Experimental Design
for
Functional MRI
David Glahn
Updated by JLL
General Experimental Design
- Neuropsychology •
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•
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What is the question?
What are appropriate controls?
Which imaging modality?
Study style?
Experimental Design: Terminology
• Variables
– Independent vs. Dependent
– Categorical vs. Continuous
• Contrasts
– Experimental vs. Control
– Parametric vs. subtractive
• Comparisons of subjects
– Between- vs. Within-subjects
• Confounding factors
• Randomization, counterbalancing
From Scott Huettel, Duke
Donder’s Method: Subtraction
Example: How long does it take to choose between
alternatives? (Mental Chronometry)
• A random series of A’s and B’s presented and the
subject must:
– Task 1 - Respond whenever event A or B occurs (RT1)
– Task 2 - Respond only to A not to B (RT2)
– Task 3 - Respond X to A and Y to B (RT3)
RT = reaction time
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•
•
•
•
RT1 = T-detect + T-response
RT2 = T-detect + T-discrimination + T-response
RT3 = T-detect + T-discrimination + T-choice + T-response
T-discrimination = RT2 - RT1
T-choice = RT3 - RT2
Criticisms of Donder
• Assumes that adding
components does not
affect other components
(i.e. assumption of pure
insertion)
• One should pick tasks
that differ along same
dimension (time in our
example)
• Although resting baseline
is good to include, it may
limit inference
(e.g. Sternberg, 1964)
What types of hypotheses are
possible for fMRI data?
From Scott Huettel, Duke
Experimental Design for fMRI
Must Account for Hemodynamic Response
(HR)
Savoy et al., 1995
Linear Systems Analysis
Boynton et al. 1996
• The linear transform model of fMRI hypothesizes that
responses are proportional to local average neural
activity averaged over a period of time.
– fMRI responses in human primary visual cortex (V1) depend on
both stimulus timing (8 Hz) and stimulus contrast (black/white).
– Responses to long-duration stimuli can be predicted from a
hemodynamic response function (HRF) derived from shorter
duration stimuli.
– The noise in the fMRI data is independent of stimulus contrast
and stimulus temporal period.
• Because the linear transform model is consistent with
our data, we proceeded to estimate the temporal fMRI
response function and the underlying (presumably
neural) contrast response function using HRF…
• Assumption is that HRF is linear and shift-invariant!
Linearity of BOLD response
Dale & Buckner, 1997
Reversing
Checkerboard (8 Hz)
One-trial = 1 stimulus
Two-trial – 2 stimuli
Three-trial = 3 stimuli
Stim duration (SD) = 1 s
Inter-stim interval (ISI) = 2 s
Sync each differential
response to start of trial
Not quite linear but good enough for
first order approximations
fMRI Design Types
1) Blocked Designs
2) Event-Related Designs
a) Periodic Single Trial
b) Jittered Single Trial
3) Mixed Designs
- Combination blocked/event-related
Blocked Designs
What are Blocked Designs?
• Blocked designs segregate different cognitive
tasks into distinct time periods (blocks)
Paradigm – pattern or model; detailed plan for the experiment
Task A
Task B
Task A
Task B
Task A
Task B
Task A
Task B
Task A
REST
Task B
REST
Task A
REST
Task B
REST
fMRI brain images acquired continuously
“Loose” vs. “Tight”
Block Designs
• Loose: 1 Task, 1 contrast (with Baseline)
• Tight: more than 1 Task, multiple
contrasts (including baseline)
Types of Blocked Design
• Task A vs. Task B (… vs. Task C…)
– Example: Squeezing Right Hand vs. Left Hand
– Allows you to distinguish differential activation
between conditions
– Does not allow identification of activity common to
both tasks
• Can control for uninteresting activity
• Task A vs. No-task (… vs. Task C…)
– Example: Squeezing Right Hand vs. Rest
– Shows you activity associated with task
– May introduce unwanted results if not matched
properly
(example would be if rest acquired with eyes closed
but task had eyes open)
(E - Bad Control Design)
Adapted from Gusnard & Raichle (2001)
A True Baseline?
Different Areas may have different baselines
Cerebral
Blood Flow
Cerebral
Metabolic
Rate of O2
Oxygen
Extraction
Fraction
Adapted from Gusnard & Raichle (2001)
Depends on what is measured!
