Cortical mapping of the neuronal circuits modulating the muscle tone. Introduction to the electrophysiological treatment of the spastic hand

L'objectiu d'aquest estudi es investigar l'organització cortical junt amb la connectivitat còrtico-subcortical en subjectes sans, com a estudi preliminar. Els mapes corticals s'han fet per TMS navegada, i els punts motors obtinguts s'han exportant per estudi tractogràfic i anàlisi de las seves connexions. El coneixement precís de la localització de l'àrea cortical motora primària i les seves connexions es la base per ser utilitzada en estudis posteriors de la reorganització cortical i sub-cortical en pacients amb infart cerebral. Aquesta reorganització es deguda a la neuroplasticitat i pot ser influenciada per els efectes neuromoduladors de la estimulació cerebral no invasiva.The purpose of this study is to investigate the motor cortex organisation together with the cortico-subcortical connectivity in healthy subjects, as a preliminary study. Cortical maps have been performed by navigated TMS and the motor points have been exported to DTI to study their subcortical connectivity. The precise knowledge of localization of the primary motor cortex area and its connectivity is the base to be used in later studies of cortical and subcortical re-organisation in stroke patients. This re-organisation is due to the neuroplascity and can be influenced by the neuromodulation effects of the non-invasive cerebral stimulation therapy by TMS

MEG Source Imaging and Group Analysis Using VBMEG

Variational Bayesian Multimodal EncephaloGraphy (VBMEG) is a MATLAB toolbox that estimates distributed source currents from magnetoencephalography (MEG)/electroencephalography (EEG) data by integrating functional MRI (fMRI) (https://vbmeg.atr.jp/). VBMEG also estimates whole-brain connectome dynamics using anatomical connectivity derived from a diffusion MRI (dMRI). In this paper, we introduce the VBMEG toolbox and demonstrate its usefulness. By collaborating with VBMEG's tutorial page (https://vbmeg.atr.jp/docs/v2/static/vbmeg2_tutorial_neuromag.html), we show its full pipeline using an open dataset recorded by Wakeman and Henson (2015). We import the MEG data and preprocess them to estimate the source currents. From the estimated source currents, we perform a group analysis and examine the differences of current amplitudes between conditions by controlling the false discovery rate (FDR), which yields results consistent with previous studies. We highlight VBMEG's characteristics by comparing these results with those obtained by other source imaging methods: weighted minimum norm estimate (wMNE), dynamic statistical parametric mapping (dSPM), and linearly constrained minimum variance (LCMV) beamformer. We also estimate source currents from the EEG data and the whole-brain connectome dynamics from the MEG data and dMRI. The observed results indicate the reliability, characteristics, and usefulness of VBMEG

Brachial Plexus Injury

In this book, specialists from different countries and continents share their knowledge and experience in brachial plexus surgery. It discusses the different types of brachial plexus injury and advances in surgical treatments

Heterogeneous data fusion for brain psychology applications

This thesis aims to apply Empirical Mode Decomposition (EMD), Multiscale Entropy (MSE), and collaborative adaptive filters for the monitoring of different brain consciousness states. Both block based and online approaches are investigated, and a possible extension to the monitoring and identification of Electromyograph (EMG) states is provided. Firstly, EMD is employed as a multiscale time-frequency data driven tool to decompose a signal into a number of band-limited oscillatory components; its data driven nature makes EMD an ideal candidate for the analysis of nonlinear and non-stationary data. This methodology is further extended to process multichannel real world data, by making use of recent theoretical advances in complex and multivariate EMD. It is shown that this can be used to robustly measure higher order features in multichannel recordings to robustly indicate ‘QBD’. In the next stage, analysis is performed in an information theory setting on multiple scales in time, using MSE. This enables an insight into the complexity of real world recordings. The results of the MSE analysis and the corresponding statistical analysis show a clear difference in MSE between the patients in different brain consciousness states. Finally, an online method for the assessment of the underlying signal nature is studied. This method is based on a collaborative adaptive filtering approach, and is shown to be able to approximately quantify the degree of signal nonlinearity, sparsity, and non-circularity relative to the constituent subfilters. To further illustrate the usefulness of the proposed data driven multiscale signal processing methodology, the final case study considers a human-robot interface based on a multichannel EMG analysis. A preliminary analysis shows that the same methodology as that applied to the analysis of brain cognitive states gives robust and accurate results. The analysis, simulations, and the scope of applications presented suggest great potential of the proposed multiscale data processing framework for feature extraction in multichannel data analysis. Directions for future work include further development of real-time feature map approaches and their use across brain-computer and brain-machine interface applications

