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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.

High-density surface electromyography (HD-sEMG) is a recent technique that overcomes the limitations of monopolar and bipolar sEMG recordings and enables the collection of physiological and topographical informations concerning muscle activation. However, HD-sEMG channels are usually contaminated by noise in an heterogeneous manner. The sources of noise are mainly power line interference (PLI), white Gaussian noise (WGN) and motion artifacts (MA). The spectral components of these disruptive signals overlap with the sEMG spectrum which makes classical filtering techniques non effective, especially during low contraction level recordings. In this study, we propose to denoise HD-sEMG recordings at 20 % of the maximum voluntary contraction by using a second-order blind source separation technique, named canonical component analysis (CCA). For this purpose, a specific and automatic canonical component selection, using noise ratio thresholding, and a channel selection procedure for the selective version (sCCA) are proposed. Results obtained from the application of the proposed methods (CCA and sCCA) on realistic simulated data demonstrated the ability of the proposed approach to retrieve the original HD-sEMG signals, by suppressing the PLI and WGN components, with high accuracy (for five different simulated noise dispersions using the same anatomy). Afterward, the proposed algorithms are employed to denoise experimental HD-sEMG signals from five healthy subjects during biceps brachii contractions following an isometric protocol. Obtained results showed that PLI and WGN components could be successfully removed, which enhances considerably the SNR of the channels with low SNR and thereby increases the mean SNR value among the grid. Moreover, the MA component is often isolated on specific estimated sources but requires additional signal processing for a total removal. In addition, comparative study with independent component analysis, CCA-wavelet and CCA-empirical mode decomposition (EMD) proved a higher efficiency of the presented method over existing denoising techniques and demonstrated pointless a second filtering stage for denoising HD-sEMG recordings at this contraction level.

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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.

The Brachialis (BR) is placed under the Biceps Brachii (BB) deep in the upper arm. Therefore, the detection of the corresponding surface Electromyogram (sEMG) is a complex task. The BR is an important elbow flexor, but it is usually not considered in the sEMG based force estimation process. The aim of this study was to attempt to separate the two sEMG activities of the BR and the BB by using a High Density sEMG (HD-sEMG) grid placed at the upper arm and Canonical Component Analysis (CCA) technique. For this purpose, we recorded sEMG signals from seven subjects with two 8 × 4 electrode grids placed over BB and BR. Four isometric voluntary contraction levels were recorded (5, 10, 30 and 50 %MVC) for 90° elbow angle. Then using CCA and image processing tools the sources of each muscle activity were separated. Finally, the corresponding sEMG signals were reconstructed using the remaining canonical components in order to retrieve the activity of the BB and the BR muscles.

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ObjectiveStereotactic-EEG (SEEG) and scalp EEG recordings can be modeled using mesoscale neural mass population models (NMM). 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.ApproachWe provide a framework combining the multi-compartmental modeling formalism and a detailed geometrical model to simulate the transmembrane currents that appear in a 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 consequent 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 as a proof of concept.Main resultsCSD depends strongly on the distribution of the synaptic inputs onto the different cortical layers and the equivalent current dipole strengths display considerable 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 neural mass models 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).SignificanceOur 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.