Multiscale neuro-inspired models for interpretation of EEG signals in patients with epilepsy

OBJECTIVE

The aim is to gain insight into the pathophysiological mechanisms underlying interictal epileptiform discharges observed in electroencephalographic (EEG) and stereo-EEG (SEEG, depth electrodes) recordings performed during pre-surgical evaluation of patients with drug-resistant epilepsy.

METHODS

We developed novel neuro-inspired computational models of the human cerebral cortex at three different levels of description: i) microscale (detailed neuron models), ii) mesoscale (neuronal mass models) and iii) macroscale (whole brain models). Although conceptually different, micro- and mesoscale models share some similar features, such as the typology of neurons (pyramidal cells and three types of interneurons), their spatial arrangement in cortical layers, and their synaptic connectivity (excitatory and inhibitory). The whole brain model consists of a large-scale network of interconnected neuronal masses, with connectivity based on the human connectome.

RESULTS

For these three levels of description, the fine-tuning of free parameters and the quantitative comparison with real data allowed us to reproduce interictal epileptiform discharges with a high degree of fidelity and to formulate hypotheses about the cell- and network-related mechanisms underlying the generation of fast ripples and SEEG-recorded epileptic spikes and spike-waves.

CONCLUSIONS

The proposed models provide valuable insights into the pathophysiological mechanisms underlying the generation of epileptic events. The knowledge gained from these models effectively complements the clinical analysis of SEEG data collected during the evaluation of patients with epilepsy.

SIGNIFICANCE

These models are likely to play a key role in the mechanistic interpretation of epileptiform activity.

EEG changes induced by meditative practices: State and trait effects in healthy subjects and in patients with epilepsy

The effect of meditation on brain activity has been the topic of many studies in healthy subjects and in patients suffering from chronic diseases. These effects are either explored during meditation practice (state effects) or as a longer-term result of meditation training during the resting-state (trait). The topic of this article is to first review these findings by focusing on electroencephalography (EEG) changes in healthy subjects with or without experience in meditation. Modifications in EEG baseline rhythms, functional connectivity and advanced nonlinear parameters are discussed in regard to feasibility in clinical applications. Secondly, we provide a state-of-the-art of studies that proposed meditative practices as a complementary therapy in patients with epilepsy, in whom anxiety and depressive symptoms are prevalent. In these studies, the effects of standardized meditation programs including elements of traditional meditation practices such as mindfulness, loving-kindness and compassion are explored both at the level of psychological functioning and on the occurrence of seizures. Lastly, preliminary results are given regarding our ongoing study, the aim of which is to quantify the effects of a mindfulness self-compassion (MSC) practice on interictal and ictal epileptic activity. Feasibility, difficulties, and prospects of this study are discussed.

Electrophysiological brain imaging based on simulation-driven deep learning in the context of epilepsy

Identifying the location, the spatial extent and the electrical activity of distributed brain sources in the context of epilepsy through ElectroEncephaloGraphy (EEG) recordings is a challenging task because of the highly ill-posed nature of the underlying Electrophysiological Source Imaging (ESI) problem. To guarantee a unique solution, most existing ESI methods pay more attention to solve this inverse problem by imposing physiological constraints. This paper proposes an efficient ESI approach based on simulation-driven deep learning. Epileptic High-resolution 256-channels scalp EEG (Hr-EEG) signals are simulated in a realistic manner to train the proposed patient-specific model. More particularly, a computational neural mass model developed in our team is used to generate the temporal dynamics of the activity of each dipole while the forward problem is solved using a patient-specific three-shell realistic head model and the boundary element method. A Temporal Convolutional Network (TCN) is considered in the proposed model to capture local spatial patterns. To enable the model to observe the EEG signals from different scale levels, the multi-scale strategy is leveraged to capture the overall features and fine-grain features by adjusting the convolutional kernel size. Then, the Long Short-Term Memory (LSTM) is used to extract temporal dependencies among the computed spatial features. The performance of the proposed method is evaluated through three different scenarios of realistic synthetic interictal Hr-EEG data as well as on real interictal Hr-EEG data acquired in three patients with drug-resistant partial epilepsy, during their presurgical evaluation. A performance comparison study is also conducted with two other deep learning-based methods and four classical ESI techniques. The proposed model achieved a Dipole Localization Error (DLE) of 1.39 and Normalized Hamming Distance (NHD) of 0.28 in the case of one patch with SNR of 10 dB. In the case of two uncorrelated patches with an SNR of 10 dB, obtained DLE and NHD were respectively 1.50 and 0.28. Even in the more challenging scenario of two correlated patches with an SNR of 10 dB, the proposed approach still achieved a DLE of 3.74 and an NHD of 0.43. The results obtained on simulated data demonstrate that the proposed method outperforms the existing methods for different signal-to-noise and source configurations. The good behavior of the proposed method is also confirmed on real interictal EEG data. The robustness with respect to noise makes it a promising and alternative tool to localize epileptic brain areas and to reconstruct their electrical activities from EEG signals.

Signal processing and computational modeling for interpretation of SEEG-recorded interictal epileptiform discharges in epileptogenic and non-epileptogenic zones

OBJECTIVE

In partial epilepsies, interictal epileptiform discharges (IEDs) are paroxysmal events observed in epileptogenic and non-epileptogenic zones. IEDs' generation and recurrence are subject to different hypotheses: they appear through glutamatergic and GABAergic processes; they may trigger seizures or prevent seizure propagation. This paper focuses on a specific class of IEDs, spike-waves (SWs), characterized by a short-duration spike followed by a longer duration wave, both of the same polarity. Signal analysis and neurophysiological mathematical models are used to interpret puzzling IED generation.

APPROACH

Interictal activity was recorded by intracranial stereo-electroencephalography (SEEG) electrodes in five different patients. SEEG experts identified the epileptic and non-epileptic zones in which IEDs were detected. After quantifying spatial and temporal features of the detected IEDs, the most significant features for classifying epileptic and non-epileptic zones were determined. A neurophysiologically-plausible mathematical model was then introduced to simulate the IEDs and understand the underlying differences observed in epileptic and non-epileptic zone IEDs.

