Gamma Band Oscillation Response to Somatosensory Feedback Stimulation Schemes Constructed on Basis of Biphasic Neural Touch Representation

abstract: Prosthetic users abandon devices due to difficulties performing tasks without proper graded or interpretable feedback. The inability to adequately detect and correct error of the device leads to failure and frustration. In advanced prostheses, peripheral nerve stimulation can be used to deliver sensations, but standard schemes used in sensorized prosthetic systems induce percepts inconsistent with natural sensations, providing limited benefit. Recent uses of time varying stimulation strategies appear to produce more practical sensations, but without a clear path to pursue improvements. This dissertation examines the use of physiologically based stimulation strategies to elicit sensations that are more readily interpretable. A psychophysical experiment designed to investigate sensitivities to the discrimination of perturbation direction within precision grip suggests that perception is biomechanically referenced: increased sensitivities along the ulnar-radial axis align with potential anisotropic deformation of the finger pad, indicating somatosensation uses internal information rather than environmental. Contact-site and direction dependent deformation of the finger pad activates complimentary fast adapting and slow adapting mechanoreceptors, exhibiting parallel activity of the two associate temporal patterns: static and dynamic. The spectrum of temporal activity seen in somatosensory cortex can be explained by a combined representation of these distinct response dynamics, a phenomenon referred in this dissertation to “biphasic representation.” In a reach-to-precision-grasp task, neurons in somatosensory cortex were found to possess biphasic firing patterns in their responses to texture, orientation, and movement. Sensitivities seem to align with variable deformation and mechanoreceptor activity: movement and smooth texture responses align with potential fast adapting activation, non-movement and coarse texture responses align with potential increased slow adapting activation, and responses to orientation are conceptually consistent with coding of tangential load. Using evidence of biphasic representations’ association with perceptual priorities, gamma band phase locking is used to compare responses to peripheral nerve stimulation patterns and mechanical stimulation. Vibrotactile and punctate mechanical stimuli are used to represent the practical and impractical percepts commonly observed in peripheral nerve stimulation feedback. Standard patterns of constant parameters closely mimic impractical vibrotactile stimulation while biphasic patterns better mimic punctate stimulation and provide a platform to investigate intragrip dynamics representing contextual activation.Dissertation/ThesisDoctoral Dissertation Biomedical Engineering 201

Augmenting Sensorimotor Control Using “Goal-Aware” Vibrotactile Stimulation during Reaching and Manipulation Behaviors

We describe two sets of experiments that examine the ability of vibrotactile encoding of simple position error and combined object states (calculated from an optimal controller) to enhance performance of reaching and manipulation tasks in healthy human adults. The goal of the first experiment (tracking) was to follow a moving target with a cursor on a computer screen. Visual and/or vibrotactile cues were provided in this experiment, and vibrotactile feedback was redundant with visual feedback in that it did not encode any information above and beyond what was already available via vision. After only 10 minutes of practice using vibrotactile feedback to guide performance, subjects tracked the moving target with response latency and movement accuracy values approaching those observed under visually guided reaching. Unlike previous reports on multisensory enhancement, combining vibrotactile and visual feedback of performance errors conferred neither positive nor negative effects on task performance. In the second experiment (balancing), vibrotactile feedback encoded a corrective motor command as a linear combination of object states (derived from a linear-quadratic regulator implementing a trade-off between kinematic and energetic performance) to teach subjects how to balance a simulated inverted pendulum. Here, the tactile feedback signal differed from visual feedback in that it provided information that was not readily available from visual feedback alone. Immediately after applying this novel “goal-aware” vibrotactile feedback, time to failure was improved by a factor of three. Additionally, the effect of vibrotactile training persisted after the feedback was removed. These results suggest that vibrotactile encoding of appropriate combinations of state information may be an effective form of augmented sensory feedback that can be applied, among other purposes, to compensate for lost or compromised proprioception as commonly observed, for example, in stroke survivors

