Localisation of epileptic foci using novel imaging modalities

Semi-Automated and Direct Localization and Labeling of EEG Electrodes Using MR Structural Images for Simultaneous fMRI-EEG

Electroencephalography (EEG) source reconstruction estimates spatial information from the brain’s electrical activity acquired using EEG. This method requires accurate identification of the EEG electrodes in a three-dimensional (3D) space and involves spatial localization and labeling of EEG electrodes. Here, we propose a new approach to tackle this two-step problem based on the simultaneous acquisition of EEG and magnetic resonance imaging (MRI). For the step of spatial localization of electrodes, we extract the electrode coordinates from the curvature of the protrusions formed in the high-resolution T1-weighted brain scans. In the next step, we assign labels to each electrode based on the distinguishing feature of the electrode’s distance profile in relation to other electrodes. We then compare the subject’s electrode data with template-based models of prelabeled distance profiles of correctly labeled subjects. Based on this approach, we could localize EEG electrodes in 26 head models with over 90% accuracy in the 3D localization of electrodes. Next, we performed electrode labeling of the subjects’ data with progressive improvements in accuracy: with ∼58% accuracy based on a single EEG-template, with ∼71% accuracy based on 3 EEG-templates, and with ∼76% accuracy using 5 EEG-templates. The proposed semi-automated method provides a simple alternative for the rapid localization and labeling of electrodes without the requirement of any additional equipment than what is already used in an EEG-fMRI setup.

Seizure prediction - ready for a new era

Epilepsy is a common disorder characterized by recurrent seizures. An overwhelming majority of people with epilepsy regard the unpredictability of seizures as a major issue. More than 30 years of international effort have been devoted to the prediction of seizures, aiming to remove the burden of unpredictability and to couple novel, time-specific treatment to seizure prediction technology. A highly influential review published in 2007 concluded that insufficient evidence indicated that seizures could be predicted. Since then, several advances have been made, including successful prospective seizure prediction using intracranial EEG in a small number of people in a trial of a real-time seizure prediction device. In this Review, we examine advances in the field, including EEG databases, seizure prediction competitions, the prospective trial mentioned and advances in our understanding of the mechanisms of seizures. We argue that these advances, together with statistical evaluations, set the stage for a resurgence in efforts towards the development of seizure prediction methodologies. We propose new avenues of investigation involving a synergy between mechanisms, models, data, devices and algorithms and refine the existing guidelines for the development of seizure prediction technology to instigate development of a solution that removes the burden of the unpredictability of seizures.

Spatio-temporal reconstruction of brain dynamics from EEG with a Markov prior

Electroencephalography (EEG) can capture brain dynamics in high temporal resolution. By projecting the scalp EEG signal back to its origin in the brain also high spatial resolution can be achieved. Source localized EEG therefore has potential to be a very powerful tool for understanding the functional dynamics of the brain. Solving the inverse problem of EEG is however highly ill-posed as there are many more potential locations of the EEG generators than EEG measurement points. Several well-known properties of brain dynamics can be exploited to alleviate this problem. More short ranging connections exist in the brain than long ranging, arguing for spatially focal sources. Additionally, recent work (Delorme et al., 2012) argues that EEG can be decomposed into components having sparse source distributions. On the temporal side both short and long term stationarity of brain activation are seen. We summarize these insights in an inverse solver, the so-called "Variational Garrote" (Kappen and Gómez, 2013). Using a Markov prior we can incorporate flexible degrees of temporal stationarity. Through spatial basis functions spatially smooth distributions are obtained. Sparsity of these are inherent to the Variational Garrote solver. We name our method the MarkoVG and demonstrate its ability to adapt to the temporal smoothness and spatial sparsity in simulated EEG data. Finally a benchmark EEG dataset is used to demonstrate MarkoVG's ability to recover non-stationary brain dynamics.

International Veterinary Epilepsy Task Force recommendations for a veterinary epilepsy-specific MRI protocol

Epilepsy is one of the most common chronic neurological diseases in veterinary practice. Magnetic resonance imaging (MRI) is regarded as an important diagnostic test to reach the diagnosis of idiopathic epilepsy. However, given that the diagnosis requires the exclusion of other differentials for seizures, the parameters for MRI examination should allow the detection of subtle lesions which may not be obvious with existing techniques. In addition, there are several differentials for idiopathic epilepsy in humans, for example some focal cortical dysplasias, which may only apparent with special sequences, imaging planes and/or particular techniques used in performing the MRI scan. As a result, there is a need to standardize MRI examination in veterinary patients with techniques that reliably diagnose subtle lesions, identify post-seizure changes, and which will allow for future identification of underlying causes of seizures not yet apparent in the veterinary literature.

There is a need for a standardized veterinary epilepsy-specific MRI protocol which will facilitate more detailed examination of areas susceptible to generating and perpetuating seizures, is cost efficient, simple to perform and can be adapted for both low and high field scanners. Standardisation of imaging will improve clinical communication and uniformity of case definition between research studies. A 6–7 sequence epilepsy-specific MRI protocol for veterinary patients is proposed and further advanced MR and functional imaging is reviewed.

