Experimental Designfor

Functional MRI

David GlahnUpdated by JLL

General Experimental Design

• What is the question you are trying to ask?

• What are the appropriate controls?

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

From Scott Huettel, Duke

Donder’s Method: Subtraction

• A random series of A’s and B’s presented and the subject must:– Task 1 - Respond whenever event A or B occurs (RT1)– Task 2 - Respond only to A not to B (RT2)– Task 3 - Respond X to A and Y to B (RT3)

RT = reaction time

• RT1 = RT(detect) + RT(response)• RT2 = RT(detect) + RT(discrimination) + RT(response)• RT3 = RT(detect) + RT(discrimination) + RT(choice) +

RT(response)• RT(discrimination) = RT2 - RT1

• RT(choice) = RT3 - RT2

Example: How long does it take to choose between alternatives? (Mental Chronometry)

Criticisms of Donder

• Assumes that adding components does not affect other components (i.e. assumption of pure insertion)

• One should pick tasks that differ along same dimension

• Although resting baseline is good to include, it may limit inference

(e.g. Sternberg, 1964)

What types of hypotheses are possible for fMRI data?

From Scott Huettel, Duke

Experimental Design for fMRI

Hemodynamic Response Function(HRF)

Savoy et al., 1995

Linear Systems Analysis Boynton et al. 1996

• The linear transform model of fMRI hypothesizes that responses are proportional to local average neural activity averaged over a period of time. – fMRI responses in human primary visual cortex (V1) depend on

both stimulus timing (8 Hz) and stimulus contrast (black/white). – Responses to long-duration stimuli can be predicted from HRC

derived from shorter duration stimuli. – The noise in the fMRI data is independent of stimulus contrast

and temporal period.

• Because the linear transform model is consistent with our data, we proceeded to estimate the temporal fMRI response function and the underlying (presumably neural) contrast response function using HRF…

• Assumption is that HRF is linear and shift-invariant!

Linearity of BOLD responseDale & Buckner, 1997

Sync each differential response to start of trial

Not quite linear but good enough for first order approximations

Reversing Checkerboard (8 Hz)

One-trial = 1 stimulus

Two-trial – 2 stimuli

Three-trial = 3 stimuli

Stim duration (SD) = 1 s

Inter-stim interval (ISI) = 2 s

fMRI Design Types

1) Blocked Designs2) Event-Related Designs

a) Periodic Single Trial b) Jittered Single Trial

3) Mixed Designs- Combination blocked/event-

related

Blocked Designs

What are Blocked Designs?

• Blocked designs segregate different cognitive tasks 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

“Loose” vs. “Tight” Block Designs

• Loose: 1 Task, 1 contrast (with Baseline)

• Tight: more than 1 Task, multiple contrasts (including baseline)

Choosing Length of Blocks

• Longer block lengths allow for stability of extended responses– Hemodynamic response saturates following extended stimulation

• After about 10s, activation reaches plateau– Many tasks require extended intervals

• Brain processing may differ throughout the task period

• Shorter block lengths move your signal to higher frequencies– Away from low-frequency noise: scanner drift, etc.– Not possible in O-15 PET rCBF studies

• Periodic blocks may result in aliasing of other variance in the data– Example: if the person breathes at a regular rate of 12

breaths/min and the blocks are 10s long (6 blocks/min)

From Scott Huettel, Duke

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 if not matched

properly(e.g. Rest with eyes closed but task had eyes open)

Adapted from Gusnard & Raichle (2001)

Adapted from Gusnard & Raichle (2001)

Oxygen Extractio

n Fraction

Cerebral Metabolic Rate of

O2

Cerebral Blood Flow

A True Baseline?

Depends on what is measured!

Non-Task Processing

• In experiments activation can be 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 Network”

Default Mode!

Damoiseaux 2006 analyzed separate 10-subject resting-state data sets, using

Independent Components analysis (ICA).

Vision.

Memory.

Power in Blocked Designs

1. Summation of responses results in large signals then plateaus (at 8-16 s duration)

1. Duration does not plateau

Stimulus duration

and interval

short compared with HRF

What are the temporal limits?What is the shortest stimulus duration that fMRI can detect?

Blamire et al. (1992) – 2 secBandettini (1993): 0.5 secSavoy et al (1995): 34 msec

• With enough averaging, anything seems possible.