Investigation of differences in cortical activation during wrist flexion and extension performed under real, passive and motor imagined paradigms

The neuromuscular control comparison between flexion and extension of the upper extremities has been conducted in a number of studies. It has been speculated that differences in the corticospinal pathway between flexion and extension may play a role in the cortical difference detected between flexion and extension, resulting in higher cortical activation for extension. However, it is still unclear as to what roles these pathways play, and to what degree other factors (muscle force activation, sensory feedback, frequency of movement, structural and/or functional differences) might influence the cortical activation in the brain. It has been speculated that the difference in cortical muscular pathways is due to flexion movements being used more often in day to day activities, therefore requiring less cortical activation for that movement. Through the investigation of the cortical differences present during different movement types, a deeper understanding into the differences between flexion and extension may be obtained. No previous study has compared the cortical differences between flexion and extension of the upper extremities during different movement types. In this study, an offline investigation is conducted between wrist flexion and extension, during real, passive and motor imaginary movement with the help of a servo controlled hand device. Simultaneous recording of EEG, EMG and wrist dynamics (velocity, angle, strain) were made on fifteen healthy right handed subjects performing 60 randomized repartitions of right wrist flexion and extension, for kinaesthetic motor imaginary, passively moved, and voluntary real active movements. Real movements were conducted at 10% relative subject maximum voluntary contraction (MVC). A servo controlled hand device was used to regulate dynamic force applied for real movements, and provide motion during passive movements. The use of different movement types with the aid of a servo controlled hand device, may give a deeper understanding into the effects of muscle force activation, rate of movement and corticospinal pathway on flexion and extension. In order to investigate the cortical differences between flexion and extension, subjects perceived difficulty, movement dynamics, movement related cortical potential (MRCP), event related desynchronization and synchronization (ERD/ERS), and phase locking value (PLV) were measured. Each measurement examines a different aspect of the cortical activation present in the brain, during the different movement types. Although relative muscle force activation between wrist real flexion and extension was similar, the motor cortex activation during extension was higher than during flexion, by MRCP and mu-band ERD, with subjects also perceiving real wrist extension to be more difficult to perform. Passive movements found higher motor cortex activation for flexion (MRCP, beta-band ERD), however higher somatosensory cortical activation was present during extension, by mu-band ERS and PLV. Motor imagined wrist flexion showed higher cortical activation during wrist flexion, by MRCP and beta-band ERD. Although numerous variables were tested (each in difference frequency bands), with some being significant and others being non-significant, overall it can be suggested that there was higher cortical activation for extension. The higher cortical activation during wrist extension movements may be due to corticospinal and somatosensory motor control pathways to motor neuron and from sensory neuron pools for extensor/flexor muscle and muscle spindle of the upper extremities. This investigation contributes to the current literature relating to cortical differences between flexion and extension of the upper extremities, by including the real, passive and motor imaginary differences between flexion and extension

Neurotechnology for Brain Repair:Imaging, Enhancing and Restoring Human Motor Function