MRI characterization of temporal lobe epilepsy using rapidly measurable spatial indices with hemisphere asymmetries and gender features

INTRODUCTION

The paucity of morphometric markers for hemispheric asymmetries and gender variations in hippocampi and amygdalae in temporal lobe epilepsy (TLE) calls for better characterization of TLE by finding more useful prognostic MRI parameter(s).

METHODS

T1-weighted MRI (3 T) morphometry using multiple parameters of hippocampus-parahippocampus (angular and linear measures, volumetry) and amygdalae (volumetry) including their hemispheric asymmetry indices (AI) were evaluated in both genders. The cutoff values of parameters were statistically estimated from measurements of healthy subjects to characterize TLE (57 patients, 55% male) alterations.

RESULTS

TLE had differential categories with hippocampal atrophy, parahippocampal angle (PHA) acuteness, and several other parametric changes. Bilateral TLE categories were much more prevalent compared to unilateral TLE categories. Female patients were considerably more disposed to bilateral TLE categories than male patients. Male patients displayed diverse categories of unilateral abnormalities. Few patients (both genders) had combined bilateral appearances of hippocampal atrophy, amygdala atrophy, PHA acuteness, and increase in hippocampal angle (HA) where medial distance ratio (MDR) varied among genders. TLE had gender-specific and hemispheric dominant alterations in AI of parameters. Maximum magnitude of parametric changes in TLE includes (a) AI increase in HA of both genders, (b) HA increase (bilateral) in female patients, and (c) increase in ratio of amygdale/hippocampal volume (unilateral, right hemispheric), and AI decrease in MDR, in male patients.

CONCLUSION

Multiparametric MRI studies of hippocampus and amygdalae, including their hemispheric asymmetry, underscore better characterization of TLE. Rapidly measurable single-slice parameters (HA, PHA, MDR) can readily delineate TLE in a time-constrained clinical setting, which contrasts with customary three-dimensional hippocampal volumetry that requires many slice computation.

Emerging neuroimaging contribution to the diagnosis and management of the ring chromosome 20 syndrome

Ring chromosome 20 [r(20)] syndrome is an underdiagnosed chromosomal anomaly characterized by severe epilepsy, behavioral problems, and mild-to-moderate cognitive deficits. Since the cognitive and behavioral decline follows seizure onset, this syndrome has been proposed as an epileptic encephalopathy (EE). The recent overwhelming development of advanced neuroimaging techniques has opened a new era in the investigation of the brain networks subserving the EEs. In particular, functional neuroimaging tools are well suited to show alterations related to epileptiform discharges at the network level and to build hypotheses about the mechanisms underlying the cognitive disruption observed in these conditions. This paper reviews the brain circuits and their disruption as revealed by functional neuroimaging studies in patients with [r(20)] syndrome. It discusses the clinical consequences of the neuroimaging findings on the management of patients with [r(20)] syndrome, including their impact to an earlier diagnosis of this disorder. Based on the available lines of evidences, [r(20)] syndrome is characterized by interictal and ictal dysfunctions within basal ganglia-prefrontal lobe networks and by long-lasting effects of the peculiar theta-delta rhythm, which represents an EEG marker of the syndrome on integrated brain networks that subserve cognitive functions.

Detection of abnormal resting-state networks in individual patients suffering from focal epilepsy: an initial step toward individual connectivity assessment

The spatial coherence of spontaneous slow fluctuations in the blood-oxygen-level dependent (BOLD) signal at rest is routinely used to characterize the underlying resting-state networks (RSNs). Studies have demonstrated that these patterns are organized in space and highly reproducible from subject to subject. Moreover, RSNs reorganizations have been suggested in pathological conditions. Comparisons of RSNs organization have been performed between groups of subjects but have rarely been applied at the individual level, a step required for clinical application. Defining the notion of modularity as the organization of brain activity in stable networks, we propose Detection of Abnormal Networks in Individuals (DANI) to identify modularity changes at the individual level. The stability of each RSN was estimated using a spatial clustering method: Bootstrap Analysis of Stable Clusters (BASC) (Bellec et al., 2010). Our contributions consisted in (i) providing functional maps of the most stable cores of each networks and (ii) in detecting “abnormal” individual changes in networks organization when compared to a population of healthy controls. DANI was first evaluated using realistic simulated data, showing that focussing on a conservative core size (50% most stable regions) improved the sensitivity to detect modularity changes. DANI was then applied to resting state fMRI data of six patients with focal epilepsy who underwent multimodal assessment using simultaneous EEG/fMRI acquisition followed by surgery. Only patient with a seizure free outcome were selected and the resected area was identified using a post-operative MRI. DANI automatically detected abnormal changes in 5 out of 6 patients, with excellent sensitivity, showing for each of them at least one “abnormal” lateralized network closely related to the epileptic focus. For each patient, we also detected some distant networks as abnormal, suggesting some remote reorganization in the epileptic brain.