3  Experimental paradigm

for fMRI

In this section

  • Technical aspects of an fMRI experiment

  • Software

  • Implementation details

Software

Feature PsychoPy Psychtoolbox
GUI limited no
Mac/Windows yes if payed
Linux (no) yes
Degree of hardware control and precision less more
Advanced functionality no yes
Timing accuracy less more
Version compativility no yes
Standalone yes no (MATLAB or Octave)

Runs

  • chunks of 5-10 minute length

  • ideally containing all experimental conditions (there are exceptions)

  • one run - one start of the experimental script

  • one paradigm run corresponds to one fMRI time series file

  • Typically repeated multiple times to fill about 1 hour of scanning

Start screen

  • The script should start by showing a welcome message or instruction reminder

  • It should be waiting for key “5” (scanner “trigger”)

  • This is needed to synchronize the fMRI signal acquisition with your paradigm

T1 equilibration wait time

  • After the start screen, there should be a period of 5 - 10 seconds of nothing happening; this is needed to discard initial scanner artifacts

This is more of a historical practice that remains from old times.

Back in the day, people even waited a certain amount of volumes before starting the paradigm. This led to misalignment of fMRI and paradigm timings, and to analysis errors.

Modern Siemens scanners discard initial volumes automatically without saving them, so you don’t need to care about it. Furthermore, preprocessing software runs additional tests to detect nonsteady state volumes. These volumes (if detected), can be added as niussance regressors to the analysis.

Behind the scenes

Video presentation

Audio

Problem: continuous scanner noise

Solution: High-quality auditory presentation or acquisition interruption

Ear phones

Head phones

Subject responses

Subject keys

1, 2, 3, 4, [], 6, 7, 8, 9

Joystick

Vigilance check

  • If the subject does not have a task, it is important to make sure they are still paying attention to the paradigm and not falling asleep.
  • This can be done by introducing a fixation task
  • For example, the fixation point can change color or shape, and the subject has to press a button when they notice it.
  • In any case, it is useful to give subjects feedback on their performance!

Paradigm events

Throughout the experimental run, we save all important information that happened, such as: - Which condition was presented at which time - Which button was pressed at which time

It is generally encouraged to save as much information as possible, as it can be useful for later analysis and troubleshooting. This is especially important for complex paradigms with many conditions and/or response options. Better save to much than too little!

Carryover effects

(Brooks 2012); code available at https://supp.apa.org/psycarticles/supplemental/a0029310/MET-Brooks20100184-R-S1.pdf

Software

Neurodesign advantages

  • Python code

  • Optimization of multiple aspects simultaneously

  • Less requirements on experiment timings

On neurodesk

BIDS plugin for PsychoPy

Should be installed and enabled to save events (conditions and button presses) in a comprehensive and standardized manner

This will also make our data analysis easier

A warning on BIDS event:

  • do not rely on BIDS events exclusively; save as much information as possible on your experimental design

  • check the timing of your paradigm to make sure there are no unexpected delays

References

Brooks, Joseph L. 2012. “Counterbalancing for Serial Order Carryover Effects in Experimental Condition Orders.” Psychological Methods 17 (4): 600–614. https://doi.org/10.1037/a0029310.
Buračas, Giedrius T., and Geoffrey M. Boynton. 2002. “Efficient Design of Event-Related fMRI Experiments Using M-Sequences.” NeuroImage 16 (3): 801–13. https://doi.org/10.1006/nimg.2002.1116.
Dale, Anders M. 1999. “Optimal Experimental Design for Event-Related fMRI.” Human Brain Mapping 8 (2-3): 109–14. https://doi.org/10.1002/(sici)1097-0193(1999)8:2/3<109::aid-hbm7>3.0.co;2-w.
Wager, Tor D., and Thomas E. Nichols. 2003. “Optimization of Experimental Design in fMRI: A General Framework Using a Genetic Algorithm.” NeuroImage 18 (2): 293–309. https://doi.org/10.1016/s1053-8119(02)00046-0.