1. Designing a behavioral experiment
Chris Rorden
Designing fMRI studies
fMRI signal is sluggish and additive.
Efficient designs maximize predictable changes in HRF.
Efficient designs are often very predictable
Participant may anticipate events.
Techniques for balancing efficiency and psychological validity.

2. Finding effects
Statistics are based on the ratio of explained predictable versus unexplained variability:
We can improve statistical efficiency by
Increasing the task related variance (signal)
Designing Experiments (today’s lecture)
Decreasing unrelated variance (noise)
Spatial and temporal processing lectures.
Good signal in our fMRI data
Physics lectures
Signal+Noise Noise F=
Signal Noise t=

3. There are two crucial aspects of the BOLD effect:
fMRI Signal
There are two crucial aspects of the BOLD effect:
The HRF is very sluggish
Delay between brain activity and changes in fMRI images (~5s).
The HRF is additive
Doing a task twice causes about twice as much change as doing it once.

4. Visual cortex shows peak response ~5s after visual stimuli.
The BOLD timecourse
Visual cortex shows peak response ~5s after visual stimuli.
Indirect measure 2 1
% Signal Change
Time (seconds)

5. Temporal Properties of fMRI Signal
We predict the HRF by convolving the neural signal by the HRF.
We want to maximize the amount of predictable variability.
Neural Signal
HRF
Convolved Response =

6. BOLD effects are additive
Three stimuli presented rapidly result in almost 3 times the signal of a single stimuli (e.g. Dale & Buckner, 1997).
Crucial finding for experimental design.
Note there are limits to this additivity effect, but the basic point is that more stimuli generate more signal (see Birn et al. 2001)

7. Comparing predictable HRF
Consider 3 paradigms:
Fixed ISI: one stimuli every 16 seconds.
Inefficient
Fixed ISI: one stimuli every 4 seconds.
Insanely inefficient: virtually no task-related variability
Block design: cluster five stimuli in 8 seconds, pause 12 seconds, repeat.
Very efficient.
Cluster of events is additive. Note peak amplitude is x3 the 16s design.

8. aka ‘Box Car’, or ‘Epoch’ designs.
Block Designs
aka ‘Box Car’, or ‘Epoch’ designs.
Different cognitive processes occur in distinct time periods
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9. Optimal Design Block designs are optimal.
Present trials as rapidly as possible for ~12 sec
Summation maximizes additive effect of HRF.
Consider experiment:
Three conditions, each condition repeated 14 times (once every 900ms)
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Note huge predictable variability in signal.

10. While efficient, block designs are often predictable.
May not be experimentally valid.
Optimal block length around 12s, followed by around 12s until condition is repeated.
Avoid long blocks:
Reduced signal variability
Low frequency signal will be hard to distinguish from low frequency signals such as drift in MRI signal.

11. Block design limitations
Block designs good for detection, poor for estimating HDR.
Detection: which areas are active?
Estimation: what is the timecourse of activity?

12. Block design limitations
While block designs offer statistical power, they are very predictable.
E.G. our participants will know they will press the same finger 14 times in a row.
Many tasks not suitable for block design
E.G. Novelty detection, memory, etc.
Your can not post-hoc sort data from block designs, e.g. Konishi, et al., 2000 examine correct rejection vs hits on episodic memory task.

13. Event related designs Much less power than block designs.
Simply randomizing trial order of our block design, the typical event related design has one quarter the efficiency.
Here, we ran 50 iterations and selected the most efficient event related design.
Still half as efficient as the block design.
Note this design is not very random: runs of same condition make it efficient.

14. Permuted Blocks
Permuted block designs (Liu, 2004) offer possible some unpredictability…
Permuted Design:
Start with a block design
Randomly swap stimuli
Repeat step 2 for n iterations
More iterations = less predictable, less power

15. Permuted Blocks
Below you can see our study after 10 permutations during the first minute of scanning.
Permuted block designs can offer a balance of power and predictability.

16. Jittered Inter-Stimulus Interval
Dale et al. suggest using exponential distribution for inter-trial intervals.
Exponential Distribution:
Many trials have short duration
A few trials have long duration
Efficient because jittering makes events block-like
1 condition, fixed ISI = little variability
1 condition, exponential ISI = more variability

17. Interstimulus Intervals and Power
Fixed ISI: low statistical power
Fixed ISI have most power if >12sec between stimuli
At that rate, only a few dozen trials in a 10 minute scan.
In theory, variable ISI can offer much more efficiency than fixed ISI.
Exponential Distribution

18. Should you use variable ISIs?
In practice, variable ISIs often reduce power.
Most experiments have more than one condition, so fixed ISI designs also have temporal variability.
Unless you are looking at low-level processes (e.g. early vision), trials must be separated by a couple seconds.
For multi-condition studies, the minimum time between trials is crucial.
People are faster to respond to fixed ISI than variable ISI
Therefore, fixed ISI are often more powerful
However, variable ISI may help us reconstruct the true shape of the HRF measured.

19. Tips
For event related designs – helpful if TR is either variable or a not evenly divisible by the interstimulus interval.
Allows you to accurately estimate whether conditions influence the latency of response.
TR not divisible by ISI
TR divisible by ISI

20. Generate your own experiments…
Set the TR (time per volume)
Set the number of volumes
Set minimum ISI – this will be time between trials for block designs.
Set the mean ISI – this will be the average time between trials for event related designs.
Set the number of conditions.
Iterations – you can compute hundreds of event related designs and choose the most efficient High iterations will lead to efficient but predictable designs.
Permutations – select the number of permutations for the permuted block design. Fewer permutations lead to efficient but predictable designs.
Press the type of study you want to generate
Block
Permuted Block
Fixed ISI Event
Exponential ISI Event

21. Experiment generator Software reports variance.
Higher variance corresponds with more power.
Power relative – do not directly compare studies with different TR or volumes.
Only approximate estimate of power: does not ensure conditions have uncorrelated responses.
Press ‘i’ button to see text file of condition onset times (you can paste into e-prime).

22. General guidelines (Nichols et al)
If possible, use block design
Keep blocks <40s (temporal processing lecture describes why)
Limit number of conditions
Pairwise comparisons far apart in time may be confounded by low frequency noise.
Randomize order of events that are close to each other in time.
Randomize SOA between events that need to be distinguished.
Run as many people as possible for as long as possible.
Have testable anatomical prediction

23. However, long sessions can lead to problems:
Increasing power
Increasing the sample size (more people, more scans per person) is a fantastic way to increase statistical power.
However, long sessions can lead to problems:
Increased head motion
Poor task compliance (bored = fall asleep)
Learning effects (make sure the different conditions balanced throughout session).