MAIN RESULTS

Two classes of SWs were identified according to subtle differences in morphology and timing of the spike and wave component. Results showed that type-1 SWs were generated in epileptogenic regions also involved at seizure onset, while type-2 SWs were produced in the propagation or non-involved areas. The modeling study indicated that synaptic kinetics, cortical organization, and network interactions determined the morphology of the simulated SEEG signals. Modeling results suggested that the IED morphologies were linked to the degree of preserved inhibition.

SIGNIFICANCE

This work contributes to the understanding of different mechanisms generating IEDs in epileptic networks. The combination of signal analysis and computational models provides an efficient framework for exploring IEDs in partial epilepsies and classifying epileptogenic and non-epileptogenic zones.

Selective enhancement of low-gamma activity by tACS improves phonemic processing and reading accuracy in dyslexia

The phonological deficit in dyslexia is associated with altered low-gamma oscillatory function in left auditory cortex, but a causal relationship between oscillatory function and phonemic processing has never been established. After confirming a deficit at 30 Hz with electroencephalography (EEG), we applied 20 minutes of transcranial alternating current stimulation (tACS) to transiently restore this activity in adults with dyslexia. The intervention significantly improved phonological processing and reading accuracy as measured immediately after tACS. The effect occurred selectively for a 30-Hz stimulation in the dyslexia group. Importantly, we observed that the focal intervention over the left auditory cortex also decreased 30-Hz activity in the right superior temporal cortex, resulting in reinstating a left dominance for the oscillatory response. These findings establish a causal role of neural oscillations in phonological processing and offer solid neurophysiological grounds for a potential correction of low-gamma anomalies and for alleviating the phonological deficit in dyslexia.

COALIA: A Computational Model of Human EEG for Consciousness Research

Understanding the origin of the main physiological processes involved in consciousness is a major challenge of contemporary neuroscience, with crucial implications for the study of Disorders of Consciousness (DOC). The difficulties in achieving this task include the considerable quantity of experimental data in this field, along with the non-intuitive, nonlinear nature of neuronal dynamics. One possibility of integrating the main results from the experimental literature into a cohesive framework, while accounting for nonlinear brain dynamics, is the use of physiologically-inspired computational models. In this study, we present a physiologically-grounded computational model, attempting to account for the main micro-circuits identified in the human cortex, while including the specificities of each neuronal type. More specifically, the model accounts for thalamo-cortical (vertical) regulation of cortico-cortical (horizontal) connectivity, which is a central mechanism for brain information integration and processing. The distinct neuronal assemblies communicate through feedforward and feedback excitatory and inhibitory synaptic connections implemented in a template brain accounting for long-range connectome. The EEG generated by this physiologically-based simulated brain is validated through comparison with brain rhythms recorded in humans in two states of consciousness (wakefulness, sleep). Using the model, it is possible to reproduce the local disynaptic disinhibition of basket cells (fast GABAergic inhibition) and glutamatergic pyramidal neurons through long-range activation of vasoactive intestinal-peptide (VIP) interneurons that induced inhibition of somatostatin positive (SST) interneurons. The model (COALIA) predicts that the strength and dynamics of the thalamic output on the cortex control the local and long-range cortical processing of information. Furthermore, the model reproduces and explains clinical results regarding the complexity of transcranial magnetic stimulation TMS-evoked EEG responses in DOC patients and healthy volunteers, through a modulation of thalamo-cortical connectivity that governs the level of cortico-cortical communication. This new model provides a quantitative framework to accelerate the study of the physiological mechanisms involved in the emergence, maintenance and disruption (sleep, anesthesia, DOC) of consciousness.

Identification of epileptogenic networks from dense EEG: A model-based study

Epilepsy is a network disease. Identifying the epileptogenic networks from noninvasive recordings is a challenging issue. In this context, M/EEG source connectivity is a promising tool to identify brain networks with high temporal and spatial resolution. In this paper, we analyze the impact of the two main factors that intervene in EEG source connectivity processing: i) the algorithm used to solve the EEG inverse problem and ii) the method used to measure the functional connectivity. We evaluate three inverse solutions algorithms (dSPM, wMNE and cMEM) and three connectivity measures (r(2), h(2) and MI) on data simulated from a combined biophysical/physiological model able to generate realistic interictal epileptic spikes reflected in scalp EEG. The performance criterion used here is the similarity between the network identified by each of the inverse/connectivity combination and the original network used in the source model. Results show that the choice of the combination has a high impact on the identified network. Results suggest also that nonlinear methods (nonlinear correlation coefficient and mutual information) for measuring the connectivity are more efficient than the linear one (the cross correlation coefficient). The dSPM as inverse solution shows the lowest performance compared to cMEM and wMNE.

Classification of high frequency oscillations in epileptic intracerebral EEG

High Frequency Oscillations (HFOs 40-500 Hz), recorded from intracerebral electroencephalography (iEEG) in epileptic patients, are categorized into four distinct sub-bands (Gamma, High-Gamma, Ripples and Fast Ripples). They have recently been used as a reliable biomarker of epileptogenic zones. The objective of this paper is to investigate the possibility of discriminating between the different classes of HFOs which physiological/pathological value is critical for diagnostic but remains to be clarified. The proposed method is based on the definition of a relevant feature vector built from energy ratios (computed using Wavelet Transform-WT) in a-priori-defined frequency bands. It makes use of a multiclass Linear Discriminant Analysis (LDA) and is applied to iEEG signals recorded in patients candidate to epilepsy surgery. Results obtained from bootstrap on training/test datasets indicate high performances in terms of sensitivity and specificity.

Source reconstruction via the spatiotemporal Kalman filter and LORETA from EEG time series with 32 or fewer electrodes

The clinical routine of non-invasive electroencephalography (EEG) is usually performed with 8-40 electrodes, especially in long-term monitoring, infants or emergency care. There is a need in clinical and scientific brain imaging to develop inverse solution methods that can reconstruct brain sources from these low-density EEG recordings. In this proof-of-principle paper we investigate the performance of the spatiotemporal Kalman filter (STKF) in EEG source reconstruction with 9-, 19- and 32- electrodes. We used simulated EEG data of epileptic spikes generated from lateral frontal and lateral temporal brain sources using state-of-the-art neuronal population models. For validation of source reconstruction, we compared STKF results to the location of the simulated source and to the results of low-resolution brain electromagnetic tomography (LORETA) standard inverse solution. STKF consistently showed less localization bias compared to LORETA, especially when the number of electrodes was decreased. The results encourage further research into the application of the STKF in source reconstruction of brain activity from low-density EEG recordings.

