Accurate localization of epileptic zones from high-resolution electroencephalography (HR-EEG) data can be challenging, especially when multiple synchronous zones are involved, and is highly dependent on the chosen EEG Source Imaging (ESI) method. Since a given scalp-level electrical pattern can result from multiple source configurations, ESI methods address this ill-posed inverse problem by imposing constraints on the structure of underlying sources. Here, we present an efficient approach that imposes sparsity on both the source-level activity and its spatial gradient. Unlike other methods that generally require a heuristic choice of a regularization parameter that balances between data fidelity and constraints, our method iteratively adjusts the parameter value based on the noise level in a fully automated way. The performance of the new method is evaluated across different scenarios of realistic synthetic HR-EEG data, including unifocal and synchronous multifocal cortical epileptic activity. Based on multiple performance indices, we demonstrate that the proposed approach outperforms traditional methods in accurately reconstructing epileptic sources. We also show that the method reduces polarity artifacts responsible for ghost sources and spatial discontinuities. Its ability to recover homogeneous and well-delineated epileptic regions is further confirmed based on real HR-EEG data of a typical absence seizure.

Electroencephalography (EEG) recordings are widely used in neuroscience to identify healthy individual brain rhythms and to detect alterations associated with various brain diseases. However, understanding the cellular origins of scalp EEG signals and their spatiotemporal changes during the resting state (RS) in humans remains challenging, as cellular-level recordings are typically restricted to animal models. The objective of this study was to simulate individual-specific spatiotemporal features of RS EEG and measure the degree of similarity between real and simulated EEG. Using a physiologically grounded whole-brain computational model (based on known neuronal subtypes and their structural and functional connectivity) that simulates interregional cortical circuitry activity, realistic individual EEG recordings during RS of three healthy subjects were created. The model included interconnected neural mass modules simulating activities of different neuronal subtypes, including pyramidal cells and four types of GABAergic interneurons. High-definition EEG and source localization were used to delineate the cortical extent of alpha and beta-gamma rhythms. To evaluate the realism of the simulated EEG, we developed a similarity index based on cross-correlation analysis in the frequency domain across various bipolar channels respecting standard longitudinal montage. Alpha oscillations were produced by strengthening the somatostatin-pyramidal loop in posterior regions, while beta-gamma oscillations were generated by increasing the excitability of parvalbumin-interneurons on pyramidal neurons in anterior regions. The generation of realistic individual RS EEG rhythms represents a significant advance for research fields requiring data augmentation, including brain-computer interfaces and artificial intelligence training.

Background: Childhood Absence epilepsy is a common generalized epileptic syndrome in children. The seizures involve momentary lapses in consciousness, aligning with generalized spike-wave discharges on EEG. This study proposes an AI-based algorithm for early detection of absence seizure onset, allowing for application of sensory stimulation, such as acoustic stimulation, prone to abort the seizure. Method: We propose a deep learning-based model designed for early detection of the onset of absence seizures in children. The use of deep learning algorithms offers a promising solution to this problem by leveraging their ability to analyze complex patterns. The model was evaluated under two configurations. Clinical configuration to assess feasibility of an accurate detector of the onset seizures, and a wearable device configuration intended to implement the model on a portable closed-loop stimulator. Results: The performance analysis, in term of accuracy and time delay, assessed on a clinical EEG database of 117 patients with confirmed childhood absence epilepsy, are promising: sensitivity of 0.859, precision of 0.819, F1-score of 0.837, and a mean time delay of 0.522 s. Furthermore, the algorithm performance evaluated using reduced number of electrodes, as required for a wearable device, is still stable with a sensitivity = 0.837, precision = 0.808, F1-score = 0.820 and detection delays around 0.5 s. Conclusion: The performance of the proposed method on clinical configuration demonstrates the feasibility of a robust and universal detector of the onset of absence seizures in children. In addition, the consistency of results when only two bipolar EEG channels are utilized makes the pipeline suitable to be embedded in a wearable stimulator.

