Localization in Epilepsy

Effects of Botulinum Toxin Injection on Reducing Myogenic Artifacts during Video-EEG Monitoring: A Longitudinal Study

Medically refractory seizures affect one-third of patients with epilepsy (PwE), for whom epilepsy surgery is considered. Video electroencephalography (vEEG) monitoring is a fundamental tool for pre-operative seizure localization. Facial and cranial myogenic artifacts can obscure vEEG findings, thus interfering with seizure localization. Studies have shown the beneficial effects of botulinum toxin type A (BTX-A) injection into cranial muscles for reducing myogenic artifacts. This longitudinal study aimed to assess the effects of BTX-A injection on these artifacts. Twenty-two patients with medically refractory hypermotor seizures with daily seizure frequency and undetermined epilepsy localization were included in this study and underwent Dysport® injection (200 units) into the frontotemporal region. vEEG recordings were performed at baseline (one week before the injection), and at three days and six days post-injection. Before and after the injection, the amplitudes of myogenic artifacts were compared during various states (ictal, blinking, chewing, bruxism, head lateralization, scowling, talking, and yawning). BTX-A injection significantly reduced the amplitudes of EEG myogenic artifacts, except during blinking (day three) and talking (days three and six). On day six, significant reduction in EEG myogenic artifacts were noted during blinking, chewing, and bruxism for the greatest number of patients (95.5%, 90.9%, 81.8%), while significant reductions in EEG myogenic artifacts during talking, head lateralization, and ictal phase were associated with the least number of patients (22.7%, 36.3%, and 40.9%). Therefore, BTX-A injection could be a convenient method for filtering myogenic contamination, improving EEG interpretation, and facilitating seizure localization in patients with medically refractory seizures.

High-Resolution EEG Source Localization in Segmentation-Free Head Models Based on Finite-Difference Method and Matching Pursuit Algorithm

Electroencephalogram (EEG) is a method to monitor electrophysiological activity on the scalp, which represents the macroscopic activity of the brain. However, it is challenging to identify EEG source regions inside the brain based on data measured by a scalp-attached network of electrodes. The accuracy of EEG source localization significantly depends on the type of head modeling and inverse problem solver. In this study, we adopted different models with a resolution of 0.5 mm to account for thin tissues/fluids, such as the cerebrospinal fluid (CSF) and dura. In particular, a spatially dependent conductivity (segmentation-free) model created using deep learning was developed and used for more realist representation of electrical conductivity. We then adopted a multi-grid-based finite-difference method (FDM) for forward problem analysis and a sparse-based algorithm to solve the inverse problem. This enabled us to perform efficient source localization using high-resolution model with a reasonable computational cost. Results indicated that the abrupt spatial change in conductivity, inherent in conventional segmentation-based head models, may trigger source localization error accumulation. The accurate modeling of the CSF, whose conductivity is the highest in the head, was an important factor affecting localization accuracy. Moreover, computational experiments with different noise levels and electrode setups demonstrate the robustness of the proposed method with segmentation-free head model.

Comparison of electroencephalographic findings with hippocampal magnetic resonance imaging volumetry in dogs with idiopathic epilepsy

BACKGROUND

In humans, temporal lobe epilepsy (TLE), is a type of focal epilepsy occurring mainly in the mesial TLE (mTLE), commonly associated with hippocampal sclerosis (HS).

OBJECTIVES

According to recent studies, TLE might also occur in dogs and could be associated with hippocampal atrophy (HA)/HS. To date, hippocampal lesions have not been correlated with electroencephalographic (EEG) findings in epileptic dogs.

ANIMALS

An EEG examination, brain magnetic resonance imaging, and volumetric assessment of the hippocampus were performed in 16 nonepileptic and 41 epileptic dogs.

METHODS

In this retrospective study, the presence and localization of EEG-defined epileptiform discharges (EDs) was blindly evaluated. The hippocampus was measured and assessed for unilateral atrophy. The results of EEG and volumetric findings were correlated to determine whether the functional epileptic focus is equivalent to structural changes.

RESULTS

The median hippocampal asymmetric ratio (AR) in epileptic dogs was significantly greater than in the control group (P < .001). Using a cut-off threshold AR of >6%, 56% (23/41) of the dogs were characterized with unilateral HA. Of those animals, 35% (8/23) had EDs in the temporal leads and 26% (6/23) had no EDs. In 88% (7/8) of dogs with EDs in the temporal leads that had unilateral HA, the EDs correlated with the side of the decreased hippocampal volume.

