MNE software for processing MEG and EEG data

Gramfort, Alexandre; Luessi, Martin; Larson, Eric; Engemann, Denis A.; Strohmeier, Daniel; Brodbeck, Christian; Parkkonen, Lauri; Hämäläinen, Matti S.

doi:10.1016/j.neuroimage.2013.10.027

Cited by 1,679 publications

(1,430 citation statements)

References 99 publications

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“…To do so, individual MRIs were first segmented using the Freesurfer software (Reuter et al, 2012). Then, the MEG forward model was computed for two orthogonal tangential current dipoles placed on a homogeneous 2-mm grid source space covering the whole brain (MNE suite; Gramfort et al, 2014). Coherence maps were produced within the computed source space at delta (0.5 Hz; frequency corresponding to sentence level prosody) and theta (4-8 Hz; frequency corresponding to syllable production rate) using a LCMVB (Hillebrand et al, 2005;Van Veen et al, 1997).…”

Section: Coherent Source Analysismentioning

confidence: 99%

Contrasting functional imaging parametric maps: The mislocation problem and alternative solutions

2018

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A B S T R A C TIn the field of neuroimaging, researchers often resort to contrasting parametric maps to identify differences between conditions or populations. Unfortunately, contrast patterns mix effects related to amplitude and location differences and tend to peak away from sources of genuine brain activity to an extent that scales with the smoothness of the maps. Here, we illustrate this mislocation problem on source maps reconstructed from magnetoencephalographic recordings and propose a novel, dedicated location-comparison method. In realistic simulations, contrast mislocation was on average~10 mm when genuine sources were placed at the same location, and was still above 5 mm when sources were 20 mm apart. The dedicated location-comparison method achieved a sensitivity of~90% when inter-source distance was 12 mm. Its benefit is also illustrated on real brain-speech entrainment data. In conclusion, contrasts of parametric maps provide precarious information for source location. To specifically address the question of location difference, one should turn to dedicated methods as the one proposed here.

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Section: Coherent Source Analysismentioning

confidence: 99%

Contrasting functional imaging parametric maps: The mislocation problem and alternative solutions

2018

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“…The inverse computation was done using a loose orientation constraint (loose = 0.2, depth = 0.8) (Lin et al, 2006). The cortically constrained reconstructed sources were then registered, morphed, onto the FreeSurfer average brain for group-level statistical analysis that was performed with MNE-python (Gramfort et al, 2013(Gramfort et al, , 2014.…”

Section: Meg Source Reconstructionmentioning

confidence: 99%

Encoding of event timing in the phase of neural oscillations

2014

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a b s t r a c t a r t i c l e i n f oTime perception is a critical component of conscious experience. To be in synchrony with the environment, the brain must deal not only with differences in the speed of light and sound but also with its computational and neural transmission delays. Here, we asked whether the brain could actively compensate for temporal delays by changing its processing time. Specifically, can changes in neural timing or in the phase of neural oscillation index perceived timing? For this, a lag-adaptation paradigm was used to manipulate participants' perceived audiovisual (AV) simultaneity of events while they were recorded with magnetoencephalography (MEG). Desynchronized AV stimuli were presented rhythmically to elicit a robust 1 Hz frequency-tagging of auditory and visual cortical responses. As participants' perception of AV simultaneity shifted, systematic changes in the phase of entrained neural oscillations were observed. This suggests that neural entrainment is not a passive response and that the entrained neural oscillation shifts in time. Crucially, our results indicate that shifts in neural timing in auditory cortices linearly map participants' perceived AV simultaneity. To our knowledge, these results provide the first mechanistic evidence for active neural compensation in the encoding of sensory event timing in support of the emergence of time awareness.

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“…All processing stages and topographical maps were performed with MNE-python [22,23] and custom made python libraries. The Case Report guidelines were followed throughout [24] .…”

Section: Materiel and Methodsmentioning

confidence: 99%