fMRI: Advice and thoughts on fMRI experiments

Trying to design your first neuroimaging study? Here are some thoughts you might want to consider (see also at the end some very useful links plus a PDF of my slides from my presentation at the DUKE-NUS Neuroimaging Short Course (2012)):


·       Make sure you have concrete independent variables and concrete predictions. Assume you had collected the data and imagine some ideal results. How would you interpret them?

·       The brain records more than what we think! Therefore:

o   Aim for orthogonal designs; make sure that you do not actually measure more than one dimensions that are collinear.

o   Watch out for low-level differences: visual effects,  reaction time, eye movements.

o   Try to identify the underlying cognitive processes that might overshadow your main process of interest (attention is a usual suspect).

·       If you are using event-related design then jitter appropriately; randomize presentation of stimuli.

·       Look for appropriate control tasks.

·       Allow for at least 20-25 measurements  per condition

·       Make sure you have a balanced number of trials per condition.

·       Go simple! Do not try to answer everything in one experiment

·       Do the task yourself. Watch out for boredom, especially for tasks that study ‘higher’ cognitive functions

·       Test – test – test! It is great if you have behavioral differences!

·       Interpretation: Watch out for reverse inferences and other conceptual errors.

·       Ask!


Also, here are some algorithms to help you optimize your design:

1. Design Magic – Multicollinearity assessment for fMRI designs SPM99 SPM2

Summary: Allows you to assess the multicollinearity in your fMRI-design by calculating the amount of factor variance that is also accounted for by the other factors in the design (expressed in R^2). Essentially, the higher R^2 is, the lower the t-values will be. Also can calculate (and create) the most optimal high-pass filter for your design.

Author: Matthijs Vink


2. GA – Genetic Algorithm for Experimental Design SPM99 SPM2 SPM5 SPM8

Summary: Optimizes event related fMRI designs by statistical and psychological criterion. (Matlab Signal Processing Toolbox required).

Author: Tor Wager