulse Response (FIR)
Fourier
Temporal basis function
Gamma functionSlide18
Canonical
Haemodynamic Response Function (HRF) used in SPM2 gamma functionsAssumed to be the same everywhere in the brain
Canonical
Temporal basis functions
the standard HRFSlide19
Canonical
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 derivativesSlide20
Canonical
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 regionsSlide21
Simple convolution
Which design is more efficient?Slide22
Overview
Event-related design vs block designModelling eventsOptimising the designSlide23
Optimising 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 orderITISOASlide24
Which SOA is optimal?
Not very efficient…
Very inefficient…
Which design is more efficient?
Neither are very good
16s SOA
4s SOASlide25
Short randomised SOA
Stimulus (“Neural”)HRF
Predicted Data
More efficient…
=
Null eventsSlide26
Block 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 transformSlide28
Randomised Design, SOAmin
= 4s, highpass filter = 1/120sFourier TransformFourier Transform
=
=
Stimulus (“Neural”)
HRF
Predicted Data
Analysing
efficiency: Fourier transformSlide29
Sinusoidal modulation, f=1/33s
Fourier TransformFourier Transform=
Stimulus (“Neural”)
HRF
Predicted Data
=
=
The optimal SOASlide30
X: 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 valueSlide31
Happy (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 SOASlide32
Contrast 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.Slide33
Two 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 #2Slide34
Efficiency 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?Slide35
Non-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., 1999Slide36
Efficiency 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<2sSlide37
Summary
Choosing whether to use an event-related or block designChoosing how to model the BOLD responseOptimising the timing of the experiment (design efficiency)Slide38
Further 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