During the third trimester of gestation in humans, the auditory cortex displays spontaneous and auditory-evoked EEG patterns of intermittent local oscillatory activity nested in delta waves - delta brushes (DBs). To test whether the spatiotemporal dynamics of evoked DBs depends on stimulus type, we studied auditory evoked responses (AERs) to voice and "click" using 32-electrode EEG in 30 healthy neonates aged 30 to 38 post-menstrual weeks. Both stimuli elicited two peaks at approximately 250 ms and 600 ms, the second corresponding to the first principal components of the AER and the evoked DB. The DB showed stimulus-specific topography, temporal posterior and mid-temporal for "click", and mid-temporal and pre-central inferior for voice, and contained theta to gamma oscillations more widespread for the "click"response. Gamma oscillations increased with age. AERs predominated on the right but shifted toward the left with age for voice response. Auditory evoked DBs may therefore underlie specific auditory processing during fetal development.

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.

OBJECTIVE:To evaluate the effects of personalized multichannel tDCS on seizure frequency, severity, quality of life, and psychiatric comorbidities in patients with drug-resistant focal epilepsy. Secondary goals include assessing the safety and feasibility of this approach. METHODS:This open-label pilot study involved 16 patients with drug-resistant focal epilepsy. Patients underwent 3 cycles of personalized multichannel tDCS over 6 months, targeting the EZ defined by stereoelectroencephalography (SEEG). Each cycle consisted of five consecutive days of tDCS, with two daily sessions of 20 min each. The primary endpoint was a reduction in seizure frequency, with secondary endpoints addressing quality of life (QOLIE-31 scores), seizure severity (NHS3 scores), and psychiatric comorbidities (NDDI-E and GAD-7 scales). RESULTS:Across all participants, a statistically significant 20% reduction in seizure frequency was observed (p = 0.044). Six patients (37%) were identified as responders (≥50% seizure reduction), with one achieving seizure freedom. The mean seizure reduction among responders was 68%. Significant improvements were noted in overall quality of life (QOLIE-31, p = 0.009), with greater benefits for patients with poorer baseline scores. No overall significant changes were observed in depression, anxiety, and seizure severity scores, though individual variability was noted. The treatment was well tolerated, with mild adverse events, primarily skin-related. SIGNIFICANCE:Personalized multichannel tDCS shows promise as a noninvasive therapeutic option for drug-resistant focal epilepsy, with benefits in seizure reduction and quality of life. Although results were variable, the method's safety and feasibility support further exploration through randomized controlled trials to refine protocols, better select potential responders' patients, and validate findings. PLAIN LANGUAGE SUMMARY:This study tested a personalized brain stimulation technique called tDCS in people with difficult-to-treat epilepsy. The treatment led to fewer seizures in some patients and improved their quality of life. The approach was safe and caused only mild side effects. These results suggest that this type of noninvasive brain stimulation may be a helpful new option for people who do not benefit from medication or surgery.

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.

Objective.Computational modeling has recently emerged as a powerful tool to better understand seizure dynamics and guide new treatment strategies. This work aims to develop and personalize whole-brain computational models in epilepsy using multimodal clinical data to simulate and evaluate individualized therapeutic strategies.Approach.We present a computational framework that constructs patient-specific whole-brain models of seizure propagation by integrating SEEG, MRI, and diffusion MRI data. The pipeline uses neural mass models for each node in the network, simulating whole-brain dynamics. Model personalization involves adjusting global and local parameters representing the excitability of individual brain areas, using an evolutionary algorithm that aims to maximize the correlation between empirical and synthetic functional connectivity matrices derived from SEEG data.Main results.The resulting personalized models successfully reproduce individual seizure propagation patterns and can be used to simulate therapeutic interventions like surgery, stimulation, or pharmacological interventions within a unified physiological framework. Notably, model predictions reveal distinct patient-specific responses across interventions, including variable sensitivity to different pharmacological agents and identification of critical regions whose removal or modulation reduced seizure spread.Significance.This framework provides a mechanistic, interpretable approach to simulate and compare individualized treatment strategies. By integrating multimodal data into a unified whole-brain model, it has the potential to improve clinical decision-making in epilepsy by identifying accessible and functionally relevant targets.

