Efficiency – practical

1. Get better fMRI results Efficiency – practical Design matrix and Dummy-in-chief Joel Winston

2. Experimental design & efficiency • Getting the “right” results for a given amount of scanner time requires maximising your efficiency in detecting the experimental effect • Because of the temporal smoothing that the HRF applies in translating neural responses to BOLD signal, we know something a priori about how to maximise an experimental effect • As Paul has shown (?), mathematically the block design turns out to be highly efficient, essentially by maximising the experimental variance within a time frame that escapes two filters: HRF (low pass) and SPM (high pass)

3. Temporal filtering and neuroimaging As mentioned, there are two components to temporal filtering routinely applied to fMRI data, one by the brain, the other by us… The brain’s temporal filter is the Haemodynamic Response Function (HRF), whose form we all know and love:

4. HRF HRF – power spectrum Peak at ~0.04Hz => Max sensitivity for designs with on-off cycles of 0.04-1 = 25s What this means in reality is that the HRF acts as a low pass filter on our recording of the brain’s activity

5. Why the high-pass filter? • We routinely apply a high pass filter in SPM • The reason for this is simply because we can • For the most part, we use SPM to analyse designed experiments where we have some control over the interesting parameters, and little control over disinteresting ones • Disinteresting parameters are often slow-moving things, like scanner drift, physiological noise, and occur outside the temporal space of designed experiments • So we get rid of these by high-pass filtering the data but not so severely that we lose our experimental effects…

6. How can I check that I’m not losing anything interesting by high pass filtering?

7. The importance of being event-related When is it necessary/advantageous to use event-related designs? • Trials that by definition can’t be blocked • e.g. oddballs • Post-hoc classification • e.g. classification by memory, parametric scores, subjective perception • Randomise trial order • Where phasic/tonic effects might be dissociable • Where anticipation/predictability might be a problem

8. OK, so you’ve persuaded me that I have to use event-related fMRI for my experiment (the neural correlates of doughnut eating…) How do I make the most of an event-related paradigm? • Two things that we’ll talk about: • Spacing of events • Sequences of events

9. The spacing of events Simulations show that efficiency to detect differential effects between event types increases with shorter SOAs:

10. But I’m also interested in detecting main effects relative to baseline (“evoked responses”)… Consider including null events as an extra event type:

11. So the bottom line is… …pack it in!!

12. But my events have to be spaced out! Then you might want to consider not randomising event orders, but having them alternate or nearly alternate (permuted designs):

13. Planning in advance… One of the best ways to increase the efficiency of event-related designs is to ensure mini-runs of same stimuli… …and one way of ensure mini-runs is to modulate the probability of different event-types over experimental time

14. Stochastic designs • Essentially a stochastic design defines a (variable) probability of a given event type at each SOAmin • Stochastic designs can be stationary or dynamic • One incarnation of dynamic stochastic designs (implemented in SPM99) is to modulate the underlying probability of events at each SOA by a sine wave:

15. How will this translateinto an event train? (Not that sort of train, dummies)

16. This sort of train:

17. A practical example • Faces vs scrambled faces • SOA was fixed at 2.97s • TR was 2.5s • Three runs of 128 scans: • Blocked faces and scrambled faces • Fully randomised stimulus order • Modulated probability of face/scrambled face • Task was detection of very infrequent (1%!) targets (chairs)

18. The design matrix M M M S S S F C F C F C F = faces S = scrambled faces C = chairs M = movement parameters Blocked Fully randomised Dynamic stochastic

19. Calculated efficiency for the 3 sessions

20. Fully randomised Superficial comparison between sessions

21. Randomised Dynamic stochastic Blocked Superficial comparison between sessions

22. Posterior cingulate (-9,-54,42) Right anterior fusiform (36,-24,-30) Visual cortex (-12,-78,-6) Results –Interaction of efficiency type andfaces vs scrambled faces Left STS (-57,-33,6) Differential effect (faces > scrambled faces) Right anterior fusiform Randomised Randomised Dynamic stochastic Dynamic stochastic Blocked Blocked

23. Occipital pole Anterior cingulate “Chair” area Dorsal occipital pole Results –Chairs vs other visual stimuli

24. 5 15 0 10 20 Results – Chairs vs other visual stimuli Raw time series from anterior cingulate:

25. …and another thing:

26. Take home messages • Efficiency can be estimated before you do your study to allow a comparison between different designs • However, on any one implementation, a given design may prove less successful in detecting effects than another, less efficient design • Psychological validity is an important design constraint