Spatial projection as a preprocessing step for EEG source reconstruction using spatiotemporal Kalman filtering

The reconstruction of brain sources from non-invasive electroencephalography (EEG) or magnetoencephalography (MEG) via source imaging can be distorted by information redundancy in case of high-resolution recordings. Dimensionality reduction approaches such as spatial projection may be used to alleviate this problem. In this proof-of-principle paper we apply spatial projection to solve the problem of information redundancy in case of source reconstruction via spatiotemporal Kalman filtering (STKF), which is based on state-space modeling. We compare two approaches for incorporating spatial projection into the STKF algorithm and select the best approach based on its performance in source localization with respect to accurate estimation of source location, lack of spurious sources, computational speed and small number of required optimization steps in state-space model parameter estimation. We use state-of-the-art simulated EEG data based on neuronal population models, for which the number and location of sources is known, to validate the source reconstruction results of the STKF. The incorporation of spatial projection into the STKF algorithm solved the problem of information redundancy, resulting in correct source localization with no spurious sources, and decreased the overall computational time in STKF analysis. The results help make STKF analyses of high-density EEG, MEG or simultaneous MEG-EEG data more feasible.

Computational modeling of high frequency oscillations recorded with clinical intracranial macroelectrodes

High Frequency Oscillations (HFOs) are a potential biomarker of epileptogenic regions. They have been extensively investigated in terms of automatic detection, classification and feature extraction. However, the mechanisms governing the generation of HFOs as well as the observability conditions on clinical intracranial macroelectrodes remain elusive. In this paper, we propose a novel physiologically-relevant macroscopic model for accurate simulation of HFOs as invasively recorded in epileptic patients. This model accounts for both the temporal and spatial properties of the cortical patch at the origin of epileptiform activity. Indeed, neuronal populations are combined with a 3D geometrical representation to simulate an extended epileptic source. Then, by solving the forward problem, the contributions of neuronal population signals are projected onto intracerebral electrode contacts. The obtained signals are qualitatively and quantitatively compared to real HFOs, and a relationship is drawn between macroscopic model parameters such as synchronization and spatial extent on the one hand, and HFO features such as the wave and fast ripple (200-600 Hz) components, on the other hand.

Identification of Interictal Epileptic Networks from Dense-EEG

Epilepsy is a network disease. The epileptic network usually involves spatially distributed brain regions. In this context, noninvasive M/EEG source connectivity is an emerging technique to identify functional brain networks at cortical level from noninvasive recordings. In this paper, we analyze the effect of the two key factors involved in EEG source connectivity processing: (i) the algorithm used in the solution of the EEG inverse problem and (ii) the method used in the estimation of the functional connectivity. We evaluate four inverse solutions algorithms (dSPM, wMNE, sLORETA and cMEM) and four connectivity measures (r ², h ², PLV, and MI) on data simulated from a combined biophysical/physiological model to generate realistic interictal epileptic spikes reflected in scalp EEG. We use a new network-based similarity index to compare between the network identified by each of the inverse/connectivity combination and the original network generated in the model. The method will be also applied on real data recorded from one epileptic patient who underwent a full presurgical evaluation for drug-resistant focal epilepsy. In simulated data, results revealed that the selection of the inverse/connectivity combination has a significant impact on the identified networks. Results suggested that nonlinear methods (nonlinear correlation coefficient, phase synchronization and mutual information) for measuring the connectivity are more efficient than the linear one (the cross correlation coefficient). The wMNE inverse solution showed higher performance than dSPM, cMEM and sLORETA. In real data, the combination (wMNE/PLV) led to a very good matching between the interictal epileptic network identified from noninvasive EEG recordings and the network obtained from connectivity analysis of intracerebral EEG recordings. These results suggest that source connectivity method, when appropriately configured, is able to extract highly relevant diagnostic information about networks involved in interictal epileptic spikes from non-invasive dense-EEG data.

Feasibility of blind source separation methods for the denoising of dense-array EEG

High-density electroencephalographic recordings have recently been proved to bring useful information during the pre-surgical evaluation of patients suffering from drug-resistant epilepsy. However, these recordings can be particularly obscured by noise and artifacts. This paper focuses on the denoising of dense-array EEG data (e.g. 257 channels) contaminated with muscle artifacts. In this context, we compared the efficiency of several Independent Component Analysis (ICA) methods, namely SOBI, SOBIrob, PICA, InfoMax, two different implementations of FastICA, COM2, ERICA, and SIMBEC, as well as that of Canonical Correlation Analysis (CCA). We evaluated the performance using the Normalized Mean Square Error (NMSE) criterion and calculated the numerical complexity. Quantitative results obtained on realistic simulated data show that some of the ICA methods as well as CCA can properly remove muscular artifacts from dense-array EEG.

Localization of spatially distributed brain sources after a tensor-based preprocessing of interictal epileptic EEG data

This paper addresses the localization of spatially distributed sources from interictal epileptic electroencephalographic data after a tensor-based preprocessing. Justifying the Canonical Polyadic (CP) model of the space-time-frequency and space-time-wave-vector tensors is not an easy task when two or more extended sources have to be localized. On the other hand, the occurrence of several amplitude modulated spikes originating from the same epileptic region can be used to build a space-time-spike tensor from the EEG data. While the CP model of this tensor appears more justified, the exact computation of its loading matrices can be limited by the presence of highly correlated sources or/and a strong background noise. An efficient extended source localization scheme after the tensor-based preprocessing has then to be set up. Different strategies are thus investigated and compared on realistic simulated data: the "disk algorithm" using a precomputed dictionary of circular patches, a standardized Tikhonov regularization and a fused LASSO scheme.

EEG source connectivity analysis: from dense array recordings to brain networks

The recent past years have seen a noticeable increase of interest for electroencephalography (EEG) to analyze functional connectivity through brain sources reconstructed from scalp signals. Although considerable advances have been done both on the recording and analysis of EEG signals, a number of methodological questions are still open regarding the optimal way to process the data in order to identify brain networks. In this paper, we analyze the impact of three factors that intervene in this processing: i) the number of scalp electrodes, ii) the combination between the algorithm used to solve the EEG inverse problem and the algorithm used to measure the functional connectivity and iii) the frequency bands retained to estimate the functional connectivity among neocortical sources. Using High-Resolution (hr) EEG recordings in healthy volunteers, we evaluated these factors on evoked responses during picture recognition and naming task. The main reason for selection this task is that a solid literature background is available about involved brain networks (ground truth). From this a priori information, we propose a performance criterion based on the number of connections identified in the regions of interest (ROI) that belong to potentially activated networks. Our results show that the three studied factors have a dramatic impact on the final result (the identified network in the source space) as strong discrepancies were evidenced depending on the methods used. They also suggest that the combination of weighted Minimum Norm Estimator (wMNE) and the Phase Synchronization (PS) methods applied on High-Resolution EEG in beta/gamma bands provides the best performance in term of topological distance between the identified network and the expected network in the above-mentioned cognitive task.