Objective. Transcranial alternating current stimulation (tACS) enables non-invasive modulation of brain activity, holding promise for clinical and research applications. Yet, it remains unclear how the stimulation frequency differentially impacts various neuron types. Here, we aimed to quantify the frequency-dependent behavior of key neocortical cell types. Approach. We used both detailed (anatomical multicompartments) and simplified (three compartments) single-cell modeling approaches based on the Hodgkin-Huxley formalism to study neocortical excitatory and inhibitory cells under various tACS intensities and frequencies within the 5-50 Hz range at rest and during basal 10 Hz activity. Main results. L5 pyramidal cells (PCs) exhibited the highest polarizability at direct current, ranging from 0.21 to 0.25 mm and decaying exponentially with frequency. Inhibitory neurons displayed membrane resonance in the 5-15 Hz range with lower polarizability, although bipolar cells had higher polarizability. Layer 5 PC demonstrated the highest entrainment close to 10 Hz, which decayed with frequency. In contrast, inhibitory neurons entrainment increased with frequency, reaching levels akin to PC. Results from simplified models could replicate phase preferences, while amplitudes tended to follow opposite trends in PC. Significance. tACS-induced membrane polarization is frequency-dependent, revealing observable resonance behavior. Whilst optimal phase entrainment of sustained activity is achieved in PC when tACS frequency matches endogenous activity, inhibitory neurons tend to be entrained at higher frequencies. Consequently, our results highlight the potential for precise, cell-specific targeting for tACS.

Transcranial direct current stimulation shows promise as a non-invasive therapeutic method for patients with focal drug-resistant epilepsy. However, there is considerable variability in individual responses to transcranial direct current stimulation, and the factors influencing treatment effectiveness in targeted regions are not well understood. We aimed to assess how the extent and depth of the epileptogenic zone and associated networks impact patient responses to transcranial direct current stimulation therapy. We conducted a retrospective analysis of stereoelectroencephalography data from 23 patients participating in a personalized multichannel transcranial direct current stimulation protocol. We evaluated the extent and depth of the epileptogenic zone network, propagation zone network, and the combined network of the entire epileptogenic and propagation zones, correlating these factors with clinical response measured by the reduction in seizure frequency following repeated transcranial direct current stimulation sessions. Among the patients, 10 (43.5%) were classified as responders (R), experiencing a significant (>50%) decrease in seizure frequency, while 13 were non-responders, showing minimal improvement or increased seizure frequency. Importantly, we found a significant positive correlation between the extent of the epileptogenic zone network and changes in seizure frequency. A smaller epileptogenic zone network extent was associated with better transcranial direct current stimulation efficacy, with responders demonstrating a significantly smaller epileptogenic and propagation zones compared with non-responders. Additionally, non-responders tended to have a significantly deeper epileptogenic zone network compared with responders. Our results highlight the significant impact of the extent and depth of the epileptogenic zone network on transcranial direct current stimulation efficacy in patients with refractory focal epilepsy. Responders typically exhibited a smaller and shallower epileptogenic zone network compared with non-responders. These findings suggest that utilizing individualized epileptogenic zone network characteristics could help refine patient selection for personalized transcranial direct current stimulation protocols, potentially improving therapeutic outcomes.

Objective: Non-invasive neuromodulation techniques, particularly transcranial direct current stimulation (tDCS), are promising for drug-resistant epilepsy (DRE), though the mechanisms of their efficacy remain unclear. This study aims to (i) investigate tDCS neurophysiological mechanisms using a personalized multichannel protocol with magnetoencephalography (MEG) and (ii) assess post-tDCS changes in brain connectivity, correlating them with clinical outcomes. Methods: Seventeen patients with focal DRE underwent three cycles of tDCS over five days, each consisting of 40-minute stimulations targeting the epileptogenic zone (EZ) identified via stereo-EEG. MEG was performed before and after sessions to assess functional connectivity (FC) and power spectral density (PSD),estimated at source level (beamforming). Results: Five of fourteen patients experienced a seizure frequency reduction > 50 %. Distinct PSD changes were seen across frequency bands, with reduced FC in responders and increased connectivity in non-responders (p < 0.05). No significant differences were observed between EZ network and non-involved networks. Responders also had higher baseline FC, suggesting it could predict clinical response to tDCS in DRE. Conclusions: Personalized multichannel tDCS induces neurophysiological changes associated with seizure reduction in DRE.

