Designing a behavioral experiment

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 (todays lecture)
• Decreasing unrelated variance (noise)
• Spatial and temporal processing lectures.
• Good signal in our fMRI data
• Physics lectures

3
fMRI Signal

• There are two crucial apects of the BOLD effect
• The HRF is very sluggish
• The is a long 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.

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The BOLD timecourse

• Visual cortex shows peak response 5s after
  visual stimuli.
• Indirect measure

2 1 0
Signal Change
0 6 12 18
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Time (seconds)
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Temporal Properties of fMRI Signal

• Hemodynamic response function (HRF) is sluggish
  peak signal above 5s after activation.
• We predict the HRF by convolving the neural
  signal by the HRF.
• We want to maximize the amount of predictable
  variability.

Convolved Response
Neural Signal
HRF

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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)

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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.

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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)
• Press left index finger when you see ç
• Press right index finger when you see è
• Do nothing when you see é

Note huge predictable variability in signal.
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Block designs

• 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.

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Block Designs

• aka Box Car, or Epoch designs.
• Different cognitive processes occur in distinct
  time periods
• Press left index finger when you see ç
• Press right index finger when you see è
• Do nothing when you see é

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Block design limitations

• Block designs good for detection, poor for
  estimating HDR.

Detection which areas are active? Estimation
what is the timecourse of activity?
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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.

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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.

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Permuted Blocks

• Permuted block designs (Liu, 2004) offer possible
  some unpredictability

• Permuted Design
• Start with a block design
• Randomly swap stimuli
• Repeat step to for n iterations
• More iterations less predictable, less power

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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.

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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, exponential ISI more variability
1 condition, fixed ISI little variability
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Interstimulus Intervals and Power

• Fixed ISI low statistical power
• Fixed ISI have most power if gt12sec 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
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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.

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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
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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
  efficientHigh 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

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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).

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General guidelines (Nichols et al)

• If possible, use block design
• Keep blocks lt40s
• 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

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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).