EEG biomarker candidates for the identification of epilepsy

Abstract

Electroencephalography (EEG) is one of the main pillars used for the diagnosis and study of epilepsy, readily employed after a possible first seizure has occurred. The most established biomarker of epilepsy, in case seizures are not recorded, are interictal epileptiform discharges (IEDs). In clinical practice, however, IEDs are not always present and the EEG may appear completely normal despite an underlying epileptic disorder, often leading to difficulties in the diagnosis of the disease. Thus, finding other biomarkers that reliably predict whether an individual suffers from epilepsy even in the absence of evident epileptic activity would be extremely helpful, since they could allow shortening the period of diagnostic uncertainty and consequently decreasing the risk of seizure. To date only a few EEG features other than IEDs seem to be promising candidates able to distinguish between epilepsy, i.e. > 60 % risk of recurrent seizures, or other (pathological) conditions. The aim of this narrative review is to provide an overview of the EEG-based biomarker candidates for epilepsy and the techniques employed for their identification.

Keywords: Connectivity; EEG biomarkers; Epilepsy; HFOs; IEDs; Microstates.

Fig. 1

Examples of A) polyspikes, B) focal spike-waves, C) temporal intermittent rhythmic delta activity (TIRDA), and D) generalized spike-waves. Red rectangles and arrows indicate EEG patterns of interest. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

Raw stereo-EEG signal containing a spike-ripple event (A), the same signal transformed (whitened) in order to have a balanced power across frequencies (B), and its time–frequency representation (C).

Fig. 3

Scalp EEG signal showing a HFO pattern over left fronto-temporal areas (sampling rate 256 Hz).

Fig. 4

A schematic representation of the modular organization of the brain. Nodes (basic units of a network, represented by the grey circles) and edges (links between basic units, represented by the grey lines) form modules (light yellow, green, and blue backgrounds) and brain networks which communicate with each other. When a node is densely interconnected with other nodes either of the same network or of other networks it is defined as a hub (green, red and blue circles), and can be characterized by different degrees of inter- and intra-modular connectivity. Brain connectivity can be defined as structural, functional and effective connectivity. Structural connectivity highlights anatomical connections between different brain regions (dashed lines), functional connectivity is defined by measures of e.g. correlation between the activity of different brain regions (solid lines), and effective connectivity reveals which node of a given network (driver) exerts a certain influence over another node (receiver) and it is estimated by measures of e.g. granger causality (red arrows). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 5

A) EEG and MRI recordings are performed both on healthy controls and epileptic patients. Signals are pre-processed and the result constitutes the input used for source connectivity analyses. B) EEG electrode positions and MRI segmentation are combined in order to estimate source distributions. Brain parcellation leads to a predefined number of regions of interest (ROIs), and for each of them a time course is reconstructed. C) A connectivity matrix representing the pair-wise connections between all ROIs is then computed. D) Nodes and edges constituting brain networks are then analyzed and differences between population groups can be investigated.

Fig. 6

The four most frequently described microstate maps (A, B, C, D). Around 70% of an EEG signal can be explained by a limited number of microstates. These four maps have been repeatedly found in the majority of studies, thus showing high reproducibility and robustness.