Kristy DeDuck & Luzia Troebinger
MFD – Wednesday 18th January 2012
Image time-series
Realignment
Spatial filter
Design matrix
Smoothing
General Linear Model
Statistical Parametric Map
Statistical
Inference
Normalisation
Anatomical
reference Parameter estimates
RFT
p <0.05
Overview
 Experimental Design
 Types of Experimental Design
 Timing parameters – Blocked and Event-Related &
Mixed design
Main take home message of
experimental design…
Make sure you’ve chosen your analysis method and
contrasts before you start your experiment!
Why is it so important to correctly design
your experiment?
 Main design goal: To test specific hypotheses
 We
want to manipulate the participants
experience and behaviour in some way that is
likely to produce a functionally specific
neurovascular response.
 What can we manipulate?
 Stimulus type and properties
 Stimulus timing
 Participant instructions
http://blogs.plos.org/blog/2011/05/06/the-secret-of-experimental-design/
Adaptation - Repetition suppression
– Repeated viewing of the same face elicits lower BOLD activity in face-selective
regions
– Repetition suppression / adaptation designs:
BOLD decreases for repetition used to infer functional specialization
for this task/stimulus
Henson, Dolan, Shallice (2000) Science
Henson et al (2002) Cereb Cortex
Types of experimental design
1. Categorical - comparing the activity between
stimulus types
2. Factorial - combining two or more factors within a
task and looking at the effect of one factor on the
response to other factor
3. Parametric - exploring systematic changes in brain
responses according to some performance
attributes of the task
Categorical Design
Categorical design: comparing the activity between stimulus types
Example:
Stimulus: visual presentation of 12 common nouns.
Tasks: decide for each noun whether it refers to an animate or inanimate
object.
goat
bucket
Factorial design
combining two or more factors within a task and looking at the effect of one factor on the response to other factor
 Simple main effects
e.g. A-B = Simple main effect of motion (vs. no
motion) in the context of low load
LOW
 Main effects
e.g. (A + B) – (C + D) = the main effect of low LOAD
load (vs. high load) irrelevant of motion
HIGH
 Interaction terms
e.g. (A - B) – (C – D) = the interaction effect of
motion (vs. no motion) greater under low (vs.
high) load
MOTION
NO MOTION
A
B
C
D
Factorial design in SPM
 Main effect of low load:
A
B
C
 (A + B) – (C + D)
[1
1
A
B
C
-1
0
D
-1
-1]
 Simple main effect of motion in the context
of low load:
 (A – B)
[1
D
0]
 Interaction term of motion greater under low
load:
 (A – B) – (C – D)
A
[1
B
-1
C
D
-1
1]
Factorial design in SPM
Parametric design
= exploring systematic changes in brain responses according to some
performance attributes of the task
 Parametric designs use continuous rather than
categorical design.
 For example, we could correlate RTs with brain
activity.
Overview
 Experimental Design
 Types of Experimental Design
 Timing parameters – Blocked, Event-Related &
Mixed Design
Experimental design based on the
BOLD signal
 A brief burst of neural activity corresponding to
presentation of a short discrete stimulus or event
will produce a more gradual BOLD response lasting
about 15sec.
 Due to noisiness of the BOLD signal multiple
repetitions of each condition are required in order
to achieve sufficient reliability and statistical
power.
Design & Neuronal Model
 Design (Randomized vs. Block)
 Neuronal Model (Events vs. Epochs)
Blocked design
= trial of one type
(e.g., face image)
= trial of another type
(e.g., place image)
Multiple repetitions from a given experimental
condition are strung together in a condition block
which alternates between one or more condition
blocks or control blocks
 Advantages and considerations in Block design
 The BOLD signal from multiple repetitions is additive
 Blocked designs remain the most statistically powerful designs for fMRI
experiments (Bandetti & Cox, 2000)
 Can look at resting baseline e.g Johnstone & colleagues
 Each block should be about 16-40sec
 Disadvantages
 Although block designs are more statistically efficient event related
designs often necessary in experimental conditions
 Habituation effects
 In affective sciences their may be cumulative effects of emotional or
social stimuli on participants moods
Event related design
time
 In an event related design, presentations of trials from
different experimental conditions are interspersed in a
randomised order, rather then being blocked together by
condition
 In order to control for possible overlapping BOLD signal
responses to stimuli and to reduce the time needed for an
experiment you can introduce ‘jittering’ (i.e. use variable
length ITI’s)
 Advantages and considerations in Event-related
design
 Avoids the problems of habituation and expectation
 Allows subsequent analysis on a trial by trial basis, using behavioural
measures such as judgment time, subjective reports or physiological
responses to correlate with BOLD
 Using jittered ITIs and randomised event order can increase statistical
power
 Disadvantages
 More complex design and analysis (esp. timing and baseline issues).
