Noise as a useful signal within the nervous system in neurorehabilitation

Focus on the neural interface

Voluntary movement is generated from the interaction between neurons in our brain and the neurons in our spinal cord that engage our muscles. A spinal cord injury destroys the connection between these two regions, but parts of their underlying neural circuits survive. A new class of treatment (the brain-machine interface) takes advantage of this fact by either a) recording neural activity from the brain and predicting the intended movement (neural prosthetics) or b) stimulating neural activity in the spinal cord to facilitate muscle activity (spinal stimulation). This thesis covers new research studying the brain-machine interface and its application for spinal injury. First, the electrical properties of the microelectrode (the main tool of the brain-machine interface) are studied during deep brain recording and stimulation. This work shows that the insulation coating the electrode forms a capacitor with the surrounding neural tissue. This capacitance causes large spikes of voltage in the surrounding tissue during deep brain stimulation, which will cause electrical artifacts in neural recordings and may damage the surrounding neurons. This work also shows that a coaxially shielded electrode will block this effect. Second, the activity of neurons in the parietal cortex is studied during hand movements, which has applications for neural prosthetics. Prior work suggests that the parietal cortex encodes a state-estimator [1], which combines sensory feedback with the internal efference copy to predict the state of the hand. To test this idea, we used a visual lag to misalign sensory feedback from the efference copy. The expectation was that a state-estimator would unknowingly combine the delayed visual feedback with the current efference information, resulting in incorrect predictions of the hand. Our results show a drop in correlation between neural activity in the parietal cortex and hand movement during a visual lag, supporting the idea that the parietal cortex encodes a state-estimator. This correlation gradually recovers over time, showing that parietal cortex is adaptive to sensory delays. Third, while the intention of spinal stimulation was to interact locally with neural circuits in the spinal cord, results from the clinic show that electrical stimulation of the lumbosacral enlargement enables paraplegic patients to regain voluntary movement of their legs [2]. This means that spinal stimulation facilitates communication across an injury site. To further study this effect, we developed a new behavioral task in the rodent. Rats were trained to kick their right hindlimb in response to an auditory cue. The animals then received a spinal injury that caused paraplegia. After injury, the animals recovered the behavior (they could kick in response to the cue), but only during spinal stimulation. Their recovered behavior was slower and more stereotyped than their pre-injury response. Administering quipazine to these rodents disrupted their ability to respond to the cue, suggesting that serotonin plays an important role in the recovered pathway. This work proves that the new behavioral task is a successful tool for studying the recovery of voluntary movement. Future work will combine cortical recordings with this behavioral task in the rodent to study plasticity in the nervous system and improve treatment of spinal cord injuries. [1] Mulliken, Grant H., Sam Musallam, and Richard A. Andersen. Forward estimation of movement state in posterior parietal cortex. Proceedings of the National Academy of Sciences105.24 (2008): 8170-8177. [2] Harkema, Susan, et al. Effect of epidural stimulation of the lumbosacral spinal cord on voluntary movement, standing, and assisted stepping after motor complete paraplegia: a case study. The Lancet 377.9781 (2011): 1938-1947.

Nervous system disorders are among the most severe disorders. Significant breakthroughs in contemporary clinical practice may provide brain-computer interfaces (BCIs) and neuroprostheses (NPs). The aim of this article is to investigate the extent to which the ethical considerations in the clinical application of brain-computer interfaces and associated threats are being identified. Ethical considerations and implications may significantly influence further development of BCIs and NPs. Moreover, there is significant public interest in supervising this development. Awareness of BCIs’ and NPs’ threats and limitations allow for wise planning and management in further clinical practice, especially in the area of long-term neurorehabilitation and care.

