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

Part I - Study Design

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

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

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?

What can we manipulate? ❖ Stimulus type ❖ Stimulus properties ❖ Stimulus timing ❖ Participant instructions

Experimental Designs ❖ Categorical ❖ Factorial ❖ Parametric

Categorical ❖ Comparing the activity between stimulus types ❖ e.g: being presented with different nouns and deciding whether each is animate or inanimate goatbucket VSVS Stimulus Tas k

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!)

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

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!)

An example Motion (Low) A No motion (Low) B Motion (High) C No Motion (High) D 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

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

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.

Types of presentation strategies ❖ Blocked ❖ Event Related ❖ Mixed ❖ fMRI adaptation

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

❖ 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

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.

❖ 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

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.

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

Part II: Efficiency and optimisation of fMRI designs

Terminology 2 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

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

Fixed SOA = 16s 4 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 = 4s 5 Overall signal is high, but variance is low Majority of signal will be lost after high-pass filtering  even less efficient design

Stochastic design, SOA min = 4s 6 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

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

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

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

High-pass filtering 10 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

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

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

Efficiency equation 13 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(σ 2, c, X) = {σ 2 c T (X T X) -1 c} -1 σ 2 = noise variance, (X T X) -1 = design variance

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

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

Correlation between regressors 16 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

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

General advice 18 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

References 19 Rik Henson’s SPM guide: Amaro Jr., E., & Barker, G. J. (2006). Study design in fMRI: Basic principles. Brain and Cognition, 60, doi: /j.bandc Previous MfD slides Thanks to our expert Tom Fitzgerald