Abstract
Background: Modern Elekta Neuromag MEG devices include 102 sensor triplets containing one magnetometer and two planar gradiometers. The first processing step is often a signal space separation (SSS), which provides a powerful noise reduction. A question commonly raised by researchers and reviewers relates to which data should be employed in analyses: (1) magnetometers only, (2) gradiometers only, (3) magnetometers and gradiometers together. The MEG community is currently divided with regard to the proper answer. Methods: First, we provide theoretical evidence that both gradiometers and magnetometers result from the backprojection of the same SSS components. Then, we compare resting state and task-related sensor and source estimations from magnetometers and gradiometers in real MEG recordings before and after SSS. Results: SSS introduced a strong increase in the similarity between source time series derived from magnetometers and gradiometers (r2 = 0.3–0.8 before SSS and r2 > 0.80 after SSS). After SSS, resting state power spectrum and functional connectivity, as well as visual evoked responses, derived from both magnetometers and gradiometers were highly similar (Intraclass Correlation Coefficient > 0.8, r2 > 0.8). Conclusions: After SSS, magnetometer and gradiometer data are estimated from a single set of SSS components (usually ≤ 80). Equivalent results can be obtained with both sensor types in typical MEG experiments.
Highlights
The signal space separation method (SSS) [1] and its spatiotemporal extension [2] are powerful noise-reduction methods commonly used as a first preprocessing step in raw MEG data analysis
Pearson correlation brevity and simplicity, we focused here on four sources of interest, which are spread across the cortex andr2commonly are used in the neuroimaging literature: cortex (MNI coordinates coefficients were computed between source timevisual series derived from magnetometers and mm), primary somatosensory cortex (MNI: [−38, −27, 52] mm), precuneus (MNI: [1, −57, 28] mm) and gradiometers for each pair of λmag and λgrad and averaged over trials and2 subjects
Squared coefficients averaged across subjects selected as a Pearson function ofcorrelation the regularization parameters λ for magnetometer (x-axis)are andshown for gradiometer beamforming reconstructions
Summary
The signal space separation method (SSS) [1] and its spatiotemporal extension (tSSS) [2] are powerful noise-reduction methods commonly used as a first preprocessing step in raw MEG data analysis. They have repeatedly been proven to be successful in the suppression of unwanted magnetic noise originating from distant [3] and nearby [2] sources, or even from orthodontic material [4]. After SSS, resting state power spectrum and functional connectivity, as well as visual evoked responses, derived from both magnetometers and gradiometers were highly similar
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