EEG extended source localization: tensor-based vs. conventional methods

The localization of brain sources based on EEG measurements is a topic that has attracted a lot of attention in the last decades and many different source localization algorithms have been proposed. However, their performance is limited in the case of several simultaneously active brain regions and low signal-to-noise ratios. To overcome these problems, tensor-based preprocessing can be applied, which consists in constructing a space-time-frequency (STF) or space-time-wave-vector (STWV) tensor and decomposing it using the Canonical Polyadic (CP) decomposition. In this paper, we present a new algorithm for the accurate localization of extended sources based on the results of the tensor decomposition. Furthermore, we conduct a detailed study of the tensor-based preprocessing methods, including an analysis of their theoretical foundation, their computational complexity, and their performance for realistic simulated data in comparison to conventional source localization algorithms such as sLORETA, cortical LORETA (cLORETA), and 4-ExSo-MUSIC. Our objective consists, on the one hand, in demonstrating the gain in performance that can be achieved by tensor-based preprocessing, and, on the other hand, in pointing out the limits and drawbacks of this method. Finally, we validate the STF and STWV techniques on real measurements to demonstrate their usefulness for practical applications.

Transcranial current brain stimulation (tCS): models and technologies

In this paper, we provide a broad overview of models and technologies pertaining to transcranial current brain stimulation (tCS), a family of related noninvasive techniques including direct current (tDCS), alternating current (tACS), and random noise current stimulation (tRNS). These techniques are based on the delivery of weak currents through the scalp (with electrode current intensity to area ratios of about 0.3-5 A/m2) at low frequencies (typically < 1 kHz) resulting in weak electric fields in the brain (with amplitudes of about 0.2-2 V/m). Here we review the biophysics and simulation of noninvasive, current-controlled generation of electric fields in the human brain and the models for the interaction of these electric fields with neurons, including a survey of in vitro and in vivo related studies. Finally, we outline directions for future fundamental and technological research.

Abstract P4-05-08: Oncotype Dx assay in BRCA positive ER positive breast cancer patients

Background:

Oncotype DX is a 21-gene RT-PCR assay which quantifies the likelihood of breast cancer recurrence and the potential benefit of chemotherapy in patients with early stage, ER positive, Tamoxifen treated breast cancer. Breast cancer in BRCA carriers is considered more aggressive. The aim of our study was to evaluate whether Oncotype Dx recurrence score distribution is different in breast cancer patients with inherited BRCA mutation.

Methods:

The Oncotype DX assay has been used at Hadassah Medical Center since 2004 on specimens from over 450 patients. We analyzed and compared clinicopathological characteristics and Oncotype Dx recurrence scores of BRCA carriers versus non- BRCA or unknown status of BRCA patients.

Results:

Ten patients had validated inherited BRCA mutation, five of them are BRCA1 carriers and five BRCA2 carriers. There were no significant differences in the clinicopathological characteristics between the two groups. Oncotype Dx recurrence score distribution between low, intermediate and high risk groups was not significantly different. The mean recurrence score was 18.48 for the non- BRCA or unknown status of BRCA patients and 22.8 for the BRCA carriers patients. This difference was not statistically significant.

Conclusion:

Estrogen receptor positive breast cancer tumors from BRCA carriers does not display a significantly different Oncotype Dx recurrence score result distribution.

These preliminary data suggest Oncotype Dx assay might be used to help tailor treatment in this subset of patients, although further follow up is needed.

All patients evaluated for oncotype_DX  All evaluated (except BRCA+) n = 456BRCA positiveP valueAgeMean57.4557.60.7 Median5857  Range56 (25-81)34 (42-76) T stageT167.7%70%0.9 T230.7%30%  T31.6%0% Tumor sizeMean1.71.360.2 Median1.51.26  Range7.8 (0.2-8)1.3 (0.7-2) GradeGrade 1-278.8%60%0.15 Grade 321.2%40% RSLow50%40%0.57 Intermediate39.8%40%  High10.2%20% RSMean18.4822.80.16 Median17.522.5  Range64 (0-64)27 (12-39) 

Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P4-05-08.

From oscillatory transcranial current stimulation to scalp EEG changes: a biophysical and physiological modeling study

Both biophysical and neurophysiological aspects need to be considered to assess the impact of electric fields induced by transcranial current stimulation (tCS) on the cerebral cortex and the subsequent effects occurring on scalp EEG. The objective of this work was to elaborate a global model allowing for the simulation of scalp EEG signals under tCS. In our integrated modeling approach, realistic meshes of the head tissues and of the stimulation electrodes were first built to map the generated electric field distribution on the cortical surface. Secondly, source activities at various cortical macro-regions were generated by means of a computational model of neuronal populations. The model parameters were adjusted so that populations generated an oscillating activity around 10 Hz resembling typical EEG alpha activity. In order to account for tCS effects and following current biophysical models, the calculated component of the electric field normal to the cortex was used to locally influence the activity of neuronal populations. Lastly, EEG under both spontaneous and tACS-stimulated (transcranial sinunoidal tCS from 4 to 16 Hz) brain activity was simulated at the level of scalp electrodes by solving the forward problem in the aforementioned realistic head model. Under the 10 Hz-tACS condition, a significant increase in alpha power occurred in simulated scalp EEG signals as compared to the no-stimulation condition. This increase involved most channels bilaterally, was more pronounced on posterior electrodes and was only significant for tACS frequencies from 8 to 12 Hz. The immediate effects of tACS in the model agreed with the post-tACS results previously reported in real subjects. Moreover, additional information was also brought by the model at other electrode positions or stimulation frequency. This suggests that our modeling approach can be used to compare, interpret and predict changes occurring on EEG with respect to parameters used in specific stimulation configurations.

