fMRI Paradigm Design

1. fMRI Paradigm Design Chris Dodds Property of GlaxoSmithKline

2. Overview Aims of an fMRI experiment Limitations and trade-offs of fMRI Blocked vs event-related designs Importance of strong anatomical hypotheses Subtraction logic and the importance of the baseline Tips on design efficiency Good and bad fMRI experiments Example fMRI project – effects of BDNF polymorphism on episodic memory

3. Neurons fire → increase in blood flow. But, the increase in O2utilisation is much lower. Concentration of deoxyHb decreases and leads to a decrease in the distortion of magnetic field (e.g., image appears brighter with decreaseddistortion from deoxyHb). T2* relaxation signal is sensitive to this Blood Oxygenation Level (BOLD) change and we measure this difference due to psychological tasks. fMRI and the BOLD signal Local Field Potential correlates with BOLD: (Logothetis et al., 2001)

4. Main Goal of an fMRI experiment Design fMRI experiments that are sensitive to a specific hypothesis (i.e, statistical planned comparison or "contrast"). Example: Is brain region X more involved in processing syntax or semantics of language? Translation in real life: Design study that maximizes the difference in BOLD signal between the experimental (‘Task on’ and control condition (‘Task off’). This is another way of saying that you want maximize ‘design efficiency’

5. Subtraction Logic and the importance of the baseline • An fMRI study is like any other experiment in establishing the relationship between two variables • -Explanatory / independent variable : psychological task • -Outcome / dependent variable : brain activity We are evaluating the brain’s response to an experimental manipulation consisting of an experimental condition (sometimes called ‘activation task’ and a control condition (sometimes called ‘baseline task’). - fMRI brain response is arbitrary and has no meaning independent of the baseline -Experimental Condition or Activation Task: involves the psychological process of interest (e.g., face-processing) -Control Condition or Baseline Task: should be as similar as possible to the activation task but should lack the psychological process of interest (e.g. object processing) Activation for PsychProcess = Experimental – Control

6. Experimental Control Control

7. Assumptions Pure Insertion: Psychological tasks can be elaborated on by inserting additional psychological processes. The validity of this assumption depends on the psychological model being tested. This may work well for low-level psychological processes (vision), where certain it is evident what is added or subtracted from one another. But what about higher level cognitive processes, whereby subtracting out one process cannot be easily achieved without altering the other psychological process that is leftover? Can we really know what is psychologically ‘leftover’ from a cognitive subtraction? Careful thought is needed when designing your experiment... Your experimental condition is only as good as what your control condition is. Cognitive subtraction What is being subtracted? aardvark krvarada Objects > Textures object shapes, irregular shapes, familiarity, visual features (e.g., brightness, contrast, etc.), actability, attention... Word > NonWord phonology, semantics, lexical properties, imagery, animacy...

8. Limitations • Expense • Depending on academic/industry study, can be up to £1,200 per 90 minute scan • Assuming 40 volunteers, each scanned twice, costs can be £100,000+ for a single study • Think carefully – do we need fMRI? • What extra information does imaging provide? • Is it useful to know where in the brain something happens? • Can we design experiments to go beyond localisation?

9. Limitations • Time • Maximum approx 90 mins in the scanner • Includes structural scans (approx 20 mins) and rest time • Perhaps 60 mins for task performance • Multiple tasks? • Alertness/attention • tradeoff between lots of data and good data

10. Limitations Environment/Equipment limits what we can do

11. Limitations Behavioural Performance • Affects interpretation of results • E.g. Reduced activation + worse performance in patients (vs controls) could indicate a) Neuronal dysfunction causing worse performance or • Worse performance causing reduction in neuronal signal. • Match behavioural performance • Difficult to interpret differences in activation • Increase in activation in one group may indicate better neuronal function or less efficient neuronal function fMRI data relies on correlation, not causation

12. Importance of converging evidence from neuropsychology Aron et al – Motor inhibition performance associated with volume of damage to right inferior frontal cortex Reduced activation in this region in ADHD patients relative to controls

