Tuning Microelectrodes' Impedance to Improve Fast Ripples Recording.

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.

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.

Towards a mesoscale physical modeling framework for stereotactic-EEG recordings.

Objective.Stereotactic-electroencephalography (SEEG) and scalp EEG recordings can be modeled using mesoscale neural mass population models (NMMs). However, the relationship between those mathematical models and the physics of the measurements is unclear. In addition, it is challenging to represent SEEG data by combining NMMs and volume conductor models due to the intermediate spatial scale represented by these measurements.Approach.We provide a framework combining the multi-compartmental modeling formalism and a detailed geometrical model to simulate the transmembrane currents that appear in layer 3, 5 and 6 pyramidal cells due to a synaptic input. With this approach, it is possible to realistically simulate the current source density (CSD) depth profile inside a cortical patch due to inputs localized into a single cortical layer and the induced voltage measured by two SEEG contacts using a volume conductor model. Based on this approach, we built a framework to connect the activity of a NMM with a volume conductor model and we simulated an example of SEEG signal as a proof of concept.Main results.CSD depends strongly on the distribution of the synaptic inputs onto the different cortical layers and the equivalent current dipole strengths display substantial differences (of up to a factor of four in magnitude in our example). Thus, the inputs coming from different neural populations do not contribute equally to the electrophysiological recordings. A direct consequence of this is that the raw output of NMMs is not a good proxy for electrical recordings. We also show that the simplest CSD model that can accurately reproduce SEEG measurements can be constructed from discrete monopolar sources (one per cortical layer).Significance.Our results highlight the importance of including a physical model in NMMs to represent measurements. We provide a framework connecting microscale neuron models with the neural mass formalism and with physical models of the measurement process that can improve the accuracy of predicted electrophysiological recordings.

Structural brain connectivity in children after neonatal stroke: A whole-brain fixel-based analysis.

INTRODUCTION: Neonatal arterial ischemic stroke (NAIS) has been shown to affect white matter (WM) microstructure beyond the lesion. Here, we employed fixel-based analysis, a technique which allows to model and interpret WM alterations in complex arrangements such as crossing fibers, to further characterize the long-term effects of NAIS on the entire WM outside the primary infarct area. MATERIALS AND METHODS: 32 children (mean age 7.3 years (SD 0.4), 19 male) with middle cerebral artery NAIS (18 left hemisphere, 14 right hemisphere) and 31 healthy controls (mean age 7.7 years (SD 0.6), 16 male) underwent diffusion MRI scans and clinical examination for manual dexterity. Microstructural and macrostructural properties of the WM were investigated in a fixel-based whole-brain analysis, which allows to detect fiber-specific effects. Additionally, tract-averaged fixel metrics in interhemispheric tracts, and their correlation with manual dexterity, were examined. RESULTS: Significantly reduced microstructural properties were identified, located within the parietal and temporal WM of the affected hemisphere, as well as within their interhemispheric connecting tracts. Tract-averaged fixel metrics showed moderate, significant correlation with manual dexterity of the affected hand. No increased fixel metrics or contralesional alterations were observed. DISCUSSION: Our results show that NAIS leads to long-term alterations in WM microstructure distant from the lesion site, both within the parietal and temporal lobes as well as in their interhemispheric connections. The functional significance of these findings is demonstrated by the correlations with manual dexterity. The localization of alterations in structures highly connected to the lesioned areas shift our perception of NAIS from a focal towards a developmental network injury.

A connectome-based approach to assess motor outcome after neonatal arterial ischemic stroke.

OBJECTIVE: Studies of motor outcome after Neonatal Arterial Ischemic Stroke (NAIS) often rely on lesion mapping using MRI. However, clinical measurements indicate that motor deficit can be different than what would solely be anticipated by the lesion extent and location. Because this may be explained by the cortical disconnections between motor areas due to necrosis following the stroke, the investigation of the motor network can help in the understanding of visual inspection and outcome discrepancy. In this study, we propose to examine the structural connectivity between motor areas in NAIS patients compared to healthy controls in order to define the cortical and subcortical connections that can reflect the motor outcome. METHODS: Thirty healthy controls and 32 NAIS patients with and without Cerebral Palsy (CP) underwent MRI acquisition and manual assessment. The connectome of all participants was obtained from T1-weighted and diffusion-weighted imaging. RESULTS: Significant disconnections in the lesioned and contra-lesioned hemispheres of patients were found. Furthermore, significant correlations were detected between the structural connectivity metric of specific motor areas and manuality assessed by the Box and Block Test (BBT) scores in patients. INTERPRETATION: Using the connectivity measures of these links, the BBT score can be estimated using a multiple linear regression model. In addition, the presence or not of CP can also be predicted using the KNN classification algorithm. According to our results, the structural connectome can be an asset in the estimation of gross manual dexterity and can help uncover structural changes between brain regions related to NAIS.