Effects of transcranial Direct Current Stimulation (tDCS) on cortical activity: a computational modeling study

Although it is well-admitted that transcranial Direct Current Stimulation (tDCS) allows for interacting with brain endogenous rhythms, the exact mechanisms by which externally-applied fields modulate the activity of neurons remain elusive. In this study a novel computational model (a neural mass model including subpopulations of pyramidal cells and inhibitory interneurons mediating synaptic currents with either slow or fast kinetics) of the cerebral cortex was elaborated to investigate the local effects of tDCS on neuronal populations based on an in-vivo experimental study. Model parameters were adjusted to reproduce evoked potentials (EPs) recorded from the somatosensory cortex of the rabbit in response to air-puffs applied on the whiskers. EPs were simulated under control condition (no tDCS) as well as under anodal and cathodal tDCS fields. Results first revealed that a feed-forward inhibition mechanism must be included in the model for accurate simulation of actual EPs (peaks and latencies). Interestingly, results revealed that externally-applied fields are also likely to affect interneurons. Indeed, when interneurons get polarized then the characteristics of simulated EPs become closer to those of real EPs. In particular, under anodal tDCS condition, more realistic EPs could be obtained when pyramidal cells were depolarized and, simultaneously, slow (resp. fast) interneurons became de- (resp. hyper-) polarized. Geometrical characteristics of interneurons might provide some explanations for this effect.

On joint diagonalization of cumulant matrices for independent component analysis of MRS and EEG signals

An extension of the original implementation of JADE, named eJADE((1)) hereafter, was proposed in 2001 to perform independent component analysis for any combination of statistical orders greater than or equal to three. More precisely, eJADE((1)) relies on the joint diagonalization of a set of several cumulant matrices corresponding to different matrix slices of one or several higher order cumulant tensors. An efficient way, without lose of statistical information, of reducing the number of third and fourth order cumulant matrices to be jointly diagonalized is proposed in this paper. The resulting approach, named eJADE(3,4)((2)), can be interpreted as an improvement of the eJADE(3,4)((1)) method. A performance comparison with classical methods is conducted in the context of MRS and EEG signals showing the good behavior of our technique.

Interictal brain 5-HT1A receptors binding in migraine without aura: a 18F-MPPF-PET study

In this study we aimed to assess the brain distribution of 5-HT(1A) receptors in migraine patients without aura. Ten female migraine patients and 24 female healthy volunteers underwent magnetic resonance imaging and positron emission tomography using a radioligand antagonist of 5-HT(1A) receptors [4-(2'-methoxyphenyl)-1-[2'-(N-2-pirydynyl)-p-fluorobenzamido]-ethylpiperazine ((18)F-MPPF)]. A simplified reference tissue model was used to generate parametric images of 5-HT(1A) receptor binding potential (BP) values. Statistical Parametrical Mapping (SPM) analysis showed increased MPPF BP in posterior cortical areas and hippocampi bilaterally in patients compared with controls. Region of interest (ROI) analysis showed a non-significant trend in favour of a BP increase patients in cortical regions identified by the SPM analysis except in hippocampi, left parietal areas and raphe nuclei. During the interictal period of migraine patients without aura, the increase of MPPF BP in posterior cortical and limbic areas could reflect an increase in receptor density or a decrease of endogenous serotonin, which could explain their altered cortical excitability.

Fourth order approaches for localization of brain current sources

Two high resolution methods solving inverse problems potentially ill-posed, named 4-MUSIC and 4-RapMUSIC, are proposed. They allow for localization of brain current sources with unconstrained orientations from surface electro-or magneto-encephalographic data using spherical or realistic head geometries. The 4-MUSIC and 4-RapMUSIC methods are based on i) the separability of the data transfer matrix as a function of location and orientation parameters and ii) the fourth order (FO) virtual array theory. In addition, 4-RapMUSIC uses the deflation concept extended to FO statistics accounting for the presence of potentially but not totally coherent sources. Computer results display the superiority of the 4-RapMUSIC approach in different situations (two closed sources, additive Gaussian noise with unknown spatial covariance, ...) especially over classical algorithms.

[Drug-induced dementia: a case/non-case study in the French Pharmacovigilance database]

The increased incidence of dementia on the aging population makes this disease a major public health problem. Among known causes of dementia, drug etiology is under considered. We investigated the relationship between exposure to drug therapy and dementia with a case/non-case study using reports of the French Pharmacovigilance database. Among 263 962 adverse effects recorded between 1985 and 2005, 79 (0.03%) are dementia. Median age is 66 (range 3-91). There was 41 women and 37 men. The therapeutic drug class associated with dementia were anticonvulsants, antiparkinsonians, antidepressants, anxiolytics, hypnotics, antipsychotics and morphinics. An association between reporting of dementia and non neurotropic drugs were also found, i.e. interferon alfa-2B, vancomycin and allopurinol. The term "Dementia" is only mentioned in the summary of the characteristics of valproate, but it may concern other drugs. Drug etiology for dementia is a reality but is not necessarily attributed as a cause in aging population, in particular.

Brain source localization using a fourth-order deflation scheme

In this paper, a high-resolution method for solving potentially ill-posed inverse problems is proposed. This method named FO-D-MUSIC allows for localization of brain current sources with unconstrained orientations from surface electroencephalographic (EEG) or magnetoencephalographic (MEG) data using spherical or realistic head geometries. The FO-D-MUSIC method is based on the following: 1) the separability of the data transfer matrix as a function of location and orientation parameters, 2) the fourth-order (FO) virtual array theory, and 3) the deflation concept extended to FO statistics accounting for the presence of potentially but not completely statistically dependent sources. Computer results display the superiority of the FO-D-MUSIC approach in different situations (very closed sources, small number of electrodes, additive Gaussian noise with unknown spatial covariance, etc.) compared to classical algorithms.

A 18F-MPPF PET normative database of 5-HT1A receptor binding in men and women over aging

Neurotransmission imaging studies require normative data for the statistical assessment of neurophysiologic dysfunctions. 2'-Methoxyphenyl-(N-2'-pyridinyl)-p-18F-fluoro-benzamidoethylpiperazine (18F-MPPF) is a specific serotonin 5-HT1A antagonist PET tracer recently characterized, modeled, and used for clinical research to explore abnormalities in the serotoninergic system. Our study reports, to our knowledge, the first large normative imaging database of 18F-MPPF binding potential (BP) over aging, for both males and females.

