Pieter van Mierlo

Functional brain connectivity from EEG in epilepsy: Seizure prediction and epileptogenic focus localization

Today, neuroimaging techniques are frequently used to investigate the integration of functionally specialized brain regions in a network. Functional connectivity, which quantifies the statistical dependencies among the dynamics of simultaneously recorded signals, allows to infer the dynamical interactions of segregated brain regions. In this review we discuss how the functional connectivity patterns obtained from intracranial and scalp electroencephalographic (EEG) recordings reveal information about the dynamics of the epileptic brain and can be used to predict upcoming seizures and to localize the seizure onset zone. The added value of extracting information that is not visibly identifiable in the EEG data using functional connectivity analysis is stressed. Despite the fact that many studies have showed promising results, we must conclude that functional connectivity analysis has not made its way into clinical practice yet.

Ictal-onset localization through connectivity analysis of intracranial EEG signals in patients with refractory epilepsy.

Fifteen percent to 25% of patients with refractory epilepsy require invasive video–electroencephalography (EEG) monitoring (IVEM) to precisely delineate the ictal‐onset zone. This delineation based on the recorded intracranial EEG (iEEG) signals occurs visually by the epileptologist and is therefore prone to human mistakes. The purpose of this study is to investigate whether effective connectivity analysis of intracranially recorded EEG during seizures provides an objective method to localize the ictal‐onset zone.

Influence of skull modeling approaches on EEG source localization

Electroencephalographic source localization (ESL) relies on an accurate model representing the human head for the computation of the forward solution. In this head model, the skull is of utmost importance due to its complex geometry and low conductivity compared to the other tissues inside the head. We investigated the influence of using different skull modeling approaches on ESL. These approaches, consisting in skull conductivity and geometry modeling simplifications, make use of X-ray computed tomography (CT) and magnetic resonance (MR) images to generate seven different head models. A head model with an accurately segmented skull from CT images, including spongy and compact bone compartments as well as some air-filled cavities, was used as the reference model. EEG simulations were performed for a configuration of 32 and 128 electrodes, and for both noiseless and noisy data. The results show that skull geometry simplifications have a larger effect on ESL than those of the conductivity modeling. This suggests that accurate skull modeling is important in order to achieve reliable results for ESL that are useful in a clinical environment. We recommend the following guidelines to be taken into account for skull modeling in the generation of subject-specific head models: (i) If CT images are available, i.e., if the geometry of the skull and its different tissue types can be accurately segmented, the conductivity should be modeled as isotropic heterogeneous. The spongy bone might be segmented as an erosion of the compact bone; (ii) when only MR images are available, the skull base should be represented as accurately as possible and the conductivity can be modeled as isotropic heterogeneous, segmenting the spongy bone directly from the MR image; (iii) a large number of EEG electrodes should be used to obtain high spatial sampling, which reduces the localization errors at realistic noise levels.

Tracking slow modulations in synaptic gain using dynamic causal modelling: Validation in epilepsy

In this work we propose a proof of principle that dynamic causal modelling can identify plausible mechanisms at the synaptic level underlying brain state changes over a timescale of seconds. As a benchmark example for validation we used intracranial electroencephalographic signals in a human subject. These data were used to infer the (effective connectivity) architecture of synaptic connections among neural populations assumed to generate seizure activity. Dynamic causal modelling allowed us to quantify empirical changes in spectral activity in terms of a trajectory in parameter space — identifying key synaptic parameters or connections that cause observed signals. Using recordings from three seizures in one patient, we considered a network of two sources (within and just outside the putative ictal zone). Bayesian model selection was used to identify the intrinsic (within-source) and extrinsic (between-source) connectivity. Having established the underlying architecture, we were able to track the evolution of key connectivity parameters (e.g., inhibitory connections to superficial pyramidal cells) and test specific hypotheses about the synaptic mechanisms involved in ictogenesis. Our key finding was that intrinsic synaptic changes were sufficient to explain seizure onset, where these changes showed dissociable time courses over several seconds. Crucially, these changes spoke to an increase in the sensitivity of principal cells to intrinsic inhibitory afferents and a transient loss of excitatory–inhibitory balance.

Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing

INTRODUCTION In this paper we propose a technique based on reservoir computing (RC) to mark epileptic seizures on the intra-cranial electroencephalogram (EEG) of rats. RC is a recurrent neural networks training technique which has been shown to possess good generalization properties with limited training. MATERIALS The system is evaluated on data containing two different seizure types: absence seizures from genetic absence epilepsy rats from Strasbourg (GAERS) and tonic-clonic seizures from kainate-induced temporal-lobe epilepsy rats. The dataset consists of 452hours from 23 GAERS and 982hours from 15 kainate-induced temporal-lobe epilepsy rats. METHODS During the preprocessing stage, several features are extracted from the EEG. A feature selection algorithm selects the best features, which are then presented as input to the RC-based classification algorithm. To classify the output of this algorithm a two-threshold technique is used. This technique is compared with other state-of-the-art techniques. RESULTS A balanced error rate (BER) of 3.7% and 3.5% was achieved on the data from GAERS and kainate rats, respectively. This resulted in a sensitivity of 96% and 94% and a specificity of 96% and 99% respectively. The state-of-the-art technique for GAERS achieved a BER of 4%, whereas the best technique to detect tonic-clonic seizures achieved a BER of 16%. CONCLUSION Our method outperforms up-to-date techniques and only a few parameters need to be optimized on a limited training set. It is therefore suited as an automatic aid for epilepsy researchers and is able to eliminate the tedious manual review and annotation of EEG.

Increased motor preparation activity during fluent single word production in DS: A correlate for stuttering frequency and severity.

Abnormal speech motor preparation is suggested to be a neural characteristic of stuttering. One of the neurophysiological substrates of motor preparation is the contingent negative variation (CNV). The CNV is an event-related, slow negative potential that occurs between two defined stimuli. Unfortunately, CNV tasks are rarely studied in developmental stuttering (DS). Therefore, the present study aimed to evaluate motor preparation in DS by use of a CNV task. Twenty five adults who stutter (AWS) and 35 fluent speakers (FS) were included. They performed a picture naming task while an electro-encephalogram was recorded. The slope of the CNV was evaluated at frontal, central and parietal electrode sites. In addition, a correlation analysis was performed with stuttering severity and frequency measures. There was a marked increase in CNV slope in AWS as compared to FS. This increase was observed over the entire scalp with respect to stimulus onset, and only over the right hemisphere with respect to lip movement onset. Moreover, strong positive correlations were found between CNV slope and stuttering frequency and severity. As the CNV is known to reflect the activity in the basal ganglia-thalamo-cortical-network, the present findings confirm an increased activation of this loop during speech motor preparation in stuttering. The more a person stutters, the more neurons of this cortical-subcortical network seem to be activated. Because this increased CNV slope was observed during fluent single word production, it is discussed whether or not this observation refers to a successful compensation strategy.

When will a stuttering moment occur? The determining role of speech motor preparation.

The present study aimed to evaluate whether increased activity related to speech motor preparation preceding fluently produced words reflects a successful compensation strategy in stuttering. For this purpose, a contingent negative variation (CNV) was evoked during a picture naming task and measured by use of electro-encephalography. A CNV is a slow, negative event-related potential known to reflect motor preparation generated by the basal ganglia-thalamo-cortical (BGTC) - loop. In a previous analysis, the CNV of 25 adults with developmental stuttering (AWS) was significantly increased, especially over the right hemisphere, compared to the CNV of 35 fluent speakers (FS) when both groups were speaking fluently (Vanhoutte et al., (2015) doi: 10.1016/j.neuropsychologia.2015.05.013). To elucidate whether this increase is a compensation strategy enabling fluent speech in AWS, the present analysis evaluated the CNV of 7 AWS who stuttered during this picture naming task. The CNV preceding AWS stuttered words was statistically compared to the CNV preceding AWS fluent words and FS fluent words. Though no difference emerged between the CNV of the AWS stuttered words and the FS fluent words, a significant reduction was observed when comparing the CNV preceding AWS stuttered words to the CNV preceding AWS fluent words. The latter seems to confirm the compensation hypothesis: the increased CNV prior to AWS fluent words is a successful compensation strategy, especially when it occurs over the right hemisphere. The words are produced fluently because of an enlarged activity during speech motor preparation. The left CNV preceding AWS stuttered words correlated negatively with stuttering frequency and severity suggestive for a link between the left BGTC - network and the stuttering pathology. Overall, speech motor preparatory activity generated by the BGTC - loop seems to have a determining role in stuttering. An important divergence between left and right hemisphere is hypothesized.

