Sense of agency for intracortical brain-machine interfaces

Intracortical brain-machine interfaces decode motor commands from neural signals and translate them into actions, enabling movement for paralysed individuals. The subjective sense of agency associated with actions generated via intracortical brain-machine interfaces, the neural mechanisms involved and its clinical relevance are currently unknown. By experimentally manipulating the coherence between decoded motor commands and sensory feedback in a tetraplegic individual using a brain-machine interface, we provide evidence that primary motor cortex processes sensory feedback, sensorimotor conflicts and subjective states of actions generated via the brain-machine interface. Neural signals processing the sense of agency affected the proficiency of the brain-machine interface, underlining the clinical potential of the present approach. These findings show that primary motor cortex encodes information related to action and sensing, but also sensorimotor and subjective agency signals, which in turn are relevant for clinical applications of brain-machine interfaces. Serino et al. studied the sense of agency for actions generated via a brain-machine interface. They show that primary motor cortex encodes not only motor and sensory signals, but also subjective agency signals, enabling improved brain-machine interface proficiency.

Motor and cognitive brain circuits of brain-machine interactions

Silvia Marchesotti

Brain-machine interfaces (BMIs) allow the user to control an external device such as a robotic arm, a cursor, or an avatar in a virtual world through the real-time decoding of brain signals and without the involvement of the musculoskeletal system. Although BMIs hold great promise for providing motor-impaired patients with means of control and interaction with the external world, the neurocognitive mechanisms that are involved in the use of a BMI remain poorly understood. In addition, BMIs allow researchers to investigate and separate the brain processes underlying cognitive functions from those related to motor control and afferent sensory signals that reliably accompany movements. Thus, BMIs are powerful tools for basic and applied neuroscience research. The present work investigates different stages of a BMI-mediated action and targets the subjective, behavioral, and neural signals of BMI control based on electroencephalography (EEG) signals. More specifically, the main study of this thesis has focused on the development of a multimodal imaging platform that allows EEG-based BMI control inside the magnetic resonance imaging (MRI) scanner and during the acquisition of functional MRI (fMRI). This platform has enabled us to exploit both the high temporal resolution of EEG and the high spatial resolution of fMRI to precisely identify the brain mechanisms underlying BMI control and those reflecting the subjective feeling of being in control over a BMI-action (i.e., the sense of agency, SoA). In this study we found an extended cortico-subcortical network involved in operating a motor-imagery BMI. Overall BMI performance was associated with activity in a set of regions including contralateral premotor cortex and the posterior cingulate cortex. Finally, cortical midline regions and the basal ganglia were involved in the subjective sense of controlling the BMI. In a second study, we further investigated whether the ability to control a BMI relates to the ability to perform motor imagery. Despite decades of technical advances, effective BMI-control remains limited to a subset of users. We show that inter-subject variability in BMI proficiency is associated with differences in motor imagery accuracy as captured by subjective and behavioral measurements, pointing to a prominent role of kinesthetic rather than visual imagery. We also identified enhanced lateralized ÎŒ-band oscillations over sensorimotor cortices during motor imagery in high- versus low-aptitude BMI users. Finally, we developed a novel paradigm for joint BMI actions, allowing two users to be jointly engaged in BMI control; our preliminary data provide evidence in support of the hypothesis that joint actions yield BMI-performance improvement even in absence of a physical connection. Our findings also show that during joint BMI-control the SoA over the imagery-mediated actions is significantly enhanced, and is affected by BMI abilities. This work show the potential of applying a multimodal imaging approach to the field of BMI: exploiting our EEG-BMI-fMRI platform we were able to identify, the neural mechanisms involved in motor and cognitive aspects of imagery-mediated BMI. Furthermore, our results enable us to better understand the subjective and behavioral aspects of BMI-actions, and reveal strategies that could potentially reinforce BMI control. Our findings can ultimately be of relevance for the field of neuroprosthetics and make BMI more accessible to a broader range of users.

EPFL2016

Brain-controlled neuroprosthetic interventions to restore locomotion after contusion spinal cord injury in the rat

Marco Bonizzato

Spinal cord injury (SCI), second only to stroke, is the leading cause of paralysis. Therapies based on electrical stimulation of the spinal cord and other locomotor areas of the Nervous System improve motor control in people with neurotrauma. Neuromodulation of the sensorimotor systems can indeed reactivate circuitries that are left dormant after SCI and turn them into an active locomotor state. These results have been found in animal models and translated to clinical fruition. Recent medical research has demonstrated that the involvement of volition and motor intent is a decisive factor for success of rehabilitation therapies that involve functional electrical stimulation of spinal cord and muscles. A key brain area that encodes information related to the conscious processing and execution of movement is the primary sensory-motor cortex. In this thesis I present two novel neuroprosthetic systems for neuromodulation based on cortical population activity in rats with SCI. We tested the hypotheses that neurons in the motor cortex could provide a reliable input for closed-loop neuroprosthetic systems designed to electrically stimulate different locomotor areas of the nervous system in a locomotor-phase-specific manner and thus enhance the locomotor output. During the experiments, we connected cortical correlates of intended movements with patterns of stimulation delivered either at the midbrain or at the sublesional spinal cord. Our brain-controlled functional stimulation reduced locomotor deficits caused by spinal cord injury and restored voluntary control of foot movement. These findings and the control policies we developed could be applied to clinical trials to improve the results of neurorehabilitation, for the benefit of people living with SCI.

EPFL2017

Framework for the Development of Neuroprostheses: From Basic Understanding by Sciatic and Median Nerves Models to Bionic Legs and Hands

Francesco Maria Petrini, Stanisa Raspopovic, Giacomo Valle, Marek Zelechowski

Neuroprostheses based on electrical stimulation are becoming a therapeutic reality, dramatically improving the life of disabled people. They are based on neural interfaces that are designed to create an intimate contact with neural cells. These devices speak the language of electron currents, while the human nervous system uses ionic currents to communicate. A deep understanding of the complex interplay between these currents, during the electrical stimulation, is essential for the development of optimized neuroprostheses. Neural electrodes can have different geometries, placement within the nervous system, and the stimulation protocols (paradigms of use). This high-dimensional problem is not tractable by an empiric, brute-force approach and should be tackled by exact computational models, making use of our accumulated knowledge. In pursuit of this goal, a hybrid finite element method-NEURON modeling-is used for a solution of electrical field generated by stimulation, within the different neural structures having anisotropic conductivity, and a corresponding neural response computation. In this work, an important correction of perineurium electrical conductivity is computed. Models of median and sciatic nerves, innervating the hand and foot areas, relevant to the development of bionic hands and legs with sensory feedback, are implemented. The obtained results have the potential to optimize the design of neural interfaces, in terms of shape and number of stimulating contacts. Guidelines for the neurosurgical planning are proposed, by indicating the optimal number of implants for a specific nerve to obtain the best efficacy with the lowest invasiveness. The interpretation is proposed for one of the basic problems of neural interfaces, consisting in the change of the stimulation threshold due to fibrotic reaction of tissue. We show that it is possible to use human microstimulation as an experimental setup for testing of afferent stimulation paradigms, which can be translated to further chronic implants. In the future, models will have a key role in the decision of the most appropriate design of customized neuroprostheses, their optimal modality of use, understanding the effects that occur during their use, and minimizing animal and human experimentation.