Biology subjects

Deslauriers-Gauthier, S.

Publications and source records attributed to Deslauriers-Gauthier, S..

8 recordsLinked to original sources

bioRxiv2023

NeuroMotion: Open-source Simulator with Neuromechanical and Deep Network Models to Generate Surface EMG signals during Voluntary Movement

Neuromechanical studies investigate how the nervous system interacts with the musculoskeletal (MSK) system to generate volitional movements. Such studies have been supported by simulation models that provide insights into variables that cannot be measured experimentally and allow a large number of conditions to be tested before the experimental analysis. However, current simulation models of electromyography (EMG), a core physiological signal in neuromechanical analyses, are mainly limited to static contractions and cannot fully represent the dynamic modulation of EMG signals during volitional movements. Here, we overcome these limitations by presenting NeuroMotion, an open-source simulator that provides a full-spectrum synthesis of EMG signals during voluntary movements. NeuroMotion is comprised of three modules. The first module is an upper-limb MSK model with OpenSim API to estimate the muscle fibre lengths and muscle activations during movements. The second module is BioMime, a deep neural network-based EMG generator that receives nonstationary physiological parameter inputs, such as muscle fibre lengths, and efficiently outputs motor unit action potentials (MUAPs). The third module is a motor unit pool model that transforms the muscle activations into discharge timings of motor units. The discharge timings are convolved with the output of BioMime to simulate EMG signals during the movement. Here we also provide representative applications of NeuroMotion. We first show how simulated MUAP waveforms change during different levels of physiological parameter variations and different movements. We then show that the synthetic EMG signals during two-degree-of-freedom hand and wrist movements can be used to augment experimental data for regression. Ridge regressors trained on the synthetic dataset were directly used to predict joint angles from experimental data. NeuroMotion is the first full-spectrum EMG generative model to simulate human forearm electrophysiology during voluntary hand, wrist, and forearm movements. All intermediate variables are available, which allows the user to study cause-effect relationships in the complex neuromechanical system, fast iterate algorithms before collecting experimental data, and validate algorithms that estimate non-measurable parameters in experiments. We expect this full-spectrum model will complement experimental approaches and facilitate neuromechanical research. Author summaryNeuromechanical studies investigate how the nervous system and musculoskeletal system interact to generate movements. Such studies heavily rely on simulation models, which provide non-measurable variables to complement the experimental analyses. However, the simulation models of surface electromyography (EMG), the core physiological signal widely used in neuromechanical analyses, are limited to static conditions. We bridged this gap by proposing NeuroMotion, the first full-spectrum EMG simulator that can be used to generate EMG signals during voluntary movements. NeuroMotion integrates a musculoskeletal model, a neural network-based EMG generator, and an advanced motoneuron model. With representative applications of this simulator, we show that it can be used to investigate the variabilities of EMG signals during voluntary movement. We also demonstrate that the synthetic signals generated by NeuroMotion can be used to augment experimental data for regressing joint angles. We expect the functionality provided by NeuroMotion, which is provided open-source, will stimulate progress in neuromechanics.

bioengineering↗

bioRxiv2022

The length of the thalamo-cortical white matter fibers brings insight into sex differences in sleep spindle frequency

Sleep spindles (SS) are crucial to brain functions like memory and learning. SS characteristics result from the propagation of nerve impulses along white matter (WM) projections underlying an intricate loop between the thalamus and the cortex. SS amplitude and density have been associated with WM diffusion microarchitecture but physiological mechanisms underlying individual and sex-related variations in SS frequency are unknown. Here, we tested a model of traveling signals along the thalamo-cortico-thalamic projections to explain individual differences in spindle frequency. We predicted the presence of a relationship between the length of the thalamo-cortical WM bundles and a specific characteristic of this functional network, SS frequency. Thirty young participants underwent a polysomnographic recording and a 3T MRI including a diffusion sequence. The length of WM fiber bundles between the thalamus and the frontal cortex was derived from probabilistic tractography computed through constrained spherical deconvolution. Longer WM fiber bundles between the thalamus and specific regions of the frontal cortex (rostral middle frontal gyrus and anterior and middle part of the superior frontal gyrus) were associated with slower SS frequency. Moreover, the length of these WM fiber bundles statistically mediated the sex-related differences in SS frequency. By providing a neuroanatomical marker of individual and sex-related differences in SS frequency, this study is the first to highlight the association between the anatomy of a specific brain network and a specific functional characteristic of this network, the frequency of oscillations produced during sleep.

neuroscience↗

bioRxiv2021

Ultra fast and highly realistic numerical modelling of the surface EMG

Muscle electrophysiology has emerged as a powerful tool to drive human machine interfaces, with many new recent applications outside the traditional clinical domains. It is currently a crucial component of control systems in robotics and virtual reality. However, more sophisticated, functional, and robust decoding algorithms are required to meet the fine control requirements of these new applications. Deep learning approaches have shown the highest potential in this regard. To be effective, deep learning requires a large amount of high-quality annotated data for training; the only option today is the use of experimental electromyography data. Yet the acquisition and labelling of training data is time-consuming and expensive. Moreover, the high-quality annotation of this data is often not possible because the ground truth labels are hidden. Data augmentation using simulations, a strategy applied in other deep learning applications, has never been attempted in electromyography due to the absence of computationally efficient and realistic models. Here, we present a new highly realistic and ultra-fast computational model tailored for the training of deep learning algorithms. For the first time, we are able to simulate arbitrary large datasets of realistic electromyography signals with high internal variability and leverage it to train deep learning algorithms. Because the computational model provides access to all the hidden parameters of the simulation, it also allows us to use some annotation strategies that are impossible with experimental data. We believe that this concept of Myoelectric Digital Twin allows new unprecedented approaches to muscular signals decoding and will accelerate the development of human-machine interfaces.