Objective.Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms, including cognitive impairment. Its diagnosis, which used to be based on clinical assessment, increasingly relies on biomarkers. While electroencephalography (EEG) biomarkers are still at an experimental stage, they have been studied using deep learning (DL) models. Our aim was to determine whether a cognitive task could improve the accuracy of EEG-based disease detection by activating cortical regions affected by the disease.Approach. We trained a DL model to discriminate PD patients from controls based on their high-density EEG recordings. Previous studies have employed a range of preprocessing techniques, models and, predominantly, resting state (RS) EEG. We also investigated different network architectures and hyperparameters, and the role of spatial and temporal resolution.Main results. The best model gave a classification accuracy of 83% on the cognitive task EEG dataset and 76% on the RS EEG dataset. Sensitivity analysis indicated that the model predominantly uses specific temporal and spatial components of the EEG in the cognitive task condition, differing from the RS.Significance. Our results suggest that cortical activation by the cognitive task unveils EEG features that are effective in distinguishing between PD and controls. These features can be used by the model, thereby improving its diagnostic accuracy.

The therapeutic application of centromedian nucleus stimulation (CMS) has been limited by uncertainties regarding its mechanism of action. In this study, we used stereoelectro-encephalography (SEEG) signals recorded from a patient with refractory epilepsy, caused by focal cortical dysplasia, which is a malformation of cortical development. SEEG recordings revealed that neocortical interictal discharges could be suppressed by CMS. These effects were found to be frequency-dependent: while 50 Hz CMS induced no change in neocortical epileptiform activity, CMS at 70 Hz, 100 Hz and 150 Hz led to periods of suppression of neocortical epileptiform activity. These periods were shown to have different durations depending on the stimulation protocol. We developed a neurophysiologically-plausible thalamocortical model to explain these observations. This model included glutamatergic subpopulations and GABAergic subpopulations in the neocortical and the thalamic compartments. Synaptic inhibition and short-term plasticity mechanisms were integrated into the latter compartment. We hypothesized that the enhanced activation of thalamic inhibitory subpopulations during high frequency CMS (>70Hz) would result in GABA spillover which activated synaptic GABAergic receptors on the thalamocortical relay cells. This decreased the thalamic driving-input to the neocortex, hence suppressing interictal discharges in the dysplastic neocortical tissue. While inhibition of thalamocortical relay cells was maximal for CMS at 70 Hz and 100 Hz, this was not the case for 150 Hz CMS, suggesting that presynaptic GABAergic receptors were activated and that the rate of GABA reuptake was increased. Thus, our model suggests that the transient suppression of the neocortical epileptic activity with CMS may be primarily due to extra-synaptic tonic inhibition in the thalamocortical relay cells. These findings contribute to a deeper understanding of high-frequency CMS in epilepsy and pave the way for further research and optimization of this therapeutic approach.

This study aimed to create a neurophysiologically grounded computer model of focal epilepsy. This is a feature frequently lacking to simulations in this domain, making the translation from in silico to in vivo results questionable and difficult to understand for clinical electrophysiologists. We adapted a whole-brain neuronal mass model for EEG generation in various conscious states to replicate the EEG patterns of a type 2 focal cortical dysplasia (FCD), a condition associated with epilepsy. Our model successfully simulated both intracranial and scalp EEGs of a complex patient with type 2 FCD, who was later cured through surgery. Importantly, the simulated lesion location matched the patient's epileptogenic zone, and removing this area in the model eliminated epileptic activity in the EEG, demonstrating the model's accuracy.

ObjectiveThe 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.MethodsWe 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.ResultsFor 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.ConclusionsThe 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.SignificanceThese models are likely to play a key role in the mechanistic interpretation of epileptiform activity.

