A computational biomarker of idiopathic generalized epilepsy from resting state EEG

Abstract

SummaryEpilepsy is one of the most common serious neurologic conditions. It is characterized by the tendency to have recurrent seizures, which arise against a backdrop of apparently normal brain activity. At present, clinical diagnosis relies on the following: (1) case history, which can be unreliable; (2) observation of transient abnormal activity during electroencephalography (EEG), which may not be present during clinical evaluation; and (3) if diagnostic uncertainty occurs, undertaking prolonged monitoring in an attempt to observe EEG abnormalities, which is costly. Herein, we describe the discovery and validation of an epilepsy biomarker based on computational analysis of a short segment of resting‐state (interictal) EEG. Our method utilizes a computer model of dynamic networks, where the network is inferred from the extent of synchrony between EEG channels (functional networks) and the normalized power spectrum of the clinical data. We optimize model parameters using a leave‐one‐out classification on a dataset comprising 30 people with idiopathic generalized epilepsy (IGE) and 38 normal controls. Applying this scheme to all 68 subjects we find 100% specificity at 56.7% sensitivity, and 100% sensitivity at 65.8% specificity. We believe this biomarker could readily provide additional support to the diagnostic process.

Dynamic network properties of the interictal brain determine whether seizures appear focal or generalised

“…In previous work, we found this critical coupling value to be significantly lower in a cohort of subjects with IGE in comparison to healthy controls. 12,13 This indicates that the resting state functional networks of people with IGE support transitions to seizures more readily than those from healthy controls. Here, we can regard the critical coupling value as a generic marker of the propensity of a brain to generate seizures of any type (focal or generalised).…”

“…18 This choice of frequency band has been demonstrated previously to be at the basis of significant differences between generalised epilepsies and healthy controls in resting-state EEG. [12][13][14]19 We emphasise here that our analysis of EEG was based entirely on apparently normal, resting-state EEG free from interictal discharges, seizures or artefacts. This is a critical point.…”

“…Under the assumption that the properties of large-scale brain networks are a critical component of the onset of seizures and interictal discharges, alterations in network properties should be an enduring feature of the seizure prone brain and thus reflected within EEG regardless of whether seizures or interictal discharges are present. In support of this concept, we have developed methods for extracting functional network properties from 20s segments of resting-state EEG, 12,13 and shown how properties of these networks are altered in people with idiopathic generalised epilepsy (IGE). 14 Here we provide an objective method to quantify large-scale brain network features, using short epochs of apparently normal resting-state EEG.…”

Critical fluctuations and coupling of stochastic neural mass models

“…Examples of application of one-dimensional phase oscillator models to synchrony in the nervous system include non-antisymmetric coupling between neural oscillations allowing collective oscillator death , pulsatile coupling causing stable phase locking (Ermentrout and Kopell, 1991), entrainment of a mesoscopic population by its input while individual neurons remain unsynchronized (Popovych and Tass, 2011), and experimental results that the functional network structures of epilepsy patients, inferred from EEG, cause global synchrony at weaker coupling than those of healthy controls (Schmidt et al, 2014(Schmidt et al, , 2016. Breakspear et al (2010) provides an overview of the theoretical background of general one-dimensional phase oscillator models and a comprehensive review of the use of these models in neuroscience.…”

“…For example transitions to pathological oscillations are characteristic of epileptic seizure (Wendling et al, 2016) and changes in synchrony may facilitate ictal propagation between cortical regions as one aspect of the epileptogenic process (Mormann et al, 2005;Schmidt et al, 2016).…”