Neurotechnology is the application of scientific knowledge to the practical purpose of understanding, interacting and/or repairing the brain or, in a broader sense, the nervous system. The development of novel approaches to decode functional information from the brain, to enhance specific properties of neural tissue and to restore motor output in real end-users is a fundamental challenge to translate these novel solutions into clinical practice. In this Thesis, I introduce i) a novel imaging method to characterize movement-related electroencephalographic (EEG) potentials; ii) a brain stimulation strategy to improve brain-computer interface (BCI) control; iii) and a therapy for motor recovery involving a neuroprosthesis. Overall, results show i) that stable EEG topographies present a subject-independent organization that can be used to robustly decode actual or attempted movements in sub-acute stroke patients and healthy controls, with minimal a-priori information; ii) that transcranial direct-current stimulation (tDCS) enhances the modulability of sensorimotor rhythms used for brain-computer interaction in chronic Spinal Cord Injured (SCI) individuals and healthy controls; iii) that neuromuscular electrical stimulation (NMES) controlled via closed-loop neural activity induces significantly stronger upper limb functional recovery in chronic stroke patients than sham NMES therapy, and that these changes are clinically relevant. These results have or might have important implications in i) disease diagnostics and monitoring through EEG; ii) assistive technology and reduction of permanent disability following SCI; iii) rehabilitation and recovery of upper limb function following a stroke, also after several years of complete paralysis. Briefly, this Thesis provides the conceptual framework, scientific rationale, technical details and clinical evidence supporting translational Neurotechnology that improves, optimizes and disrupts current medical practice in monitoring, substituting and recovering lost upper limb function

Neurophysiological Adaptations to Resistance Training and Repetitive Grasping

Perhaps the most prominent feature of the central nervous system is its ability to respond to experience and its environment. Understanding the processes and mechanisms that govern adaptive behavior provides insights into its plastic nature. Capitalizing on this plasticity is of critical importance in response to injury and recovery: 35, 106), and the importance of its promotion is increasingly recognized by rehabilitation scientists. Neurophysiological techniques permitting study of cortical function in vivo may play a significant role in validating exercise interventions and disease management approaches: 14). It may be possible that with these advances we may better understand the relationship between brain function and therapeutic approaches. For this purpose, we present data on both cumulative and acute effects of motor training to better understand adaptive processes. Neural adaptations accompany resistance training, but current evidence regarding the nature of these adaptations is best characterized as indirect, particularly with respect to adaptation within central or supraspinal centers: 56). To this end, we recorded movement-related cortical potentials: MRCP), i.e. electroencephalography: EEG)-derived event-related potentials, in healthy adults prior to and following a program of lower body resistance training. The cumulative effects of nine progressive training sessions resulted in attenuation of relative MRCP amplitudes. We interpreted these findings in terms of neural efficiency such that for the same pre-training load, central effort is diminished post-training. These data demonstrate the impact of cumulative motor training sessions in fostering a reduction in the level of cortical motor activation. Such a program may be of a particular utility for individuals with limited motor reserves such as those with Parkinson disease: PD). Although cumulative effects may foster a more efficient cortical network, the acute demands of a training session have received less attention. It is reasonable to assume that the reverse might be expected: i.e. augmented amplitude) during a motor training session, much like the muscular system is taxed during resistance training exercise. At the level of the cortex, neural activity was studied by recording the MRCP during 150 repetitive handgrip contractions at a high intensity. The goal of this work was to examine whether central adaptive processes used to maintain task performance vary as a function of age or PD. We found that for healthy young adults, augmented activation of motor cortical centers is responsible for maintaining performance. However, this was not observed for older adults with and without PD, where minimal changes in cortical activity were observed over the duration of the protocol. Our findings suggest that older adults and those with PD may rely on alternative mechanisms: i.e. mobilization of additional cortical and subcortical structures) to maintain task performance as compared to increasing activity locally as seen with younger adults. Taken together, our work further supports the adaptable nature of the central nervous system. We note in passing the utility of the MRCP paradigm for observing such effects

A non-invasive human-machine interfacing framework for investigating dexterous control of hand muscles