Bayesian model selection of template forward models for EEG source reconstruction.

Several EEG source reconstruction techniques have been proposed to identify the generating neuronal sources of electrical activity measured on the scalp. The solution of these techniques depends directly on the accuracy of the forward model that is inverted. Recently, a parametric empirical Bayesian (PEB) framework for distributed source reconstruction in EEG/MEG was introduced and implemented in the Statistical Parametric Mapping (SPM) software. The framework allows us to compare different forward modeling approaches, using real data, instead of using more traditional simulated data from an assumed true forward model. In the absence of a subject specific MR image, a 3-layered boundary element method (BEM) template head model is currently used including a scalp, skull and brain compartment. In this study, we introduced volumetric template head models based on the finite difference method (FDM). We constructed a FDM head model equivalent to the BEM model and an extended FDM model including CSF. These models were compared within the context of three different types of source priors related to the type of inversion used in the PEB framework: independent and identically distributed (IID) sources, equivalent to classical minimum norm approaches, coherence (COH) priors similar to methods such as LORETA, and multiple sparse priors (MSP). The resulting models were compared based on ERP data of 20 subjects using Bayesian model selection for group studies. The reconstructed activity was also compared with the findings of previous studies using functional magnetic resonance imaging. We found very strong evidence in favor of the extended FDM head model with CSF and assuming MSP. These results suggest that the use of realistic volumetric forward models can improve PEB EEG source reconstruction.

Neurophysiological investigation of phonological input: Aging effects and development of normative data

The current study investigated attended and unattended auditory phoneme discrimination using the P300 and Mismatch Negativity event-related potentials (ERPs). Three phonemic contrasts present in the Dutch language were compared. Additionally, auditory word recognition was investigated by presenting rare pseudowords among frequent words. Two main goals were: (1) obtain normative data for ERP latencies (ms) and amplitudes (μV) and (2) examine aging influences. Seventy-one healthy subjects (21-83 years) were included. During phoneme discrimination aging was associated with increased latencies and decreased amplitudes. However, a discrepancy between attended and unattended processing, as well as between phonemic contrasts, was found. During word recognition aging only had an impact on ERPs elicited by real words, indicating that mainly semantic processes were altered leaving lexical processes unharmed. Early sensory-perceptual processes, reflected by N100 and P50, were free from aging influences. In future, neurophysiological normative data can be applied in the evaluation of acquired language disorders.

Seizure Onset Zone Localization from Ictal High-Density EEG in Refractory Focal Epilepsy

Epilepsy surgery is the most efficient treatment option for patients with refractory epilepsy. Before surgery, it is of utmost importance to accurately delineate the seizure onset zone (SOZ). Non-invasive EEG is the most used neuroimaging technique to diagnose epilepsy, but it is hard to localize the SOZ from EEG due to its low spatial resolution and because epilepsy is a network disease, with several brain regions becoming active during a seizure. In this work, we propose and validate an approach based on EEG source imaging (ESI) combined with functional connectivity analysis to overcome these problems. We considered both simulations and real data of patients. Ictal epochs of 204-channel EEG and subsets down to 32 channels were analyzed. ESI was done using realistic head models and LORETA was used as inverse technique. The connectivity pattern between the reconstructed sources was calculated, and the source with the highest number of outgoing connections was selected as SOZ. We compared this algorithm with a more straightforward approach, i.e. selecting the source with the highest power after ESI as the SOZ. We found that functional connectivity analysis estimated the SOZ consistently closer to the simulated EZ/RZ than localization based on maximal power. Performance, however, decreased when 128 electrodes or less were used, especially in the realistic data. The results show the added value of functional connectivity analysis for SOZ localization, when the EEG is obtained with a high-density setup. Next to this, the method can potentially be used as objective tool in clinical settings.