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Event-related fMRI Rik Henson With thanks to: Karl Friston, Oliver Josephs

Event-related fMRI Rik Henson With thanks to: Karl Friston, Oliver Josephs. Overview. 1. Advantages of efMRI 2. BOLD impulse response 3. General Linear Model 4. Temporal Basis Functions 5. Timing Issues 6. Design Optimisation 7. Nonlinear Models 8. Example Applications.

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Event-related fMRI Rik Henson With thanks to: Karl Friston, Oliver Josephs

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  1. Event-related fMRI Rik Henson With thanks to: Karl Friston, Oliver Josephs

  2. Overview 1. Advantages of efMRI 2. BOLD impulse response 3. General Linear Model 4. Temporal Basis Functions 5. Timing Issues 6. Design Optimisation 7. Nonlinear Models 8. Example Applications

  3. Advantages ofEvent-related fMRI 1. Randomised trial order c.f. confounds of blocked designs (Johnson et al 1997) 2. Post hoc / subjective classification of trials e.g, according to subsequent memory (Wagner et al 1998) 3. Some events can only be indicated by subject (in time) e.g, spontaneous perceptual changes (Kleinschmidt et al 1998) 4. Some trials cannot be blocked e.g, “oddball” designs (Clark et al., 2000) 5. More accurate models even for blocked designs?e.g, “state-item” interactions (Chawla et al, 1999)

  4. (Disadvantages of Randomised Designs) 1. Less efficient for detecting effects than are blocked designs (see later…) 2. Some psychological processes may be better blocked (eg task-switching, attentional instructions)

  5. Overview 1. Advantages of efMRI 2. BOLD impulse response 3. General Linear Model 4. Temporal Basis Functions 5. Timing Issues 6. Design Optimisation 7. Nonlinear Models 8. Example Applications

  6. Peak Brief Stimulus Undershoot Initial Undershoot BOLD Impulse Response • Function of blood oxygenation, flow, volume (Buxton et al, 1998) • Peak (max. oxygenation) 4-6s poststimulus; baseline after 20-30s • Initial undershoot can be observed (Malonek & Grinvald, 1996) • Similar across V1, A1, S1… • … but differences across:other regions (Schacter et al 1997) individuals (Aguirre et al, 1998)

  7. Peak Brief Stimulus Undershoot Initial Undershoot BOLD Impulse Response • Early event-related fMRI studies used a long Stimulus Onset Asynchrony (SOA) to allow BOLD response to return to baseline • However, if the BOLD response is explicitly modelled, overlap between successive responses at short SOAs can be accommodated… • … particularly if responses are assumed to superpose linearly • Short SOAs are more sensitive…

  8. Overview 1. Advantages of efMRI 2. BOLD impulse response 3. General Linear Model 4. Temporal Basis Functions 5. Timing Issues 6. Design Optimisation 7. Nonlinear Models 8. Example Applications

  9. h(t)= ßi fi (t) u(t) T 2T 3T ... convolution General Linear (Convolution) Model GLM for a single voxel: y(t) = u(t)  h(t) + (t) u(t) = neural causes (stimulus train) u(t) =   (t - nT) h(t) = hemodynamic (BOLD) response h(t) =  ßi fi (t) fi(t) = temporal basis functions y(t) =  ßi fi (t - nT) + (t) y = X ß + ε sampled each scan Design Matrix

  10. Auditory words every 20s Gamma functions ƒi() of peristimulus time  (Orthogonalised) SPM{F} Sampled every TR = 1.7s Design matrix, X [x(t)ƒ1() | x(t)ƒ2() |...] … 0 time {secs} 30 General Linear Model (in SPM)

  11. T=16, TR=2s T0=16 o T0=9 o Scan 1 0 A word about down-sampling x2 x3

  12. Overview 1. Advantages of efMRI 2. BOLD impulse response 3. General Linear Model 4. Temporal Basis Functions 5. Timing Issues 6. Design Optimisation 7. Nonlinear Models 8. Example Applications

  13. Temporal Basis Functions • Fourier Set Windowed sines & cosines Any shape (up to frequency limit) Inference via F-test

  14. Temporal Basis Functions • Finite Impulse Response Mini “timebins” (selective averaging) Any shape (up to bin-width) Inference via F-test

  15. Temporal Basis Functions • Fourier Set Windowed sines & cosines Any shape (up to frequency limit) Inference via F-test • Gamma Functions Bounded, asymmetrical (like BOLD) Set of different lags Inference via F-test

  16. Temporal Basis Functions • Fourier Set Windowed sines & cosines Any shape (up to frequency limit) Inference via F-test • Gamma Functions Bounded, asymmetrical (like BOLD) Set of different lags Inference via F-test • “Informed” Basis Set Best guess of canonical BOLD response Variability captured by Taylor expansion “Magnitude” inferences via t-test…?

