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Event-related fMRI Will Penny (this talk was made by Rik Henson) Event-related fMRI Will Penny (this talk was made by Rik Henson)

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Presentation on theme: "Event-related fMRI Will Penny (this talk was made by Rik Henson) Event-related fMRI Will Penny (this talk was made by Rik Henson)"— Presentation transcript:

1 Event-related fMRI Will Penny (this talk was made by Rik Henson) Event-related fMRI Will Penny (this talk was made by Rik Henson)

2 OverviewOverview 1. Advantages of efMRI 2. BOLD impulse response 3. Timing Issues Temporal Basis Functions 4. Temporal Basis Functions 5. Design Optimisation 6. Nonlinear Models 7. Examples 1. Advantages of efMRI 2. BOLD impulse response 3. Timing Issues Temporal Basis Functions 4. Temporal Basis Functions 5. Design Optimisation 6. Nonlinear Models 7. Examples

3 1. Post hoc / subjective classification of trials e.g, according to subsequent memory (Wagner et al 1998) 1. Post hoc / subjective classification of trials e.g, according to subsequent memory (Wagner et al 1998) 2. Some events can only be indicated by subject (in time) e.g, spontaneous perceptual changes (Kleinschmidt et al 1998) 2. Some events can only be indicated by subject (in time) e.g, spontaneous perceptual changes (Kleinschmidt et al 1998) 3. Some trials cannot be blocked e.g, “oddball” designs (Clark et al., 2000) 1. Post hoc / subjective classification of trials e.g, according to subsequent memory (Wagner et al 1998) 1. Post hoc / subjective classification of trials e.g, according to subsequent memory (Wagner et al 1998) 2. Some events can only be indicated by subject (in time) e.g, spontaneous perceptual changes (Kleinschmidt et al 1998) 2. Some events can only be indicated by subject (in time) e.g, spontaneous perceptual changes (Kleinschmidt et al 1998) 3. Some trials cannot be blocked e.g, “oddball” designs (Clark et al., 2000) Advantages of Event-related fMRI

4 1. Less efficient for detecting effects than are blocked designs (see later…) 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) 1. Less efficient for detecting effects than are blocked designs (see later…) 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) Potential Disadvantages

5 Function of blood oxygenation, flow, volume (Buxton et al, 1998)Function of blood oxygenation, flow, volume (Buxton et al, 1998) Peak (max. oxygenation) 4-6s poststimulus; baseline after 20-30sPeak (max. oxygenation) 4-6s poststimulus; baseline after 20-30s Initial undershoot can be observed (Malonek & Grinvald, 1996)Initial undershoot can be observed (Malonek & Grinvald, 1996) Similar across V1, A1, S1…Similar across V1, A1, S1… Function of blood oxygenation, flow, volume (Buxton et al, 1998)Function of blood oxygenation, flow, volume (Buxton et al, 1998) Peak (max. oxygenation) 4-6s poststimulus; baseline after 20-30sPeak (max. oxygenation) 4-6s poststimulus; baseline after 20-30s Initial undershoot can be observed (Malonek & Grinvald, 1996)Initial undershoot can be observed (Malonek & Grinvald, 1996) Similar across V1, A1, S1…Similar across V1, A1, S1… BOLD Impulse Response Brief Stimulus Undershoot Initial Undershoot Peak

6 OverviewOverview 1. Advantages of efMRI 2. BOLD impulse response 3. Timing Issues Temporal Basis Functions 4. Temporal Basis Functions 5. Design Optimisation 6. Nonlinear Models 7. Examples 1. Advantages of efMRI 2. BOLD impulse response 3. Timing Issues Temporal Basis Functions 4. Temporal Basis Functions 5. Design Optimisation 6. Nonlinear Models 7. Examples

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

8 x2x2 x3x3 A word about down-sampling T=16, TR=2s Scan 0 1 o T 0 =9 o T 0 =16

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

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

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

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

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

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

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

16 OverviewOverview 1. Advantages of efMRI 2. BOLD impulse response 3. Timing Issues Temporal Basis Functions 4. Temporal Basis Functions 5. Design Optimisation 6. Nonlinear Models 7. Examples 1. Advantages of efMRI 2. BOLD impulse response 3. Timing Issues Temporal Basis Functions 4. Temporal Basis Functions 5. Design Optimisation 6. Nonlinear Models 7. Examples

