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Experimental Design
An Introduction to MRI Physics and Analysis
Michael Jay Schillaci, PhDMonday, March 17th, 2008
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Overview
Design terminology Blocked Designs Event-Related Designs Mixed Designs Summary
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Design Terminology
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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
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What is fMRI Experimental Design?
Controlling the timing and quality of cognitive operations (IVs) to influence brain activation (DVs)
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)
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What types of hypotheses are possible for fMRI data?
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Optimal Experimental Design
Maximizing both Detection and Estimation Maximal variance in signal (incr. detect.) Maximal variance in stimulus timing (incr. est.)
Limitations on Optimal Design Refractory effects Signal saturation Subject’s predictability
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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+NoiseNoise
F=SignalNoise
t=
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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
0 6 12 18 24
% Signal Change
2
1
0
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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Blocked Designs
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What are Blocked Designs?
Blocked designs segregate different cognitive processes into distinct time periods
Task A Task B Task A Task B Task A Task B Task A Task B
Task A Task BREST REST Task A Task BREST REST
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Comparing predictable HRF Consider 3 paradigms:
1. Fixed ISI: one stimuli every 16 seconds.
– inefficient
2. Fixed ISI: one stimuli every 4 seconds.
– Insanely inefficient: virtually no task-related variability
3. 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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Choosing Length of Blocks
Longer block lengths allow for stability of extended responses Hemodynamic response saturates following extended stimulation
After about 10s, activation reaches max Many tasks require extended intervals
Processing may differ throughout the task period
Shorter block lengths move your signal to higher frequencies Away from low-frequency noise: scanner drift, etc.
Periodic blocks may result in aliasing of other variance in the data Example: if the person breathes at a regular rate of 1 breath/5sec, and the blocks
occur every 10s
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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)1. Press left index finger when you see 2. Press right index finger when you see 3. Do nothing when you see
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 periods1. Press left index finger when you see 2. Press right index finger when you see 3. Do nothing when you see
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Block designs good for detection, poor for estimating HDR.
Block design limitations
Detection: which areas are active?
Estimation: what is the timecourse of activity?
-10 0 10 20 30 40
R_Tap L_Tap right left
-0.005
0.000
0.005
0.010
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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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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
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25 Adapted from Gusnard & Raichle (2001)
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26 Adapted from Gusnard & Raichle (2001)
Oxygen Extraction Fraction
Cerebral Metabolic Rate of O2
Cerebral Blood Flow
Any true baseline?
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Non-Task Processing
In many experiments, activation is greater in baseline conditions than in task conditions! Requires interpretations of significant activation
Suggests the idea of baseline/resting mental processes Gathering/evaluation about the world around you Awareness (of self) Online monitoring of sensory information Daydreaming
This collection of processes is often called the “Default Mode”
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Default Mode!
Damoiseaux 2006 analyzed separate 10-subject resting-state data sets, using Independent
Components analysis.
Vision.
Memory.
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Power in Blocked Designs
1. Summation of responses results in large variance
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HDR Estimation: Blocked Designs
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Deeper concept…
We want the changes evoked by the task to be at different parts of the frequency spectrum than non-task-evoked changes.
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Limitations of Blocked Designs
Very sensitive to signal drift
Poor choice of conditions/baseline may preclude meaningful conclusions
Many tasks cannot be conducted repeatedly
Difficult to estimate the Hemodynamic Response
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Event-Related Designs
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What are Event-Related Designs? Event-related designs associate brain processes with
discrete events, which may occur at any point in the scanning session.
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Why use event-related designs?
Some experimental tasks are naturally event-related
Allows studying of trial effects Improves relation to behavioral factors Simple analyses
Selective averagingGeneral linear models
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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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2a. Periodic Single Trial Designs
Stimulus events presented infrequently with long interstimulus intervals
500 ms 500 ms 500 ms 500 ms
18 s 18 s 18 s
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38 McCarthy et al., (1997)
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Trial Spacing Effects: Periodic Designs
20sec
8sec 4sec
12sec
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From Bandettini and Cox, 2000
ISI: Interstimulus Interval
SD: Stimulus Duration
Why not short, periodic designs?
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2b. Jittered Single Trial Designs
Varying the timing of trials within a run Varying the timing of events within a trial
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Effects of Jittering on Stimulus Variance
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43 Dale and Buckner (1997)
How rapidly can we present stimuli?
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Effects of ISI on Power
Birn et al, 2002
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Mixed Designs
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3a. 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.
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Permuted Blocks
Permuted block designs (Liu, 2004) offer possible some unpredictability…
Permuted Design:
1. Start with a block design
2. Randomly swap stimuli
3. Repeat step to for n iterations
More iterations = less predictable, less power
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Summary
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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
You can model a block with events…
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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