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.

Epilepsy is a chronic neurological disorder characterized by recurrent seizures resulting from abnormal neuronal hyperexcitability. In the case of pharmacoresistant epilepsy requiring resection surgery, the identification of the Epileptogenic Zone (EZ) is critical. Fast Ripples (FRs; 200–600 Hz) are one of the promising biomarkers that can aid in EZ delineation. However, recording FRs requires physically small electrodes. These microelectrodes suffer from high impedance, which significantly impacts FRs’ observability and detection. In this study, we investigated the potential of a conductive polymer coating to enhance FR observability. We employed biophysical modeling to compare two types of microelectrodes: Gold (Au) and Au coated with the conductive polymer poly(3,4-ethylenedioxythiophene)-poly(styrene sulfonate) (Au/PEDOT:PSS). These electrodes were then implanted into the CA1 hippocampal neural network of epileptic mice to record FRs during epileptogenesis. The results showed that the polymer-coated electrodes had a two-order lower impedance as well as a higher transfer function amplitude and cut-off frequency. Consequently, FRs recorded with the PEDOT:PSS-coated microelectrode yielded significantly higher signal energy compared to the uncoated one. The PEDOT:PSS coating improved the observability of the recorded FRs and thus their detection. This work paves the way for the development of signal-specific microelectrode designs that allow for better targeting of pathological biomarkers.

Objective: Clinical exploitation of transcranial electrical stimulation for focal epilepsy treatment lacks quantification of the underlying neurophysiological changes. This study explores the immediate effects of transcranial alternating (tACS) and direct (tDCS) current stimulation on local and network brain activity using simultaneous stereoelectroencephalography (SEEG) recordings. Methods: Patients were randomized for personalized tACS (n = 5) or tDCS (n = 6). Active stimulation (20 min) was preceded by sham stimulation (20 min). Changes in interictal epileptiform discharges (IED), functional connectivity (FC) and power spectral density (PSD) were quantified against baseline. Results: Results demonstrated variable responses. Spike rate decreased in 2/6 subjects following sham and tDCS, while 2/6 showed an increase. Alpha power and aperiodic PSD components generally increased during and after tDCS but decreased following tACS. FC changes varied among subjects and were detectable even during sham sessions. Conclusions: Strong variability suggests that tES does not have a univocal effect on immediate changes in IED or FC, possibly due to the single session format and challenges in affecting subcortical areas. Significance: This is the first study to examine intracranial FC changes during tACS and tDCS, highlighting the importance of sham comparisons and individual variability in tES response, offering valuable insights into its application for epilepsy treatment. (c) 2024 Published by Elsevier B.V. on behalf of International Federation of Clinical Neurophysiology.

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.

Abstract Devices that safely interface with the brain are critical to advancing neuroengineering. Thin and flexible neural implants show great promise alongside established silicon technologies. They therefore require a physical stiffener to allow their insertion into brain tissue. Bioresorbable polymer shanks are novel transient guides enabling accurate implantation using biocompatible materials that will be absorbed by the body over time. The development of materials with optimized stiffness and degradation is needed to provide minimally invasive probes with precise insertion capability under surgical conditions. A microfabrication protocol for the patterning of polyvinyl alcohol and its physical cross‐linking is presented, resulting in insertion guides with precise shapes and tunable degradation and stiffness. The results demonstrate a remarkable improvement in batch fabricating micro‐scale neural shanks with designed crystallinity. It results in their prolonged degradation time, evaluated in agarose gel, and remarkably improved penetrability due to the increase in mechanical stiffness. In vitro and in vivo studies support the high acceptability of this combination in interfacing with neural cells and tissue. This work represents a novel approach to the material and process engineering of bioresorbable polymers for developing fully organic and safe implants.

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.