MEG

About MEG

Magnetoencephalography (MEG) is a functional neuroimaging technique for mapping brain activity by recording magnetic fields produced by electrical currents occurring naturally in the brain, using very sensitive magnetometers. Arrays of SQUIDs (superconducting quantum interference devices) are currently the most common magnetometer.

Motion Correction

This is less of an issue with MEG than it is with fMRI because head motion is explicitly recorded with HPI coils. Motion correction is part of the initial Maxfilter processing which also removes noise from external magnetic fields.

Dealing with Volume Conduction

Spurious zero-lag correlations stemming from volume conduction are the main obstacle when using MEG data for connectivity analysis. Different solutions have been proposed, for instance the Phase Lag Index. The signal-envelope based approach by Hipp et al. 2012 seems to work well for some.

Wavelet Decomposition and Hilbert transform

If using R, the waveslim package provides all the necessary functions. However, if the Hipp method is used, it is more straightforward to simply bandpass the data and apply a Hilbert transform to get the complex valued analytic signal ${\hat {X}}(t)$ with respect to the original signal $x(t)$ by calculating:

${\hat {S}}(f)=2\operatorname {u} (f)S(f)=\left[1+\operatorname {sgn}(f)\right]S(f)$

where:

• $S(f)$ is the Fourier transform of $x(t)$,
• $\operatorname {u} (f)$ is the Heaviside step function,
• $\operatorname {sgn}(f)$ is the sign function,

Then the analytic signal of $x(t)$ is the inverse Fourier transform of ${\hat {S}}(f)$:

${\hat {X}}(t)={\mathcal {F}}^{-1}[{\hat {S}}(f)]$

In this way one obtains both signal envelope and phase which can subsequently be fed into the orthogonalized envelope correlation ${\rm {C}}_{\perp }({\hat {X}},{\hat {Y}})$ proposed by Hipp:

${\begin{aligned}{\hat {Y}}_{\perp ^{X}}(t,f)&=&\Im \left({\hat {Y}}(t,f){\frac {{\hat {X}}^{\dagger }(t,f)}{|{\hat {X}}(t,f)|}}\right){\hat {e}}_{\perp ^{X}}(t,f)\\{\rm {C}}_{\perp }({\hat {X}},{\hat {Y}};t,f)&=&{\frac {{\rm {Corr}}(|{\hat {X}}|,|{\hat {Y}}_{\perp ^{X}}|)+{\rm {Corr}}(|{\hat {Y}}|,|{\hat {X}}_{\perp ^{Y}}|)}{2}}\end{aligned}}$

See also

The CBU Wiki is the best resource for everything MEG.