CONCLUSIONS AND CLINICAL IMPORTANCE

The results indicate an association between the presence of EDs detectable on EEG and a decrease in the unilateral hippocampal volume in some cases of canine idiopathic epilepsy that might reflect features of human mTLE.

Physical Feature Encoding and Word Recognition Abilities Are Altered in Children with Intractable Epilepsy: Preliminary Neuromagnetic Evidence

Objective evaluation of language function is critical for children with intractable epilepsy under consideration for epilepsy surgery. The purpose of this preliminary study was to evaluate word recognition in children with intractable epilepsy by using magnetoencephalography (MEG). Ten children with intractable epilepsy (M/F 6/4, mean ± SD 13.4 ± 2.2 years) were matched on age and sex to healthy controls. Common nouns were presented simultaneously from visual and auditory sensory inputs in "match" and "mismatch" conditions. Neuromagnetic responses M1, M2, M3, M4, and M5 with latencies of ~100 ms, ~150 ms, ~250 ms, ~350 ms, and ~450 ms, respectively, elicited during the "match" condition were identified. Compared to healthy children, epilepsy patients had both significantly delayed latency of the M1 and reduced amplitudes of M3 and M5 responses. These results provide neurophysiologic evidence of altered word recognition in children with intractable epilepsy.

MEG source localization of spatially extended generators of epileptic activity: comparing entropic and hierarchical bayesian approaches

Localizing the generators of epileptic activity in the brain using Electro-EncephaloGraphy (EEG) or Magneto-EncephaloGraphy (MEG) signals is of particular interest during the pre-surgical investigation of epilepsy. Epileptic discharges can be detectable from background brain activity, provided they are associated with spatially extended generators. Using realistic simulations of epileptic activity, this study evaluates the ability of distributed source localization methods to accurately estimate the location of the generators and their sensitivity to the spatial extent of such generators when using MEG data. Source localization methods based on two types of realistic models have been investigated: (i) brain activity may be modeled using cortical parcels and (ii) brain activity is assumed to be locally smooth within each parcel. A Data Driven Parcellization (DDP) method was used to segment the cortical surface into non-overlapping parcels and diffusion-based spatial priors were used to model local spatial smoothness within parcels. These models were implemented within the Maximum Entropy on the Mean (MEM) and the Hierarchical Bayesian (HB) source localization frameworks. We proposed new methods in this context and compared them with other standard ones using Monte Carlo simulations of realistic MEG data involving sources of several spatial extents and depths. Detection accuracy of each method was quantified using Receiver Operating Characteristic (ROC) analysis and localization error metrics. Our results showed that methods implemented within the MEM framework were sensitive to all spatial extents of the sources ranging from 3 cm² to 30 cm², whatever were the number and size of the parcels defining the model. To reach a similar level of accuracy within the HB framework, a model using parcels larger than the size of the sources should be considered.

Functional MRI applications in epilepsy surgery

Functional magnetic resonance imaging (fMRI) is a non-invasive neuroimaging technique that has grown rapidly in popularity over the past decade. It is already prevalent in psychology, cognitive and basic neuroscience research and is being used increasingly as a tool for clinical decision-making in epilepsy. It has been used to determine language location and laterality in patients, sometimes eliminating the need for invasive tests. fMRI can been used pre-surgically to guide resection margins, preserving eloquent cortex. Other fMRI paradigms assessing memory, visual and somatosensory systems have limited clinical applications currently, but show great promise. Simultaneous recording of electroencephalogram (EEG) and fMRI has also provided insights into the networks underlying seizure generation and is increasingly being used in epilepsy centres. In this review, we present some of the current clinical applications for fMRI in the pre-surgical assessment of epilepsy patients, and examine a number of new techniques that may soon become clinically relevant.

Computational modeling of epilepsy for an experimental neurologist

Computational modeling can be a powerful tool for an experimentalist, providing a rigorous mathematical model of the system you are studying. This can be valuable in testing your hypotheses and developing experimental protocols prior to experimenting. This paper reviews models of seizures and epilepsy at different scales, including cellular, network, cortical region, and brain scales by looking at how they have been used in conjunction with experimental data. At each scale, models with different levels of abstraction, the extraction of physiological detail, are presented. Varying levels of detail are necessary in different situations. Physiologically realistic models are valuable surrogates for experimental systems because, unlike in an experiment, every parameter can be changed and every variable can be observed. Abstract models are useful in determining essential parameters of a system, allowing the experimentalist to extract principles that explain the relationship between mechanisms and the behavior of the system. Modeling is becoming easier with the emergence of platforms dedicated to neuronal modeling and databases of models that can be downloaded. Modeling will never be a replacement for animal and clinical experiments, but it should be a starting point in designing experiments and understanding their results.