A novel integrated MEG and EEG analysis method for dipolar sources

Abstract

The ability of magnetoencephalography (MEG) to accurately localize neuronal currents and obtain tangential components of the source is largely due to MEG's insensitivity to the conductivity profile of the head tissues. However, MEG cannot reliably detect the radial component of the neuronal current. In contrast, the localization accuracy of electroencephalography (EEG) is not as good as MEG, but EEG can detect both the tangential and radial components of the source. In the present study, we investigated the conductivity dependence in a new approach that combines MEG and EEG to accurately obtain, not only the location and tangential components, but also the radial component of the source. In this approach, the source location and tangential components are obtained from MEG alone, and optimal conductivity values of the EEG model are estimated by best-fitting EEG signal, while precisely matching the tangential components of the source in EEG and MEG. Then, the radial components are obtained from EEG using the previously estimated optimal conductivity values. Computer simulations testing this integrated approach demonstrated two main findings. First, there are well-organized optimal combinations of the conductivity values that provide an accurate fit to the combined MEG and EEG data. Second, the radial component, in addition to the location and tangential components, can be obtained with high accuracy without needing to know the precise conductivity profile of the head. We then demonstrated that this new approach performed reliably in an analysis of the 20-ms component from human somatosensory responses elicited by electric median-nerve stimulation.

Magnetoencephalography Slow-Wave Detection in Patients with Mild Traumatic Brain Injury and Ongoing Symptoms Correlated with Long-Term Neuropsychological Outcome

“…[51][52][53][54] A Realistic Boundary Element Method head model was used for MEG forward calculation. 55,56 The BEM mesh was constructed by tessellating the inner skull surface from the T1-weighted MRI into *6000 triangular elements with *5-mm size. A cubic source grid with 5-mm size was used for calculating the MEG gain (i.e., lead-field) matrix, which leads to a grid with *7000 nodes covering the cortical and subcortical gray matter of the whole brain.…”

Influence of the head model on EEG and MEG source connectivity analyses

“…For each point in the source space the source orientation for which the MEG was minimal was defined as radial. Singular value decomposition was performed for the lead field of three orthogonal dipoles at each source position (Ahlfors et al, 2010;Huang et al, 2007;Lew et al, 2013): [l x (q i ), l y (q i ), l z (q i )] = USV T , where U and V are the left and right singular vectors, respectively, and S is the diagonal matrix Table 1 Conductivity values (unit: S/m) of the head models (w is the white matter, g is the gray matter, c is the compact bone, and s is the spongy bone).…”

Magnetoencephalography (MEG) Slow-Wave Imaging for Diagnosing Non-acute Mild Traumatic Brain Injury

“…The T1-weighted images were also used to extract the brain volume and innermost skull surface. Realistic boundary element method (BEM) head model was used for MEG forward calculation [29,30]. The BEM mesh was constructed by tessellating the inner skull surface from the T1-weighted MRI.…”