bioengineering↗

bioRxiv2021

A Paradigm Free Regularization Approach to Recover Brain Activation from Functional MRI Data

ContextFunctional MRI is a non-invasive imaging technique that provides an indirect view into brain activity, via the blood-oxygen-level-dependent (BOLD) response. In particular, resting-state fMRI poses challenges to the recovery of brain activity without prior knowledge on the experimental paradigm, as it is the case for task-fMRI. Conventional methods to infer brain activity from the fMRI signals, for example the general linear model (GLM), require the knowledge of the experimental paradigm to define regressors and estimate the contribution of each voxels time course to the task. To overcome this limitation, approaches to deconvolve the BOLD response and recover the underlying neural activation without a priori information on the task have been proposed. State-of-the-art techniques, and in particular the Total Activation (TA), formulates the deconvolution as an optimization problem with decoupled spatial and temporal regularization terms. This increases the number of hyperparameters to be set and requires an optimization strategy that alternates between the constraints. ApproachIn this work, we propose a paradigm-free regularization algorithm named Paradigm-Free fMRI (PF-fMRI) that is applied on the 4-D fMRI image, acting simultaneously in the 3-D space and 1-D time dimensions. Based on the idea that large image variations should be preserved as they occur during brain activation, whereas small variations considered as noise should be removed, the PF-fMRI applies an anisotropic regularization, thus recovering the location and the duration of brain activation. ResultsUsing the experimental paradigm as ground truth, the PF-fMRI is validated on synthetic and real task-fMRI data from 51 subjets, and its performance is compared to the TA. Results show higher correlations of the recovered time-courses with the ground truth compared to the TA and lower computational times. In addition, we show that the PF-fMRI recovers activity that agrees with the GLM, without requiring or using any knowledge of the experimental paradigm.

neuroscience↗

bioRxiv2021

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge.

Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. Most predictions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.

neuroscience↗

bioRxiv2021

Multi-Tissue Multi-Compartment models of diffusion MRI

State-of-the-art multi-compartment microstructural models of diffusion MRI (dMRI) in the human brain have limited capability to model multiple tissues at the same time. In particular, the available techniques that allow this multi-tissue modelling are based on multi-TE acquisitions. In this work we propose a novel multi-tissue formulation of classical multi-compartment models that relies on more common single-TE acquisitions and can be employed in the analysis of previously acquired datasets. We show how modelling multiple tissues provides a new interpretation of the concepts of signal fraction and volume fraction in the context of multi-compartment modelling. The software that allows to inspect single-TE diffusion MRI data with multi-tissue multi-compartment models is included in the publicly available Dmipy Python package.

neuroscience↗

bioRxiv2020

Network alignment and similarity reveal atlas-based topological differences in structural connectomes

The interactions between different brain regions can be modeled as a graph, called connectome, whose nodes correspond to parcels from a predefined brain atlas. The edges of the graph encode the strength of the axonal connectivity between regions of the atlas which can be estimated via diffusion Magnetic Resonance Imaging (MRI) tractography. Herein, we aim at providing a novel perspective on the problem of choosing a suitable atlas for structural connectivity studies by assessing how robustly an atlas captures the network topology across different subjects in a homogeneous cohort. We measure this robustness by assessing the alignability of the connectomes, namely the possibility to retrieve graph matchings that provide highly similar graphs. We introduce two novel concepts. First, the graph Jaccard index (GJI), a graph similarity measure based on the well-established Jaccard index between sets; the GJI exhibits natural mathematical properties that are not satisfied by previous approaches. Second, we devise WL-align, a new technique for aligning connectomes obtained by adapting the Weisfeiler-Lehman (WL) graph-isomorphism test. We validated the GJI and WL-align on data from the Human Connectome Project database, inferring a strategy for choosing a suitable parcellation for structural connectivity studies. Code and data are publicly available. AUTHOR SUMMARYAn important part of our current understanding of the structure of the human brain relies on the concept of brain network, which is obtained by looking at how different brain regions are connected with each other. In this paper we present a strategy for choosing a suitable parcellation of the brain for structural connectivity studies by making use of the concepts of network alignment and similarity. To do so, we design a novel similarity measure between weighted networks called graph Jaccard index, and a new network alignment technique called WL-align. By assessing the possibility to retrieve graph matchings that provide highly similar graphs, we show that morphology- and structure-based atlases define brain networks which are more topologically robust across a wide range of resolutions.

neuroscience↗

bioRxiv2016

Tractography-based connectomes are dominated by false-positive connections

Fiber tractography based on non-invasive diffusion imaging is at the heart of connectivity studies of the human brain. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain dataset with ground truth white matter tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. While most state-of-the-art algorithms reconstructed 90% of ground truth bundles to at least some extent, on average they produced four times more invalid than valid bundles. About half of the invalid bundles occurred systematically in the majority of submissions. Our results demonstrate fundamental ambiguities inherent to tract reconstruction methods based on diffusion orientation information, with critical consequences for the approach of diffusion tractography in particular and human connectivity studies in general.

neuroscience↗