A synergy-based hand control is encoded in human motor cortical areas

How the human brain controls hand movements to carry out different tasks is still debated. The concept of synergy has been proposed to indicate functional modules that may simplify the control of hand postures by simultaneously recruiting sets of muscles and joints. However, whether and to what extent synergic hand postures are encoded as such at a cortical level remains unknown. Here, we combined kinematic, electromyography, and brain activity measures obtained by functional magnetic resonance imaging while subjects performed a variety of movements towards virtual objects. Hand postural information, encoded through kinematic synergies, were represented in cortical areas devoted to hand motor control and successfully discriminated individual grasping movements, significantly outperforming alternative somatotopic or muscle-based models. Importantly, hand postural synergies were predicted by neural activation patterns within primary motor cortex. These findings support a novel cortical organization for hand movement control and open potential applications for brain-computer interfaces and neuroprostheses

A synergy-based hand control is encoded in human motor cortical areas

abstract: How the human brain controls hand movements to carry out different tasks is still debated. The concept of synergy has been proposed to indicate functional modules that may simplify the control of hand postures by simultaneously recruiting sets of muscles and joints. However, whether and to what extent synergic hand postures are encoded as such at a cortical level remains unknown. Here, we combined kinematic, electromyography, and brain activity measures obtained by functional magnetic resonance imaging while subjects performed a variety of movements towards virtual objects. Hand postural information, encoded through kinematic synergies, were represented in cortical areas devoted to hand motor control and successfully discriminated individual grasping movements, significantly outperforming alternative somatotopic or muscle-based models. Importantly, hand postural synergies were predicted by neural activation patterns within primary motor cortex. These findings support a novel cortical organization for hand movement control and open potential applications for brain-computer interfaces and neuroprostheses

I, NEURON: the neuron as the collective

Purpose – In the last half-century, individual sensory neurons have been bestowed with characteristics of the whole human being, such as behavior and its oft-presumed precursor, consciousness. This anthropomorphization is pervasive in the literature. It is also absurd, given what we know about neurons, and it needs to be abolished. This study aims to first understand how it happened, and hence why it persists. Design/methodology/approach – The peer-reviewed sensory-neurophysiology literature extends to hundreds (perhaps thousands) of papers. Here, more than 90 mainstream papers were scrutinized. Findings – Anthropomorphization arose because single neurons were cast as “observers” who “identify”, “categorize”, “recognize”, “distinguish” or “discriminate” the stimuli, using math-based algorithms that reduce (“decode”) the stimulus-evoked spike trains to the particular stimuli inferred to elicit them. Without “decoding”, there is supposedly no perception. However, “decoding” is both unnecessary and unconfirmed. The neuronal “observer” in fact consists of the laboratory staff and the greater society that supports them. In anthropomorphization, the neuron becomes the collective. Research limitations/implications – Anthropomorphization underlies the widespread application to neurons Information Theory and Signal Detection Theory, making both approaches incorrect. Practical implications – A great deal of time, money and effort has been wasted on anthropomorphic Reductionist approaches to understanding perception and consciousness. Those resources should be diverted into more-fruitful approaches. Originality/value – A long-overdue scrutiny of sensory-neuroscience literature reveals that anthropomorphization, a form of Reductionism that involves the presumption of single-neuron consciousness, has run amok in neuroscience. Consciousness is more likely to be an emergent property of the brain

On the structure of natural human movement

Understanding of human motor control is central to neuroscience with strong implications in the fields of medicine, robotics and evolution. It is thus surprising that the vast majority of motor control studies have focussed on human movement in the laboratory while neglecting behaviour in natural environments. We developed an experimental paradigm to quantify human behaviour in high resolution over extended periods of time in ecologically relevant environments. This allows us to discover novel insights and contradictory evidence to well-established findings obtained in controlled laboratory conditions. Using our data, we map the statistics of natural human movement and their variability between people. The variability and complexity of the data recorded in these settings required us to develop new tools to extract meaningful information in an objective, data-driven fashion. Moving from descriptive statistics to structure, we identify stable structures of movement coordination, particularly within the arm-hand area. Combining our data with numerous published findings, we argue that current hypotheses that the brain simplifies motor control problems by dimensionality reduction are too reductionist. We propose an alternative hypothesis derived from sparse coding theory, a concept which has been successfully applied to the sensory system. To investigate this idea, we develop an algorithm for unsupervised identification of sparse structures in natural movement data. Our method outperforms state-of-the-art algorithms for accuracy and data-efficiency. Applying this method to hand data reveals a dictionary of \emph{sparse eigenmotions} (SEMs) which are well preserved across multiple subjects. These are highly efficient and invariant representation of natural movement, and suggest a potential higher-order grammatical structure or ``movement language''. Our findings make a number of testable predictions about neural coding of movement in the cortex. This has direct consequences for advancing research on dextrous prosthetics and robotics, and has profound implications for our understanding of how the brain controls our body.Open Acces

