Basics of Experimental Design for fMRI: Event-Related Designs Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University of Western Ontario Jody Culham Brain and Mind Institute Department of Psychology University of Western Ontario

Part III Choosing an Event-Related Design

Convolution of Single Trials Neuronal Activity Haemodynamic Function BOLD Signal Time Slide from Matt Brown

BOLD Summates Neuronal Activity BOLD Signal Slide from Matt Brown

BOLD Overlap and Jittering Closely-spaced haemodynamic impulses summate. Constant ITI causes tetanus. Burock et al

Design Types Block Design Slow ER Design Rapid Counterbalanced ER Design Rapid Jittered ER Design Mixed Design = null trial (nothing happens) = trial of one type (e.g., face image) = trial of another type (e.g., place image)

Block Designs Early Assumption: Because the hemodynamic response delays and blurs the response to activation, the temporal resolution of fMRI is limited. = trial of one type (e.g., face image) = trial of another type (e.g., place image) WRONG !!!!! Block Design Positive BOLD response Initial Dip Overshoot Post-stimulus Undershoot BOLD Response (% signal change) Time Stimulus

What are the temporal limits? What is the briefest stimulus that fMRI can detect? Blamire et al. (1992): 2 sec Bandettini (1993): 0.5 sec Savoy et al (1995): 34 msec Although the shape of the HRF delayed and blurred, it is predictable. Event-related potentials (ERPs) are based on averaging small responses over many trials. Can we do the same thing with fMRI? Data: Blamire et al., 1992, PNAS Figure: Huettel, Song & McCarthy, s stimuli single events Data: Robert Savoy & Kathy O’Craven Figure: Rosen et al., 1998, PNAS

Detection vs. Estimation detection: determination of whether activity of a given voxel (or region) changes in response to the experimental manipulation Definitions modified from: Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging % Signal Change 0 Time (sec) estimation: measurement of the time course within an active voxel in response to the experimental manipulation

Block Designs: Poor Estimation Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging

Pros & Cons of Block Designs Pros high detection power has been the most widely used approach for fMRI studies accurate estimation of hemodynamic response function is not as critical as with event-related designs Cons poor estimation power subjects get into a mental set for a block very predictable for subject can’t look at effects of single events (e.g., correct vs. incorrect trials, remembered vs. forgotten items) becomes unmanagable with too many conditions (e.g., more than 4 conditions + baseline)

Slow Event-Related Designs Slow ER Design

Slow Event-Related Design: Constant ITI Bandettini et al. (2000) What is the optimal trial spacing (duration + intertrial interval, ITI) for a Spaced Mixed Trial design with constant stimulus duration? Block 2 s stim vary ISI Sync with trial onset and average Source: Bandettini et al., 2000

Optimal Constant ITI Brief (< 2 sec) stimuli: optimal trial spacing = 12 sec For longer stimuli: optimal trial spacing = 8 + 2*stimulus duration Effective loss in power of event related design: = -35% i.e., for 6 minutes of block design, run ~9 min ER design Source: Bandettini et al., 2000

Trial to Trial Variability Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging

How Many Trials Do You Need? standard error of the mean varies with square root of number of trials Number of trials needed will vary with effect size Function begins to asymptote around 15 trials Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging

Effect of Adding Trials Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging

Pros & Cons of Slow ER Designs Pros good estimation power allows accurate estimate of baseline activation and deviations from it useful for studies with delay periods very useful for designs with motion artifacts (grasping, swallowing, speech) because you can tease out artifacts analysis is straightforward Cons poor detection power because you get very few trials per condition by spending most of your sampling power on estimating the baseline subjects can get VERY bored and sleepy with long inter-trial intervals  Hand motion artifact % signal change Time Activation 

“Do You Wanna Go Faster?” Yes, but we have to test assumptions regarding linearity of BOLD signal first Rapid Jittered ER Design Mixed Design Rapid Counterbalanced ER Design

Linearity of BOLD response Source: Dale & Buckner, 1997 Linearity: “Do things add up?” red = green = Sync each trial response to start of trial Not quite linear but good enough!

