Basics of Experimental Design for fMRI: Event-Related Designs

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Basics of Experimental Designfor
fMRIEvent-Related Designs
Jody Culham Brain and Mind Institute Department
of Psychology University of Western Ontario
http//www.fmri4newbies.com/
Last Update January 18, 2012 Last Course
Psychology 9223, W2010, University of Western
Ontario
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Part III

• Choosing an Event-Related Design

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Convolution of Single Trials
Neuronal Activity
BOLD Signal
Haemodynamic Function
Time
Time
Slide from Matt Brown
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BOLD Summates
Neuronal Activity
BOLD Signal
Slide from Matt Brown
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BOLD Overlap and Jittering

• Closely-spaced haemodynamic impulses summate.
• Constant ITI causes tetanus.

Burock et al. 1998.
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Design Types
null trial (nothing happens)
trial of one type (e.g., face image)
trial of another type (e.g., place image)
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Block Designs
trial of one type (e.g., face image)
trial of another type (e.g., place image)
Block Design

• Early Assumption Because the hemodynamic
  response delays and blurs the response to
  activation, the temporal resolution of fMRI is
  limited.

WRONG!!!!!
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What are the temporal limits?
What is the briefest stimulus that fMRI can
detect? Blamire et al. (1992) 2 sec Bandettini
(1993) 0.5 sec Savoy et al (1995) 34 msec
2 s stimuli single events
Data Blamire et al., 1992, PNAS Figure Huettel,
Song McCarthy, 2004
Data Robert Savoy Kathy OCraven Figure Rosen
et al., 1998, PNAS
Although the shape of the HRF delayed and
blurred, it is predictable. Event-related
potentials (ERPs) are based on averaging small
responses over many trials. Can we do the same
thing with fMRI?
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Detection vs. Estimation

• detection determination of whether activity of a
  given voxel (or region) changes in response to
  the experimental manipulation

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• estimation measurement of the time course within
  an active voxel in response to the experimental
  manipulation

Signal Change
0
0
4
8
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Time (sec)
Definitions modified from Huettel, Song
McCarthy, 2004, Functional Magnetic Resonance
Imaging
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Block Designs Poor Estimation
Huettel, Song McCarthy, 2004, Functional
Magnetic Resonance Imaging
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Pros Cons of Block Designs

• Pros
• high detection power
• has been the most widely used approach for fMRI
  studies
• accurate estimation of hemodynamic response
  function is not as critical as with event-related
  designs
• Cons
• poor estimation power
• subjects get into a mental set for a block
• very predictable for subject
• cant look at effects of single events (e.g.,
  correct vs. incorrect trials, remembered vs.
  forgotten items)
• becomes unmanagable with too many conditions
  (e.g., more than 4 conditions baseline)

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Slow Event-Related Designs
Slow ER Design
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Slow Event-Related Design Constant ITI
Bandettini et al. (2000) What is the optimal
trial spacing (duration intertrial interval,
ITI) for a Spaced Mixed Trial design with
constant stimulus duration?
2 s stim vary ISI
Block
Source Bandettini et al., 2000
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Optimal Constant ITI
Source Bandettini et al., 2000
Brief (lt 2 sec) stimuli optimal trial spacing
12 sec For longer stimuli optimal trial spacing
8 2stimulus duration Effective loss in
power of event related design -35 i.e., for 6
minutes of block design, run 9 min ER design
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Trial to Trial Variability
Huettel, Song McCarthy, 2004, Functional
Magnetic Resonance Imaging
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How Many Trials Do You Need?
Huettel, Song McCarthy, 2004, Functional
Magnetic Resonance Imaging

• standard error of the mean varies with square
  root of number of trials
• Number of trials needed will vary with effect
  size
• Function begins to asymptote around 15 trials

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Effect of Adding Trials
Huettel, Song McCarthy, 2004, Functional
Magnetic Resonance Imaging
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Pros Cons of Slow ER Designs

• Pros
• good estimation power
• allows accurate estimate of baseline activation
  and deviations from it
• useful for studies with delay periods
• very useful for designs with motion artifacts
  (grasping, swallowing, speech) because you can
  tease out artifacts
• analysis is straightforward

• Cons
• poor detection power because you get very few
  trials per condition by spending most of your
  sampling power on estimating the baseline
• subjects can get VERY bored and sleepy with long
  inter-trial intervals

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Do You Wanna Go Faster?