Intrinsic and Extrinsic Biomechanical Factors in a Co-adaptive ECoG-based Brain Computer Interface

Paralysis, due to spinal cord injury, amyotrophic lateral sclerosis (ALS), or stroke, is the result of severed communication between the brain and the motor periphery. Brain computer interfaces (BCIs) are neuroprosthetic devices that create novel communication pathways by measuring and transforming neural activity into operational commands. State of the art BCI systems measure brain activity using penetrating electrode arrays able to record from hundreds of individual cortical neurons simultaneously. Unfortunately, these systems are highly susceptible to signal degradation which limits their efficacy to 1-2 years. However, electrocorticography (ECoG) signals recorded from the surface of the brain deliver a more competitive balance between surgical risk, long-term stability, signal bandwidth, and signal-to-noise ratio when compared to both the aforementioned intracortical systems and the more common non-invasive electroencephalography (EEG) technologies. Historically, neural signals for controlling a computer cursor or robotic arm have been mapped to extrinsic, kinematic (i.e. position or velocity) variables. Although this strategy is adequate for use in simple environments, it may not be ideal for control of real-world prosthetic devices that are subject to external and unexpected forces. When reaching for an object, the trajectory of the hand through space can be defined in either extrinsic (e.g. Cartesian) or intrinsic (e.g. joint angles, muscle forces) frames of reference. During this movement, the brain has to perform a series of sensorimotor transformations that involve solving a complex, 2nd order differential equation (i.e. musculoskeletal biomechanics) in order to determine the appropriate muscle activations. Functional neuromuscular stimulation (FNS) is a desirable BCI application because it attempts to restore motor function to paralyzed limbs through electrical excitation of muscles. Rather than applying the conventional extrinsic kinematic control signals to such a system, it may be more appropriate to map neural activity to muscle activation directly and allow the brain to develop its own transfer function. This dissertation examines the application of intrinsic decoding schemes to control an upper limb using ECoG in non-human primates. ECoG electrode arrays were chronically implanted in rhesus monkeys over sensorimotor cortex. A novel multi-joint reaching task was developed to train the subjects to control a virtual arm simulating muscle and inertial forces. Utilizing a co-adaptive algorithm (where both the brain adapts via biofeedback and the decoding algorithm adapts to improve performance), new decoding models were initially built over the course of the first 3-5 minutes of each daily experimental session and then continually adapted throughout the day. Three subjects performed the task using neural control signals mapped to 1) joint angular velocity, 2) joint torque, and 3) muscle forces of the virtual arm. Performance exceeded 97%, 93%, and 89% accuracy for the three control paradigms respectively. Neural control features in the upper gamma frequency bands (70-115 and 130-175 Hz) were found to be directionally tuned in an ordered fashion, with preferred directions varying topographically in the mediolateral direction without distinction between motor and sensory areas. Long-term stability was demonstrated by all three monkeys, which maintained performance at 42, 55, and 57 months post-implantation. These results provide insights into the capabilities of sensorimotor cortex for control of non-linear multi-joint reaching dynamics and present a first step toward design of intrinsic, force-based BCI systems suitable for long-term FNS applications

Clinical neuroscience and neurotechnology: An amazing symbiosis

In the last decades, clinical neuroscience found a novel ally in neurotechnologies, devices able to record and stimulate electrical activity in the nervous system. These technologies improved the ability to diagnose and treat neural disorders. Neurotechnologies are concurrently enabling a deeper understanding of healthy and pathological dynamics of the nervous system through stimulation and recordings during brain implants. On the other hand, clinical neurosciences are not only driving neuroengineering toward the most relevant clinical issues, but are also shaping the neurotechnologies thanks to clinical advancements. For instance, understanding the etiology of a disease informs the location of a therapeutic stimulation, but also the way stimulation patterns should be designed to be more effective/naturalistic. Here, we describe cases of fruitful integration such as Deep Brain Stimulation and cortical interfaces to highlight how this symbiosis between clinical neuroscience and neurotechnology is closer to a novel integrated framework than to a simple interdisciplinary interaction