The recent fast development of virtual reality and robotic assistive devices enables to augment the capabilities of able-body individuals as well as to overcome the motor missing functions of neurologically impaired or amputee individuals. To control these devices, movement intentions can be captured from biological structures involved in the process of motor planning and execution, such as the central nervous system (CNS), the peripheral nervous system (in particular the spinal motor neurons) and the musculoskeletal system. Thus, human-machine interfaces (HMI) enable to transfer neural information from the neuro-muscular system to machines. To prevent any risks due to surgical operations or tissue damage in implementing these HMIs, a non-invasive approach is proposed in this thesis. In the last five decades, surface electromyography (sEMG) has been extensively explored as a non-invasive source of neural information. EMG signals are constituted by the mixed electrical activity of several recruited motor units, the fundamental components of muscle contraction. High-density sEMG (HD-sEMG) with the use of blind source separation methods enabled to identify the discharge patterns of many of these active motor units. From these decomposed discharge patterns, the net common synaptic input (CSI) to the corresponding spinal motor neurons was quantified with cross-correlation in the time and frequency domain or with principal component analysis (PCA) on one or few muscles. It has been hypothesised that this CSI would result from the contribution of spinal descending commands sent by supra-spinal structures and afferences integrated by spinal interneurons. Another motor strategy implying the integration of descending commands at the spinal level is the one regarding the coordination of many muscles to control a large number of articular joints. This neurophysiological mechanism was investigated by measuring a single EMG amplitude per muscle, thus without the use of HD-sEMG and decomposition. In this case, the aim was to understand how the central nervous system (CNS) could control a large set of muscles actuating a vast set of combinations of degrees of freedom in a modular way. Thus, time-invariant patterns of muscle coordination, i.e. muscle synergies , were found in animals and humans from EMG amplitude of many muscles, modulated by time-varying commands to be combined to fulfil complex movements. In this thesis, for the first time, we present a non-invasive framework for human-machine interfaces based on both spinal motor neuron recruitment strategy and muscle synergistic control for unifying the understanding of these two motor control strategies and producing control signals correlated to biomechanical quantities. This implies recording both from many muscles and using HD-sEMG for each muscle. We investigated 14 muscles of the hand, 6 extrinsic and 8 intrinsic. The first two studies, (in Chapters 2 and 3, respectively) present the framework for CSI quantification by PCA and the extraction of the synergistic organisation of spinal motor neurons innervating the 14 investigated muscles. For the latter analysis, in Chapter 3, we proposed the existence of what we named as motor neuron synergies extracted with non-negative matrix factorisation (NMF) from the identified motor neurons. In these first two studies, we considered 7 subjects and 7 grip types involving differently all the four fingers in opposition with the thumb. In the first study, we found that the variance explained by the CSI among all motor neuron spike trains was (53.0 ± 10.9) % and its cross-correlation with force was 0.67 ± 0.10, remarkably high with respect to previous findings. In the second study, 4 motor neuron synergies were identified and associated with the actuation of one finger in opposition with the thumb, finding even higher correlation values with force (over 0.8) for each synergy associated with a finger during the actuation of the relative finger. In Chapter 4, we then extended the set of analysed movements in a vast repertoire of gestures and repeated the analysis of Chapter 3 by finding a different synergistic organisation during the execution of tens of tasks. We divided the contribution among extrinsic and intrinsic muscles and we found that intrinsic better enable single-finger spatial discrimination, while no difference was found in regression of joint angles by dividing the two groups of muscles. Finally, in Chapter 5 we proposed the techniques of the previous chapters for cases of impairment due both to amputation and stroke. We analysed one case of pre and post rehabilitation sessions of a trans-humeral amputee, the case of a post-stroke trans-radial amputee and three cases of acute stroke, i.e. less than one month from the stroke event. We present future perspectives (Chapter 6) aimed to design and implement a platform for both rehabilitation monitoring and myoelectric control. Thus, this thesis provides a bridge between two extensively studied motor control mechanisms, i.e. motor neuron recruitment and muscle synergies, and proposes this framework as suitable for rehabilitation monitoring and control of assistive devices.Open Acces

Dynamic Information Flow Based on EEG and Diffusion MRI in Stroke: A Proof-of-Principle Study