METHODS

Fifty-three healthy volunteers (27 females, 26 males; age, 20-70 y) were selected to undergo structural MRI and single-injection 18F-MPPF multiframe dynamic PET. 18F-MPPF BP values were computed using a nonlinear modeling method with tissue reference. The statistical assessment of the effect of age and sex was performed both at the anatomic structure level, using regions of interest drawn manually on individual MR images, and at the voxel level, using normalized BP parametric images in different statistical parametric mapping designs.

RESULTS

A negative linear correlation between age and 18F-MPPF binding (3.6% decrease by decade) was found in females but not in males and involved most of the limbic and paralimbic regions; on the other hand, males in their 30s showed decreased binding in most cerebral regions.

CONCLUSION

A comparison of males and females revealed higher BP values independent of age in females in the right hemisphere and a different evolution of BP over aging. These results confirm the necessity of a database for further statistical analysis in individuals or groups with pathology.

Statistical parametric mapping of 5-HT1A receptor binding in temporal lobe epilepsy with hippocampal ictal onset on intracranial EEG

Experimental data in animals show that 5-HT(1A) receptors are predominantly located in limbic areas and suggest that serotonin, via these receptors, mediates an antiepileptic and anticonvulsant effect. In this PET study, we used an antagonist of the 5-HT(1A) receptor, [(18)F]MPPF, to assess the extent of 5-HT(1A) receptor binding changes in a group of seven temporal lobe epilepsy (TLE) patients with hippocampal ictal onset demonstrated by intracerebral EEG recording. On the basis of MRI-measured hippocampal volumes (HV), patients were classified into "normal HV" or "hippocampal atrophy" (HA). Voxel-based analyses (SPM99) were performed to objectively assess the differences in [(18)F]MPPF binding potential (BP) between patients (taken as a group or as individuals) and a database of 48 controls subjects. In the full group of patients, a significant decreased BP was detected ipsilateral to the epileptogenic zone in the hippocampus, temporal pole, insula, and temporal neocortex. This result was confirmed in the subgroup of patients with HA. In patients with normal HV, the BP decrease was restricted to the temporal pole. TLE patients also demonstrated an increased BP in various regions contralateral to the epileptogenic zone. These data suggest that in TLE patients with hippocampal seizure onset, the decrease in 5-HT(1A) receptor binding partly reflects hippocampal neuronal loss, but is also observed in various regions involved in temporo-limbic epileptogenic networks that appeared normal on MRI. Further studies are warranted to evaluate the clinical usefulness of [(18)F]MPPF-PET as compared to other established PET tracers in drug resistant TLE.

5-HT1A receptor binding and intracerebral activity in temporal lobe epilepsy: an [18F]MPPF-PET study

The aim of our study was to assess abnormalities in 5-hydroxytryptamine-1A (5-HT1A) receptor density in patients suffering from refractory temporal lobe epilepsy (TLE). Experimental data in animals show that 5-HT1A receptors are predominantly located in limbic areas, and that serotonin, via these receptors, mediates an antiepileptic and anticonvulsant effect. In TLE patients, we quantified 5-HT1A receptor density in epileptogenic and non-epileptogenic areas, as defined by intracranial recordings with stereo-electroencephalography (SEEG). Nine TLE patients and 53 control subjects were studied by PET using a 5-HT1A receptor antagonist ([18F]MPPF). Anatomical regions of interest (ROIs) were drawn on patient and control MRIs co-registered with PET. PET data were quantified using a simplified model to assess binding potential (BP) values in each ROI, with cerebellum as reference. For each patient, a normalized percentage BP change was calculated as the relative variation of BP in each ROI compared with the corresponding ROI in control subjects. In patients, ROIs explored by SEEG were categorized according to their degree of epileptic activity (ictal onset, ictal spreading, interictal spikes, no epileptic activity) and according to their lesional aspect and volume (lesional with volume loss, lesional without volume loss, non-lesional). Compared with control values, the binding to 5-HT1A receptors in TLE patients was decreased in the epileptogenic temporal lobe. BP decrease was significantly greater in: (i) regions involved in the seizure onset than regions where only interictal paroxysms or no epileptic activity was recorded; and (ii) regions where the discharge propagated than regions where only interictal paroxysms or no epileptic activity was recorded. BP decrease was shown to be significantly influenced by the existence of a lesion on MRI. However, in the group of ROIs with normal quantitative and qualitative MRI aspect, BP decrease remained strongly correlated to the degree of epileptic activity. This study shows that in vivo availability of 5-HT1A receptors is decreased in epileptic patients compared with normal subjects. This decrease is highly correlated to the degree of epileptogenicity of cortical areas explored by intracerebral recordings, and does not reflect only pathological changes or neuronal loss in the epileptic focus.

Modeling [18 F]MPPF positron emission tomography kinetics for the determination of 5-hydroxytryptamine(1A) receptor concentration with multiinjection

The selectivity of [18F]MPPF (fluorine-18-labeled 4-(2;-methoxyphenyl)-1-[2;-(N-2"-pirydynyl)-p-fluorobenzamido]ethylpiperazine) for serotonergic 5-hydroxytryptamine(1A) (5-HT1A) receptors has been established in animals and humans. The authors quantified the parameters of ligand-receptor exchanges using a double-injection protocol. After injection of a tracer and a coinjection dose of [18F]MPPF, dynamic positron emission tomography (PET) data were acquired during a 160-minute session in five healthy males. These PET and magnetic resonance imaging data were coregistered for anatomical identification. A three-compartment model was used to determine six parameters: Fv (vascular fraction), K1, k2 (plasma/free compartment exchange rate), koff, kon/Vr (association and dissociation rate), Bmax (receptor concentration), and to deduce Kd (apparent equilibrium dissociation rate). The model was fitted with regional PET kinetics and arterial input function corrected for metabolites. Analytical distribution volume and binding potential were compared with indices generated by Logan-Patlak graphical analysis. The 5HT1A specificity for MPPF was evidenced. A Bmax of 2.9 pmol/mL and a Kd of 2.8 nmol/L were found in hippocampal regions, Kd and distribution volume in the free compartment were regionally stable, and the Logan binding potential was linearly correlated to Bmax. This study confirms the value of MPPF in the investigation of normal and pathologic systems involving the limbic network and 5-HT1A receptors. Standard values can be used for the simulation of simplified protocols.