Model-Guided Design of Microelectrodes for HFO Recording.

High Frequency Oscillations (HFOs, 200-600 Hz) are recognized as a biomarker of epileptogenic brain areas. This work aims at designing novel microelectrodes in order to optimize the recording and further detection of HFOs in brain (intracerebral electroencephalography, iEEG). The quality of the recorded iEEG signals is highly dependent on the electrode contact impedance, which is determined by the characteristics of the recording electrode (geometry, position, material). These properties are essential for the observability of HFOs. In this study, a previously published hippocampal neural network model is used for the simulation of interictal HFOs. An additional microelectrode model layer is implemented in order to simulate the impact of using different types and characteristics of microelectrodes on the recorded HFOs. Results indicate that a small layer PEDOT/PSS and PEDOT/CNT on microelectrodes can effectively decrease their impedance resulting in the increase of HFOs observability. This model-based study can lead to the actual design of new electrodes that will ultimately contribute to improved diagnosis prior to invasive therapies.

Is the Blood Oxygenation Level-Dependent fMRI Response to Motor Tasks Altered in Children After Neonatal Stroke?

Functional MRI is increasingly being used in the assessment of brain activation and connectivity following stroke. Many of these studies rely on the Blood Oxygenation Level Dependent (BOLD) contrast. However, the stability, as well as the accuracy of the BOLD response to motor task in the ipsilesional hemisphere, remains ambiguous. In this work, the BOLD signal acquired from both healthy and affected hemispheres was analyzed in 7-year-old children who sustained a Neonatal Arterial Ischemic Stroke (NAIS). Accordingly, a repetitive motor task of the contralesional and the ipsilesional hands was performed by 33 patients with unilateral lesions. These patients were divided into two groups: those without cerebral palsy (NAIS), and those with cerebral palsy (CP). The BOLD signal time course was obtained from distinctly defined regions of interest (ROIs) extracted from the functional activation maps of 30 healthy controls with similar age and demographic characteristics as the patients. An ROI covering both the primary motor cortex (M1) and the primary somatosensory cortex (S1) was also tested. Compared with controls, NAIS patients without CP had similar BOLD amplitude variation for both the contralesional and the ipsilesional hand movements. However, in the case of NAIS patients with CP, a significant difference in the averaged BOLD amplitude was found between the healthy and affected hemisphere. In both cases, no progressive attenuation of the BOLD signal amplitude was observed throughout the task epochs. Besides, results also showed a correlation between the BOLD signal percentage variation of the lesioned hemisphere and the dexterity level. These findings suggest that for patients who sustained a NAIS with no extensive permanent motor impairment, BOLD signal-based data analysis can be a valuable tool for the evaluation of functional brain networks.

Alterations in Cortical Morphology after Neonatal Stroke: Compensation in the Contralesional Hemisphere?

Although neonatal arterial ischemic stroke is now well-studied, its complex consequences on long-term cortical brain development has not yet been solved. In order to understand the brain development after focal early brain lesion, brain morphometry needs to be evaluated using structural parameters. In this work, our aim was to study and analyze the changes in morphometry of ipsi- and contralesional hemispheres in seven-year-old children following neonatal stroke. Therefore, we used surface-based morphometry in order to examine the cortical thickness, surface area, cortical volume, and local gyrification index in two groups of children that suffered from neonatal stroke in the left (n = 19) and right hemispheres (n = 15) and a group of healthy controls (n = 30). Reduced cortical thickness, surface area, and cortical volumes were observed in the ipsilesional hemispheres for both groups in comparison with controls. For the group with left-sided lesions, higher gyrification of the contralesional hemisphere was observed primarily in the occipital region along with higher surface area and cortical volume. As for the group with right-sided lesions, higher gyrification was detected in two separate clusters also in the occipital lobe of the contralesional hemisphere, without a significant change in cortical thickness, surface area, or cortical volume. This is the first time that alterations of structural parameters are detected in the "healthy" hemisphere after unilateral neonatal stroke indicative of a compensatory phenomenon. Moreover, findings presented in this work suggest that lesion lateralization might have an influence on brain development and maturation.

