rapid self-paced event- related functional mri: feasibility and implications of stimulus- versus...

Rapid Self-Paced Event-Related Functional MRI: Feasibility and Implications of Stimulus- versus Response-

Locked Timing

Maccotta, Zacks & Buckner, 2001

Outline

This is a methods paper Problems with other fMRI methods A new Method! Rapid self-paced

event-related fMRI Empirical test Conclusions

Blocked designs

All trials in a given block are of the same type

Can’t see what happens on individual trials

Strategies could be employed Some questions require that events

happen at unpredictable times

Event-Related Designs

Allow you to examine single trials Stimulus onset is locked to

beginning of a TR (repetition time) Require a lot of down-time for

BOLD response to return to baseline

Rapid Event-Related Designs

Dale and Buckner, 1997 Bold signals are additive You don’t need to wait for the

BOLD signal to return to baseline before the next trial

But trials still began at the beginning of a TR

Joseph et al. (1997)

In the context of event-related designs, the stimulus does not have to be time locked to image acquisition

Gives temporal resolution better than TR

Over-samples the hemodynamic function

The questions here

Can a self-paced event-related fMRI:• successfully detect task correlated

activation during trials that are fully self-paced by the subject?

• Precisely characterize the hemodynamic response?

The Subjects

17 right handers 8 males no history of significant neurological

or psychiatric problems What exactly qualifies a NON-

significant neurological or psychiatric problem?

Imaging Stuff

Scanner: 1.5 T Coil type: Circularly polarized head

coil Restraints: Thermoplastic face

mask and foam cushions

Structural Images

Resolution: 1.25 x 1 x 1 mm T1 weighted TR (Repetition time) = 9.7 ms TE (Time to echo) = 4 ms flip angle = 10º TI = 20 ms TD = 500 ms

Functional Images

Echo-planar asymmetric spin-echo sequence

TR (repetition time)= 2.36 s TE (Time to echo)= 37 ms Resolution: 3.75 x 3.75 mm Flip angle 90º

Functional Images

Each image acquisition consisted of 16 contiguous, 8 mm-thick axial images parallel to the anterior-posterior commisural plane

There were 128 image acquisitions per run

Each run took 5 minutes (4 runs per subject)

The Cognitive Paradigm

Mental rotation A pair of human stick figures were

presented left and right of fixation Each figure was rotated by some

amount (in 30º increments) Figures could be same or mirror

images

The Cognitive Paradigm (cont’d)

A left or a right hand keypress was required (counterbalanced across subjects)

Stimuli remained on-screen until a response was made

750 ms later, the next trial began

Logic

The task is not important The main thing here is test the

feasibility of doing this Left vs. Right respond hand should

light up known areas Manipulating the amount of rotation

will manipulation response time

Functional Data Preprocessing

Rigid-body rotation and translation was performed to correct for motion (Snyder, 1996)

Images were translated into standardized atlas space (Talairach & Tournoux, 1988)

Over-Sampling

Stimulus onset could happen at any point in a TR (0 - 2360 ms from onset)

This means you get over-sampling of the hemodynamic response function

Usually stimulus onsets occur at integer multiples of TR

Over-Sampling

This means your smallest temporal bin that’s useful is TR

But if stimulus onsets can occur anywhere over TR, you can get smaller temporal bins

With rapid presentation and smaller temporal bins, you should be able to get better temporal resolution

Proportion of stimulus onsets/bin

0

0.2

0.4

0.6

0.8

1

0 - 590 591 - 1180 1181 - 1770 1771 - 2360

Time from beginning of TR (ms)

Prop

ortio

n of

st

imul

us o

nset

s

Over-Sampling

To take advantage of this, each trial was rebinned to the time bin closest to the true trial onset

Bin size was varied from TR, TR/2 to TR/4 to see what would happen

Statistical Map Generation

Trials were sorted and averaged according to response hand (Left vs. Right) and response time (Slow, Medium, Fast)


For each voxel, difference time courses between trial types were generated (Left - Right)

This was then regressed with a set of idealized hemodynamic response curves


This regression represents a difference between conditions

Because conditions only differ in response hand (left vs. right) activation is expected in motor areas

Regions of Interest

Six regions of interests were located:• right and left motor cortex;• right and left supplementary motor

area (SMA), and;• right and left cerebellar cortex

Regions of Interest

ROIs were identified by 19 or more suprathreshold (Z > 3.3) 8 mm3 cubic voxels

Most significant peaks in 12 mm radius were kept

ROIs were defined to include all voxels within 12 mm of a peak

Regions of Interest

Time courses for each trial type were extracted for each region of interest, averaging across all voxels within the region

Time course differences between trial types were then generated, which gave the BOLD hemodynamic response associated with each trial type comparison

The Hemodynamic Response

Three parameters were estimated:• Amplitude• Time-to-onset• Time-to-peak

Sig

nal

Cha

nge

Time

Time-to-onset

Time-to-peak

Amplitude

Behavioural Results

518 mean correct responses/session 92 % correct (72 - 98 %) RT(Rhand) = 1303 ms SD = 666 ms RT(Lhand) = 1348 ms SD = 792 ms

Imaging Results

Initial analysis was done to see if the procedure could get accurate maps of task correlated activation

Left - Right and Right - Left trials were examined, with bin sizes of TR, collapsed across response speed

Imaging Results

Observed activation corresponded to the left and right motor networks (motor cortex, SMA and cerebellar cortex), as expected

Results for time-to-onset (2 s) and time-to-peak (4 - 6 s) are similar to those from fixed-pace fMRI studies

Temporal Sampling of BOLD

Was the over-sampling of the hemodynamic response even across the TR?

Yep.

Temporal Resolution

Does using smaller bins (TR/2 or TR/4) still allow for estimates of hemodynamic response?

Yep. And the precision is better, because the bin size is smaller

Regardless of bin size, the hemodynamic response function can be modeled by a gamma function

Hemodynamic Response Timing and Behavioural

Response Timing What happen to the hemodynamic

response (for motor cortex) as the time taken to perform the task increases?

Slow responses are associated with longer time-to-onsets, time-to-peaks and smaller amplitudes

Response-Locked Timing

You can measure time-to-onset, time-to-peak and amplitude of the hemodynamic response from both the stimulus onset and the response onset

This can tell you if a manipulation has it’s effect before or after activation reaches a site


Remember that trials were binned according to response time

Slower trials were probably caused by a requirement for more mental rotation

This processing should occur before neural activity hits motor cortex


If this is the case, hemodynamic response parameters should be invariant across behavioural response times when response-locked

This more or less happened, but there was some differences still

Summary

You can get robust hemodynamic response estimates from rapid, arbitrarily timed events in an event-related fMRI paradigm

Multiple regions known to be active during motor response execution were showed significant activation

Summary

This was true at the group and individual level

Results paralleled those from fixed-pace studies

presentation rate is not dictated by TR

Presentation rate can be less than TR

Summary

Self-paced paradigms produced even sampling of the hemodynamic response across TR, which allows for better temporal resolution (resolution better than TR) of the hemodynamic response

rapid self-paced event- related functional mri: feasibility and implications of stimulus- versus...

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