 Generally have reduced statistical power
 May be unsuitable when conditions have large switching cost
Mixed designs
 More recently, researchers have recognised the need to
take into account two distinct types of neural processes
during fMRI tasks
1 – sustained activity throughout task (‘sustained activity’)
e.g. taking exams
2 – brain activity evoked by each trial of a task (‘transient
activity’)
Mixed designs can dissociate these transient and
sustained events (but this is actually quite hard!)
Study design and efficiency
The Basics…

General linear model:
Y = X*β+E
Where…
 Y is the Matrix of BOLD signals (what you collect),
 X is the Design Matrix (what you put into SPM),
 β represents the Matrix Parameters (need to be
estimated),
 E represents the error matrix (residual error for
each voxel).
Terminology
 Trials
…replication of condition.
 Either
…epochs: sustained neural activity
…or events: bursts of neural activity
 ITI
…time between start of one and start of the next trial
 SOA (stimulus onset asynchrony)
…time between onset of components.
BOLD response
The BOLD response to a brief burst of activity typically exhibits a
peak at around 4-6 s and an undershoot at around 10-30 s.
To get predicted response…
 Convolve the haemodynamic response with the stimulus.
 Convolution is a mathematical operation on two functions
that produces a third function which typically represents a
modified version of one of the original functions.
On timing…
Fixed SOA of 16 s – not particularly efficient.
Try much shorter SOA of 4 s…
IR to events now overlaps considerably. Variability in response is low which
means most of the signal will be lost after high pass filtering, so this is not an
efficient design, either.
What if we vary SOA randomly?
SOA is still 4s, but with a 50% probability of event occurring every 4 s. More
efficient because there is larger variability in signal, and we know how the signal
varies (even though it is generated randomly, we know this from observing the
resulting sequence).
Blocked design
Runs of events followed by ‘rest periods’ (periods of null events) – blocked design,
very efficient
Fourier transform
 decomposes signal into its constituent frequencies
 represents signal in frequency space
 allows us to gain insight into how much of the signal lies
within each frequency band
Why is it useful?
Take the Fourier transform of each function in the top row, and plot amplitude
(magnitude) against Frequency. The neural activity represents the original
data, IR acts as a filter (low pass in this case).
What is the most efficient design?
 From what we have seen so far, the most efficient design
means varying the neural activity in a sinusoidal fashion
with a frequency that matches the peak of the amplitude
spectrum of the IR filter.
Sinusoidal modulation places all the stimulus energy at the peak frequency as
represented by the single line in the bottom RH corner.
High pass filtering
 We know that there is some noise associated with the
scanner.
 This basically consists of low frequency ‘1/f’ noise and
background white noise.
 We need to filter such that noise is minimised while we
keep as much of the signal as possible.
For example…
Consequences of high pass filtering for long blocks. Much of the signal
is lost because the fundamental frequency (1/160s ~ 0.006 Hz) is lower
than the high pass cutoff. This is why block length should not be too
long.
Revisiting our stochastic design…
Here, the signal is spread across a range of frequencies. Some of the signal is
lost due to filtering, but a lot of it is passed which makes this a reasonable
design.
General linear model revisited…
 Recall:
Y = X*β+E
 Efficiency is basically the ability to estimate β given data X
and contrast c
e (c, X) = inverse (σ2 cT Inverse(XTX) c)
 Can only alter c and X
Timing – differential vs. main effect
 Differential effect = A-B
 Optimal SOA (randomised design) = minimal SOA (<2s)
 Main effect = A+B
 Optimal SOA = 16-20s because we are comparing to
baseline.
Sampling/jitter
 Jitter is used to randomise SOA
 Null events can be introduced using jitter
 Efficient for differential and main effects at short SOA
For SPM
Conclusions
1.
Do not contrast conditions that are far apart in time (because of
low-frequency noise in the data).
2.
Randomize the order, or randomize the SOA, of conditions that
are close in time.
Also:
 Blocked designs generally most efficient (with short SOAs, given
optimal block length is not exceeded)
 Think about both your study design and contrasts before you start!
References
 http://imaging.mrc-cbu.cam.ac.uk/imaging/DesignEfficiency
 Harmon-Jones, E. y Beer, J. S. (Eds.) (2009). Methods in social
neuroscience. Nueva York: The Guilford Press.
 Johnstone T et al., 2005. Neuroimage 25(4):1112-1123
 Previous MfD slides
Thanks to our expert Tom Fitzgerald