Abstract

a b s t r a c t a r t i c l e i n f o recordings are a rich source of information about the neural dynamics underlying cognitive processes in the brain, with excellent temporal and good spatial resolution. In recent years there have been considerable advances in MEG hardware developments and methods. Sophisticated analysis techniques are now routinely applied and continuously improved, leading to fascinating insights into the intricate dynamics of neural processes. However, the rapidly increasing level of complexity of the different steps in a MEG study make it difficult for novices, and sometimes even for experts, to stay aware of possible limitations and caveats. Furthermore, the complexity of MEG data acquisition and data analysis requires special attention when describing MEG studies in publications, in order to facilitate interpretation and reproduction of the results. This manuscript aims at making recommendations for a number of important data acquisition and data analysis steps and suggests details that should be specified in manuscripts reporting MEG studies. These recommendations will hopefully serve as guidelines that help to strengthen the position of the MEG research community within the field of neuroscience, and may foster discussion in order to further enhance the quality and impact of MEG research. © 2012 Elsevier Inc. Introduction Recent methodological and technological developments in the field of magnetoencephalography (MEG) have led to a new level of sophistication for performing state-of-the-art data acquisition and analysis. Thanks to the unique features of MEG data in combination with improved analysis techniques, a steadily increasing number of researchers have realized the potential of MEG to answer neuroscientific questions. These developments pose specific challenges, both to the methods developers and to the empirical scientists. Given the breadth of expertise required (time-resolved paradigm designs, multidimensional time series analysis, source reconstruction, statistical analysis, etc.), even experienced researchers in the field may find it difficult to keep up with new developments. New methods and technologies may have been introduced without rigorous testing, validation and comparison with existing techniques. In addition, the level of experience and the sophistication of data acquisition and analysis are highly variable, not only within but especially between groups, and there is often little room left for exchange of data, ideas and people across the MEG community. Open access under CC BY license. Open access under CC BY license. Similar procedures are often developed several times independently in different labs (or even in the same lab); sometimes with little awareness of existing procedures/toolboxes. We realize a genuine need for the development of good-practice guidelines, the implementation of validated analysis pipelines and the sharing of practical knowledge across the MEG community. Such an endeavor could have several dimensions: documentation of best practice (recommended procedures for a certain type of analysis), quality control (how to ascertain that the analysis step was successful/meaningful), reproducible research (how to ascertain that the result of an analysis can be reproduced by the same or another researcher), report of results (recommendations for reporting MEG analysis and results suitable for peer-reviewed publications), and dissemination of source code implementing recommended analysis pipelines. Achieving these goals requires substantial contributions from the MEG community, but we believe that the benefits are manifold: First, contributions to this concerted development, rather than isolated efforts, are more likely to bring long-term benefits. Second, contributions from multiple groups are likely to be synergistic, and avoid redundant efforts. Third, a concerted effort will foster development of the entire field by consistently raising the standards of MEG research. Fourth, the reproducibility of research will be facilitated. Fifth, a community consensus will enable a more unified presentation of the MEG community to other areas in neuroscience in general, and neuroimaging in particular. Sixth, it will help novices in the field to become productive in their MEG research more rapidly. Seventh, it will facilitate training and sharing of expertise and knowledge in the field. Here, we take the first step 1 in this direction and propose research guidelines for the acquisition and analysis of MEG data. These guidelines are intended to be taken as general recommendations and to provide a basis for further discussion and improvement by the entire MEG community. These recommendations may be useful as a guide for those new to the field of MEG by providing the novice with information and references to guide them through their initial MEG data recording and analysis (see, for example, recent guidelines for fMRI by This manuscript does not give recommendations related to hardware, software, data handling, participant questionnaires, or consent forms. It is also not meant as a recommendation to perform MEG analysis with all the stages described here or as an endorsement of a specific analysis (for example the analysis of event-related fields may not be meaningful for some data). The manuscript rather covers the most common analysis stages and the researcher must decide on the suitability of each stage for the respective project or may employ a different analysis not described here. In addition, these guidelines are intended for basic research, not clinical applications: timely with the present initiative, the American Clinical MEG Society has recently published the first set of guidelines for clinical MEG (Burgess et al., 2011). The paper is divided into seven sections, each of which represents a separate stage in the data collection and analysis process. It begins with data acquisition and is followed by different analysis steps: data preprocessing, analysis of event-related fields, spectral analysis, source reconstruction, connectivity analysis and statistics. Each section provides a general background of the topic and concludes with two sub-sections: recommendations and caveats, and suggestions for reporting results in peer-reviewed publications. Data acquisition Background Data acquisition is a critical component of a MEG study as it affects data quality directly. To ensure optimal data quality, the following elements need to be taken into account: • Operating status of the system and set-up (sufficient helium level, optimum calibration/tuning of the sensors, proper functioning of the interference suppression system including magnetic shielding, sufficiently low ambient magnetic interference level). • Preparation of the participant (removal of magnetic materials that may distort the measurements), explaining the task and giving instructions to avoid movements, eye-blinks and eye movements during the trials. • Experimental design (e.g., allowing for sufficient number of data trials after rejection of artifacts). • General acquisition setup (sufficiently high sampling rate and filter pass-band, recording of supplementary data such as electrooculogram (EOG), electrocardiogram (ECG), electromyogram (EMG), eyetracking, digitized head shape). The following section describes recommended procedures falling into these four categories, to optimize data quality directly or to improve performance of subsequent analysis steps such as source estimation. Recommendations and caveats • Before the participant enters the MEG laboratory: We recommend that the condition of the MEG system is recorded by performing an "empty room" recording of about 2 minute duration before and after the experiment. These records are snapshots of the noise level (and their changes) at the time of measurement, allowing for the identification of problems with the system. We suggest simple spectral analysis and storage of original data and spectra for later comparison. These datasets allow monitoring the noise level and system status over longer periods of time. Tuning of individual sensors may be required for optimal performance. A log book is helpful to record and communicate the status of sensors and the system in general.