• Yes, but we have to test assumptions regarding
  linearity of BOLD signal first

Rapid Counterbalanced ER Design
Rapid Jittered ER Design
Mixed Design
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Linearity of BOLD response
Linearity Do things add up?
Not quite linear but good enough!
Source Dale Buckner, 1997
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Optimal Rapid ITI
Source Dale Buckner, 1997
Rapid Mixed Trial Designs Short ITIs (2 sec) are
best for detection power Do you know why?
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Efficiency (Power)
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Design Types
trial of one type (e.g., face image)
trial of another type (e.g., place image)
Rapid Counterbalanced ER Design
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Detection with Rapid ER Designs
Figure Huettel, Song McCarthy, 2004

• To detect activation differences between
  conditions in a rapid ER design, you can create
  HRF-convolved reference time courses
• You can perform contrasts between beta weights as
  usual

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Variability Between Subjects/Areas

• greater variability between subjects than between
  regions
• deviations from canonical HRF cause false
  negatives (Type II errors)
• Consider including a run to establish
  subject-specific HRFs from robust area like M1

Handwerker et al., 2004, Neuroimage
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Event-Related Averaging
In this example an event is the start of a
block In single-trial designs, an event may be
the start of a single trial

• First, we compute an event related average for
  the blue condition
• Define a time window before (2 volumes) and
  after (15 volumes) the event
• Extract the time course for every event (here
  there are four events in one run)
• Average the time courses across all the events

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Event-Related Averaging
Second, we compute an event related average for
the gray condition
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Event-Related Averaging
Third, we can plot the average ERA for the blue
and gray conditions on the same graph
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Event-Related Averaging in BV
Define which subjects/runs to include
Set time window
Define which conditions to average (usually
exclude baseline)
We can tell BV where to put the y0 baseline.
Here its the average of the two circled data
points at x0.
Determine how you want to define the y-axis
values, including zero
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But what if the curves dont have the same
starting point?
In the data shown, the curves started at the same
level, as we expect they should because both
conditions were always preceded by a resting
baseline period
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Epoch-based averaging
FILE-BASED AVERAGING zero baseline determined
across all conditions (for 0 to 0 points in red
circles)
In the latter two cases, we could simply shift
the curves so they all start from the same (zero)
baseline
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File-based vs. Epoch-based Averaging
time courses may start at different points
because different event histories or noise
0

• File-based Averaging
• zero is based on average starting point of all
  curves
• works best when low frequencies have been
  filtered out of your data
• similar to what your GLM stats are testing

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What if?

• This design has the benefit that each condition
  epoch is preceded by a baseline, which is nice
  for making event-related averages

• However, we might decide that this design takes
  too much time because we are spending over half
  of the time on the baseline.
• Perhaps we should use the following paradigm
  instead?

• This regular triad sequence has some nice
  features, but it can make ERAs more complicated
  to understand.

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Regular Ordering and ERAs

• We might have a time course that looks like this

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Example of ERA Problems

• If you make an ERA the usual way, you might get
  something that looks like this

File-Based (Pre2, Post10, baseline 0 to 0)
Intact
One common newbie mistake is to make ERAs for all
conditions, including the baseline
(Fixation). This situation will illustrate some
of the confusion with that
Scrambled
Fixation

• Initially some people can be confused how to
  interpret this ERA because the pre-event
  activation looks wonky.

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Example of ERA Problems
File-Based (Pre2, Post10, baseline 0 to 0)
File-Based (Pre8, Post18, baseline 0 to 0)

• If you make the ERA over a longer time window,
  the situation becomes clearer.
• You have three curves that are merely shifted in
  time with respect to one another.

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Example of ERA Problems
File-Based (Pre2, Post10, baseline 0 to 0)
Intact
End of Intact
Scrambled
End of Scrambled
End of Fixation
Fixation

• Now you should realize that the different
  pre-epoch baselines result from the fact that
  each condition has different preceding conditions
• Intact is always preceded by Fixation
• Scrambled is always preceded by Intact
• Fixation is always preceded by Scrambled

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Example of ERA Problems
File-Based (Pre2, Post10, baseline 0 to 0)
Intact
Scrambled
Fixation

• Because of the different histories, changes with
  respect to baseline are hard to interpret.
  Nevertheless, ERAs can show you how much the
  conditions differed once the BOLD response
  stabilized
• This period shows, rightly so, Intact gt Scrambled
  gt Fixation

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Example of ERA Problems
Epoch-Based (Pre2, Post10, baseline -2 to -2)

• Because the pre-epoch baselines are so different
  (due to differences in preceding conditions),
  here it would be really stupid to do epoch-based
  averaging (e.g., with x-2 as the y0 baseline)
• In fact, it would lead us to conclude (falsely!)
  that there was more activation for Fixation than
  for Scrambled

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Example of ERA Problems

• In a situation with a regular sequence like this,
  instead of making an ERA with a short time window
  and curves for all conditions, you can make one
  single time window long enough to show the series
  of conditions (and here you can also pick a
  sensible y 0 based on x-2)

File-Based average for Intact condition only
(Pre2, Post23, baseline -2 to -2)
Intact
Scrambled
Fixation
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Partial confounding

• In the case we just considered, the histories for
  various conditions were completely confounded
• Intact was always preceded by Fixation
• Scrambled was always preceded by Intact
• Fixation was always preceded by Scrambled

