Christian Bénar

High-performance transistors for bioelectronics through tuning of channel thickness

Transistors with tunable transconductance allow high-quality recordings of human brain rhythms. Despite recent interest in organic electrochemical transistors (OECTs), sparked by their straightforward fabrication and high performance, the fundamental mechanism behind their operation remains largely unexplored. OECTs use an electrolyte in direct contact with a polymer channel as part of their device structure. Hence, they offer facile integration with biological milieux and are currently used as amplifying transducers for bioelectronics. Ion exchange between electrolyte and channel is believed to take place in OECTs, although the extent of this process and its impact on device characteristics are still unknown. We show that the uptake of ions from an electrolyte into a film of poly(3,4-ethylenedioxythiophene) doped with polystyrene sulfonate (PEDOT:PSS) leads to a purely volumetric capacitance of 39 F/cm3. This results in a dependence of the transconductance on channel thickness, a new degree of freedom that we exploit to demonstrate high-quality recordings of human brain rhythms. Our results bring to the forefront a transistor class in which performance can be tuned independently of device footprint and provide guidelines for the design of materials that will lead to state-of-the-art transistor performance.

Source localization of ictal epileptic activity investigated by high resolution EEG and validated by SEEG

High resolution electroencephalography (HR-EEG) combined with source localization methods has mainly been used to study interictal spikes and there have been few studies comparing source localization of scalp ictal patterns with depth EEG. To address this issue, 10 patients with four different scalp ictal patterns (ictal spikes, rhythmic activity, paroxysmal fast activity, obscured) were investigated by both HR-EEG and stereoelectroencephalography (SEEG). Sixty-four scalp-EEG sensors and a sampling rate of 1kHz were used to record scalp ictal patterns. Five different source models (moving dipole, rotating dipole, MUSIC, LORETA, and sLORETA) were used in order to perform source localization. Seven to 10 intracerebral electrodes were implanted during SEEG investigations. For each source model, the concordance between ictal source localization and epileptogenic zone defined by SEEG was assessed. Results were considered to agree if they localized in the same sublobar area as defined by a trained epileptologist. Across the study population, the best concordance between source localization methods and SEEG (9/10) was obtained with equivalent current dipole modeling. MUSIC and LORETA had a concordance of 7/10 whereas sLORETA had a concordance of only 5/10. Four of our patients classified into different groups (ictal spikes, paroxysmal fast activity, obscured) had complete concordance between source localization methods and SEEG. A high signal to noise ratio, a short time window of analysis (<1s) and bandpass filtering around the frequency of rhythmic activity allowed improvement of the source localization results. A high level of agreement between source localization methods and SEEG can be obtained for ictal spike patterns and for scalp-EEG paroxysmal fact activities whereas scalp rhythmic discharges can be accurately localized but originated from seizure propagation network.

Conducting polymer electrodes for electroencephalography.

Conducting polymer electrodes are developed on a flexible substrate for electroencephalography applications. These electrodes yield higher quality recordings than dry electrodes made from metal. Their performance is equivalent to commercial gel-assisted electrodes, paving the way for non-invasive, long-term monitoring of the human brain.

The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread

ABSTRACT Individual variability has clear effects upon the outcome of therapies and treatment approaches. The customization of healthcare options to the individual patient should accordingly improve treatment results. We propose a novel approach to brain interventions based on personalized brain network models derived from non‐invasive structural data of individual patients. Along the example of a patient with bitemporal epilepsy, we show step by step how to develop a Virtual Epileptic Patient (VEP) brain model and integrate patient‐specific information such as brain connectivity, epileptogenic zone and MRI lesions. Using high‐performance computing, we systematically carry out parameter space explorations, fit and validate the brain model against the patients empirical stereotactic EEG (SEEG) data and demonstrate how to develop novel personalized strategies towards therapy and intervention. HighlightsA novel approach to brain interventions is proposed based on personalized large‐scale brain network models.The approach relies on the fusion of structural data of individual patients and mathematical modeling of brain activations.Personalization is achieved by integrating patient specific brain connectivity, epileptogenic zone and MRI lesions.High‐performance computing enables systematic parameter space explorations, fitting and validation of the brain model.Large‐scale brain models foster the development of personalized strategies towards therapy and intervention.

Organic electrochemical transistors for clinical applications

The ability of organic electrochemical transistors is explored to record human electrophysiological signals of clinical relevance. An organic electrochemical transistor (OECT) that shows a high (>1 mS) transconductance at zero applied gate voltage is used, necessitating only one power supply to bias the drain, while the gate circuit is driven by cutaneous electrical potentials. The OECT is successful in recording cardiac rhythm, eye movement, and brain activity of a human volunteer. These results pave the way for applications of OECTs as an amplifying transducer for human electrophysiology.

From perception to recognition memory: Time course and lateralization of neural substrates of word and abstract picture processing

Through study of clinical cases with brain lesions as well as neuroimaging studies of cognitive processing of words and pictures, it has been established that material-specific hemispheric specialization exists. It remains however unclear whether such specialization holds true for all processes involved in complex tasks, such as recognition memory. To investigate neural signatures of transition from perception to recognition, according to type of material (words or abstract pictures), high-resolution scalp ERPs were recorded in adult humans engaged either in categorization or in memory recognition tasks within the same experimental setup. Several steps in the process from perception to recognition were identified. Source localization showed that the early stage of perception processing (N170) takes place in the fusiform gyrus and is lateralized according to the nature of stimuli (left side for words and right side for pictures). Late stages of processing (N400/P600) corresponding to recognition are material independent and involve anterior medial-temporal and ventral prefrontal structures bilaterally. A crucial transitional process between perception (N170) and recognition (N400/P600) is reflected by the N270, an often overlooked component, which occurs in anterior rhinal cortices and shows material-specific hemispheric lateralization.