OBJECTIVE:For the pre-surgical evaluation of patients with drug-resistant focal epilepsy, stereo-electroencephalographic (SEEG) signals are routinely recorded to identify the epileptogenic zone network (EZN). This network consists of remote brain regions involved in seizure initiation. However, the pathophysiological mechanisms underlying typical SEEG patterns that occur during the transition from interictal to ictal activity in distant brain nodes of the EZN remain poorly understood. The primary aim is to identify and explain these mechanisms using a novel physiologically-plausible model of the EZN. METHODS:We analyzed SEEG signals recorded from the EZN in 10 patients during the transition from interictal to ictal activity. This transition consisted of a sequence of periods during which SEEG signals from distant neocortical regions showed stereotypical patterns of activity: sustained preictal spiking activity preceding a fast activity occurring at seizure onset, followed by the ictal activity. Spectral content and non-linear correlation of SEEG signals were analyzed. In addition, we developed a novel neuro-inspired computational model consisting of bidirectionally coupled neuronal populations. RESULTS:The proposed model captured the essential characteristics of the patient signals, including the quasi-synchronous onset of rapid discharges in distant interconnected epileptogenic zones. Statistical analysis confirmed the dynamic correlation/de-decorrelation pattern observed in the patient signals and accurately reproduced in the simulated signals. SIGNIFICANCE:This study provides insight into the abnormal dynamic changes in glutamatergic and γ-aminobutyric acid (GABA)ergic synaptic transmission that occur during the transition to seizures. The results strongly support the hypothesis that bidirectional connections between distant neuronal populations of the EZN (from pyramidal cells to vaso-intestinal peptide-positive interneurons) play a key role in this transition, while parvalbumin-positive interneurons intervene in the emergence of rapid discharges at seizure onset.

Background: Understanding the pathophysiological dynamics that underline Interictal Epileptiform Events (IEEs) such as epileptic spikes, spike-and-waves or High-Frequency Oscillations (HFOs) is of major importance in the context of neocortical refractory epilepsy, as it paves the way for the development of novel therapies. Typically, these events are detected in Local Field Potential (LFP) recordings obtained through depth electrodes during pre-surgical investigations. Although essential, the underlying pathophysiological mechanisms for the generation of these epileptic neuromarkers remain unclear. The aim of this paper is to propose a novel neurophysiologically relevant reconstruction of the neocortical microcircuitry in the context of epilepsy. This reconstruction intends to facilitate the analysis of a comprehensive set of parameters encompassing physiological, morphological, and biophysical aspects that directly impact the generation and recording of different IEEs. Method: a novel microscale computational model of an epileptic neocortical column was introduced. This model incorporates the intricate multilayered structure of the cortex and allows for the simulation of realistic interictal epileptic signals. The proposed model was validated through comparisons with real IEEs recorded using intracranial stereo-electroencephalography (SEEG) signals from both humans and animals. Using the model, the user can recreate epileptiform patterns observed in different species (human, rodent, and mouse) and study the intracellular activity associated with these patterns. Results: Our model allowed us to unravel the relationship between glutamatergic and GABAergic synaptic transmission of the epileptic neural network and the type of generated IEE. Moreover, sensitivity analyses allowed for the exploration of the pathophysiological parameters responsible for the transitions between these events. Finally, the presented modeling framework also provides an Electrode Tissue Model (ETI) that adds realism to the simulated signals and offers the possibility of studying their sensitivity to the electrode characteristics. Conclusion: The model (NeoCoMM) presented in this work can be of great use in different applications since it offers an in silico framework for sensitivity analysis and hypothesis testing. It can also be used as a starting point for more complex studies.

Objective: This study aims to detect the seizure onset, in childhood absence epilepsy, as early as possible. Indeed, interfering with absence seizures with sensory simulation has been shown to be possible on the condition that the stimulation occurs soon enough after the seizure onset. Methods: We present four variations (two supervised, two unsupervised) of an algorithm designed to detect the onset of absence seizures from 4 scalp electrodes, and compare their performance with that of a state -of -the -art algorithm. We exploit the characteristic shape of spike-wave discharges to detect the seizure onset. Their performance is assessed on clinical electroencephalograms from 63 patients with confirmed childhood absence epilepsy. Results: The proposed approaches succeed in early detection of the seizure onset, contrary to the classical detection algorithm. Indeed, the results clearly show the superiority of the proposed methods for small delays of detection, under 750 ms from the onset. Conclusion: The performance of the proposed unsupervised methods is equivalent to that of the supervised ones. The use of only four electrodes makes the pipeline suitable to be embedded in a wearable device. Significance: The proposed pipelines perform early detection of absence seizures, which constitutes a prerequisite for a closed-loop system. (c) 2024 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Neuroplasticity refers to functional and structural changes in brain regions in response to healthy and pathological activity. Activity dependent plasticity induced by epileptic activity can involve healthy brain regions into the epileptogenic network by perturbing their excitation/inhibition balance. In this article, we present a new neural mass model, which accounts for neuroplasticity, for investigating the possible mechanisms underlying the epileptogenic network expansion. Our multiple-timescale model is inspired by physiological calcium-mediated synaptic plasticity and pathological extrasynaptic N-methyl-D-aspartate (NMDA) dependent plasticity dynamics. The model highlights that synaptic plasticity at excitatory connections and structural changes in the inhibitory system can transform a healthy region into a secondary epileptic focus under recurrent seizures and interictal activity occurring in the primary focus. Our results suggest that the latent period of this transformation can provide a window of opportunity to prevent the expansion of epileptogenic networks, formation of an epileptic focus, or other comorbidities associated with epileptic activity.