• We can also run into problems (less obvious but
  with the same ERA issues) if the histories of
  conditions are partially confounded (e.g.,
  quasi-random orders)

• Intact is preceded by Scrambled 3X and by
  Fixation 3X
• Scrambled is preceded by Intact 4X and Fixation
  1X
• Fixation is preceded by Intact 2X, by Scrambled
  2X and by nothing 1X
• No condition is ever preceded by itself

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The Problem of Trial/Block History

• This problem also occurs for single trial
  designs.
• This problem also occurs even if the history is
  only partially confounded (e.g., if Condition A
  is preceded by Condition X twice as often as
  Condition B is preceded by Condition X).
• If we knew with certainty what a given subjects
  HRF looked like, we could model it (but thats
  rarely the case).
• Thus we have only two solutions
• Counterbalance trial history so that each curve
  should start with the same baseline
• Jitter the intertrial intervals so that we can
  estimate the HRF
• more on this in analysis when we talk about
  deconvolution

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One Approach to Estimation Counterbalanced Trial
Orders

• Each condition must have the same history for
  preceding trials so that trial history subtracts
  out in comparisons
• For example if you have a sequence of Face, Place
  and Object trials (e.g., FPFOPPOF), with 30
  trials for each condition, you could make sure
  that the breakdown of trials (yellow) with
  respect to the preceding trial (blue) was as
  follows
• Face ? Face x 10
• Place ? Face x 10
• Object ? Face x 10
• Face ? Place x 10
• Place ? Place x 10
• Object ? Place x 10
• Face ? Object x 10
• Place ? Object x 10
• Object ? Object x 10
• Most counterbalancing algorithms do not control
  for trial history beyond the preceding one or two
  items

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Analysis of Single Trials with Counterbalanced
Orders

• Approach used by Kourtzi Kanwisher (2001,
  Science) for pre-defined ROIs
• for each trial type, compute averaged time
  courses synced to trial onset then subtract
  differences

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Pros Cons of Counterbalanced Rapid ER Designs

• Pros
• high detection power with advantages of ER
  designs (e.g., can have many trial types in an
  unpredictable order)
• Cons and Caveats
• reduced detection compared to block designs
• estimation power is better than block designs but
  not great
• accurate detection requires accurate HRF
  modelling
• counterbalancing only considers one or two trials
  preceding each stimulus have to assume that
  higher-order history is random enough not to
  matter
• what do you do with the trials at the beginning
  of the run just throw them out?
• you cant exclude error trials and keep
  counterbalanced trial history
• you cant use this approach when you cant
  control trial status (e.g., items that are later
  remembered vs. forgotten)

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Design Types
trial of one type (e.g., face image)
trial of another type (e.g., place image)
Rapid Jittered ER Design
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BOLD Overlap With Regular Trial Spacing
Neuronal activity from TWO event types with
constant ITI
Partial tetanus BOLD activity from two event types
Slide from Matt Brown
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BOLD Overlap with Jittering
Neuronal activity from closely-spaced, jittered
events
BOLD activity from closely-spaced, jittered events
Slide from Matt Brown
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BOLD Overlap with Jittering
Neuronal activity from closely-spaced, jittered
events
BOLD activity from closely-spaced, jittered events
Slide from Matt Brown
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Fast fMRI Detection
Slide from Matt Brown
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Post Hoc Trial Sorting Example
Wagner et al., 1998, Science
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Algorithms for Picking Efficient DesignsOptseq2
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Algorithms for Picking Efficient DesignsGenetic
Algorithms
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Design Types
trial of one type (e.g., face image)
trial of another type (e.g., place image)
Mixed Design
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Example of Mixed Design

• Otten, Henson, Rugg, 2002, Nature Neuroscience
• used short task blocks in which subjects encoded
  words into memory
• In some areas, mean level of activity for a block
  predicted retrieval success

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Pros and Cons of Mixed Designs

• Pros
• allow researchers to distinguish between
  state-related and item-related activation
• Cons
• sensitive to errors in HRF modelling

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EXTRA SLIDES
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A Variant of Mixed Designs Semirandom Designs

• a type of event-related design in which the
  probability of an event will occur within a given
  time interval changes systematically over the
  course of an experiment

First period P of event 25
Middle period P of event 75
Last period P of event 25

• probability as a function of time can be
  sinusoidal rather than square wave

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Pros and Cons of Semirandom Designs

• Pros
• good tradeoff between detection and estimation
• simulations by Liu et al. (2001) suggest that
  semirandom designs have slightly less detection
  power than block designs but much better
  estimation power
• Cons
• relies on assumptions of linearity
• complex analysis
• However, if the process of interest differs
  across ISIs, then the basic assumption of the
  semirandom design is violated. Known causes of
  ISI-related differences include hemodynamic
  refractory effects, especially at very short
  intervals, and changes in cognitive processes
  based on rate of presentation (i.e., a task may
  be simpler at slow rates than at fast rates).
• -- Huettel, Song McCarthy, 2004