This chapter provides an overview of the achieved results with the existing neuroprostheses (NPs) and discusses some of the challenges of this technology which is currently facing to reorganize the brain by creating new neural pathways to regain body function due to brain lesions‚ as tumors‚ stroke‚ traumatic brain injury‚ spinal cord lesion‚ and sensory processing disorders among others; that produce motor disabilities. Neuroprostheses with myoelectric control (motor NPs) have a high potential for restoring or improving motor function as a result of applying Functional Electrical Stimulation (FES) to selected nerves and/or muscles. FES tries to mimic the central nervous system to attain sensory function or muscle activation. Individuals are able to increase their range of motion‚ their reaching ability‚ improve their overall gait and stability‚ and to diminish their spastic pattern. Therefore‚ the main challenge of motor NPs is to achieve the muscle synergies that would result in the desired movement. The burst sequence to apply FES seems to be the key to achieving them. The use of NPs in rehabilitation is often called Functional Electrical Therapy (FET). The stimulation pattern integrated in FET is timed to mimic the sequence of muscle activation in able-bodied subjects. FET is not only for therapeutic purposes but also for assistive activation that enhances neuroplasticity. The review summarizes who could benefit from the new technologies and what are the limitations of the neuroprostheses available today. It is presented the state-of-the-art of the basic architecture of FES used for electrical stimulation of the central nervous system and stimulation of peripheral sensory-motor systems. As well‚ the types of stimulation electrodes either through surface electrodes attached to the skin over nerves‚ percutaneous interface with the target or through electrodes implanted in close proximity to nerves are presented. Examples of motor neuroprostheses technologies include foot-drop and postural NPs‚ vestibular control implant‚ sensory/motor prosthetics‚ as well as‚ specialized software and hardware which support the function of autonomous nervous system‚ increase mobility‚ or improve gait and balance capacities. The coupling of a neuroprosthesis with systems as a Brain–Computer Interface (BCI) based on Electroencephalography signal (EEG) or biofeedback by Electromyography signal (EMG) to record‚ for example muscle activity is explored as an option to the control of motor neuroprostheses. The fact that the user learned to control the BCI in a comparatively short time indicates that this method may also be an alternative approach for clinical purposes to enhance neuroplasticity and sensory patterns. Clinical applications of FES devices‚ and NPs as consequence‚ require extraordinary‚ diverse‚ lengthy and intimate collaborations among basic scientists‚ engineers and clinicians. In this review‚ the most important physiological principles are shown regarding the neuroprosthetics approach and emphasize the role of electrical stimulation in order to achieve desired functional outcomes or to reduce medical problems such as‚ walking disorders‚ spasticity‚ or vertigo. Full restoration function with the current technologies is unlikely in the near future‚ continued research and development in neuroprostheses technology will likely result in a substantial improvement in the quality of life of disabled individuals.

