HOW-TO #3
BACKGROUND: GENERAL INFORMATION
FMRI Experimental Design
Studies of functional magnetic resonance imaging (FMRI) employ various
types of experiment designs, most of which fall into one of two categories:
1) block design, or 2) event-related design. In this 'How-To', block design and
various forms of event-related design will be presented, along with the
advantages and disadvantages of using each paradigm. Below is an outline of the
experiment designs that will be discussed, along with information regarding
randomization of stimulus trials and fixed versus stochastic or "jittered"
inter-stimulus intervals (ISI) in rapid event-related design:
1) Block Design
2) Event-Related Design (ER-FMRI)
A. "Slow" Event-Related Design
B. "Rapid" Event-Related Design
1. Fixed ISI and NON-RANDOMIZED stimulus presentation
2. Fixed ISI and RANDOMIZED stimulus presentation
3. "Jittered" ISI and RANDOMIZED stimulus presentation
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1) BLOCK DESIGN
Block design was the first type of experimental paradigm to be used in FMRI
research, as well as the first to involve more complex statistical analysis.
It is still the most commonly used experimental paradigm in FMRI studies.
The block design consists of several discrete epochs of on-off periods,
with the "on" representing a period of stimulus presentations, and the
"off" referring to a state of rest or baseline. Although blocks may range
in duration from 16 seconds to a minute or more (average is about 20-30
seconds), they all share the same basic on-off pattern. These on-off
states are alternated throughout the experiment to ensure that signal
variation from small changes in scanner sensitivity, subject movement, or
attention shifts have a similar effect on the signal responses associated
with each of the different states. Below is an example of a block design
with two experimental conditions: pictures (red blocks) and words (green
blocks), along with their resulting hemodynamic response functions.
Once an experiment has been run and the data have been collected, the
appropriate statistical analysis must be implemented. With block design,
individual trials are not compared. Rather, the underlying hemodynamic
responses acquired during one blocked condition are compared to the
signals acquired from baseline, or from other blocks involving different
task conditions (e.g., "picture" blocks versus "word" blocks). As such,
regions of signal activity that change between one condition and another
can be identified with considerable statistical power.
Advantages of Block Design:
* A simple block design is adequate for many types of experiments,
especially in early, exploratory stages of research projects.
* Block designs allow for considerable experimental flexibility,
allowing parametric designs and multi-factorial designs to be
employed.
* Block design can be especially advantageous and a good starting point
for newcomers to the field of FMRI research.
* Block design is statistically powerful and straightforward to analyze,
as the shape of the response function can be assumed to be simple.
Disadvantages of Block Design:
* Block design can be predictable and boring, making it prone to
potential confounds such as rapid habituation, anticipation, set, or
other strategy effects.
* It may be difficult to control a specific cognitive state for the
relatively long periods of each block. A 'rest' state is rarely true
rest, as the mind may wander in a subject who is not engaged in a
specific task.
* Information regarding activation response time courses cannot be
obtained with block design because individual responses are lost
within the block.
* The high predictability of block design makes it inappropriate for
certain cognitive tasks, such as an 'oddball' paradigm where a
reaction to an unexpected stimulus is probed.
* The BOLD signal may not remain constant across the epoch of interest.
Within a block, the underlying hemodynamic response can change from
the first trial in the block to the last trial within the block. This
result may be a consequence of anticipatory effects.
* Block design is not feasible for certain patient populations. For
instance, hallucinatory schizophrenics who often display irregular or
uncontrollable behaviors cannot be forced into a block design.
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2) EVENT-RELATED DESIGN (ER-FMRI)
Event-related designs associate brain processes with discrete (rather than
blocked) events, which may occur at any point in the scanning session.
That is, different trials or stimuli are presented in arbitrary sequences.
This type of design mimics the format of a behavioral study more closely
than block design. With behavioral studies, stimulus events such as
pictures or words are presented one at a time, usually in a randomized
fashion, and separated by an inter-stimulus interval of a specified length.
