The value of magnetoencephalography to guide electrode implantation in epilepsy

To investigate if Magnetoencephalography (MEG) can add non-redundant information to guide implantation sites for intracranial recordings (IR). The contribution of MEG to intracranial recording planning was evaluated in 12 consecutive patients assessed pre-surgically with MEG followed by IR. Primary outcome measures were the identification of focal seizure onset in IR and favorable surgical outcome. Outcome measures were compared to those of 12 patients matched for implantation type in whom non-invasive pre-surgical assessment suggested clear hypotheses for implantation (non-MEG group). In the MEG group, non-invasive assessment without MEG was inconclusive, and MEG was then used to further help identify implantation sites. In all MEG patients, at least one virtual MEG electrode generated suitable hypotheses for the location of implantations. No differences in outcome measures were found between non-MEG and MEG groups. Although the MEG group included more complex patients, it showed similar percentage of successful implantations as the non-MEG group. This suggests that MEG can contribute to identify implantation sites where standard methods failed. © 2013 Springer Science+Business Media New York.

Magnetoencephalography in epilepsy

Magnetoencephalographic (MEG) signals, like electroencephalographic (EEG) measures, are the direct extracranial manifestations of neuronal activation. The two techniques can detect time-varying changes in electromagnetic activity with a sub-millisecond time resolution. Extra-cranial electromagnetic measures are the cornerstone of the non-invasive diagnostic armamentarium in patients with epilepsy. Their extremely high temporal resolution – comparable to intracranial recordings – is the basis for a precise definition of onset and propagation of ictal and interictal abnormalities. Given the cost of the infrastructure and equipment, MEG has yet to develop into a routinely applicable diagnostic tool in clinical settings. However, in recent years, an increasing number of patients with epilepsy have been investigated – usually in the context of presurgical evaluation of refractory epilepsies – and initial encouraging results have been reported. We will briefly review the principles and the technology behind MEG and its contribution in the diagnostic work-up of patients with epilepsy.

Source localization methods

One of the most pressing demands on electrophysiology applied to the diagnosis of epilepsy is the non-invasive localization of the neuronal generators responsible for brain electrical and magnetic fields (the so-called inverse problem). These neuronal generators produce primary currents in the brain, which together with passive currents give rise to the EEG signal. Unfortunately, the signal we measure on the scalp surface doesn't directly indicate the location of the active neuronal assemblies. This is the expression of the ambiguity of the underlying static electromagnetic inverse problem, partly due to the relatively limited number of independent measures available. A given electric potential distribution recorded at the scalp can be explained by the activity of infinite different configurations of intracranial sources. In contrast, the forward problem, which consists of computing the potential field at the scalp from known source locations and strengths with known geometry and conductivity properties of the brain and its layers (CSF/meninges, skin and skull), i.e. the head model, has a unique solution. The head models vary from the computationally simpler spherical models (three or four concentric spheres) to the realistic models based on the segmentation of anatomical images obtained using magnetic resonance imaging (MRI). Realistic models – computationally intensive and difficult to implement – can separate different tissues of the head and account for the convoluted geometry of the brain and the significant inter-individual variability. In real-life applications, if the assumptions of the statistical, anatomical or functional properties of the signal and the volume in which it is generated are meaningful, a true three-dimensional tomographic representation of sources of brain electrical activity is possible in spite of the ‘ill-posed’ nature of the inverse problem (Michel et al., 2004). The techniques used to achieve this are now referred to as electrical source imaging (ESI) or magnetic source imaging (MSI). The first issue to influence reconstruction accuracy is spatial sampling, i.e. the number of EEG electrodes. It has been shown that this relationship is not linear, reaching a plateau at about 128 electrodes, provided spatial distribution is uniform. The second factor is related to the different properties of the source localization strategies used with respect to the hypothesized source configuration.