Efficient fMRI Study Design for Brain Research Insights

1. fMRI Study Design & Efficiency • Mrudul Bhatt (Muddy) & Natalie Berger

2. Part I - Study Design

3. Main considerations before starting… • METHOD OF ANALYSIS AND CONTRASTS • This forms the basis upon which an experiment is designed… • The simpler the better

4. Don’t forget BOLD is a signal based in physiology…

5. Study Design • “based on an intervention in a system (brain) and observation of the modulation of the system response (BOLD effect) resulting from this ‘provocation’ (cognitive task, or in this context, paradigm)” - Amaro & Barker 2006 (Brain & Cognition) • i.e. We want to manipulate the participants experience and behaviour in some way that is likely to produce a functionally specific neurovascular response. • Can you test your hypothesis like this?

6. What can we manipulate? • Stimulus type • Stimulus properties • Stimulus timing • Participant instructions

7. Experimental Designs • Categorical • Factorial • Parametric

8. bucket goat Categorical • Comparing the activity between stimulus types • e.g: being presented with different nouns and deciding whether each is animate or inanimate Task VS Stimulus

9. Subtraction • Vestigial from PET imaging • Involves ‘subtracting’ an image taken during a control condition from an active condition • Depends on the acquisition of two (or more) conditions • Any BOLD signal difference between images (above set statistical limits) is assumed to represent all brain regions involved in that task • Implies no interactions among cognitive components of a task (Pure Insertion) • This assumption is false most of the time (if not always!)

10. However…. • When used with block designs can be of some use • Modelling the BOLD response is simple (comparatively) • Leads to reproducible and ‘statistically confident’ data • Often used for assessing phylogenetically old regions of CNS

11. Factorial • Allows testing for interaction between cognitive components • Requires neuropsychological evidence when defining task components (preferably with behavioural data too!) • Subjects perform tasks which are cognitively intermingled one moment, then separated in another instance • Simpler if one assumes linearity between BOLD responses from the different conditions (but can be done non-linearly also!)

12. An example • Simple Main effect:A-B simple main effect of motion V no motion under context of low cognitive load • Main Effect:(A+B) - (C+D) Main effect of low cognitive load vs. high cognitive load, irrelevant of motion • Interaction effect:(A-B) - (C - D) Investigates whether the interaction effect of motion (vs. no motion) is greater under high or low cognitive loads

13. Parametric • Altering some performance attribute of task • Intrinsic structure of task remains the same • Any increase in BOLD between trials would imply a heavy association between active regions and parameter being manipulated • Can separate functionally relevant areas from others involved in the maintenance/basis of the cognitive process • Simple in principle… • Can pose a challenge to systematically ‘step-up’ cognitive demand and maintain it • Might involve recruiting other cognitive processes not present at lower levels

14. Stimulus presentation strategies • NB: A brief burst of neural activity corresponding to presentation of a short discrete stimulus or event will produce a more gradual BOLD response lasting about 15sec. • Due to noisiness of the BOLD signal multiple repetitions of each condition are required in order to achieve sufficient reliability and statistical power.

15. Types of presentation strategies • Blocked • Event Related • Mixed • fMRI adaptation

16. Blocked • Multiple repetitions from a given experimental condition are strung together in a condition block which alternates between one or more condition blocks or control blocks • Each block should be about 16-40sec

17. PROS: • BOLD signal from multiple repetitions is additive • Statistically Powerful • Can look at resting baseline e.g Johnstone & colleagues • CONS: • Whilst statistically powerful, not all hypothesis can be probed in this manner • Habituation effects • In affective sciences their may be cumulative effects of emotional or social stimuli on participants moods

18. Event Related Design • In an event related design, presentations of trials from experimental conditions are interspersed in a randomised order, rather then being blocked together by condition • In order to control for possible overlapping BOLD signal responses to stimuli and to reduce the time needed for an experiment you can introduce ‘jittering’ (i.e. use variable length ITI’s) • Can do rapid erfMRI - reduce ISI to 4 seconds (HRF doesn’t reach baseline) and deconvolve afterwards. Must use all combination of trial sequences and jitter ITI’s.

19. PROS: • Avoids expectation and habituation • Randomisation possible! • Allows subsequent analysis on a trial by trial basis, using behavioural measures such as judgment time, subjective reports or physiological responses to correlate with BOLD • CONS: • More complex design and analysis (especially timing and baseline issues) • Reduced statistical power • If conditions have large switching costs then may be unsuitable

20. Mixed Designs • Combination of Block and Event related • To account for two types of neuronal behaviour; sustained and transient • Sustained: continues throughout task (e.g. exam taking) • Transient: activity evoked by each trial of a task • Can dissociate these using mixed designs, but difficult post hoc analysis, and poorer HRF shape.