17 Similar across V1, A1, S1…Similar across V1, A1, S1… … but differences across: other regions (Schacter et al 1997) individuals (Aguirre et al, 1998)… but differences across: other regions (Schacter et al 1997) individuals (Aguirre et al, 1998) Similar across V1, A1, S1…Similar across V1, A1, S1… … but differences across: other regions (Schacter et al 1997) individuals (Aguirre et al, 1998)… but differences across: other regions (Schacter et al 1997) individuals (Aguirre et al, 1998) BOLD Impulse Response Brief Stimulus Undershoot Initial Undershoot Peak

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

19 Temporal Basis Functions

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

21 Temporal Basis Sets: Which One? + FIR+ Dispersion+ TemporalCanonical …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) In this example (rapid motor response to faces, Henson et al, 2001)…

22 General Linear Model (in SPM) Auditory words every 20s SPM{F} 0 time {secs} 30 Sampled every TR = 1.7s Design matrix, X [x(t)  ƒ 1 (  ) | x(t)  ƒ 2 (  ) |...] … Gamma functions ƒ i (  ) of peristimulus time  (Orthogonalised)

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

24 OverviewOverview 1. Advantages of efMRI 2. BOLD impulse response 3. Timing Issues Temporal Basis Functions 4. Temporal Basis Functions 5. Design Optimisation 6. Nonlinear Models 7. Examples 1. Advantages of efMRI 2. BOLD impulse response 3. Timing Issues Temporal Basis Functions 4. Temporal Basis Functions 5. Design Optimisation 6. Nonlinear Models 7. Examples

25  = Fixed SOA = 16s Not particularly efficient… Stimulus (“Neural”)HRFPredicted Data

26  = Fixed SOA = 4s Very Inefficient… Stimulus (“Neural”)HRFPredicted Data

27  = Randomised, SOA min = 4s More Efficient… Stimulus (“Neural”)HRFPredicted Data

28  = Blocked, SOA min = 4s Even more Efficient… Stimulus (“Neural”)HRFPredicted Data

29  =  Blocked, epoch = 20s = Blocked-epoch (with small SOA) and Time-Freq equivalences Stimulus (“Neural”)HRFPredicted Data

30  = Sinusoidal modulation, f = 1/33s  = The most efficient design of all! Stimulus (“Neural”)HRFPredicted Data

31 Randomised, SOA min =4s, highpass filter = 1/120s  =  = (Randomised design spreads power over frequencies) Stimulus (“Neural”)HRFPredicted Data

32 4s smoothing; 1/60s highpass filtering Efficiency - Multiple Event-types Design parametrised by:Design parametrised by: SOA min Minimum SOA p i (h) Probability of event-type i given history h of last m events With n event-types p i (h) is a n m  n Transition MatrixWith n event-types p i (h) is a n m  n Transition Matrix Example: Randomised ABExample: Randomised AB AB A AB A B => ABBBABAABABAAA... Design parametrised by:Design parametrised by: SOA min Minimum SOA p i (h) Probability of event-type i given history h of last m events With n event-types p i (h) is a n m  n Transition MatrixWith n event-types p i (h) is a n m  n Transition Matrix Example: Randomised ABExample: Randomised AB AB A AB A B => ABBBABAABABAAA... Differential Effect (A-B) Common Effect (A+B)

33 4s smoothing; 1/60s highpass filtering Efficiency - Multiple Event-types Example: Null eventsExample: Null events AB AB A B => AB-BAA--B---ABB... Efficient for differential and main effects at short SOAEfficient for differential and main effects at short SOA Equivalent to stochastic SOA (Null Event like third unmodelled event-type)Equivalent to stochastic SOA (Null Event like third unmodelled event-type) Selective averaging of data (Dale & Buckner 1997)Selective averaging of data (Dale & Buckner 1997) Example: Null eventsExample: Null events AB AB A B => AB-BAA--B---ABB... Efficient for differential and main effects at short SOAEfficient for differential and main effects at short SOA Equivalent to stochastic SOA (Null Event like third unmodelled event-type)Equivalent to stochastic SOA (Null Event like third unmodelled event-type) Selective averaging of data (Dale & Buckner 1997)Selective averaging of data (Dale & Buckner 1997) Null Events (A+B) Null Events (A-B)