Advantages of ER-FMRI:
* This type of design allows for stimulus events from various experiment
conditions (e.g., conditions A, B, and C) to be presented randomly in
one run. This type of scenario is not possible with block design.
* By detecting signals to individual trial events, ER-FMRI can parallel
behavioral studies by examining responses to individual trials rather
than blocks of trials.
* Event-related paradigms allow for greater flexibility and
randomization than block design, leading to more clever and less
predictable experiments.
* Unlike block design, ER-FMRI allows the experimenter to estimate the
hemodynamic response function from a single event type. The
hemodynamic response can be identified by averaging data acquired
after many discrete events. This approach is more powerful than
block design because it allows considerable flexibility for
determining, for example, responses to novel or aperiodically
presented stimuli, or exploring changes over time.
* Post-hoc sorting of stimulus trials can be done with ER-FMRI (e.g.,
correct vs. incorrect responses, aware vs. unaware, remembered vs.
forgotten items, quick vs. slow response times, etc.).
Disadvantages of ER-FMRI:
* The event-related design requires a greater understanding and grasp of
functional MRI because the design and statistical measures that follow
are more complex than those of block design. As a result, a newcomer
to the world of FMRI may initially encounter some difficulty when
attempting to design an event-related study.
* One major disadvantage of event-related design involves the signal-to-
noise (SNR) ratio. The timing of single events results in a lower SNR
for event-related FMRI. Specifically, for block design, the percent
signal change may be in the range of 3% to 5% while for event-related
design, it may be less than 1%. To compensate for this loss in
statistical power, the number of trials should be increased by
approximately 50 to 100 trials per condition. The result, however, is
longer scanning runs (more on this issue in the sections that follow).
The inter-stimulus interval between stimulus trials can vary, and this time
interval determines whether the event-related design is identified as
'SLOW' or 'RAPID.' An explanation of slow and rapid event-related design
is provided in the sections below.
A. SLOW EVENT-RELATED DESIGN
Within the scanner, a patient's exposure to a stimulus event may result in
a significant increase in brain activation, which is correlated with
localized changes in blood flow, oxygenation, and volume. These local
increases in blood flow and microvascular oxygenation take some time to
occur. The result is a delay in onset of the BOLD signal, which evolves
over an extended period of time, even for brief neuronal events. In fact,
the "plateau" of the hemodynamic response may not occur until 6-9 seconds
after the stimulus onset. The result will be a hemodynamic response
function that is spread out, usually far beyond the stimulus duration.
This phenomenon is known as "dispersion." On average, one should expect
the BOLD signal to rise and fall within 12-20 seconds. When implementing
an event-related design, one must consider this post-stimulus delay of the
BOLD signal. Stimulus trials spaced too close together will result in an
overlapping of their respective hemodynamic response functions, causing
them to become "tangled" or convolved. When this happens, more sophisticated
statistical measures are required to deconvolve the data.
With SLOW event-related design (a.k.a. 'widely spaced' or 'simple' event-
related design), the individual stimulus trials are spaced far apart in
time to prevent overlap of their hemodynamic functions. In other words,
the hemodynamic response that results from a single trial is allowed to
rise and fall completely before the next trial begins. Below is an
illustration of a slow event-related design, along with the resulting
hemodynamic response functions.
Advantages of slow event-related design:
* Since there is no overlap of the hemodynamic responses, slow event-
related paradigms do not require deconvolution analysis and are
therefore fairly easy to analyze statistically.
Disadvantages of slow event-related design:
* The long rest periods between stimulus presentations mitigate
habituation, expectation, and boredom, which can taint the experiment
with anticipatory effects.
* This type of design tends to be extremely time inefficient. Since
scanner time is limited, it is wasteful to spend so much time waiting
for the hemodynamic response to return to baseline.
* In addition to being wasteful, a disproportionate amount of time at
baseline results in the collection of less non-baseline data. Since
FMRI is a measurement of differences in response signal, it is perhaps
more efficient to get half of one's data at or near the baseline state,
and half at the non-baseline state. If too much time is spent at
baseline, the result is a good estimate of baseline ('small sigma' in
statistical terms), but a bad estimate of the activation ('large
Sigma'). If too much time is spent in activation, then the reverse is
true. However, an equal number of baseline and active trials will
increase the likelihood of sucessfully detecting a statistically
significant difference in response signal between baseline and
non-baseline states.