Advanced Functional Materials Solutions to Engineering the Neural Interface

Movement disorder surgery Part I: historical background and principle of surgery

There are a number of residual issues patients face after experiencing even mild closed head injury, as well as moderate and severe injuries, including changes in memory, attention, cognitive processing, and consciousness.– It is believed that neuroprosthetic devices may both facilitate recovery from the basic head injury (i.e., to help the brain heal)– as well as synchronize and activate circuits that are deficient or impaired from damage caused by the head injury.– We will discuss clinical problems associated with traumatic brain injury, depending on severity, and how brain-machine interface (BMI) approaches may help recovery or function, through a combination of brain recording and stimulation systems. The core concept of a brain-machine interface would be to measure the “intent” of the brain to perform an action, such as memory retrieval, motion, or perhaps focusing on an activity., Neuroprosthetic devices could then link the measured “intent” to an internal or external cue or device to facilitate the action. The last link in effective brain control systems and brain-machine interface is an automatic feedback to enhance and fine-tune performance on the task. Similar to intrinsic motor function, for example, where “intent” is defined, then a motor plan has to be internally created (i.e., by the basal ganglia and thalamus), then smoothly executed (by motor cortex, cerebellum and associated motor circuitry), and refinement via external vision or sensory input is critical for improving the function. We discuss several possible treatment routes relative to each level of severity of head injury, and how a feedback, control circuit might be implemented, and critical approaches now available as well as under development.As a parallel to traumatic brain injury, movement disorders treatment has evolved to include a number of regions within the brain, focusing on pathways involved with both motor pattern generation (i.e., basal ganglia in concert with cortical areas involved with generating intent), motor output (i.e., motor cortex), and sensory feedback.– As these areas have been further studied over time, additional subregions have been included as treatment possibilities, for example, the subthalamic nucleus, a new target derived from motor systems analysis in nonhuman primates, but rapidly translated into clinical usefulness.,, There are now at least three interconnected regions that appear to be involved in treatment of abnormal motion and for application of conventional deep brain stimulation (DBS). Interestingly, DBS applied to the three interconnected regions (i.e., globus pallidus, subthalamic nucleus, and ventral intermediate thalamus) provide different types of symptom relief with some overlap. DBS provides essentially a point source of electrical input into the brain (i.e., a 1.5 mm contact point; Figure 18.1) but can affect widespread regions through both direct local neuronal changes but particularly direct stimulation of afferent and efferent axons coursing through each area.– In effect, the point source stimulation that DBS provides can reset nearly the entire brain within a short period of time after activation, showing that dynamic stimulation can have widespread effects (as shown in Figure 18.1). There are multiple efforts ongoing to optimize DBS treatment effects, including improving stimulation patterns, measuring widespread EEG changes (i.e., beta oscillations) and using these as a feedback system to monitor treatment efficacy, and to transform DBS systems into “smart” neuroprosthetic systems, along the lines of brain-machine interface approaches.A natural extension of DBS for movement disorders would then be to optimize both the number of stimulation sites as well as coordinate feedback control, to extend DBS toward a full implementation of brain-machine interface. For example, detecting surrogate signals within the brain (either locally or globally) that might be linked to the movement disorder symptoms (i.e., tremor, rigidity, bradykinesia, etc.), analyzing these signals in real-time, and then altering the DBS output to dynamically route stimulation to both multiple sites and in various patterns to improve the disease symptoms. It is not clear if single or multiple sites of DBS stimulation might be more effective, but more advanced tools are needed for individual patients to assess (in a predictive sense) where to place electrodes and how to stimulate. Similarly, a revolution in epilepsy treatment is ongoing to dynamically record and stimulate seizures, with now a first-generation Neuropace device available (with four recording and four stimulating channels).– The location and predictive efficacy of sites and types of stimulation for both movement disorders and epilepsy is a current strong need, with sufficient brain detail and knowledge of the underlying circuitry to be useful for placing electrodes.

