Elsevier

Journal of Neuroscience Methods

Volume 266, 15 June 2016, Pages 137-140
Journal of Neuroscience Methods

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

https://doi.org/10.1016/j.jneumeth.2016.03.022Get rights and content

Highlights

  • We studied biased strategies for the identification of face-responsive MEG-sensors.

  • M170 amplitudes elicited by either faces of 2 friends or a loved one were analyzed.

  • Sensor-selection was based on all (unbiased) or one (biased) of 3 face-categories.

  • M170s obtained from the biased selection were increased upon the respective faces.

  • Unbiased selection including all face-categories equally revealed no true effect.

Abstract

Background

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 170 ms 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.

New method

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.

Results

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.

Comparison with existing method

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.

Conclusions

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.

Introduction

Given the large quantity of multivariate data provided by methods such as functional magnetic resonance imaging (fMRI), multi-array electroencephalography (EEG) or magnetoencephalography (MEG), selective data analysis is often necessary to study signals of interest. For instance, in MEG studies on face processing the analysis of event-related fields (ERFs) within sensor-space such as the face-selective M170 is typically restricted to regionally arranged clusters of sensors. These sensors of interest can be identified making use of previous research using different methodologies suggesting a consistent topography and chronology of activations by face stimuli occurring between 140–200 ms in posterior and lateral sensors over occipital and temporal areas (for review see Eimer, 2011). However, if sensor-selection is not only based on physiological or anatomical assumptions a phenomenon termed ‘circular analysis’ (Kriegeskorte et al., 2009) might lead to distorted and eventually flawed results. This problem would for example arise when the comparison of M170 amplitudes elicited by different types of faces were based on the averaged signal strength for only one of the face-categories. This can be verified by comparing the contrasts used for selection and analysis as vectors, which should be orthogonal to each other (i.e., their inner product is zero) to yield an independent analysis (given that presentation times, experimental design, etc. are matched between conditions). An example can be found in the article by Kriegeskorte et al. (2009, p. 536; see also the supplementary discussion in the online version of the article): when the responses to two stimulus-categories are equally used for sensor-selection (contrast A + B) before statistical comparison between both stimuli (contrast A  B); cselection [1 1]T × ctest [1 −1]T = 0.

Although the issue of circularity is not confined to a particular methodology such as fMRI but is instead relevant whenever data analysis is selective (Vul and Pashler, 2012), most of the articles on this topic concentrate almost exclusively on neuroimaging techniques (e.g., Kriegeskorte et al., 2010, Vul et al., 2009). Specifically, the extent of distortions caused by a non-independent analysis of fMRI data has been demonstrated by means of comparing artificial and realistic data as well as conducting parallel or repeated analyses (Baker et al., 2007, Kriegeskorte et al., 2009, Poldrack and Mumford, 2009, Vul et al., 2009). On the contrary, only one article concerned with the topic of selection bias in EEG and MEG data analysis has demonstrated that selection based on signal strength massively increased the type-1 error in artificial, pure noise data (Kilner, 2013). Whereas there is no doubt about the relevance of the topic for electrophysiological methods, there is an apparent lack of concrete examples and, possibly awareness of the problem. Therefore, we conducted a simplified re-analysis of MEG-data from a previously published experiment on processing of personally familiar and loved faces (Tiedt et al., 2014, Tiedt et al., 2013), adopting two different (independent compared to circular) sensor-selection strategies. Aim of the current analysis was to illustrate how sensor selection ‘favoring’ each one of three face categories for the analysis of the M170 component might not only distort the results but lead to statistically significant results.

Section snippets

Participants and experimental procedure

We analyzed data from 28 healthy subjects (mean age 25.5 years, range 20–35 years; 14 female, two were left-handed). All participants viewed photographs of three opposite-sex faces of their romantic partner as well as another two close friends for six seconds. Pictures (size 100 × 100 pixels; viewing angle 11.5°) were in color and showed only the faces of the depicted persons. Stimuli from each category were presented 30 times in randomized order with variable inter-stimulus intervals.

Results

In each of the sensor-selection conditions, an event related field (ERF) peaking within the time-window of the M170 could be identified as an outward-going magnetic flux (positive polarity) over the right hemisphere. Graphs showing the M170 obtained from the different sensor-selection strategies as well as topographic maps are depicted in Fig. 1.

In the non-circular analysis, there was no discernible effect of face-category with almost identical peak amplitudes across face categories. In

Discussion

Our re-analysis of the M170 component obtained from sensors selected by signal strength averaged across all face-categories replicated our earlier results and no effects of face-category on peak-amplitude of the right-hemispheric M170 were observed. On the contrary, selecting sensors by signal strength elicited by each one of and testing for differences between the three face categories yielded a significantly increased M170 component for the respective experimental condition. In the

Conflict of interest

None of the authors has potential conflicts of interest to be disclosed.

Acknowledgements

This research was supported by Lutz Trahms and Tilman Sander of the Biomagnetism group at the Physikalisch-Technische Bundesanstalt (PTB) in Berlin.

References (15)

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