34 Efficiency - Conclusions Optimal design for one contrast may not be optimal for anotherOptimal design for one contrast may not be optimal for another Blocked designs generally most efficient with short SOAs (but earlier restrictions and problems of interpretation…)Blocked designs generally most efficient with short SOAs (but earlier restrictions and problems of interpretation…) With randomised designs, optimal SOA for differential effect (A-B) is minimal SOA (assuming no saturation), whereas optimal SOA for main effect (A+B) is 16-20sWith randomised designs, optimal SOA for differential effect (A-B) is minimal SOA (assuming no saturation), whereas optimal SOA for main effect (A+B) is 16-20s Inclusion of null events improves efficiency for main effect at short SOAs (at cost of efficiency for differential effects)Inclusion of null events improves efficiency for main effect at short SOAs (at cost of efficiency for differential effects) Optimal design for one contrast may not be optimal for anotherOptimal design for one contrast may not be optimal for another Blocked designs generally most efficient with short SOAs (but earlier restrictions and problems of interpretation…)Blocked designs generally most efficient with short SOAs (but earlier restrictions and problems of interpretation…) With randomised designs, optimal SOA for differential effect (A-B) is minimal SOA (assuming no saturation), whereas optimal SOA for main effect (A+B) is 16-20sWith randomised designs, optimal SOA for differential effect (A-B) is minimal SOA (assuming no saturation), whereas optimal SOA for main effect (A+B) is 16-20s Inclusion of null events improves efficiency for main effect at short SOAs (at cost of efficiency for differential effects)Inclusion of null events improves efficiency for main effect at short SOAs (at cost of efficiency for differential effects)

35 OverviewOverview 1. Advantages of efMRI 2. BOLD impulse response 3. Timing Issues Temporal Basis Functions 4. Temporal Basis Functions 5. Design Optimisation 6. Nonlinear Models 7. Examples 1. Advantages of efMRI 2. BOLD impulse response 3. Timing Issues Temporal Basis Functions 4. Temporal Basis Functions 5. Design Optimisation 6. Nonlinear Models 7. Examples

36 Volterra series - a general nonlinear input-output model y(t) = 1 [u(t)] + 2 [u(t)] n [u(t)] y(t) =  1 [u(t)] +  2 [u(t)]  n [u(t)] n [u(t)] = ....  h n (t 1,..., t n )u(t - t 1 ).... u(t - t n )dt dt n  n [u(t)] = ....  h n (t 1,..., t n )u(t - t 1 ).... u(t - t n )dt dt n Volterra series - a general nonlinear input-output model y(t) = 1 [u(t)] + 2 [u(t)] n [u(t)] y(t) =  1 [u(t)] +  2 [u(t)]  n [u(t)] n [u(t)] = ....  h n (t 1,..., t n )u(t - t 1 ).... u(t - t n )dt dt n  n [u(t)] = ....  h n (t 1,..., t n )u(t - t 1 ).... u(t - t n )dt dt n Nonlinear Model  [u(t)] response y(t) input u(t) Stimulus function kernels (h) estimate

37 Nonlinear Model Friston et al (1997) SPM{F} testing H 0 : kernel coefficients, h = 0 kernel coefficients - h SPM{F} p < 0.001

38 Nonlinear Model Friston et al (1997) SPM{F} testing H 0 : kernel coefficients, h = 0 Significant nonlinearities at SOAs 0-10s: (e.g., underadditivity from 0-5s) (e.g., underadditivity from 0-5s) kernel coefficients - h SPM{F} p < 0.001

39 Nonlinear Effects Underadditivity at short SOAs Linear Prediction Volterra Prediction

40 Nonlinear Effects Underadditivity at short SOAs Linear Prediction Volterra Prediction

41 Nonlinear Effects Underadditivity at short SOAs Linear Prediction Volterra Prediction Implications for Efficiency

42 OverviewOverview 1. Advantages of efMRI 2. BOLD impulse response 3. Timing Issues Temporal Basis Functions 4. Temporal Basis Functions 5. Design Optimisation 6. Nonlinear Models 7. Examples 1. Advantages of efMRI 2. BOLD impulse response 3. Timing Issues Temporal Basis Functions 4. Temporal Basis Functions 5. Design Optimisation 6. Nonlinear Models 7. Examples