13. Limitations • Patients • Parkinson’s: tremor • Alzheimer’s: difficulty remembering task instructions • Obesity: difficulty fitting in the scanner • Ketamine users: ulcerative cystitis • ADHD: turning up • Tailor paradigms to take into account specific patient requirements • Shorter, easier tasks with regular breaks are often more appropriate

14. Limitations • Pharmacological fMRI • Practice effects/neural adaptation – pay close attention to repeatability of task (in within-subjects designs) • Side effects: Nausea can be a serious concern in the scanner • Timing of scanning – where do you want Tmax? • Controlling for vascular effects • Control task • Arterial Spin Labelling (ASL) • Behavioural performance – difficult to interpret neural effects in the absence of behavioural effect

15. Limitations • Artifacts • Artifacts (aka ‘noise’) known not to be related to neural activity are everywhere (e.g., field distortions, subject movement, respiration, low-frequency scanner drift) and reducing the contributions of these variables is always a challenge. We don’t also want bad experimental design as another contributor of ‘noise’ to our data. • Designing a good fMRI experiment is largely an exercise in maximising signal to noise ratio • Minimise (and correct for) subject motion • Pay close attention to frequency of events in design

16. Limitations BOLD signal fMRI signal is an indirect measure of neural activity and folds out "sluggishly" over time - making the design of an ‘efficient’fMRI experiment a not-so-straightforward exercise.

17. Blocked designs Predicted fMRI Data Canonical HRF Stimulus Implications (Neural) • Best for detecting amplitudedifferences between conditions (i.e. most efficient design when the aim is to detect differences in BOLD signal between conditions) • Not good at isolating responses to single events within blocks • Cannot estimate shape of HRF effectively, but are robust when there is uncertainty about the timing and shape of HRF • Can acquiremore trials in less time than event-related design, because you don’t have to worry about spacing trials apart to get an estimate for each individual event

18. Blocked designs Predicted fMRI Data Canonical HRF Stimulus Limitations (Psychological) - Highly predictable occurrence of stimuli: subjects know what is coming and may alter strategies accordingly (not always a pro) - Inflexible for more complex tasks: impact of oddball stimuli? or stimuli or events that occur uncontrollably? - Ecological validity. Does blocking trials change the psychological process you are interested in?

19. Event-related designs In freeing us from the necessity of block designs, event-related fMRIenables us to design more complex and novel experiments. History - Spaced single trial design: Present trial, wait for HRF to pan out, then present next trial. Brief stimuli every 16s: HRF rises (2s), peaks (4-6s) and falls back to baseline (10-14s). - Rapid single trial design: Individual trials spaced closer together and decompose overlapping HRFs through sophisticated jittering of time in between trials or randomization. How it works: Present events far enough apart so you can estimate the HRF for each individual event. OR randomizethe order of presentation of events or jitter(i.e. randomly space out) the stimuli with variable times between each stimuli. Randomization or jittering allows the interval between two stimuli to be on average separated far enough apart to enable estimation of the HRF to (averaged) trials containing a single event type

20. Event-related designs Predicted fMRI Data Canonical HRF Stimulus Implications (Neural) • Best for estimating the shape of HRF and looking for differences in timing. • Also allows one to estimate activation in response to single events, allowing much more flexibility in the paradigms that can be used and questions that can be tested. • Need many more trials per condition compared to block design in order to increase detection power. Trial averaging helps get rid of noise in estimating response to single events, but more trials makes averaging more robust. • Because of the need for more trials and the jittering of time between stimuli, event-related designs come at the cost of increased scanning time.

21. Event-related designs Canonical HRF Predicted fMRI Data Stimulus Implications (Psychological) • Allows researcher flexibility to design task with multiple parts and you can estimate HRF to each part of the task: E.g., Cue - + - Target - + Feedback. • Allows opportunity for trial sorting. Researchers can re-conditionalize their paradigm basedon behavioral responses. E.g. Comparing events that were subsequently remembered vs. forgotten, accurate vs. inaccurate judgments, etc.

22. Importance of anatomical hypotheses • Multiplicity problem 64 X 36 slices = 147,456 voxels - High chance of false positive results 64

23. Importance of anatomical hypotheses • Anatomical hypotheses enable us to reduce the multiplicity problem and to ask more focused, meaningful questions about brain function than mere localisation, e.g. • How does region X support process Y? • What are the response properties of region X? • What computational processes are carried out by region X? • How is activation in region X modulated by drug Y?