In hemiparetic stroke, functional recovery of paretic limb may occur with the reorganization of neural networks in the brain. Neuroimaging techniques, such as magnetic resonance imaging (MRI), have a high spatial resolution which can be used to reveal anatomical changes in the brain following a stroke. However, low temporal resolution of MRI provides less insight of dynamic changes of brain activity. In contrast, electro-neurophysiological techniques, such as electroencephalography (EEG), have an excellent temporal resolution to measure such transient events, however are hindered by its low spatial resolution. This proof-of-principle study assessed a novel multimodal brain imaging technique namely Variational Bayesian Multimodal Encephalography (VBMEG), which aims to improve the spatial resolution of EEG for tracking the information flow inside the brain and its changes following a stroke. The limitations of EEG are complemented by constraints derived from anatomical MRI and diffusion weighted imaging (DWI). EEG data were acquired from individuals suffering from a stroke as well as able-bodied participants while electrical stimuli were delivered sequentially at their index finger in the left and right hand, respectively. The locations of active sources related to this stimulus were precisely identified, resulting in high Variance Accounted For (VAF above 80%). An accurate estimation of dynamic information flow between sources was achieved in this study, showing a high VAF (above 90%) in the cross-validation test. The estimated dynamic information flow was compared between chronic hemiparetic stroke and able-bodied individuals. The results demonstrate the feasibility of VBMEG method in revealing the changes of information flow in the brain after stroke. This study verified the VBMEG method as an advanced computational approach to track the dynamic information flow in the brain following a stroke. This may lead to the development of a quantitative tool for monitoring functional changes of the cortical neural networks after a unilateral brain injury and therefore facilitate the research into, and the practice of stroke rehabilitation

Neuromodulation and rehabilitation with brain-computer interfaces and Spinal Cord Stimulation

Consequences of spinal cord injury (SCI) are often severe and life-altering. Recovery of hand and arm function is consistently reported by SCI individuals as their greatest priority in terms of rehabilitation. Yet current strategies provide poor-to-modest outcomes. Innovation is required to improve traditional approaches to upper limb rehabilitation. The current view is that, due to the multi-faceted nature of SCI pathology, effective treatment will take a combinational approach. This thesis brings together two emerging and promising technologies—transcutaneous spinal cord stimulation (tSCS) and brain-computer interfaces (BCIs)—in order to judge their complimentary nature as tools for neurophysiological assessment and rehabilitation following SCI. There is growing evidence that cervical tSCS combined with intensive physical training can lead to lasting functional improvements in individuals with chronic SCI. The mechanisms underpinning tSCS-facilitated recovery, however, are still a matter of ongoing research, with conflicting reports of the impact of tSCS on cortical and spinal excitability. Evoked and reflexes have so far been the primary method of quantifying corticospinal excitability. The research undertaken in this thesis first explores electroencephalography (EEG) as a potential complementary method for assessing neuromodulation following tSCS. Due the novelty of the research, a preliminary investigation was undertaken to establish the feasibility of EEG monitoring during cervical tSCS. In a cohort of twenty-one able-bodied individuals, it was demonstrated that tSCS presented as low-latency, high-amplitude artefacts in EEG time series, at a rate equal to the stimulation frequency. Descriptive statistics were used to characterise the impact of tSCS, and judge the effectiveness of noise-attenuation techniques. Results showed that, with artefact-suppression, EEG recorded during tSCS could be returned to levels statistically similar to that of EEG acquired without tSCS interference. Additionally, it was established that neural components, such as the individual alpha frequency, were recoverable, demonstrating the feasibility of EEG as a tool for tracking cortical activity during tSCS. A subsequent study was conducted to investigate the neuromodulatory potential of tSCS on cortical activity. EEG was recorded during upper limb movements in 30 individuals both with and without concurrent cervical tSCS. Stimulation was delivered to the cervical region of the neck at intensities matching the individual’s highest tolerance without causing pain. It was found that cortical oscillatory dynamics were unaffected over a cohort of neurologically intact participants. However, a weak inhibitory effect was measured amoing individuals who received the highest stimulation intensities. A final study was devised to explore the potential of movement priming for tSCS-facilitated upper limb therapy in an individual with chronic AIS A cervical SCI. Movement priming was achieved by encouraging the participant to engage in repetitive bimanual hand movements with respect to their sensorimotor cortical activity as measured with EEG. A BCI provided real time feedback of the participant’s motor engagement in the form of a computer game, allowing them to actively engage regardless of impairment level. The participant first underwent an initial phase of 15 sessions of tSCS training alone followed by a second phase of 15 sessions of BCI priming and tSCS training. The participant’s strength and dexterity improved across both phases of the study. BCI priming may have contributed to an enhanced effect in some measures such as improved bilateral finger strength, but due to mixed results across functional measures no firm conclusions can be drawn. Nevertheless, the functional improvements lend greater credibility to cervical tSCS as a strategy for upper limb rehabilitation