  17. Temporal Basis Functions

  18. Temporal Basis Functions “Informed” Basis Set (Friston et al. 1998) • Canonical HRF (2 gamma functions) Canonical

  19. Temporal Basis Functions “Informed” Basis Set (Friston et al. 1998) • Canonical HRF (2 gamma functions) plus Multivariate Taylor expansion in: time (Temporal Derivative) Canonical Temporal

  20. Temporal Basis Functions “Informed” Basis Set (Friston et al. 1998) • Canonical HRF (2 gamma functions) plus Multivariate Taylor expansion in: time (Temporal Derivative) width (Dispersion Derivative) Canonical Temporal Dispersion

  21. Temporal Basis Functions “Informed” Basis Set (Friston et al. 1998) • Canonical HRF (2 gamma functions) plus Multivariate Taylor expansion in: time (Temporal Derivative) width (Dispersion Derivative) • “Magnitude” inferences via t-test on canonical parameters (providing canonical is a good fit…more later) Canonical Temporal Dispersion

  22. Temporal Basis Functions “Informed” Basis Set (Friston et al. 1998) • Canonical HRF (2 gamma functions) plus Multivariate Taylor expansion in: time (Temporal Derivative) width (Dispersion Derivative) • “Magnitude” inferences via t-test on canonical parameters (providing canonical is a good fit…more later) • “Latency” inferences via tests on ratio of derivative: canonical parameters (more later…) Canonical Temporal Dispersion

  23. (Other Approaches) • Long Stimulus Onset Asychrony (SOA) • Can ignore overlap between responses (Cohen et al 1997) • … but long SOAs are less sensitive • Fully counterbalanced designs • Assume response overlap cancels (Saykin et al 1999) • Include fixation trials to “selectively average” response even at short SOA (Dale & Buckner, 1997) • … but unbalanced when events defined by subject • Define HRF from pilot scan on each subject • May capture intersubject variability (Zarahn et al, 1997) • … but not interregional variability • Numerical fitting of highly parametrised response functions • Separate estimate of magnitude, latency, duration (Kruggel et al 1999) • … but computationally expensive for every voxel

  24. Temporal Basis Sets: Which One? In this example (rapid motor response to faces, Henson et al, 2001)… Canonical + Temporal + Dispersion + FIR …canonical + temporal + dispersion derivatives appear sufficient …may not be for more complex trials (eg stimulus-delay-response) …but then such trials better modelled with separate neural components (ie activity no longer delta function) + constrained HRF (Zarahn, 1999)

  25. Overview 1. Advantages of efMRI 2. BOLD impulse response 3. General Linear Model 4. Temporal Basis Functions 5. Timing Issues 6. Design Optimisation 7. Nonlinear Models 8. Example Applications

  26. Timing Issues : Practical TR=4s Scans • Typical TR for 48 slice EPI at 3mm spacing is ~ 4s

  27. Timing Issues : Practical TR=4s Scans • Typical TR for 48 slice EPI at 3mm spacing is ~ 4s • Sampling at [0,4,8,12…] post- stimulus may miss peak signal Stimulus (synchronous) SOA=8s Sampling rate=4s

  28. Timing Issues : Practical TR=4s Scans • Typical TR for 48 slice EPI at 3mm spacing is ~ 4s • Sampling at [0,4,8,12…] post- stimulus may miss peak signal • Higher effective sampling by: 1. Asynchrony eg SOA=1.5TR Stimulus (asynchronous) SOA=6s Sampling rate=2s

  29. Timing Issues : Practical TR=4s Scans • Typical TR for 48 slice EPI at 3mm spacing is ~ 4s • Sampling at [0,4,8,12…] post- stimulus may miss peak signal • Higher effective sampling by: 1. Asynchrony eg SOA=1.5TR2. Random Jitter eg SOA=(2±0.5)TR Stimulus (random jitter) Sampling rate=2s

  30. Timing Issues : Practical TR=4s Scans • Typical TR for 48 slice EPI at 3mm spacing is ~ 4s • Sampling at [0,4,8,12…] post- stimulus may miss peak signal • Higher effective sampling by: 1. Asynchrony eg SOA=1.5TR2. Random Jitter eg SOA=(2±0.5)TR • Better response characterisation (Miezin et al, 2000) Stimulus (random jitter) Sampling rate=2s