* Compared to block design, the signal-to-noise ratio (SNR) is lost by
approximately 33% in slow ER-FMRI. As mentioned before, one way to
compensate for this loss in statistical power is to increase the
number of trials per condition for event-related averaging.
Unfortunately, the additional trials will increase the experiment
duration, thus taking up more scanner time. Logistically, this option
may not be feasible when employing a slow ER design.
B. RAPID EVENT-RELATED DESIGN
Rapid event-related design is similar to slow event-related design with the
exception that it takes individual stimulus events and spaces them at close
intervals. For instance, the ISI may be set to as little as two seconds.
The result is a significant overlap of hemodynamic response functions that
must later be disentangled to determine the effect of each stimulus
condition on brain activation.
Advantages of rapid event-related design:
* The shorter resting gaps between events leads (hopefully) to a
decrease in subject boredom. Thus, rapid ER design is much more
resistant to habituation, set, and expectation than slow ER paradigms.
* Rapid stimulus presentation makes is possible to adequately squeeze in
more stimulus trials per run, thus improving statistical power by
increasing the number of responses to be averaged per unit of time.
Disadvantages of rapid event-related design:
* The signal-to-noise ratio loss is even greater for rapid event-related
design than it is for slow ER-FMRI. Specifically, SNR loss is
approximately 17% more for rapid ER than for slow ER, and 50% more for
rapid ER compared to block design.
* The decreased inter-stimulus interval results in hemodynamic responses
that overlap substantially. Assuming linearity, the overlapping
hemodynamic responses often found in rapid designs must be separated
by a statistical process known as deconvolution. In simpler terms,
each individual hemodynamic response function must be disentangled so
that the effect of each stimulus condition (say, conditions A, B, and
C) can be differentiated and measured. This requires greater
statistical savvy and know-how on the part of the experimenter. In
addition, this overlap problem can only be resolved if the
experimental design is properly randomized. (It should become quite
clear in the next section why randomization of stimuli is so essential
in rapid event-related FMRI).
Now that the basics of rapid event-related design have been covered, the
issues of proper randomization of stimuli and fixed versus "jittered"
inter-stimulus intervals will be discussed.
1. Rapid ER-FMRI:
Fixed ISI + NON-randomized stimulus presentation = BAD DESIGN
When using an event-related design, it is important to remember
that the stimuli must be properly counterbalanced to ensure that each
trial type is preceded and followed by each trial type equally often.
If this does not happen, the result can be detrimental when it comes
time to run the statistics on the data. Primarily, a short, fixed ISI
paired with a sequential ordering of the stimulus events can lead to a
problem known as "multicollinearity" or "identification problem". The
problem of multicollinearity is illustrated below in Figure 3:
As Figure 3 demonstrates, it is problematic to combine a short, fixed
ISI with a stimulus presentation that always orders the trials in the
same exact manner (e.g., A, rest, B, rest, C, rest, A, rest, B, rest,
C, rest...). In this example, the responses always overlap in the same
way (A followed by B followed by C). As a result, there is much
ambiguity as to the source of the observed response. Is the observed
sum of the hemodynamic response functions due to stimulus A alone? Is
it due to the combined contributions of stimuli A and B? A and C? A,
B, and C? It is impossible to answer this question. It is also
important to note that multicollinearity is not a problem due to the
limitations of statistical programs that calculate the deconvolution of
time-series datasets (e.g., AFNI 3dDeconvolve). Rather, the limitation
is mathematical in nature. In such a case, it is mathematically
impossible to determine the contribution of each individual stimulus
trial to the sum of the hemodynamic responses.
Fortunately, the resolution to this dilemma can be simple. When
implementing a rapid ER design, it is important to randomize the
stimulus presentations so that every trial is preceded and followed by
every other trial type an equal number of times. The 'AFNI_howto'
section of this HowTo provides a script that does this very thing using
the AFNI program 'RSFgen' (i.e., Random Stimulus Functions generator).
