Revealing epilepsy type using a computational analysis of interictal EEG

This is the final version. Available from Nature Research via the DOI in this record.All materials (functional networks and code) are available upon request from the corresponding author.Seizure onset in epilepsy can usually be classified as focal or generalized, based on a combination of clinical phenomenology of the seizures, EEG recordings and MRI. This classification may be challenging when seizures and interictal epileptiform discharges are infrequent or discordant, and MRI does not reveal any apparent abnormalities. To address this challenge, we introduce the concept of Ictogenic Spread (IS) as a prediction of how pathological electrical activity associated with seizures will propagate throughout a brain network. This measure is defined using a person-specific computer representation of the functional network of the brain, constructed from interictal EEG, combined with a computer model of the transition from background to seizure-like activity within nodes of a distributed network. Applying this method to a dataset comprising scalp EEG from 38 people with epilepsy (17 with genetic generalized epilepsy (GGE), 21 with mesial temporal lobe epilepsy (mTLE)), we find that people with GGE display a higher IS in comparison to those with mTLE. We propose IS as a candidate computational biomarker to classify focal and generalized epilepsy using interictal EEG.Medical Research Council (MRC)Wellcome TrustEpilepsy Research UKEngineering and Physical Sciences Research Council (EPSRC)Wellcome Trus

On brain atlas choice and automatic segmentation methods: a comparison of MAPER & FreeSurfer using three atlas databases.

Several automatic image segmentation methods and few atlas databases exist for analysing structural T1-weighted magnetic resonance brain images. The impact of choosing a combination has not hitherto been described but may bias comparisons across studies. We evaluated two segmentation methods (MAPER and FreeSurfer), using three publicly available atlas databases (Hammers_mith, Desikan-Killiany-Tourville, and MICCAI 2012 Grand Challenge). For each combination of atlas and method, we conducted a leave-one-out cross-comparison to estimate the segmentation accuracy of FreeSurfer and MAPER. We also used each possible combination to segment two datasets of patients with known structural abnormalities (Alzheimer's disease (AD) and mesial temporal lobe epilepsy with hippocampal sclerosis (HS)) and their matched healthy controls. MAPER was better than FreeSurfer at modelling manual segmentations in the healthy control leave-one-out analyses in two of the three atlas databases, and the Hammers_mith atlas database transferred to new datasets best regardless of segmentation method. Both segmentation methods reliably identified known abnormalities in each patient group. Better separation was seen for FreeSurfer in the AD and left-HS datasets, and for MAPER in the right-HS dataset. We provide detailed quantitative comparisons for multiple anatomical regions, thus enabling researchers to make evidence-based decisions on their choice of atlas and segmentation method

Revealing epilepsy type using a computational analysis of interictal EEG.

Seizure onset in epilepsy can usually be classified as focal or generalized, based on a combination of clinical phenomenology of the seizures, EEG recordings and MRI. This classification may be challenging when seizures and interictal epileptiform discharges are infrequent or discordant, and MRI does not reveal any apparent abnormalities. To address this challenge, we introduce the concept of Ictogenic Spread (IS) as a prediction of how pathological electrical activity associated with seizures will propagate throughout a brain network. This measure is defined using a person-specific computer representation of the functional network of the brain, constructed from interictal EEG, combined with a computer model of the transition from background to seizure-like activity within nodes of a distributed network. Applying this method to a dataset comprising scalp EEG from 38 people with epilepsy (17 with genetic generalized epilepsy (GGE), 21 with mesial temporal lobe epilepsy (mTLE)), we find that people with GGE display a higher IS in comparison to those with mTLE. We propose IS as a candidate computational biomarker to classify focal and generalized epilepsy using interictal EEG

Abnormal temporal lobe morphology in asymptomatic relatives of patients with hippocampal sclerosis: A replication study.

We investigated gray and white matter morphology in patients with mesial temporal lobe epilepsy with hippocampal sclerosis (mTLE+HS) and first-degree asymptomatic relatives of patients with mTLE+HS. Using T1-weighted magnetic resonance imaging (MRI), we sought to replicate previously reported findings of structural surface abnormalities of the anterior temporal lobe in asymptomatic relatives of patients with mTLE+HS in an independent cohort. We performed whole-brain MRI in 19 patients with mTLE+HS, 14 first-degree asymptomatic relatives of mTLE+HS patients, and 32 healthy control participants. Structural alterations in patients and relatives compared to controls were assessed using automated hippocampal volumetry and cortical surface-based morphometry. We replicated previously reported cortical surface area contractions in the ipsilateral anterior temporal lobe in both patients and relatives compared to healthy controls, with asymptomatic relatives showing similar but less extensive changes than patients. These findings suggest morphologic abnormality in asymptomatic relatives of mTLE+HS patients, suggesting an inherited brain structure endophenotype

Heritability of alpha and sensorimotor network changes in temporal lobe epilepsy.