  31. Timing Issues : Practical • …but “Slice-timing Problem” (Henson et al, 1999) Slices acquired at different times, yet model is the same for all slices

  32. Timing Issues : Practical Bottom Slice Top Slice • …but “Slice-timing Problem” (Henson et al, 1999) Slices acquired at different times, yet model is the same for all slices => different results (using canonical HRF) for different reference slices TR=3s SPM{t} SPM{t}

  33. Timing Issues : Practical Bottom Slice Top Slice • …but “Slice-timing Problem” (Henson et al, 1999) Slices acquired at different times, yet model is the same for all slices => different results (using canonical HRF) for different reference slices • Solutions: 1. Temporal interpolation of data … but less good for longer TRs TR=3s SPM{t} SPM{t} Interpolated SPM{t}

  34. Timing Issues : Practical Bottom Slice Top Slice • …but “Slice-timing Problem” (Henson et al, 1999) Slices acquired at different times, yet model is the same for all slices => different results (using canonical HRF) for different reference slices • Solutions: 1. Temporal interpolation of data … but less good for longer TRs 2. More general basis set (e.g., with temporal derivatives) … but inferences via F-test TR=3s SPM{t} SPM{t} Interpolated SPM{t} Derivative SPM{F}

  35. Overview 1. Advantages of efMRI 2. BOLD impulse response 3. General Linear Model 4. Temporal Basis Functions 5. Timing Issues 6. Design Optimisation 7. Nonlinear Models 8. Example Applications

  36. Fixed SOA = 16s Stimulus (“Neural”) HRF Predicted Data  = Not particularly efficient…

  37. = Fixed SOA = 4s Stimulus (“Neural”) HRF Predicted Data Very Inefficient…

  38. = Randomised, SOAmin= 4s Stimulus (“Neural”) HRF Predicted Data More Efficient…

  39. = Blocked, SOAmin= 4s Stimulus (“Neural”) HRF Predicted Data Even more Efficient…

  40. = = Blocked, epoch = 20s Stimulus (“Neural”) HRF Predicted Data  Blocked-epoch (with small SOA) and Time-Freq equivalences

  41. =  = Sinusoidal modulation, f = 1/33s Stimulus (“Neural”) HRF Predicted Data The most efficient design of all!

  42. = “Effective HRF” (after highpass filtering) (Josephs & Henson, 1999)  = Blocked (80s), SOAmin=4s, highpass filter = 1/120s Stimulus (“Neural”) HRF Predicted Data Don’t have long (>60s) blocks!

  43. =  = Randomised, SOAmin=4s, highpass filter = 1/120s Stimulus (“Neural”) HRF Predicted Data (Randomised design spreads power over frequencies)

  44. Events (A-B) Design Efficiency T = cTb / var(cTb) Var(cTb) = sqrt(2cT(XTX)-1c) (i.i.d) • For max. T, want min. contrast variability (Friston et al, 1999) • If assume that noise variance (2) is unaffected by changes in X… • …then want maximal efficiency, e: e(c,X) = { cT (XTX)-1 c }-1 • = maximal bandpassed signal energy (Josephs & Henson, 1999)

  45. Efficiency - Single Event-type • Design parametrised by: SOAmin Minimum SOA p(t) Probability of event at each SOAmin

  46. Efficiency - Single Event-type • Design parametrised by: SOAmin Minimum SOA

  47. Efficiency - Single Event-type • Design parametrised by: SOAmin Minimum SOA p(t) Probability of event at each SOAmin • Deterministicp(t)=1 iff t=nT

  48. Efficiency - Single Event-type • Design parametrised by: SOAmin Minimum SOA p(t) Probability of event at each SOAmin • Deterministicp(t)=1 iff t=nSOAmin • Stationary stochastic p(t)=constant<1

  49. Efficiency - Single Event-type • Design parametrised by: SOAmin Minimum SOA p(t) Probability of event at each SOAmin • Deterministicp(t)=1 iff t=nT • Stationary stochastic p(t)=constant • Dynamic stochastic p(t) varies (eg blocked) Blocked designs most efficient! (with small SOAmin)

  50. Differential Effect (A-B) Common Effect (A+B) 4s smoothing; 1/60s highpass filtering Efficiency - Multiple Event-types • Design parametrised by: SOAmin Minimum SOA pi(h) Probability of event-type i given history h of last m events • With n event-types pi(h) is a nm nTransition Matrix • Example: Randomised AB A B A 0.5 0.5 B 0.5 0.5 => ABBBABAABABAAA...

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