Analysis of the sEMG/force relationship using HD-sEMG technique and data fusion: A simulation study.

The relationship between the surface Electromyogram (sEMG) signal and the force of an individual muscle is still ambiguous due to the complexity of experimental evaluation. However, understanding this relationship should be useful for the assessment of neuromuscular system in healthy and pathological contexts. In this study, we present a global investigation of the factors governing the shape of this relationship. Accordingly, we conducted a focused sensitivity analysis of the sEMG/force relationship form with respect to neural, functional and physiological parameters variation. For this purpose, we used a fast generation cylindrical model for the simulation of an 8×8 High Density-sEMG (HD-sEMG) grid and a twitch based force model for the muscle force generation. The HD-sEMG signals as well as the corresponding force signals were simulated in isometric non-fatiguing conditions and were based on the Biceps Brachii (BB) muscle properties. A total of 10 isometric constant contractions of 5s were simulated for each configuration of parameters. The Root Mean Squared (RMS) value was computed in order to quantify the sEMG amplitude. Then, an image segmentation method was used for data fusion of the 8×8 RMS maps. In addition, a comparative study between recent modeling propositions and the model proposed in this study is presented. The evaluation was made by computing the Normalized Root Mean Squared Error (NRMSE) of their fitting to the simulated relationship functions. Our results indicated that the relationship between the RMS (mV) and muscle force (N) can be modeled using a 3rd degree polynomial equation. Moreover, it appears that the obtained coefficients are patient-specific and dependent on physiological, anatomical and neural parameters.

Investigation of the HD-sEMG probability density function shapes with varying muscle force using data fusion and shape descriptors.

This work presents an evaluation of the High Density surface Electromyogram (HD-sEMG) Probability Density Function (PDF) shape variation according to contraction level. On that account, using PDF shape descriptors: High Order Statistics (HOS) and Shape Distances (SD), we try to address the absence of a consensus for the sEMG non-Gaussianity evolution with force variation. This is motivated by the fact that PDF shape information are relevant in physiological assessment of the muscle architecture and function, such as contraction level classification, in complement to classical amplitude parameters. Accordingly, both experimental and simulation studies are presented in this work. For data fusion, the watershed image processing technique was used. This technique allowed us to find the dominant PDF shape variation profiles from the 64 signals. The experimental protocol consisted of three isometric isotonic contractions of 30, 50 and 70% of the Maximum Voluntary Contraction (MVC). This protocol was performed by six subjects and recorded using an 8 × 8 HD-sEMG grid. For the simulation study, the muscle modeling was done using a fast computing cylindrical HD-sEMG generation model. This model was personalized by morphological parameters obtained by sonography. Moreover, a set of the model parameter configurations were compared as a focused sensitivity analysis of the PDF shape variation. Further, monopolar, bipolar and Laplacian electrode configurations were investigated in both experimental and simulation studies. Results indicated that sEMG PDF shape variations according to force increase are mainly dependent on the Motor Unit (MU) spatial recruitment strategy, the MU type distribution within the muscle, and the used electrode arrangement. Consequently, these statistics can give us an insight into non measurable parameters and specifications of the studied muscle primarily the MU type distribution.

Realistic motor unit placement in a cylindrical HD-sEMG generation model.

The aim of this work is to assess an automatic optimized algorithm for the positioning of the Motor Units (MUs) within a multilayered cylindrical High Density surface EMG (HD-sEMG) generation model representing a skeletal muscle. The multilayered cylinder is composed of three layers: muscle, adipose and skin tissues. For this purpose, two different algorithms will be compared: an unconstrained random and a Mitchell's Best Candidate (MBC) placements, both with uniform distribution for the MUs positions. These algorithms will then be compared by their fiber density within the muscle and by using a classical amplitude descriptor, the Root-Mean-Square (RMS) amplitude value obtained from 64 HD-sEMG signals recorded by an 8×8 electrode grid of circular electrode during one contraction at 70% Maximum Voluntary Contraction (MVC) in both simulation and experimental conditions for the Biceps Brachii (BB) muscle. The obtained results clearly exposed the necessity to use a specific algorithm to place the MUs within the muscle representation volume in agreement with physiology.