Experimental Design for Functional MRI David Glahn Updated by JLL

General Experimental Design - Neuropsychology - What is the question? What are appropriate controls? Which imaging modality? Study style?

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 (RT 1 ) –Task 2 - Respond only to A not to B (RT 2 ) –Task 3 - Respond X to A and Y to B (RT 3 ) RT = reaction time RT 1 = T-detect + T-response RT 2 = T-detect + T-discrimination + T-response RT 3 = T-detect + T-discrimination + T-choice + T-response T-discrimination = RT 2 - RT 1 T-choice = RT 3 - RT 2 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 (time in our example) 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 Must Account for Hemodynamic Response (HR) Savoy et al., 1995

Linear Systems Analysis Boynton et al 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 a hemodynamic response function (HRF) derived from shorter duration stimuli. –The noise in the fMRI data is independent of stimulus contrast and stimulus 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 response Dale & 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 Designs 2)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 (blocks) Task ATask BTask ATask BTask ATask BTask ATask BTask ATask BREST Task ATask BREST Paradigm – pattern or model; detailed plan for the experiment fMRI brain images acquired continuously

“Loose” vs. “Tight” Block Designs Loose: 1 Task, 1 contrast (with Baseline) Tight: more than 1 Task, multiple contrasts (including baseline)

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 (example would be if rest acquired with eyes closed but task had eyes open)

Adapted from Gusnard & Raichle (2001) (E - Bad Control Design)

Adapted from Gusnard & Raichle (2001) Oxygen Extraction Fraction Cerebral Metabolic Rate of O 2 Cerebral Blood Flow A True Baseline? Depends on what is measured! Different Areas may have different baselines

Power in Blocked Designs 1.Summation of responses results in large signals then plateaus (~10 sec) 1.Response Duration does not plateau and onset does not change Stimulus duration and interval compared with HRF ISI = 1 sec

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 temporal 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 periodic signals in the data –Example: if the person breathes at a regular rate of 12 per min and the blocks are 10s long (6 blocks/min) –Could be problem if the aliased signal falls within the range of desired signals From Scott Huettel, Duke

What are the temporal limits? What is the shortest stimulus duration that fMRI can detect? Blamire et al. (1992) – 2 sec Bandettini (1993): 0.5 sec Savoy 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 radiotracer used for CBF studies, task designs, and the time period for the image acquisition. 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 well repeatedly

Non-Task Brain Processing In experiments activation can be greater in baseline conditions than in task conditions! –Requires different processing for interpretation Suggests the idea of baseline/resting mental processes –Gathering/evaluation about the world around you –Awareness (of self) –Online monitoring of sensory information –Daydreaming –Neurons that are wired together fire together This collection of resting state brain processes is often called the “Default Mode Network” (DMN)

Default Mode! Damoiseaux 2006 analyzed separate 10-subject resting-state data sets, using Independent Components analysis (ICA). Vision. Frontal Parietal Resting State Networks (RSNs)

Event-Related Designs

Buckner et al., 1998 Event Related

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 such as changing difficulty based on error rate

Why use event-related designs? Some experimental tasks are naturally event-related (future stimuli based on response) Allows studying within-trial effects Improves relation to behavioral factors (behavior changes within blocks may be masked) Simple analyses –Selective averaging –General linear models (GLM)

Same 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 18 s

Trial Spacing Effects: Periodic Designs ISI = 8sec (~12 trials)ISI = 4sec (~45 trials) ISI = 20sec (9 trials)ISI = 12sec (15 trials) A 20 A4A4 A8A8 A 12 Want to maximize amplitude times number of trials per study

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 sec, 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 models.

Jittered Single Trial Designs Varying the timing of trials within a run Varying the timing of events within a trial Trial 1Trial 2Trial 3Trial 4 2 events3 events2 events1 event

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 Mid Response More Samples Large Response Most samples Visual stim duration = 1 s; acquisition 240 sec Trials subtracted then correlation analysis with predicted response

Variability of HRF: Evidence Aguirre, 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 sessionsame subject, different session

Variability of HRF: Implications Aguirre, 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 (do you know 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 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. True Memory Formation vs. False Memory Formation

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.)?

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.

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 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)

Experimental Design for Functional MRI David Glahn Updated by JLL