Magnetoencephalography (MEG) is increasingly being used to study brain function because of its excellentudtemporal resolution and its direct association with brain activity at the neuronal level. One possible cause of errorudin the analysis of MEG data comes from the fact that participants, even MEG-experienced ones, move their head inudthe MEG system. Head movement can cause source localization errors during the analysis of MEG data, which canudresult in the appearance of source variability that does not reflect brain activity. The MEG community places greatudimportance in eliminating this source of possible errors as is evident, for example, by recent efforts to developudhead casts that limit head movement in the MEG system. In this work we use software tools to identify, assess andudeliminate from the analysis of MEG data any possible correlations between head movement in the MEG systemudand widely-used measures of brain activity derived from MEG resting-state recordings. The measures of brainudactivity we study are a) the Hilbert-transform derived amplitude envelope of the beamformer time series and b)udfunctional networks; both measures derived by MEG resting-state recordings. Ten-minute MEG resting-state recordingsudwere performed on healthy participants, with head position continuously recorded. The sources of theudmeasured magnetic signals were localized via beamformer spatial filtering. Temporal independent componentudanalysis was subsequently used to derive resting-state networks.udSignificant correlations were observed between the beamformer envelope time series and head movement. Theudcorrelations were substantially reduced, and in some cases eliminated, after a participant-specific temporal highpassudfilter was applied to those time series. Regressing the head movement metrics out of the beamformer envelopeudtime series had an even stronger effect in reducing these correlations. Correlation trends were alsoudobserved between head movement and the activation time series of the default-mode and frontal networks.udRegressing the head movement metrics out of the beamformer envelope time series completely eliminated theseudcorrelations. Additionally, applying the head movement correction resulted in changes in the network spatialudmaps for the visual and sensorimotor networks. Our results a) show that the results of MEG resting-state studiesudthat use the above-mentioned analysis methods are confounded by head movement effects, b) suggest thatudregressing the head movement metrics out of the beamformer envelope time series is a necessary step to be addedudto these analyses, in order to eliminate the effect that head movement has on the amplitude envelope of beamformerudtime series and the network time series and c) highlight changes in the connectivity spatial maps whenudhead movement correction is applied
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