A comparison of methods for characterizing the event-related BOLD timeseries in rapid fMRI John T. Serences

Separating events ‘Sluggish’ BOLD signal Slow events: 20s ITI –Few trials per run –Not psychologically ideal BOLD signal linear & time-invariant Rapid events: > 2s ITI Jittering overcomes overlap

Jitter Fixed interval designs provide too little information to resolve the BOLD response Jittering adds information BOLD is an equation, with n unknowns:

See also Burock et al. (1998)

Event-related averaging

GLM Equation for n predictors Collapses to vector equation Least squares solution found by inverting design matrix

GLM Boxcar function Convolve with assumed HDR: Design matrix Fit to signal Beta 1 Beta 2 Beta 3

Design matrix One column = assumed BOLD response for one stimulus type In this case, 3 columns Row = # timepoints

Design matrix for deconvolution No assumed BOLD response Assumed consistent over repetitions of same type Extra column for each time points in BOLD response

Multicollinearity Each column in X must be linearly independent –Cannot make one column from linear combinations of other columns Sequential events are perfectly correlated Partial trials omit second event to reduce multicollinearity

Experimental designs 1.Independent, randomly-timed events 2.Sequentially dependant 3.Sequentially dependant with 30% partial trials

Jitter types Exponential distribution more efficient than uniform

Simulations 15 iterations of 12 runs of 256 sec BOLD response is a gamma function –Delta = 2, tau = 1.25 Noise added –Non-zero Gaussian white noise –Temporally correlated noise at 1 Hz and 0.2 Hz Time series created at 10 Hz, then sampled at 1 Hz (TR = 1000 ms) Four events (A-D) of amplitude 1, 3, 1, and 1.

Calculations Event-related averaging –All time points 6 TRs before and 20 TRs after each event averaged Deconvolution –GLM included 20 regressors for each stimulus type Repeated measures t test for each time point within averaging window –Not usually done, but valid for comparison only

Independent events

Compound trials

Partial trials

Comparison of t values

Conclusions Both event-related averaging and deconvolution can estimate the BOLD response for independent events Only deconvolution is robust for compound trials Using partial trials improves power at shorter ISIs