Decoding Motor Imagery from the Posterior Parietal Cortex of a Tetraplegic Human

Nonhuman primate and human studies have suggested that populations of neurons in the posterior parietal cortex (PPC) may represent high-level aspects of action planning that can be used to control external devices as part of a brain-machine interface. However, there is no direct neuron-recording evidence that human PPC is involved in action planning, and the suitability of these signals for neuroprosthetic control has not been tested.We recorded neural population activity with arrays of microelectrodes implanted in the PPC of a tetraplegic subject. Motor imagery could be decoded from these neural populations, including imagined goals, trajectories, and types of movement.These findings indicate that the PPC of humans represents high-level, cognitive aspects of action and that the PPC can be a rich source for cognitive control signals for neural prosthetics that assist paralyzed patients

VALIDATION OF A MODEL OF SENSORIMOTOR INTEGRATION WITH CLINICAL BENEFITS

Healthy sensorimotor integration – or how our touch influences our movements – is critical to efficiently interact with our environment. Yet, many aspects of this process are still poorly understood. Importantly, several movement disorders are often considered as originating from purely motor impairments, while a sensory origin could also lead to a similar set of symptoms. To alleviate these issues, we hereby propose a novel biologically-based model of the sensorimotor loop, known as the SMILE model. After describing both the functional, and the corresponding neuroanatomical versions of the SMILE, we tested several aspects of its motor component through functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS). Both experimental studies resulted in coherent outcomes with respect to the SMILE predictions, but they also provided novel scientific outcomes about such broad topics as the sub-phases of motor imagery, the neural processing of bodily representations, or the extend of the role of the extrastriate body area. In the final sections of this manuscript, we describe some potential clinical application of the SMILE. The first one presents the identification of plausible neuroanatomical origins for focal hand dystonia, a yet poorly understood sensorimotor disorder. The last chapter then covers possible improvements on brain-machine interfaces, driven by a better understanding of the sensorimotor system. -- La façon dont votre sens du toucher et vos mouvements interagissent est connue sous le nom d’intégration sensorimotrice. Ce procédé est essentiel pour une interaction normale avec tout ce qui nous entoure. Cependant, plusieurs aspects de ce processus sont encore méconnus. Plus important encore, l’origine de certaines déficiences motrices encore trop peu comprises sont parfois considérées comme purement motrice, alors qu’une origine sensorielle pourrait mener à un même ensemble de symptômes. Afin d’améliorer cette situation, nous proposons ici un nouveau modèle d’intégration sensorimotrice, dénommé « SMILE », basé sur les connaissances de neurobiologie actuelles. Dans ce manuscrit, nous commençons par décrire les caractéristiques fonctionnelles et neuroanatomiques du SMILE. Plusieurs expériences sont ensuite effectuées, via l’imagerie par résonance magnétique fonctionnelle (IRMf), et la stimulation magnétique transcranienne (SMT), afin de tester différents aspects de la composante motrice du SMILE. Si les résultats de ces expériences corroborent les prédictions du SMILE, elles ont aussi mis en évidences d’autres résultats scientifiques intéressants et novateurs, dans des domaines aussi divers que les sous-phases de l’imagination motrice, les processus cérébraux liés aux représentations corporelles, ou encore l’extension du rôle de l’extrastriate body area. Dans les dernières parties de ce manuscrit, nous dévoilons quelques applications cliniques potentielles de notre modèle. Nous utilisons le SMILE afin de proposer deux origines cérébrales plausibles de la dystonie focale de la main. Le dernier chapitre présente comment certaines technologies existantes, telles que les interfaces cerveaux-machines, pourraient bénéficier d’une meilleure compréhension du système sensorimoteur

Turning Science Fiction into Reality: Enhanced Motor Learning for Prosthetic Limbs

In science fiction, prosthetic limbs appear as seamless extensions of the human body that function as if the limbs were made of flesh and bone. With recent technological and scientific advancements, the prosthetic limbs of today are beginning to resemble those we once only imagined. Patients are now able to perform simple, everyday tasks like drinking from a glass of water. However, there are many limitations to this technology, including lack of fine motor movement, absence of reflexes, and missing sensory feedback from the prosthetic limb. These restrictions prohibit prosthetics patients from having the same experience as someone with a biological limb. This paper touches upon the limitations of prosthetics today and applies the findings of current neuroscience research to address these shortcomings to identify potential solutions and areas for further research