Dipole modeling of interictal and ictal EEG and MEG paroxysms

Dipole modeling procedures can be used to statistically estimate the location and the orientation of intracerebral sources of electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. These methods have been applied to interictal spikes for more than 20 years and suggest that interictal paroxysms might be generated by a network of cortical structures rather than by a focal area. In this review we address the questions of (1) the spatial extend of this network in different types of epilepsies, (2) the spatial relationship between this network and other structural of functional abnormalities as assessed by Magnetic Resonance Imaging (MRI) or Positron Emission Tomography (PET), and (3) the reliability of dipole sources of interictal and ictal paroxysms. Dipole modeling results suggest that, in temporal lobe epilepsies, both neocortical and mesio-temporal structures are involved during interictal spikes; frontal lobe epilepsies are often characterized by more complex source distributions, that, in general, involve a large area and bilateral frontal structures. In addition, dipole modeling results are also found in close agreement with MRI data in cases where focal dysplasia or heterotopia are diagnosed. Nevertheless, in most other cases, sources of interictal spikes and MRI lesions, though overlapping in space, are not totally congruent. The best concordance between sources of interictal spikes and glucose hypometabolism on PET data is usually found for temporal lobe epilepsies. Most often, intracranial and intracerebral recordings validate both the localization and the time activation of interictal spike dipoles. However, results obtained for ictal discharges are less reliable, which therefore addresses the usefulness of dipole modeling procedures in assessing sources of ictal discharges. In conclusion, dipole modeling results can rarely be used in planning a selective surgery without invasive recordings. However, the studies reviewed in this paper strongly suggest that their analysis, in combination with other non-invasive data, might be useful to better delineate the epileptogenic zone, and help the implantation of intracerebral electrodes.

Dipole modeling of scalp electroencephalogram epileptic discharges: correlation with intracerebral fields

OBJECTIVE

In order to evaluate the feasibility of modeling seizures and the reliability of dipole models, we compared source localizations of scalp seizures with the distribution of simultaneous intracerebral electroencephalogram (SEEG).

METHODS

In a first session, only scalp electroencephalogram (EEG) was recorded from 15 patients. We averaged the first detectable ictal activity in two consecutive segments of stable topography and morphology. Spatio-temporal dipole sources were estimated for each segment and projected on 3D-magnetic resonance images. In a second session, SEEG was recorded simultaneously with control scalp electrodes, allowing the identification of ictal patterns similar to those submitted to dipole modeling.

RESULTS

Ictal discharges could be analyzed in only 6 of 15 patients. In the remaining 9, scalp discharges were undetectable or non-reproducible in 6, and solutions were unstable despite an apparently stable discharge in 3. In the 6 patients successfully modeled, dipoles were found in regions where SEEG discharges were present. However, when intracerebral discharges were very focal, there was no corresponding scalp activity. When intracerebral signals were maximal in the mesial temporal regions at the seizure onset, only lateral neocortical dipoles were found. When discharges reached the frontal lobes, we could identify lateral and mesial frontal sources.

CONCLUSIONS

In most seizures, it was not possible to obtain satisfactory dipole models, probably a reflection of the high noise level or widespread generators. When modeling was possible, our results suggested that mesial temporal seizure discharges did not contribute to scalp EEG activity. This activity appears to reflect signals synchronized and distributed over the lateral temporal or frontal neocortex, as well as signals generated in mesial frontal areas.

Separation of spikes from background by independent component analysis with dipole modeling and comparison to intracranial recording

OBJECTIVE

Epileptiform discharges can be objectively separated from the EEG background by independent component analysis (ICA) into the discharge's waveform and its spatial distribution. The correspondence between ICA components, including epileptiform transients extracted from the scalp EEG and intracranial epileptic fields, was investigated.

METHODS

In 11 spike patterns from 8 patients, the scalp EEG data were decomposed by ICA. The corresponding averaged intracranial data were compared with the extracted epileptic components regarding the number of source patterns and source locations estimated from ICA maps.

RESULTS

Clear epileptic components could be separated in 10/11 spike patterns. The number of epileptic components was identical to the number of intracranial field peaks in 7 spike patterns with simple intracranial fields, and was less in the remaining 3 patterns with complex intracranial peaks. The distance between the contact of the maximal intracranial field and the dipole location estimated by the single dipole model for the clearest epileptic component ranged from 4.7 to 31.9 mm.

CONCLUSIONS

The number of epileptic ICA components largely matched the number of intracranial field patterns, and the dipole location estimated for the map of the clearest epileptic component was generally correct. This establishes the validity of epileptic components extracted by ICA from the scalp background.

Relationships between the epileptic focus and hand area in central epilepsy: combining dipole models and anatomical landmarks

OBJECT

When considering resection of epileptic generators near the central sulcus, it is essential to define the spatial relationship between the epileptic generator and the primary sensorimotor hand area. In this study, the authors assessed the accuracy of dipole modeling of electroencephalographic spikes and median nerve somatosensory evoked potentials (SSEPs) in defining this relationship preoperatively and noninvasively.

METHODS

Epileptic spikes and SSEPs in patients with focal central area epilepsy were represented by dipole models coregistered onto global magnetic resonance images. In patients who underwent surgery, spike dipoles were also compared with findings of electrocorticography (ECoG) and with the resection area. To improve the accuracy of the dipole models, anatomical landmarks of the hand area were used to assess the error in SSEP dipole location, and this error measure was used to correct the location of spike dipoles. Five patients with central epilepsy were studied, three of whom underwent ECoG-guided surgical resections. The location of SSEP dipoles correlated well with anatomical landmarks of the primary sensory hand area. The relative position of the spike and SSEP dipoles correlated well with the patients' ictal symptoms, ECoG findings, and the location of the epileptic focus (as defined by the resection cavity in patients who became seizure free postoperatively). Corrected spike dipoles were located even closer to the resection cavity.

CONCLUSIONS

The calculation of the relative location of spike and SSEP dipoles is a simple noninvasive method of determining the relationship between the primary hand area and an epileptic focus in the central area. The spatial resolution of this technique can be further improved using easily identifiable anatomical landmarks.