Many applications in recommender systems or experimental design need to make decisions online. Each decision leads to a stochastic reward with initially unknown distribution, while new decisions are made based on the observations of previous rewards. To maximize the total reward, one needs to balance between exploring different strategies and exploiting currently optimal strategies within a given set of strategies. This is the underlying trade-off of a number of clinical neural engineering problems, including brain-computer interface, deep brain stimulation, and spinal cord injury therapy. In these systems, complex electronic and computational systems interact with the human central nervous system. A critical issue is how to control the agents to produce results which are optimal under some measure, for example, efficiently decoding the user's intention in a brain-computer interface or performs temporal and spatial specific stimulation in deep brain stimulation. This dissertation is motivated by electrical sipnal cord stimulation with high dimensional inputs(multi-electrode arrays). The stimulation is applied to promote the function and rehabilitation of the remaining neural circuitry below the spinal cord injury, and enable complex motor behaviors such as stepping and standing. To enable the careful tuning of these stimuli for each patient, the electrode arrays which deliver these stimuli have become increasingly more sophisticated, with a corresponding increase in the number of free parameters over which the stimuli need to be optimized. Since the number of stimuli is growing exponentially with the number of electrodes, algorithmic methods of selecting stimuli is necessary, particularly when the feedback is expensive to get. In many online learning settings, particularly those that involve human feedback, reliable feedback is often limited to pairwise preferences instead of real valued feedback. Examples include implicit or subjective feedback for information retrieval and recommender systems, such as clicks on search results, and subjective feedback on the quality of recommended care. Sometimes with real valued feedback, we require that the sampled function values exceed some prespecified ``safety'' threshold, a requirement that existing algorithms fail to meet. Examples include medical applications where the patients' comfort must be guaranteed; recommender systems aiming to avoid user dissatisfaction; and robotic control, where one seeks to avoid controls that cause physical harm to the platform. This dissertation provides online learning algorithms for several specific online decision-making problems. \selfsparring optimizes the cumulative reward with relative feedback. RankComparison deals with ranking feedback. \safeopt considers the optimization with real valued feedback and safety constraints. \cduel is designed for specific spinal cord injury therapy. A variant of \cduel was implemented in closed-loop human experiments, controlling which epidural stimulating electrodes are used in the spinal cord of SCI patients. The results obtained are compared with concurrent stimulus tuning carried out by human experimenter. These experiments show that this algorithm is at least as effective as the human experimenter, suggesting that this algorithm can be applied to the more challenging problems of enabling and optimizing complex, sensory-dependent behaviors, such as stepping and standing in SCI patients. In order to get reliable quantitative measurements besides comparisons, the standing behaviors of paralyzed patients under spinal cord stimulation are evaluated. The potential of quantifying the quality of bipedal standing in an automatic approach is also shown in this work.

Deep brain stimulation, deontology and duty: the moral obligation of non-abandonment at the neural interface

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.

Compliant semiconductor scaffolds: building blocks for advanced neural interfaces

A translational framework for peripheral nerve stimulating electrodes: Reviewing the journey from concept to clinic

Imagine a world where individuals with lost or impaired sensory or motor function can regain independence and control through technology. This is the promise of neuroprosthetics, a rapidly evolving field that bridges the gap between the nervous system and external devices. Neuroprosthetics encompass a range of implanted or external devices designed to: Substitute for a malfunctioning part of the nervous system. Assist in the recovery or enhancement of lost or impaired function. Augment existing capabilities, creating new possibilities. These devices interact with the nervous system using various methods, including: Electrical stimulation: Directly stimulating nerves or brain tissue to evoke desired responses. Recording brain activity: Capturing electrical signals generated by the brain for further processing and interpretation. Common applications of neuroprosthetics include: Cochlear implants: Restoring hearing in individuals with severe hearing loss. Deep brain stimulation (DBS): Treating movement disorders like Parkinson's disease and essential tremor. Bionic limbs: Providing control of prosthetic arms and legs for individuals with limb loss. Brain-computer interfaces (BCIs): Enabling communication and control of external devices using brain signals alone. Neuroprosthetics offer a glimpse into the future of medicine and technology. With ongoing advancements, these devices have the potential to revolutionize how we treat neurological conditions, restore lost abilities, and even enhance human potential. However, significant challenges remain, including ensuring long-term safety, improving accuracy and reliability, and addressing ethical considerations. As research continues, neuroprosthetics holds immense potential to improve the lives of millions and redefine what it means to be human. The integration of artificial intelligence (AI) with neuroprosthetics has marked a significant milestone in the development of brain-computer interfaces (BCIs). This emerging synergy aims to enhance the quality of life for individuals with disabilities by restoring lost sensory, motor, and cognitive functions. This review article explores the advancements in AI-powered neuroprosthetics for BCIs, focusing on their design, functionality, and the ethical considerations that accompany their integration into medical practice.

Deep Brain Stimulation for OCD in a Patient With Comorbidities: Epilepsy, Tics, Autism, and Major Depressive Disorder.

Chapter 5 - Deep brain stimulation: BCI at large, where are we going to?