Optimal Rapid ITI Rapid Mixed Trial Designs Short ITIs (~2 sec) are best for detection power Do you know why? Source: Dale & Buckner, 1997

Efficiency (Power)

Design Types = trial of one type (e.g., face image) = trial of another type (e.g., place image) Rapid Counterbalanced ER Design

Detection with Rapid ER Designs To detect activation differences between conditions in a rapid ER design, you can create HRF-convolved reference time courses You can perform contrasts between beta weights as usual Figure: Huettel, Song & McCarthy, 2004

Variability Between Subjects/Areas greater variability between subjects than between regions deviations from canonical HRF cause false negatives (Type II errors) Consider including a run to establish subject-specific HRFs from robust area like M1 Handwerker et al., 2004, Neuroimage

Event-Related Averaging In this example an “event” is the start of a block In single-trial designs, an event may be the start of a single trial First, we compute an event related average for the blue condition Define a time window before (2 volumes) and after (15 volumes) the event Extract the time course for every event (here there are four events in one run) Average the time courses across all the events

Event-Related Averaging Second, we compute an event related average for the gray condition

Event-Related Averaging Third, we can plot the average ERA for the blue and gray conditions on the same graph

Event-Related Averaging in BV Define which subjects/runs to include Define which conditions to average (usually exclude baseline) Set time window Determine how you want to define the y-axis values, including zero We can tell BV where to put the y=0 baseline. Here it’s the average of the two circled data points at x=0.

But what if the curves don’t have the same starting point? In the data shown, the curves started at the same level, as we expect they should because both conditions were always preceded by a resting baseline period But what if the data looked like this? …or this?

Epoch-based averaging In the latter two cases, we could simply shift the curves so they all start from the same (zero) baseline FILE-BASED AVERAGING: zero baseline determined across all conditions (for 0 to 0: points in red circles) EPOCH-BASED AVERAGING: zero baselines are specific to each epoch

File-based vs. Epoch-based Averaging File-based Averaging zero is based on average starting point of all curves works best when low frequencies have been filtered out of your data similar to what your GLM stats are testing time courses may start at different points because different event histories or noise 0 Epoch-based Averaging each curve starts at zero can be risky with noisy data only use it if you are fairly certain your pre-stim baselines are valid (e.g., you have a long ITI and/or your trial orders are counterbalanced) can yield very different conclusions than GLM stats e.g., set EACH curve such that at time=0, %BSC=0 0 0

What if…? This design has the benefit that each condition epoch is preceded by a baseline, which is nice for making event-related averages However, we might decide that this design takes too much time because we are spending over half of the time on the baseline. Perhaps we should use the following paradigm instead…? This regular triad sequence has some nice features, but it can make ERAs more complicated to understand.

Regular Ordering and ERAs We might have a time course that looks like this

Example of ERA Problems If you make an ERA the usual way, you might get something that looks like this: Initially some people can be confused how to interpret this ERA because the pre-event activation looks wonky. Intact Scrambled Fixation One common newbie mistake is to make ERAs for all conditions, including the baseline (Fixation). This situation will illustrate some of the confusion with that File-Based (Pre=2, Post=10, baseline 0 to 0)

Example of ERA Problems If you make the ERA over a longer time window, the situation becomes clearer. You have three curves that are merely shifted in time with respect to one another. File-Based (Pre=2, Post=10, baseline 0 to 0) File-Based (Pre=8, Post=18, baseline 0 to 0)

Example of ERA Problems Now you should realize that the different pre-epoch baselines result from the fact that each condition has different preceding conditions –Intact is always preceded by Fixation –Scrambled is always preceded by Intact –Fixation is always preceded by Scrambled Intact Scrambled Fixation End of Intact End of Scrambled End of Fixation File-Based (Pre=2, Post=10, baseline 0 to 0)

Example of ERA Problems Because of the different histories, changes with respect to baseline are hard to interpret. Nevertheless, ERAs can show you how much the conditions differed once the BOLD response stabilized –This period shows, rightly so, Intact > Scrambled > Fixation Intact Scrambled Fixation File-Based (Pre=2, Post=10, baseline 0 to 0)

Example of ERA Problems Because the pre-epoch baselines are so different (due to differences in preceding conditions), here it would be really stupid to do epoch-based averaging (e.g., with x=-2 as the y=0 baseline) In fact, it would lead us to conclude (falsely!) that there was more activation for Fixation than for Scrambled Epoch-Based (Pre=2, Post=10, baseline -2 to -2)

Example of ERA Problems In a situation with a regular sequence like this, instead of making an ERA with a short time window and curves for all conditions, you can make one single time window long enough to show the series of conditions (and here you can also pick a sensible y= 0 based on x=-2) IntactScrambledFixation File-Based average for Intact condition only (Pre=2, Post23, baseline -2 to -2)

Partial confounding We can also run into problems (less obvious but with the same ERA issues) if the histories of conditions are partially confounded (e.g., quasi-random orders) In the case we just considered, the histories for various conditions were completely confounded –Intact was always preceded by Fixation –Scrambled was always preceded by Intact –Fixation was always preceded by Scrambled Intact is preceded by Scrambled 3X and by Fixation 3X Scrambled is preceded by Intact 4X and Fixation 1X Fixation is preceded by Intact 2X, by Scrambled 2X and by nothing 1X No condition is ever preceded by itself