This paper introduces eCOALIA, a Python-based environment for simulating intracranial local field potentials and scalp electroencephalography (EEG) signals with neural mass models. The source activity is modeled by a novel neural mass model respecting the layered structure of the neocortex. The whole-brain model is composed of coupled neural masses, each representing a brain region at the mesoscale and connected through the human connectome matrix. The forward solution on the electrode contracts is computed using biophysical modeling. eCOALIA allows parameter evolution during a simulation time course and visualizes the local field potential at the level of cortex and EEG electrodes. Advantaged with the neurophysiological modeling, eCOALIA advances the in silico modeling of physiological and pathological brain activity.

Epilepsia partialis continua (EPC) is a rare type of focal motor status epilepticus that causes continuous muscle jerking in a specific part of the body. Experiencing this type of seizure, along with other seizure types, such as focal motor seizures and focal to bilateral tonic-clonic seizures, can result in a disabling situation. Non-invasive brain stimulation methods like transcranial direct current stimulation (tDCS) show promise in reducing seizure frequency (SF) when medications are ineffective. However, research on tDCS for EPC and related seizures is limited. We evaluated personalized multichannel tDCS in drug-resistant EPC of diverse etiologies for long-term clinical efficacy We report three EPC patients undergoing a long-term protocol of multichannel tDCS. The patients received several cycles (11, 9, and 3) of five consecutive days of stimulation at 2 mA for 2 × 20 min, targeting the epileptogenic zone (EZ), including the central motor cortex with cathodal electrodes. The primary measurement was SF changes. In three cases, EPC was due to Rasmussen’s Encephalitis (case 1), focal cortical dysplasia (case 2), or remained unknown (case 3). tDCS cycles were administered over 6 to 22 months. The outcomes comprised a reduction of at least 75

OBJECTIVE:Microelectrodes allow the recording of neural activities with a high spatial resolution. However, their small sizes result in high impedance causing high thermal noise and poor signal-to-noise ratio. In drug-resistant epilepsy, the accurate detection of Fast Ripples (FRs) can help in the identification of epileptogenic networks. Consequently, good-quality recordings are instrumental in improving surgical outcomes. In this work, we propose a novel model-based approach for the design of microelectrodes optimized for FRs recording.METHODS:A 3D microscale computational model was developed to simulate FRs generated in the hippocampus. It was coupled with a model of the Electrode-Tissue Interface that accounts for the biophysical properties of intracortical microelectrode. This hybrid model was used to analyze the microelectrode geometrical and physical characteristics and their impact on recorded FRs. For model validation, experimental signals (local field potentials, LFPs) were recorded from CA1 using different electrode materials: stainless steel, gold, and gold coated with poly(3,4-ethylene dioxythiophene)/Poly(styrene sulfonate) (Au:PEDOT/PSS).RESULTS:results indicated that a radius between 65 and 120 μm for a wire microelectrode is the most optimal for recording FRs. In addition, in silico and in vivo quantified results showed a possible improvement in FRs observability using PEDOT/PSS coated microelectrodes.CONCLUSION:the optimization of the design of microelectrodes for FRs recording can improve the observability and detectability of FRs which are a recognized marker of epileptogenicity.SIGNIFICANCE:This model-based approach can assist in the design of hybrid electrodes that can be used in the presurgical evaluation of epileptic patients with drug-resistant epilepsy.