• Assume that the shape of the HRF is predictable.

• Event-related potentials (ERPs) are based on averaging small responses over many trials.

• Can we do the same thing with fMRI?

Assumption of steady-state dynamics.

For block designs we assume that the BOLD effect remains constant across the epoch of interest.

For PET this assumption is valid given the half-life of the tracers used to image the brain.

But the BOLD response is much more transient and more importantly may vary according to brain regions and stimulus durations and maybe even stimulus types.

Savoy et al., 1995

Limitations of Blocked Designs

• Sensitive to signal drift or MR instability

• Poor choice of conditions/baseline may preclude meaningful conclusions

• Many tasks cannot be conducted repeatedly

Event-Related Designs

What are Event-Related Designs?

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

• Can detect transient BOLD responses• Supports adapting task to response

Buckner et al., 1998

Event RelatedEvent Related

Why use event-related designs?

• Some experimental tasks are naturally event-related (future stimuli based on response)

• Allows studying of within-trial effects• Improves relation to behavioral factors

(behavior changes within blocks missed)• Simple analyses

– Selective averaging– General linear models (GLM)

Single Event

Averaging

Sorting Into Common Groups

- Behavior

- Physiological Measure

- Outlier Rejection

- Transient vs. Task level Responses

Periodic Single Trial Designs

• Stimulus events presented infrequently with long inter-stimulus intervals (ISIs)

500 ms 500 ms 500 ms 500 ms

18 s 18 s 18 s

Trial Spacing Effects: Periodic Designs

8sec 4sec

20sec 12sec

A20

A4

A8

A12

Need the signal amplitude to vary to distinguish responding areas of brain from those with no response.

Bandettini & Cox, 2000 • The optimal inter-stimulus interval (ISI) for a stimulus duration (SD), was determined.

• Empirical Observation: For SD=2sec, ISI=12 to 14 sec.• Theory Predicts: For SD<=2 sec, the optimal repetition interval (RI=ISI+SD)• Theory Predicts: For SD>2sec, RI = 8+(2*SD).

• The statistical power of ER-fMRI relative to blocked-design was determined

• Empirical: For SD=2, ER-fMRI was ~35% lower than that of blocked-design • Simulations that assumed a linear system demonstrated estimate ~65% reduction in power• Difference suggest that the ER-fMRI amplitude is greater than

that predicted by a linear shift-invariant system.

Jittered Single Trial Designs

• Varying the timing of trials within a run• Varying the timing of events within a trial

Effects of Jittering on Response

Stimulus

Response

Jittering allows us to sample BOLD response in more states

Effects of ISI on Detectability

Birn et al, 2002

Jittered ISI

Constant ISI

Detectability

Estimated

Accuracy of

HRF

Max when ½ stims are task state and ½

stims are control state

Dale and Buckner (1997)

Detecting Using Selective Averaging

Low Response

Fewer Samples

Good Response

More Samples

Best Response

Most samples

Visual stim duration = 1 s; acquisition 240 sec

Trials subtracted then correlation analysis with predicted response

Variability of HRF: EvidenceAguirre, Zarahn & D’Esposito, 1998• HRF shows considerable variability between subjects

• Within subjects, responses are more consistent, although there is still some variability between sessions

different subjects

same subject, same session same subject, different session

Variability of HRF: ImplicationsAguirre, Zarahn & D’Esposito, 1998• Generic HRF models (gamma functions) account for 70% of variance• Subject-specific models account for 92% of the variance (22% more!)• Poor modeling reduces statistical power• Less of a problem for block designs than event-related (why?)• Biggest problem with delay tasks where an inappropriate estimate of the initial and final components contaminates the delay component

• Possible solution: model the HRF individually for each subject

• Possible caveat: HRF may also vary between areas, not just subjects• Buckner et al., 1996:

• noted a delay of 0.5-1 sec between visual and prefrontal regions• vasculature difference?• processing latency?

• Bug or feature? • Menon & Kim – mental chronometry

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.

Limitations of Event-Related Designs

• Low power (maybe)– Collecting lots of data, many runs

• 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.)?

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…

Mixed Designs

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.

Mixed Design

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

What is fMRI Experimental Design?

• Controlling the timing and quality of cognitive operations to influence brain activation

• 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)