The face-responsive M170 is modulated by sensor selection: An example of circularity in the analysis of MEG-data

Magnetoencephalography (MEG) experiments create large data sets obtained from many sensors at different locations. Therefore, a process of sensor-selection prior to hypothesis-driven data analysis is common, e.g., when studying the face-selective M170 component occurring at temporal sites around 170ms post-stimulus. However, the strategies to identify sensors of interest vary across investigations, and frequently the contrast used for sensor-selection is not independent from the contrast between experimental conditions.

We re-analyzed data from a previously published MEG-experiment where participants viewed faces of either a loved person or two friends. We included different strategies for identifying face-responsive sensors based on all or each one of the face-categories before comparing M170 amplitudes across conditions.

When sensor-selection was based on signal strength elicited by one experimental condition alone, a comparison across face-categories revealed significantly increased M170 amplitudes for the respective face category. Conducting the same analysis following sensor-selection based on averaged activity across all face-categories did not yield different M170 amplitudes.

Whereas this pitfall of selective analysis has been studied and discussed in detail for fMRI methods there is no comparable re-analysis of real EEG/MEG-data.

Our results demonstrate that selection-bias is as relevant for EEG/MEG analysis as for fMRI methods. Sensor-selection must be independent from the contrast analyzed with statistical comparisons, because otherwise a distorted or 'circular' analysis might result.