Power in Blocked Designs
1. Summation of
responses results in
large signals then
plateaus (~10 sec)
1. Response Duration
does not plateau
and onset does not
change
Stimulus
duration and
interval
compared
with HRF
ISI = 1 sec
Choosing Length of Blocks
• Longer block lengths allow for stability of extended responses
– Hemodynamic response saturates following extended stimulation
• After about 10s, activation reaches plateau
– Many tasks require extended intervals
• Brain processing may differ throughout the task period
• Shorter block lengths move your signal to higher temporal
frequencies
– Away from low-frequency noise: scanner drift, etc.
– Not possible in O-15 PET rCBF studies
• Periodic blocks may result in aliasing of other periodic signals in
the data
– Example: if the person breathes at a regular rate of 12 per min and
the blocks are 10s long (6 blocks/min)
– Could be problem if the aliased signal falls within the range of desired
signals
From Scott Huettel, Duke
What are the temporal limits?
What is the shortest stimulus duration that fMRI can detect?
Blamire et al. (1992) – 2 sec
Bandettini (1993): 0.5 sec
Savoy et al (1995): 34 msec
• With enough averaging, anything seems possible.
• Assume that the shape of the HRF is predictable.
• Event-related potentials (ERPs) are based on averaging small responses over
many trials.
• Can we do the same thing with fMRI?
Assumption of steady-state dynamics.
For block designs we assume that the BOLD effect remains constant across
the epoch of interest.
For PET this assumption is valid given the half-life of the radiotracer used
for CBF studies, task designs, and the time period for the image acquisition.
But the BOLD response is much more transient and more importantly may
vary according to brain regions and stimulus durations and maybe even
stimulus types.
Savoy et al., 1995
Limitations of Blocked Designs
• Sensitive to signal drift or MR instability
• Poor choice of conditions/baseline may preclude
meaningful conclusions
• Many tasks cannot be conducted well repeatedly
Non-Task Brain Processing
• In experiments activation can be greater in baseline
conditions than in task conditions!
– Requires different processing for interpretation
• Suggests the idea of baseline/resting mental processes
–
–
–
–
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Gathering/evaluation about the world around you
Awareness (of self)
Online monitoring of sensory information
Daydreaming
Neurons that are wired together fire together
• This collection of resting state brain processes is often
called the “Default Mode Network” (DMN)
Resting State Networks (RSNs)
Vision.
Default Mode!
Frontal Parietal
Damoiseaux 2006 analyzed separate 10-subject
resting-state data sets, using Independent
Components analysis (ICA).
Event-Related Designs
Buckner et al., 1998
Event Related
What are Event-Related Designs?
• Event-related designs associate brain processes
with discrete events, which may occur at any
point in the scanning session.
• Can detect transient BOLD responses
• Supports adapting task to response such as
changing difficulty based on error rate
Why use event-related designs?
• Some experimental tasks are naturally
event-related (future stimuli based on
response)
• Allows studying within-trial effects
• Improves relation to behavioral factors
(behavior changes within blocks may be
masked)
• Simple analyses
– Selective averaging
– General linear models (GLM)
Same
Event
Averaging
Sorting Into Common Groups
- Behavior
- Physiological Measure
- Outlier Rejection
- Transient vs. Task level Responses
Periodic Single Trial Designs
• Stimulus events presented infrequently
with long inter-stimulus intervals (ISIs)
500 ms
500 ms
18 s
500 ms
18 s
500 ms
18 s
Trial Spacing Effects: Periodic Designs
ISI = 20sec (9 trials)
ISI = 12sec (15 trials)
A20
A12
ISI = 8sec (~12 trials)
ISI = 4sec (~45 trials)
A4
A8
Want to maximize amplitude times number of trials per study
Bandettini & Cox, 2000
• The optimal inter-stimulus interval (ISI) for a stimulus duration (SD),
was determined.
• Empirical Observation: For SD=2sec, ISI=12 to 14 sec.
• Theory Predicts: For SD<=2 sec, the optimal repetition interval
(RI=ISI+SD)
• Theory Predicts: For SD>2sec, RI = 8+(2*SD).
