Experimental Design

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

Experimental Design

FMRI Graduate Course (NBIO 381, PSY 362)

Dr. Scott Huettel, Course Director

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

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

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

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)

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

What types of hypotheses are possible for fMRI data?

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

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

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

fMRI Design Types

1) Blocked Designs2) Event-Related Designs

a) Periodic Single Trial b) Jittered Single Trial

3) Mixed Designs- Combination blocked/event-related

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

1. Blocked Designs

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

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

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

PET Designs

• Measurements done following injection of radioactive bolus

• Uses total activity throughout task interval (~30s)

• Blocked designs necessary– Task 1 = Injection 1– Task 2 = Injection 2

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

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

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

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

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

Adapted from Gusnard & Raichle (2001)

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

Adapted from Gusnard & Raichle (2001)

Oxygen Extractio

n Fraction

Cerebral Metabolic Rate of

O2

Cerebral Blood Flow

Any true baseline?

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

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”

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

Default Mode!

Damoiseaux 2006 analyzed separate 10-subject resting-state data sets, using Independent Components analysis.

Vision.

Memory.

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

Power in Blocked Designs

1. Summation of responses results in large variance

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

HDR Estimation: Blocked Designs

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

Deeper concept…

We want the changes evoked by the task to be at different parts of the frequency spectrum than non-task-evoked changes.

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

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

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

2. Event-Related Designs

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

What are Event-Related Designs?

• Event-related designs associate brain processes with discrete events, which may occur at any point in the scanning session.

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

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 averaging– General linear models

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

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

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

McCarthy et al., (1997)

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

Trial Spacing Effects: Periodic Designs

20sec

8sec 4sec

12sec

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

From Bandettini and Cox, 2000

ISI: Interstimulus Interval

SD: Stimulus Duration

Why not short, periodic designs?

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

2b. Jittered Single Trial Designs

• Varying the timing of trials within a run

• Varying the timing of events within a trial

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

Effects of Jittering on Stimulus Variance

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

Dale and Buckner (1997)

How rapidly can we present stimuli?

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

Effects of ISI on Power

Birn et al, 2002

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

Mean Interval between Stimuli (sec)

Rel

ativ

e E

ffici

ency

None

Maximal

Efficient Experimental Design

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

Post-Hoc Sorting of Trials

From Kim and Cabeza, 2007

Using information about fMRI activation at memory encoding to predict behavioral performance at

memory retrieval.

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

Limitations of Event-Related Designs

• None, really, at least with design itself.

• 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.)?

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

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…

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

3. Mixed Designs

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

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.

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

How do we identify efficient designs?

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

Issues in Design Efficiency

• Not all random designs are equally efficient!

• Design efficiency is defined in relation to some contrast

• Efficiency may interact with predictability & expectation

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

Iterative (Genetic) Algorithms

Eliminate inefficient designs

Retest modifications of efficient designs

Select the most efficient designs

A B C

A B CA B C

A B CDesigns

Designs

A B C

FMRI – Week 8 – Experimental Design Scott Huettel, Duke University

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