Electrocortical source imaging of intracranial EEG data in epilepsy

Abstract

Here we report first results of numerical methods for modeling the dynamic structure and evolution of epileptic seizure activity in an intracranial subdural electrode recording from a patient with partial refractory epilepsy. A 16-min dataset containing two seizures was decomposed using up to five competing adaptive mixture independent component analysis (AMICA) models. Multiple models modeled early or late ictal, or pre- or post-ictal periods in the data, respectively. To localize sources, a realistic Boundary Element Method (BEM) head model was constructed for the patient with custom open skull and plastic (non-conductive) electrode holder features. Source localization was performed using Sparse Bayesian Learning (SBL) on a dictionary of overlapping multi-scale cortical patches constructed from 80,130 dipoles in gray matter perpendicular to the cortical surface. Remaining mutual information among seizure-model AMICA components was dominated by two dependent component subspaces with largely contiguous source domains localized to superior frontal gyrus and precentral gyrus; these accounted for most of the ictal activity. Similar though much weaker dependent subspaces were also revealed in pre-ictal data by the associated AMICA model. Electrocortical source imaging appears promising both for clinical epilepsy research and for basic cognitive neuroscience research using volunteer patients who must undergo invasive monitoring for medical purposes.

Fig. 1

BEM model of the scalp, skull and the plastic sheet, represented by 10,000, 30,000, and 7,000 faces, respectively. The right figure is the plastic sheet model of the plastic grid and strip electrode matrices.

Fig. 2

CT image of the implanted grid electrodes. The two grids (6 × 8,4 × 6) and one medial strip (1×8) implanted in the patient for monitoring.

Fig. 3

The iEEG data. All channels are plotted on the same axis. The seizure periods are highligtened.

Fig. 4

Likelihood graphs for single model and 5-model Amica decomposition.

Fig. 5

Three Gaussian patches of different size centered on a cortical mesh voxel with radius 10 mm, 6 mm, and 3 mm.

Fig. 6

Projection maps (interpolated on the electrode grid and strip surfaces) and patch-basis SBL localization of the cortical source domain, shown on the whole cortical surface and in close-up.

Fig. 7

Pairwise mutual information between maximally independent components of two models in the 5-model decomposition. (a) Pre-seizure model, (b) first part of the seizure. Component subspaces exhibiting partial residual dependency are highlighted.

Fig. 8

Activations and sources of the components in the dependency clusters shown in Figure 7(a) (pre-seizure period).

Fig. 9

Activations and sources of the components in the dependency clusters shown in Figure 7(b) (seizure period)