An H(2) (15)O-PET study of cerebral blood flow changes during focal epileptic discharges induced by intracerebral electrical stimulation

Partial epileptic seizures are known to cause a focal increase in cerebral blood flow (CBF). However, quantified studies of ictal CBF changes under intracranial EEG control are still needed to assess the relationships in time and space between CBF changes and electrical discharges. Ten patients undergoing an intracerebral stereotaxic EEG (stereo-EEG) investigation for epilepsy surgery were prospectively studied for local perfusion changes. These were measured by H(2)(15)O-PET during 12 subclinical or mild symptomatic focal epileptic discharges induced by intracerebral electrical stimulation of the hippocampus (eight), amygdala (two), temporal pole (one) and fusiform gyrus (one). This study aimed to assess whether a significant focal blood flow change reflected the geographical extent of the underlying coincident epileptic discharge, as measured by this method at seizure onset. No significant CBF change was observed on test-retest at rest or during ineffective electrical stimulations outside the epileptogenic area. Compared with the resting condition, a significant focal perfusion increase of 16-55% occurred during eight discharges, there was no CBF change in three and a significant CBF decrease in one. Ictal CBF increases were mostly associated with low-voltage fast activity, but their magnitude had no obvious link with the duration of the discharge (range 8-106 s). Regional analysis of ictal PET was performed in 10 anatomical areas during each of the 12 discharges. Of all the 120 regions, 59 were not explored by intracerebral electrodes and 14 (24%) of these demonstrated ictal CBF changes. In 43 of the 61 regions explored by stereo-EEG (70.5%), PET and depth EEG findings converged, showing either a CBF change in a discharging area or no CBF change in a region unaffected by the discharge. Areas of increased CBF indicated an underlying epileptic discharge in almost 100% of the cases. Conversely, of the 18 regions showing discrepancies between intracerebral recordings and PET data, 17 were discharging regions showing no ictal CBF changes. Thus, a focal CBF increase, when detected at the seizure onset concomitantly with the initial low-voltage fast activity, was a reliable marker of an underlying epileptic discharge. It emphasizes the importance of injecting blood-flow tracers as soon as possible after detection of the discharge in routine clinical studies, even at a subclinical stage of the seizure. However, the extent of significant ictal CBF changes can be more restricted than that of the electrical discharge, thus limiting the reliability of ictal CBF images for outlining the contours of a tailored cortectomy.

Reliability of dipole models of epileptic spikes

OBJECTIVE

In order to validate dipole-modeling results, we compared dipole localizations with the distribution of intracerebral potentials occurring simultaneously with scalp EEG paroxysms.

METHODS

Firstly, scalp EEGs were recorded from 11 patients. Dipole sources were estimated on averaged spikes and projected on 3D-MRIs. Secondly, stereoelectroencephalography (SEEG) was recorded from implanted electrodes with direct identification onto MRI. Simultaneously with SEEG, control scalp electrodes were pasted where spikes peaked during the first session. SEEG was averaged, triggered by the main peak of scalp spikes.

RESULTS

SEEG activity during scalp spikes always involved several contacts. In 13 of 14 spikes, maximal fields occurred in neocortical regions. In 4 of 5 cases where intracerebral activity was simple, spikes could be modeled by one source. In all cases where intracerebral activity was complex, spikes had to be modeled by several sources. The main dipole source was 11 +/- 4.2 mm from the SEEG contact showing the maximal intracerebral potential. Early and late dipole localization and SEEG fields were concordant in two thirds of cases.

CONCLUSION

Results indicate that in our group of patients scalp spikes reflect activity in large neocortical areas and never activity limited to mesial structures. Dipole locations and time activation were confirmed most often and were more reliable for sources representing the main negative component than for early or late sources.

Simplified projection of EEG dipole sources onto human brain anatomy

This study was aimed at determining an easy way to project dipole modelling results onto brain anatomy. This simplified projection is based on the estimation of the mean location of the centre of the dipole sphere according to internal brain landmarks. The mean values for the centre location were calculated from ten epileptic patients. To define the axes of the dipole model frame on the patient's magnetic resonance image (MRI), markers were pasted at some electrode positions during the acquisition. An estimation was then made of the mean position of the model centre from the bicommissural line (anterior commissure-posterior commissure [AC-PC]), and a simple transformation to pass from the model cartesian coordinates to the anatomical correlates either in the subject MRI or in the Talairach atlas. These data were then tested in four additional subjects in whom no markers had been placed during the MRI acquisition. On average, the horizontal plane of the sphere model was pitched up 1.9 degrees +/- 1.8 only with respect to the AC-PC horizontal plane, which allowed the projection of dipoles directly onto the Talairach atlas, without pitch. The mean sphere centre was located 7.4 +/- 4.2 mm above the bicommissural line, and 8.2 +/- 1 mm in front of the posterior commissure. In the four additional subjects, projections on MRI and atlas indicated the same anatomical regions and showed high congruence with the physiology or the pathology. This simplified way we report herein has proved to give reliable results. We believe that this method will be useful as a first approximation to project dipole coordinated onto MRI data; moreover, when MRI is unavailable, our results show that dipole modelling results can be superimposed onto atlas slices provided that they are represented according to the AC-PC plane.

Topographical reliability of mesio-temporal sources of interictal spikes in temporal lobe epilepsy

PURPOSE

Localization of hippocampal paroxysmal activities in temporal lobe epilepsy (TLE) by means of dipole modeling has often been criticized because of the supposed inaccuracy of this technique in localizing deep sources of EEG signals. This study aimed at assessing the relevance of mesio-temporal dipoles, as identified by modeling of scalp recorded spikes in TLE.

METHODS

Surface and depth EEG activities were simultaneously recorded using scalp and intracranial electrodes implanted through the foramen ovale (FO) in 3 patients with refractory TLE seizures. Intracranial FO spikes were used as triggers for scalp EEG averaging. The averaged signals were modeled by current dipoles, the localization of which were fused with patients' 3D-MRI.

RESULTS

Individual FO spikes were undetectable on visual analysis of raw scalp EEG but were reflected by low-amplitude scalp EEG transients on averaged signal. Dipole modeling of this EEG deflection consistently identified a mesio-limbic source in a position close to that of the FO pole recording the intracranial spike with its maximal amplitude.

CONCLUSION

This result suggests that mesio-temporal sources can be accurately localized by modeling the signals recorded on the scalp, thus validating the anatomical and clinical relevance of hippocampal sources identified by modeling scalp interictal averaged spikes in TLE.