43 Example 1: Intermixed Trials (Henson et al 2000) Short SOA, fully randomised, with 1/3 null eventsShort SOA, fully randomised, with 1/3 null events Faces presented for 0.5s against chequerboard baseline, SOA=(2 ± 0.5)s, TR=1.4sFaces presented for 0.5s against chequerboard baseline, SOA=(2 ± 0.5)s, TR=1.4s Factorial event-types: 1. Famous/Nonfamous (F/N) 2. 1st/2nd Presentation (1/2)Factorial event-types: 1. Famous/Nonfamous (F/N) 2. 1st/2nd Presentation (1/2) Short SOA, fully randomised, with 1/3 null eventsShort SOA, fully randomised, with 1/3 null events Faces presented for 0.5s against chequerboard baseline, SOA=(2 ± 0.5)s, TR=1.4sFaces presented for 0.5s against chequerboard baseline, SOA=(2 ± 0.5)s, TR=1.4s Factorial event-types: 1. Famous/Nonfamous (F/N) 2. 1st/2nd Presentation (1/2)Factorial event-types: 1. Famous/Nonfamous (F/N) 2. 1st/2nd Presentation (1/2)

44 Lag=3 FamousNonfamous(Target)...

45 Example 1: Intermixed Trials (Henson et al 2000) Short SOA, fully randomised, with 1/3 null eventsShort SOA, fully randomised, with 1/3 null events Faces presented for 0.5s against chequerboard baseline, SOA=(2 ± 0.5)s, TR=1.4sFaces presented for 0.5s against chequerboard baseline, SOA=(2 ± 0.5)s, TR=1.4s Factorial event-types: 1. Famous/Nonfamous (F/N) 2. 1st/2nd Presentation (1/2)Factorial event-types: 1. Famous/Nonfamous (F/N) 2. 1st/2nd Presentation (1/2) Interaction (F1-F2)-(N1-N2) masked by main effect (F+N)Interaction (F1-F2)-(N1-N2) masked by main effect (F+N) Right fusiform interaction of repetition priming and familiarityRight fusiform interaction of repetition priming and familiarity Short SOA, fully randomised, with 1/3 null eventsShort SOA, fully randomised, with 1/3 null events Faces presented for 0.5s against chequerboard baseline, SOA=(2 ± 0.5)s, TR=1.4sFaces presented for 0.5s against chequerboard baseline, SOA=(2 ± 0.5)s, TR=1.4s Factorial event-types: 1. Famous/Nonfamous (F/N) 2. 1st/2nd Presentation (1/2)Factorial event-types: 1. Famous/Nonfamous (F/N) 2. 1st/2nd Presentation (1/2) Interaction (F1-F2)-(N1-N2) masked by main effect (F+N)Interaction (F1-F2)-(N1-N2) masked by main effect (F+N) Right fusiform interaction of repetition priming and familiarityRight fusiform interaction of repetition priming and familiarity

46 Example 4: Oddball Paradigm (Strange et al, 2000) 16 same-category words every 3 secs, plus …16 same-category words every 3 secs, plus … … 1 perceptual, 1 semantic, and 1 emotional oddball… 1 perceptual, 1 semantic, and 1 emotional oddball 16 same-category words every 3 secs, plus …16 same-category words every 3 secs, plus … … 1 perceptual, 1 semantic, and 1 emotional oddball… 1 perceptual, 1 semantic, and 1 emotional oddball

47 WHEAT BARLEY OATS HOPS RYE … ~3s CORN Perceptual Oddball PLUG Semantic Oddball RAPE Emotional Oddball

48 Example 4: Oddball Paradigm (Strange et al, 2000) 16 same-category words every 3 secs, plus …16 same-category words every 3 secs, plus … … 1 perceptual, 1 semantic, and 1 emotional oddball… 1 perceptual, 1 semantic, and 1 emotional oddball 16 same-category words every 3 secs, plus …16 same-category words every 3 secs, plus … … 1 perceptual, 1 semantic, and 1 emotional oddball… 1 perceptual, 1 semantic, and 1 emotional oddball Right Prefrontal Cortex Parameter Estimates Controls Oddballs 3 nonoddballs randomly matched as controls3 nonoddballs randomly matched as controls


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