OBJECTIVE: Electroencephalography (EEG) features in the alpha band have been shown to differ between people with epilepsy and healthy controls. Here, in a group of patients with mesial temporal lobe epilepsy (mTLE), we seek to confirm these EEG features, and using simultaneous functional magnetic resonance imaging, we investigate whether brain networks related to the alpha rhythm differ between patients and healthy controls. Additionally, we investigate whether alpha abnormalities are found as an inherited endophenotype in asymptomatic relatives. METHODS: We acquired scalp EEG and simultaneous EEG and functional magnetic resonance imaging in 24 unrelated patients with unilateral mTLE, 23 asymptomatic first-degree relatives of patients with mTLE, and 32 healthy controls. We compared peak alpha power and frequency from electroencephalographic data in patients and relatives to healthy controls. We identified brain networks associated with alpha oscillations and compared these networks in patients and relatives to healthy controls. RESULTS: Patients had significantly reduced peak alpha frequency (PAF) across all parietal and occipital electrodes. Asymptomatic relatives also had significantly reduced PAF over 14 of 17 parietal and occipital electrodes. Both patients and asymptomatic relatives showed a combination of increased activation and a failure of deactivation in relation to alpha oscillations compared to healthy controls in the sensorimotor network. INTERPRETATION: Genetic factors may contribute to the shift in PAF and alterations in brain networks related to alpha oscillations. These may not entirely be a consequence of anti-epileptic drugs, seizures or hippocampal sclerosis and deserve further investigation as mechanistic contributors to mTLE

Dynamic brain network states in human generalized spike-wave discharges.

Generalized spike-wave discharges in idiopathic generalized epilepsy are conventionally assumed to have abrupt onset and offset. However, in rodent models, discharges emerge during a dynamic evolution of brain network states, extending several seconds before and after the discharge. In human idiopathic generalized epilepsy, simultaneous EEG and functional MRI shows cortical regions may be active before discharges, and network connectivity around discharges may not be normal. Here, in human idiopathic generalized epilepsy, we investigated whether generalized spike-wave discharges emerge during a dynamic evolution of brain network states. Using EEG-functional MRI, we studied 43 patients and 34 healthy control subjects. We obtained 95 discharges from 20 patients. We compared data from patients with discharges with data from patients without discharges and healthy controls. Changes in MRI (blood oxygenation level-dependent) signal amplitude in discharge epochs were observed only at and after EEG onset, involving a sequence of parietal and frontal cortical regions then thalamus (P < 0.01, across all regions and measurement time points). Examining MRI signal phase synchrony as a measure of functional connectivity between each pair of 90 brain regions, we found significant connections (P < 0.01, across all connections and measurement time points) involving frontal, parietal and occipital cortex during discharges, and for 20 s after EEG offset. This network prominent during discharges showed significantly low synchrony (below 99% confidence interval for synchrony in this network in non-discharge epochs in patients) from 16 s to 10 s before discharges, then ramped up steeply to a significantly high level of synchrony 2 s before discharge onset. Significant connections were seen in a sensorimotor network in the minute before discharge onset. This network also showed elevated synchrony in patients without discharges compared to healthy controls (P = 0.004). During 6 s prior to discharges, additional significant connections to this sensorimotor network were observed, involving prefrontal and precuneus regions. In healthy subjects, significant connections involved a posterior cortical network. In patients with discharges, this posterior network showed significantly low synchrony during the minute prior to discharge onset. In patients without discharges, this network showed the same level of synchrony as in healthy controls. Our findings suggest persistently high sensorimotor network synchrony, coupled with transiently (at least 1 min) low posterior network synchrony, may be a state predisposing to generalized spike-wave discharge onset. Our findings also show that EEG onset and associated MRI signal amplitude change is embedded in a considerably longer period of evolving brain network states before and after discharge events

Thalamic volume reduction in drug-naive patients with new-onset genetic generalized epilepsy