Automatic detection of fast ripples

OBJECTIVE We propose a new method for automatic detection of fast ripples (FRs) which have been identified as a potential biomarker of epileptogenic processes. METHODS This method is based on a two-stage procedure: (i) global detection of events of interest (EOIs, defined as transient signals accompanied with an energy increase in the frequency band of interest 250-600Hz) and (ii) local energy vs. frequency analysis of detected EOIs for classification as FRs, interictal epileptic spikes or artifacts. For this second stage, two variants were implemented based either on Fourier or wavelet transform. The method was evaluated on simulated and real depth-EEG signals (human, animal). The performance criterion was based on receiving operator characteristics. RESULTS The proposed detector showed high performance in terms of sensitivity and specificity. CONCLUSIONS As designed to specifically detect FRs, the method outperforms any method simply based on the detection of energy changes in high-pass filtered signals and avoids spurious detections caused by sharp transient events often present in raw signals. SIGNIFICANCE In most of epilepsy surgery units, huge data sets are generated during pre-surgical evaluation. We think that the proposed detection method can dramatically decrease the workload in assessing the presence of FRs in intracranial EEGs.

Defining epileptogenic networks: Contribution of SEEG and signal analysis

Epileptogenic networks are defined by the brain regions involved in the production and propagation of epileptic activities. In this review we describe the historical, methodologic, and conceptual bases of this model in the analysis of electrophysiologic intracerebral recordings. In the context of epilepsy surgery, the determination of cerebral regions producing seizures (i.e., the “epileptogenic zone”) is a crucial objective. In contrast with a traditional focal vision of focal drug‐resistant epilepsies, the concept of epileptogenic networks has been progressively introduced as a model better able to describe the complexity of seizure dynamics and realistically describe the distribution of epileptogenic anomalies in the brain. The concept of epileptogenic networks is historically linked to the development of the stereoelectroencephalography (SEEG) method and subsequent introduction of means of quantifying the recorded signals. Seizures, and preictal and interictal discharges produce clear patterns on SEEG. These patterns can be analyzed utilizing signal analysis methods that quantify high‐frequency oscillations or changes in functional connectivity. Dramatic changes in SEEG brain connectivity can be described during seizure genesis and propagation within cortical and subcortical regions, associated with the production of different patterns of seizure semiology. The interictal state is characterized by networks generating abnormal activities (interictal spikes) and also by modified functional properties. The introduction of novel approaches to large‐scale modeling of these networks offers new methods in the goal of better predicting the effects of epilepsy surgery. The epileptogenic network concept is a key factor in identifying the anatomic distribution of the epileptogenic process, which is particularly important in the context of epilepsy surgery.

Whole-brain analytic measures of network communication reveal increased structure-function correlation in right temporal lobe epilepsy.

The in vivo structure-function relationship is key to understanding brain network reorganization due to pathologies. This relationship is likely to be particularly complex in brain network diseases such as temporal lobe epilepsy, in which disturbed large-scale systems are involved in both transient electrical events and long-lasting functional and structural impairments. Herein, we estimated this relationship by analyzing the correlation between structural connectivity and functional connectivity in terms of analytical network communication parameters. As such, we targeted the gradual topological structure-function reorganization caused by the pathology not only at the whole brain scale but also both in core and peripheral regions of the brain. We acquired diffusion (dMRI) and resting-state fMRI (rsfMRI) data in seven right-lateralized TLE (rTLE) patients and fourteen healthy controls and analyzed the structure-function relationship by using analytical network communication metrics derived from the structural connectome. In rTLE patients, we found a widespread hypercorrelated functional network. Network communication analysis revealed greater unspecific branching of the shortest path (search information) in the structural connectome and a higher global correlation between the structural and functional connectivity for the patient group. We also found evidence for a preserved structural rich-club in the patient group. In sum, global augmentation of structure-function correlation might be linked to a smaller functional repertoire in rTLE patients, while sparing the central core of the brain which may represent a pathway that facilitates the spread of seizures.

Modeling of the Neurovascular Coupling in Epileptic Discharges

Despite the interest in simultaneous EEG-fMRI studies of epileptic spikes, the link between epileptic discharges and their corresponding hemodynamic responses is poorly understood. In this context, biophysical models are promising tools for investigating the mechanisms underlying observed signals. Here, we apply a metabolic-hemodynamic model to simulated epileptic discharges, in part generated by a neural mass model. We analyze the effect of features specific to epileptic neuronal activity on the blood oxygen level dependent (BOLD) response, focusing on the issues of linearity in neurovascular coupling and on the origin of negative BOLD signals. We found both sub- and supra-linearity in simulated BOLD signals, depending on whether one observes the early or the late part of the BOLD response. The size of these non-linear effects is determined by the spike frequency, as well as by the amplitude of the excitatory activity. Our results additionally indicate a minor deviation from linearity at the neuronal level. According to a phase space analysis, the possibility to obtain a negative BOLD response to an epileptic spike depends on the existence of a long and strong excitatory undershoot. Moreover, we strongly suggest that a combined EEG-fMRI modeling approach should include spatial assumptions. The present study is a step towards an increased understanding of the link between epileptic spikes and their BOLD responses, aiming to improve the interpretation of simultaneous EEG-fMRI recordings in epilepsy.