24. Region of Interest (ROI) Approach • Restrict our analysis to a single region, or set of regions. • Define regions anatomically, e.g. the hippocampus, or functionally, e.g. on the basis of areas activated in our experiment • Voxelwise comparisons with ROI or average activation across voxels

25. Importance of anatomical hypotheses • Weak anatomical hypotheses lead to reverse inference...

26. The Importance of Strong Anatomical Hypotheses in fMRI • NYTimes • http://nyti.ms/pvgQnm • Reverse inference in ‘neuromarketing’:. “…most striking of all was the flurry of activation in the insular cortex of the brain, which is associated with feelings of love and compassion. The subjects’ brains responded to the sound of their phones as they would respond to the presence or proximity of a girlfriend, boyfriend or family member… In short, the subjects didn’t demonstrate the classic brain-based signs of addiction. Instead, they loved their iPhones.” Caveat: The insula is activated in 1/3 of all brain imaging studies, not just those involving love. In fact, the insula has been shown to be involved in negative emotion (e.g., disgust). We could have said with equal confidence “when subject heard their iPhones, they were disgusted… Literally.”

27. No anatomical hypothesis “Shopping list” of areas activated Baseline? What does this tell us? Certainly not going beyond localisation

28. Strong anatomical hypothesis Good baseline Beyond localisation Converging evidence

29. Efficient experimental design • Scan for as long as possible. • Power depends on df which depend on no of independent observations (scans). • *Sample size has more impact on power than # of scans though… • Minimise“dead time” whenever possible– e.g., reduce ITI. • Avoid breaks in scanning • - disrupt the spin equilibrium (i.e, require extra dummy scans at the beginning of the run) • - reduce efficiency of any temporal filtering during analysis (data no longer a single timeseries) • - introduce potential "session" effects • Don’t contrast trials that are far apart in time • - low frequency noise (scanner drift), high pass filtering tries to get rid of this, but will also get rid of experimental variability if contrasting trials very far apart. • - in block designs, avoid using blocks that are longer than 50s • Randomise trial order or stimulus onset asynchrony (SOA) – essential. • - i.e., AABBAABB with fixed ITI of 2 sec is very inefficient. • Add null events (e.g., fixation cross) to allow another ‘low level baseline’ to compare experimental and control conditions to.

30. Example: Measuring the effects of drug on hedonic brain responses to alcohol in the scanner • Expense – do we need fMRI? What will it add? • Time – do we have enough? • Environment/equipment – can we do this? • Artifacts – possible sources of noise? • Sluggish BOLD signal – an issue? • Event-related/block design? • Strong anatomical hypothesis? • Going beyond localisation?

31. Example...

33. fMRI – Recognition Memory Task Encoding - Indoor or Outdoor? Retrieval - Old or New?

34. Encode Encode Encode Encode Encode Rest Rest Rest Rest Rest Rest = Met carriers (Encode – Rest) – Val carriers (Encode – Rest)

36. Condition of interest: Subsequently recognised Baseline: Subsequently not recognised - = Rest Rest Encode

37. No effect of polymorphism on activation during successful memory encoding

38. Summary • fMRI is not a ‘magic bullet’ • Researchers often think “I’ve got this task, let’s run it with fMRI and see what happens.” • Usually leads to uninterpretable results • reverse inference – i.e. long list of activated regions and an attempt to explain them by referring to previous studies • Simplistic claims based on localisation – We have discovered the neural basis of X • Need to design fMRI experiments with a clear hypothesis and ask what information we can get from fMRI that we can’t get from a less expensive and less time-taking approach. • Designing an fMRI experiment involves a series of decisions about trade-offs and compromises, • e.g. collect as much data as possible but not at the expense of tiring out subjects and collecting low quality data • Design an interesting study but not try to ask too many questions – keep as simple as possible • Always remember, the scanner is a noisy, unfamiliar, uncomfortable environment. There is a limit to what the subject will put up with, and this will be (much) lower than what they will tolerate outside of the scanner. Property of GlaxoSmithKline