OBJECTIVE: Patients with genetic generalized epilepsy (GGE) have subtle morphologic abnormalities of the brain revealed with magnetic resonance imaging (MRI), particularly in the thalamus. However, it is unclear whether morphologic abnormalities of the brain in GGE are a consequence of repeated seizures over the duration of the disease, or are a consequence of treatment with antiepileptic drugs (AEDs), or are independent of these factors. Therefore, we measured brain morphometry in a cohort of AED-naive patients with GGE at disease onset. We hypothesize that drug-naive patients at disease onset have gray matter changes compared to age-matched healthy controls. METHODS: We performed quantitative measures of gray matter volume in the thalamus, putamen, caudate, pallidum, hippocampus, precuneus, prefrontal cortex, precentral cortex, and cingulate in 29 AED-naive patients with new-onset GGE and compared them to 32 age-matched healthy controls. We subsequently compared the shape of any brain structures found to differ in gray matter volume between the groups. RESULTS: The thalamus was the only structure to show reduced gray matter volume in AED-naive patients with new-onset GGE compared to healthy controls. Shape analysis revealed that the thalamus showed deflation, which was not uniformly distributed, but particularly affected a circumferential strip involving anterior, superior, posterior, and inferior regions with sparing of medial and lateral regions. SIGNIFICANCE: Structural abnormalities in the thalamus are present at the initial onset of GGE in AED-naive patients, suggesting that thalamic structural abnormality is an intrinsic feature of GGE and not a consequence of AEDs or disease duration

Thalamic volume reduction in drug-naive patients with new-onset genetic generalized epilepsy.

OBJECTIVE: Patients with genetic generalized epilepsy (GGE) have subtle morphologic abnormalities of the brain revealed with magnetic resonance imaging (MRI), particularly in the thalamus. However, it is unclear whether morphologic abnormalities of the brain in GGE are a consequence of repeated seizures over the duration of the disease, or are a consequence of treatment with antiepileptic drugs (AEDs), or are independent of these factors. Therefore, we measured brain morphometry in a cohort of AED-naive patients with GGE at disease onset. We hypothesize that drug-naive patients at disease onset have gray matter changes compared to age-matched healthy controls. METHODS: We performed quantitative measures of gray matter volume in the thalamus, putamen, caudate, pallidum, hippocampus, precuneus, prefrontal cortex, precentral cortex, and cingulate in 29 AED-naive patients with new-onset GGE and compared them to 32 age-matched healthy controls. We subsequently compared the shape of any brain structures found to differ in gray matter volume between the groups. RESULTS: The thalamus was the only structure to show reduced gray matter volume in AED-naive patients with new-onset GGE compared to healthy controls. Shape analysis revealed that the thalamus showed deflation, which was not uniformly distributed, but particularly affected a circumferential strip involving anterior, superior, posterior, and inferior regions with sparing of medial and lateral regions. SIGNIFICANCE: Structural abnormalities in the thalamus are present at the initial onset of GGE in AED-naive patients, suggesting that thalamic structural abnormality is an intrinsic feature of GGE and not a consequence of AEDs or disease duration

Slower alpha rhythm associates with poorer seizure control in epilepsy.

OBJECTIVE: Slowing and frontal spread of the alpha rhythm have been reported in multiple epilepsy syndromes. We investigated whether these phenomena are associated with seizure control. METHODS: We prospectively acquired resting-state electroencephalogram (EEG) in 63 patients with focal and idiopathic generalized epilepsy (FE and IGE) and 39 age- and gender-matched healthy subjects (HS). Patients were divided into good and poor (≥4 seizures/12 months) seizure control groups based on self-reports and clinical records. We computed spectral power from 20-sec EEG segments during eyes-closed wakefulness, free of interictal abnormalities, and quantified power in high- and low-alpha bands. Analysis of covariance and post hoc t-tests were used to assess group differences in alpha-power shift across all EEG channels. Permutation-based statistics were used to assess the topography of this shift across the whole scalp. RESULTS: Compared to HS, patients showed a statistically significant shift of spectral power from high- to low-alpha frequencies (effect size g = 0.78 [95% confidence interval 0.43, 1.20]). This alpha-power shift was driven by patients with poor seizure control in both FE and IGE (g = 1.14, [0.65, 1.74]), and occurred over midline frontal and bilateral occipital regions. IGE exhibited less alpha power shift compared to FE over bilateral frontal regions (g = -1.16 [-0.68, -1.74]). There was no interaction between syndrome and seizure control. Effects were independent of antiepileptic drug load, time of day, or subgroup definitions. INTERPRETATION: Alpha slowing and anteriorization are a robust finding in patients with epilepsy and might represent a generic indicator of seizure liability