• The statistical power of ER-fMRI relative to blocked-design was
determined
• Empirical: For SD=2 sec, ER-fMRI was ~35% lower than that of
blocked-design
• Simulations that assumed a linear system demonstrated estimate
~65% reduction in power
• Difference suggest that the ER-fMRI amplitude is greater than
that predicted by a linear shift-invariant system models.
Jittered Single Trial Designs
• Varying the timing of trials within a run
• Varying the timing of events within a trial
1 event
2 events
Trial 1
Trial 2
3 events
2 events
Trial 3
Trial 4
Effects of Jittering on Response
Stimulus
Response
Jittering allows us to sample BOLD response in more states
Effects of ISI on Detectability
Jittered ISI
Detectability
Constant ISI
Max when ½ stims
are task state and ½
stims are control
state
Estimated
Accuracy of
HRF
Birn et al, 2002
Detecting Using Selective Averaging
Visual stim duration = 1 s; acquisition 240 sec
Trials subtracted then correlation analysis with predicted response
Large Response
Mid Response
Low Response
Most samples
More Samples
Fewer Samples
Dale and Buckner (1997)
Variability of HRF: Evidence
Aguirre, Zarahn & D’Esposito, 1998
• HRF shows considerable variability between subjects
different subjects
• Within subjects, responses are more consistent, although there is still
some variability between sessions
same subject, same session
same subject, different session
Variability of HRF: Implications
Aguirre, Zarahn & D’Esposito, 1998
• Generic HRF models (gamma functions) account for 70% of variance
• Subject-specific models account for 92% of the variance (22% more!)
• Poor modeling reduces statistical power
• Less of a problem for block designs than event-related (do you know why?)
• Biggest problem with delay tasks where an inappropriate estimate of the initial
and final components contaminates the delay component
• Possible solution: model the HRF individually for each subject
• Possible caveat: HRF may also vary between areas, not just subjects
• Buckner et al., 1996:
• noted a delay of 0.5-1 sec between visual and prefrontal regions
• vasculature difference?
• processing latency?
• Bug or feature?
• Menon & Kim – mental chronometry
Post-Hoc Sorting of Trials
True Memory Formation
vs.
False Memory Formation
From Kim and Cabeza, 2007
Using information about fMRI
activation at memory encoding to
predict behavioral performance at
memory retrieval.
Limitations of Event-Related Designs
• Low power (maybe)
– Collecting lots of data, many runs
• The key issues are:
– Can my subjects perform the task as designed?
– Are the processes of interest independent
from each other (in time, amplitude, etc.)?
Mixed Designs
Mixed: Combination Blocked/Event
• Both blocked and event-related design aspects are used
(for different purposes)
– Blocked design: state-dependent effects
– Event-related design: item-related effects
• Analyses can model these as separate phenomena, if
cognitive processes are independent.
– “Memory load effects” vs. “Item retrieval effects”
• Or, interactions can be modeled.
– Effects of memory load on item retrieval activation.
You can model a block with events…
Blocked
(solid)
Event-Related
(dashed)
Event-related model
reaches peak sooner…
… and returns to
baseline more slowly.
In this study, some
language-related regions
were better modeled by
event-related.
From Mechelli, et al., 2003
Mixed Design
Summary of Experiment Design
• Main Issues to Consider
– What design constraints are induced by my task?
– What am I trying to measure?
– What sorts of non-task-related variability do I want to avoid?
• Rules of thumb
– Blocked Designs:
• Powerful for detecting activation
• Useful for examining state changes
– Event-Related Designs:
• Powerful for estimating time course of activity
• Allows determination of baseline activity
• Best for post hoc trial sorting
– Mixed Designs
• Best combination of detection and estimation
• Much more complicated analyses
What is fMRI Experimental Design?
• Controlling the timing and quality of cognitive
operations to influence brain activation
• What can we control?
– Stimulus properties (what is presented?)
– Stimulus timing (when is it presented?)
– Subject instructions (what do subjects do with it?)
• What are the goals of experimental design?
– To test specific hypotheses (i.e., hypothesis-driven)
– To generate new hypotheses (i.e., data-driven)
Experimental Design
for
Functional MRI
David Glahn
Updated by JLL