How the brain grasps tools: fMRI & motion-capture investigations

Humans’ ability to learn about and use tools is considered a defining feature of our species, with most related neuroimaging investigations involving proxy 2D picture viewing tasks. Using a novel tool grasping paradigm across three experiments, participants grasped 3D-printed tools (e.g., a knife) in ways that were considered to be typical (i.e., by the handle) or atypical (i.e., by the blade) for subsequent use. As a control, participants also performed grasps in corresponding directions on a series of 3D-printed non-tool objects, matched for properties including elongation and object size. Project 1 paired a powerful fMRI block-design with visual localiser Region of Interest (ROI) and searchlight Multivoxel Pattern Analysis (MVPA) approaches. Most remarkably, ROI MVPA revealed that hand-selective, but not anatomically overlapping tool-selective, areas of the left Lateral Occipital Temporal Cortex and Intraparietal Sulcus represented the typicality of tool grasping. Searchlight MVPA found similar evidence within left anterior temporal cortex as well as right parietal and temporal areas. Project 2 measured hand kinematics using motion-capture during a highly similar procedure, finding hallmark grip scaling effects despite the unnatural task demands. Further, slower movements were observed when grasping tools, relative to non-tools, with grip scaling also being poorer for atypical tool, compared to non-tool, grasping. Project 3 used a slow-event related fMRI design to investigate whether representations of typicality were detectable during motor planning, but MVPA was largely unsuccessful, presumably due to a lack of statistical power. Taken together, the representations of typicality identified within areas of the ventral and dorsal, but not ventro-dorsal, pathways have implications for specific predictions made by leading theories about the neural regions supporting human tool-use, including dual visual stream theory and the two-action systems model

The Representation of Multimodal Tactile Sensations in the Human Somatosensory System

The sense of touch is critical to executing basic motor tasks and generating a feeling of embodiment. To construct touch percepts, the brain integrates information from tactile mechanoreceptors with inputs from other senses and top-down variables such as attention and task context. In this thesis, we investigate how these factors influence neural activity within the somatosensory system at different stages of tactile processing, using electrophysiological and behavioral data from a human tetraplegic participant implanted with microelectrode arrays. First, we find that neural responses to imagined touches of different types are decodable in the primary somatosensory cortex, ventral premotor cortex, and the supra-marginal gyrus, and these responses remain stable over many months. Following this analysis, the primary somatosensory cortex is explored in greater depth to better characterize early-stage cortical tactile processing. Touches to the arm and finger are examined during a passive task, in a variety of conditions including visually observed physical touches, physical touches without vision, and visual touches without physical contact. Analysis of the two touch locations suggests that touch encoding in primary somatosensory cortex may be less rigid than in the classical topographic view. Additionally, this experiment uncovers a modulatory effect of vision in the primary somatosensory cortex when it is paired with a physical touch, but no effect of vision alone. Finally, we investigate how visual information impacts artificial tactile sensations, which can be elicited using intra-cortical microstimulation to the primary somatosensory cortex. The ability to elicit reliable, naturalistic artificial touch sensations is vital to the implementation of a tactile brain-machine interface, which would benefit patients with spinal cord injury and others with somatosensory impairments. We find that visual information biases the qualitative percept of artificial stimulation towards an interpretation that is visually plausible. The temporal binding window between vision and stimulation is found to be larger when visual information is biologically relevant, suggesting that the brain’s ability to causally relate artificial stimulation to visual cues depends on visual context. Additionally, recordings from the primary somatosensory cortex indicate that visual information relevant to artificial stimulation is represented across contexts, during an active task. The effect of task on the responsiveness of the primary somatosensory cortex to visual information points to a role of attention in mediating early cortical tactile processing. In combination, the findings presented in this thesis provide insight into the basic neuroscience of how tactile experiences are constructed by the brain, suggesting that early tactile processing is influenced by multisensory, contextual factors. These findings also have clinical applications to developing a brain-machine interface capable of providing naturalistic sensations within a complex real world environment

Neural Prosthetic Advancement: identification of circuitry in the Posterior Parietal Cortex

There are limited options for rehabilitation following an established Spinal Cord Injury (SCI) resulting in paralysis. For most of the individuals affected, SCI means a lifetime of confinement to a wheelchair and overall reduced independence. Brain-Computer and Brain-Machine Interface (BCI and BMI) techniques may be of aid when used for assistive purposes. However, these techniques are still far from being implemented in daily rehabilitative practice. Existing literature on the use of BCI and BMI techniques in SCI is limited and focuses on the extraction of motor control signals from the primary motor cortex (M1). However, evidence suggests that in long-term established SCI the functional activation of motor and premotor areas tends to decrease over time. In the present project, we explore the possibility of successful implementation of assistive BCI and BMI systems using posterior parietal areas as extraction sites of motor control activity. Firstly, we will investigate the representation of space in the posterior parietal cortex (PPC) and whether evidence of body-centered reference frames can be found in healthy individuals. We will then proceed to extract information regarding the residual level of motor imagery activity in individuals suffering from long-term and high-level SCI. Our aim is to ascertain whether functional activation of motor and posterior areas is comparable to that of matched controls. Finally, we will present work that was done in collaboration with the Netherlands Organisation for Applied Scientific Research that can offer an example of successful application of a BCI technique for rehabilitation purposes

Human Ipsilateral Motor Physiology and Neuroprosthetic Applications in Chronic Stroke