The Problem of Trial/Block History This problem also occurs for single trial designs. This problem also occurs even if the history is only partially confounded (e.g., if Condition A is preceded by Condition X twice as often as Condition B is preceded by Condition X). If we knew with certainty what a given subject’s HRF looked like, we could model it (but that’s rarely the case). Thus we have only two solutions: 1)Counterbalance trial history so that each curve should start with the same baseline 2)Jitter the intertrial intervals so that we can estimate the HRF more on this in analysis when we talk about deconvolution

One Approach to Estimation: Counterbalanced Trial Orders Each condition must have the same history for preceding trials so that trial history subtracts out in comparisons For example if you have a sequence of Face, Place and Object trials (e.g., FPFOPPOF…), with 30 trials for each condition, you could make sure that the breakdown of trials (yellow) with respect to the preceding trial (blue) was as follows: …Face  Face x 10 …Place  Face x 10 …Object  Face x 10 …Face  Place x 10 …Place  Place x 10 …Object  Place x 10 …Face  Object x 10 …Place  Object x 10 …Object  Object x 10 Most counterbalancing algorithms do not control for trial history beyond the preceding one or two items

Analysis of Single Trials with Counterbalanced Orders Approach used by Kourtzi & Kanwisher (2001, Science) for pre-defined ROI’s: for each trial type, compute averaged time courses synced to trial onset; then subtract differences … Raw data Event-related average with control period factored out A signal change = (A – F)/F B signal change = (B – F)/F Event-related average sync to trial onset A B F

Pros & Cons of Counterbalanced Rapid ER Designs Pros high detection power with advantages of ER designs (e.g., can have many trial types in an unpredictable order) Cons and Caveats reduced detection compared to block designs estimation power is better than block designs but not great accurate detection requires accurate HRF modelling counterbalancing only considers one or two trials preceding each stimulus; have to assume that higher-order history is random enough not to matter what do you do with the trials at the beginning of the run… just throw them out? you can’t exclude error trials and keep counterbalanced trial history you can’t use this approach when you can’t control trial status (e.g., items that are later remembered vs. forgotten)

Design Types Rapid Jittered ER Design = trial of one type (e.g., face image) = trial of another type (e.g., place image)

BOLD Overlap With Regular Trial Spacing Neuronal activity from TWO event types with constant ITI Partial tetanus BOLD activity from two event types Slide from Matt Brown

BOLD Overlap with Jittering Neuronal activity from closely-spaced, jittered events BOLD activity from closely-spaced, jittered events Slide from Matt Brown

BOLD Overlap with Jittering Neuronal activity from closely-spaced, jittered events BOLD activity from closely-spaced, jittered events Slide from Matt Brown

Fast fMRI Detection A) BOLD Signal B) Individual Haemodynamic Components C) 2 Predictor Curves for use with GLM (summation of B) Slide from Matt Brown

Post Hoc Trial Sorting Example Wagner et al., 1998, Science

Algorithms for Picking Efficient Designs Optseq2

Algorithms for Picking Efficient Designs Genetic Algorithms

Design Types Mixed Design = trial of one type (e.g., face image) = trial of another type (e.g., place image)

Example of Mixed Design Otten, Henson, & Rugg, 2002, Nature Neuroscience used short task blocks in which subjects encoded words into memory In some areas, mean level of activity for a block predicted retrieval success

Pros and Cons of Mixed Designs Pros allow researchers to distinguish between state- related and item-related activation Cons sensitive to errors in HRF modelling

EXTRA SLIDES

A Variant of Mixed Designs: Semirandom Designs a type of event-related design in which the probability of an event will occur within a given time interval changes systematically over the course of an experiment First period: P of event: 25% Middle period: P of event: 75% Last period: P of event: 25% probability as a function of time can be sinusoidal rather than square wave

Pros and Cons of Semirandom Designs Pros good tradeoff between detection and estimation simulations by Liu et al. (2001) suggest that semirandom designs have slightly less detection power than block designs but much better estimation power Cons relies on assumptions of linearity complex analysis “However, if the process of interest differs across ISIs, then the basic assumption of the semirandom design is violated. Known causes of ISI-related differences include hemodynamic refractory effects, especially at very short intervals, and changes in cognitive processes based on rate of presentation (i.e., a task may be simpler at slow rates than at fast rates).” -- Huettel, Song & McCarthy, 2004