The Role of Skull Modeling in EEG Source Imaging for Patients with Refractory Temporal Lobe Epilepsy

Abstract

We investigated the influence of different skull modeling approaches on EEG source imaging (ESI), using data of six patients with refractory temporal lobe epilepsy who later underwent successful epilepsy surgery. Four realistic head models with different skull compartments, based on finite difference methods, were constructed for each patient: (i) Three models had skulls with compact and spongy bone compartments as well as air-filled cavities, segmented from either computed tomography (CT), magnetic resonance imaging (MRI) or a CT-template and (ii) one model included a MRI-based skull with a single compact bone compartment. In all patients we performed ESI of single and averaged spikes marked in the clinical 27-channel EEG by the epileptologist. To analyze at which time point the dipole estimations were closer to the resected zone, ESI was performed at two time instants: the half-rising phase and peak of the spike. The estimated sources for each model were validated against the resected area, as indicated by the postoperative MRI. Our results showed that single spike analysis was highly influenced by the signal-to-noise ratio (SNR), yielding estimations with smaller distances to the resected volume at the peak of the spike. Although averaging reduced the SNR effects, it did not always result in dipole estimations lying closer to the resection. The proposed skull modeling approaches did not lead to significant differences in the localization of the irritative zone from clinical EEG data with low spatial sampling density. Furthermore, we showed that a simple skull model (MRI-based) resulted in similar accuracy in dipole estimation compared to more complex head models (based on CT- or CT-template). Therefore, all the considered head models can be used in the presurgical evaluation of patients with temporal lobe epilepsy to localize the irritative zone from low-density clinical EEG recordings.

Automated long‐term EEG analysis to localize the epileptogenic zone

“…We did model the air cavities/sinuses in the patients because not modeling them could introduce focal localization errors in the frontal and temporal regions . The skull was modeled as a single isotropic layer and not anisotropic or as 3 layers, because we recently showed that more complex skull modeling approaches did not lead to significant differences in the localization of the irritative zone from clinical EEG data recorded with low spatial sampling . We used a skull conductivity of 0.0105 S/m according to Dannhauer et al., who calculated the optimal isotropic conductivity on the basis of the conductivity measurements of the spongy and hard bone of Ahktari et al .…”

“…18 The skull was modeled as a single isotropic layer and not anisotropic or as 3 layers, because we recently showed that more complex skull modeling approaches did not lead to significant differences in the localization of the irritative zone from clinical EEG data recorded with low spatial sampling. 19 We used a skull conductivity of 0.0105 S/m according to Dannhauer et al, 20 who calculated the optimal isotropic conductivity on the basis of the conductivity measurements of the spongy and hard bone of Ahktari et al 21 It must be noted that these conductivity values were measured in dead bone tissue. Hoekema et al 22 performed measurements in living skull fragments during bone flap surgery and showed that the conductivity values ranged from 0.032 S/m to 0.080 S/m and that they vary with age.…”

EEG source connectivity to localize the seizure onset zone in patients with drug resistant epilepsy

“…After the selection and preprocessing of an ictal epoch as described in Section 2.3 , ESI was applied. For this purpose, realistic finite difference method (FDM) head models consisting of six different tissues (air (0 S/m), scalp (0.33 S/m), skull (0.0132 S/m), cerebrospinal fluid (1.79 S/m), grey matter (0.33 S/m) and white matter (0.14 S/m)) were constructed based on the individual patient's pre-operative T1-weighted MR image ( Montes-Restrepo et al, 2016 , Strobbe et al, 2016 ). The solution space was constructed as a uniform grid in the segmented grey matter, excluding the cerebellum, with a spacing of 4 mm.…”

Influence of Patient-Specific Head Modeling on EEG Source Imaging

“…Nevertheless, the use of individually defined forward models can be mandatory in several medical applications that demand a proper assisted diagnosis and adequate interpretation of patient states from the involved neurophysiological data [18][19][20]. In this regard, the combination of both approaches to enhance the model of brain structure differently implies inter-and intraobserver variability, not always decreasing enough the impact of uncertainty in the inherent geometrical complexities.…”