2. Rapid ER-FMRI:
Fixed ISI + RANDOMIZED stimulus presentation = BETTER DESIGN
Figure 3 illustrates the detrimental effects of ignoring
randomization in rapid event-related design. To successfully deconvolve
the overlapping HRF's, a rapid ER design should include every possible
combination of trial sequences. Since responses sum in an approximately
linear fashion, the responses to rapidly presented stimuli can be
extracted from the data if the stimulus presentations are randomly
varied. Figure 4 provides an example of such a design and the resulting
HRF's:
In the above figure, the effort was made to randomize the stimulus
trials in a way that ensured successful deconvolution of the overlapping
HRF's. By properly counterbalancing the trials, one can now
mathematically determine the contribution of each stimulus condition on
the observed sum of the hemodynamic responses.
Hopefully, these examples demonstrate how taking the time to carefully
design and execute an experiment is well worth the effort. The end
result will be the collection of 'deconvolvable' data, which can be
properly analyzed and understood.
3. Rapid ER-FMRI with "jittered" ISI
Up to this point, this HowTo has illustrated examples of FMRI
designs involving inter-stimulus intervals that are "fixed." In other
words, the ISI remains constant throughout the experiment. However,
just as stimulus presentations can be randomized, so can the ISI. With
a "jittered" or stochastic stimulus timing, the inter-stimulus interval
is randomized throughout the experiment. The result is a varied onset
of successive stimulus events, with randomly intervening rest intervals.
Below is an example of a rapid event-related design with a jittered
inter-stimulus interval:
A differential ISI results in an even more differential HRF overlap,
further reducing the probability that an experiment design will confront
multicollinearity problems. However, these types of designs will be more
challenging to analyze.
In some cases, differential ISI's are necessary because the timing of
the stimulus presentations is determined by the subject. Many
behavioral studies implement this type of "self-paced" timing. In other
cases, jittered stimulus timing is incorporated into the experiment
design because it introduces more overall randomness to the study. This
can be a good thing considering subjects are inquisitive creatures who
are constantly attempting to "figure out" the experiment. More
randomness and unpredictability significantly decreases anticipatory
effects.
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SUMMARY
While block design provides a fairly simple and straightforward approach to
creating an experimental paradigm, the habituation and possible hemodynamic
lag that afflict a design of this sort is problematic. Furthermore, block
design may not be feasible for certain types of cognitive tasks or patient
populations. On the other hand, event-related design is more flexible and
can introduce randomization of stimulus trials into the experiment, thus
mimicking behavioral studies more closely. The downside is that analysis
of the data may be more complicated, particularly if the hemodynamic
response functions overlap significantly as in the case of rapid event-
related design. In addition, the signal-to-noise ratio decreases
dramatically with event-related design.
When implementing an event-related design, one must decide between a fixed or
"jittered" inter-stimulus interval, and appreciate the benefits of proper
counterbalancing of stimulus events. One must also choose between a "slow"
or "rapid" ISI, and consider the pros and cons of each. While slow ER
design avoids the overlap of hemodynamic response functions, the longer ISI
mitigates habituation and results in the collection of less non-baseline
data. Conversely, rapid ER design is less boring and more non-baseline
data can be obtained, but the substantial overlap of hemodynamic response
functions reduces the SNR ratio and requires more complex deconvolution
measures.
Given the advantages and disadvantages of each research paradigm, many
researchers are opting for the best of both worlds by creating experiments
that incorporate both block and event-related design. Ultimately, it is
up to the user to decide which particular design (or combination) is most
suitable for their experimental needs. Whichever paradigm one decides to
implement, it is important that the experiment design be thought out and
planned well in advance. One should consider which experimental paradigm
will be best for the cognitive phenomenon being examined and the patient
population being tested. Likewise, one should consider which statistical
measures are appropriate for the data being gathered. By doing so, the
scanning process, data collection, and statistical analysis will go much
smoother.