21. fMRI Adaptation • Uses principle of repeat exposure • Repeat exposure to a stimulus will produce an attenuated responses • Maybe due to fatigue or haemodynamic responses • By exposing the brain to a second, different stimulus one would expect no attenuation of responses (due to recruitment of a ‘fresh’ sub population) • by seeing if the response to the second stimulus is indeed attenuated, we can determine if the same neuronal groups are involved in processing the two stimuli

22. Part II: Efficiency and optimisation of fMRI designs

23. Terminology • Trial: replication of a condition, consist of one or more components • Inter-Trial Interval (ITI): time between the onset of successive trials • Components may be brief bursts of neural activity, events, or periods of sustained neural activity, epochs • Stimulus Onset Asynchrony (SOA): time between onset of trial components (even if components are not stimuli per se) • Inter-Stimulus Interval (ISI):time between the offset of one component and the onset of the next 2

24. BOLD impulse response HRF • Response to a brief burst of neural activity • Predicted fMRI time series: Convolved stimulus function with the haemodynamic response function (HRF) Peak Undershoot 3

25. Detection of signal in background noise works best if variability of signal is maximised Signal that varies little will be difficult to detect  not particularly efficient design Fixed SOA = 16s 4

26. Overall signal is high, but variance is low Majority of signal will be lost after high-pass filtering  even less efficient design Fixed SOA = 4s 5

27. An equal number of null events and true events are randomly intermixed Much larger variability in signal (and we know how it varies)  more efficient design Stochastic design, SOAmin= 4s 6

28. Runs of events, followed by runs of no (null) events: 5 stimuli every 4s, alternating with 20s rest Very efficient design Blocked design 7

29. Waves can be described as the sum of a number of sinusoidal components FT helps to see which components will pass the IR filter Fourier transform 8

30. …in a sinusoidal fashion …with a frequency that matches the peak of the amplitude spectrum of the IR filter Most efficient design: Modulates neural activity… Peak of the amplitude spectrum of the IR filter (0.03 Hz) 9

31. High-pass filtering • fMRI noise: • Low-frequency noise (dark blue), e.g., gradual changes in ambient temperature • Background ‘white’ noise • High-pass filter in SPM: max loss of noise & min loss of signal •  Increased signal-to-noise ratio 10

32. Example: Long blocks of 80 s, 20 trials every 4s Fundamental frequency lower than high-pass cut-off  loss of signal Consequence of filtering 11

33. Signal is spread across a range of frequencies Some signal is lost due to filtering, but a lot of it is passed Reasonably efficient Revisiting stochastic design 12

34. Y = X . β + ε Data Design matrix Parameters error Efficiency is ability to estimate β, given your design matrix (X) for a particular contrast (c) e(σ2,c, X) = {σ2cT (XTX)-1 c}-1 σ2 = noise variance, (XTX)-1 = design variance Efficiency equation 13

35. Timing: Multiple event types (A & B) • Randomised design • Optimal SOA for main effect (A+B): 16-20s • Optimal SOA for differential effect (A-B): minimal SOA (>2 seconds) 14

36. Timing: Null events • Additional event type randomly intermixed with event types of interest • Efficient for main AND differential effects at short SOAs • Equivalent to stochastic design 15

37. Correlation between regressors • Can decrease efficiency depending on particular contrast • Common effect of A and B versus baseline estimated poorly, but difference between A and B estimated well 16

38. Example: Stimulus-response paradigms • Each trial consists of 2 events, one of which must follow the other 17

39. General advice • From Rik Henson: • Scan for as long as possible and keep subject as busy as possible • If group study, number of subjects more important than time per subject (though additional set-up time may encourage multiple experiments per subject) • Do not contrast trials that are far apart in time • Randomise the order, or SOA, of trials close together in time 18

40. References • Rik Henson’s SPM guide: • http://imaging.mrc-cbu.cam.ac.uk/imaging/DesignEfficiency • Amaro Jr., E., & Barker, G. J. (2006). Study design in fMRI: Basic principles. Brain and Cognition, 60, 220-232. doi: 10.1016/j.bandc.2005.11.009 • Previous MfD slides • Thanks to our expert Tom Fitzgerald 19