Improving the recovery of lost motor function in hemiplegic chronic stroke survivors is a critical need to improve the lives of these patients. Over the last several decades, neuroprosthetic systems have emerged as novel tools with the potential to restore function in a variety of patient populations. While traditional neuroprosthetics have focused on using neural activity contralateral to a moving limb for device control, an alternative control signal may be necessary to develop brain-computer interface (BCI) systems in stroke survivors that suffer damage to the cortical hemisphere contralateral to the affected limb. While movement-related neural activity also occurs in the hemisphere ipsilateral to a moving limb, it is uncertain if these signals can be used within BCI systems. This dissertation examines the motor activity ipsilateral to a moving limb and the potential use of these signals for neuroprosthetic applications in chronic stroke survivors. Patients performed three-dimensional (3D) reaching movements with the arm ipsilateral to an electrocorticography (ECoG) array in order to assess the extent of kinematic information that can be decoded from the cortex ipsilateral to a moving limb. Additionally, patients performed the same task with the arm contralateral to the same ECoG arrays, allowing us to compare the neural representations of contralateral and ipsilateral limb movements. While spectral power changes related to ipsilateral arm movements begin later and are lower in amplitude than power changes related to contralateral arm movements, 3D kinematics from both contralateral and ipsilateral arm trajectories can be decoded with similar accuracies. The ability to decode movement kinematics from the ipsilateral cortical hemisphere demonstrates the potential to use these signals within BCI applications for controlling multiple degrees of freedom. Next we examined the relationship between electrode invasiveness and signal quality. The ability to decode movement kinematics from neural activity was significantly decreased in simulated electroencephalography (EEG) signals relative to ECoG signals, indicating that invasive signals would be necessary to implement BCI systems with multiple degrees of freedom. For ECoG signals, the human dura also causes a significant decrease in signal quality when electrodes with small spatial sizes are used. This tradeoff between signal quality and electrode invasiveness should therefore be taken into account when designing ECoG BCI systems. Finally, chronic stroke survivors used activity associated with affected hand motor intentions, recorded from their unaffected hemisphere using EEG, to control simple BCI systems. This demonstrates that motor signals from the ipsilateral hemisphere are viable for BCI applications, not only in motor-intact patients, but also in chronic stroke survivors. Taken together, these experiments provide initial demonstrations that it is possible to develop BCI systems using the unaffected hemisphere in stroke survivors with multiple degrees of freedom. Further development of these BCI systems may eventually lead to improving function for a significant population of patients

EEG and ECoG features for Brain Computer Interface in Stroke Rehabilitation

The ability of non-invasive Brain-Computer Interface (BCI) to control an exoskeleton was used for motor rehabilitation in stroke patients or as an assistive device for the paralyzed. However, there is still a need to create a more reliable BCI that could be used to control several degrees of Freedom (DoFs) that could improve rehabilitation results. Decoding different movements from the same limb, high accuracy and reliability are some of the main difficulties when using conventional EEG-based BCIs and the challenges we tackled in this thesis. In this PhD thesis, we investigated that the classification of several functional hand reaching movements from the same limb using EEG is possible with acceptable accuracy. Moreover, we investigated how the recalibration could affect the classification results. For this reason, we tested the recalibration in each multi-class decoding for within session, recalibrated between-sessions, and between sessions. It was shown the great influence of recalibrating the generated classifier with data from the current session to improve stability and reliability of the decoding. Moreover, we used a multiclass extension of the Filter Bank Common Spatial Patterns (FBCSP) to improve the decoding accuracy based on features and compared it to our previous study using CSP. Sensorimotor-rhythm-based BCI systems have been used within the same frequency ranges as a way to influence brain plasticity or controlling external devices. However, neural oscillations have shown to synchronize activity according to motor and cognitive functions. For this reason, the existence of cross-frequency interactions produces oscillations with different frequencies in neural networks. In this PhD, we investigated for the first time the existence of cross-frequency coupling during rest and movement using ECoG in chronic stroke patients. We found that there is an exaggerated phase-amplitude coupling between the phase of alpha frequency and the amplitude of gamma frequency, which can be used as feature or target for neurofeedback interventions using BCIs. This coupling has been also reported in another neurological disorder affecting motor function (Parkinson and dystonia) but, to date, it has not been investigated in stroke patients. This finding might change the future design of assistive or therapeuthic BCI systems for motor restoration in stroke patients

Development of a Unique Whole-Brain Model for Upper Extremity Neuroprosthetic Control

Neuroprostheses are at the forefront of upper extremity function restoration. However, contemporary controllers of these neuroprostheses do not adequately address the natural brain strategies related to planning, execution and mediation of upper extremity movements. These lead to restrictions in providing complete and lasting restoration of function. This dissertation develops a novel whole-brain model of neuronal activation with the goal of providing a robust platform for an improved upper extremity neuroprosthetic controller. Experiments (N=36 total) used goal-oriented upper extremity movements with real-world objects in an MRI scanner while measuring brain activation during functional magnetic resonance imaging (fMRI). The resulting data was used to understand neuromotor strategies using brain anatomical and temporal activation patterns. The study\u27s fMRI paradigm is unique and the use of goal-oriented movements and real-world objects are crucial to providing accurate information about motor task strategy and cortical representation of reaching and grasping. Results are used to develop a novel whole-brain model using a machine learning algorithm. When tested on human subject data, it was determined that the model was able to accurately distinguish functional motor tasks with no prior knowledge. The proof of concept model created in this work should lead to improved prostheses for the treatment of chronic upper extremity physical dysfunction