2,431 research outputs found
Totally Implantable Bidirectional Neural Prostheses: A Flexible Platform for Innovation in Neuromodulation.
Implantable neural prostheses are in widespread use for treating a variety of brain disorders. Until recently, most implantable brain devices have been unidirectional, either delivering neurostimulation without brain sensing, or sensing brain activity to drive external effectors without a stimulation component. Further, many neural interfaces that incorporate a sensing function have relied on hardwired connections, such that subjects are tethered to external computers and cannot move freely. A new generation of neural prostheses has become available, that are both bidirectional (stimulate as well as record brain activity) and totally implantable (no externalized connections). These devices provide an opportunity for discovering the circuit basis for neuropsychiatric disorders, and to prototype personalized neuromodulation therapies that selectively interrupt neural activity underlying specific signs and symptoms
Enhancing Nervous System Recovery through Neurobiologics, Neural Interface Training, and Neurorehabilitation.
After an initial period of recovery, human neurological injury has long been thought to be static. In order to improve quality of life for those suffering from stroke, spinal cord injury, or traumatic brain injury, researchers have been working to restore the nervous system and reduce neurological deficits through a number of mechanisms. For example, neurobiologists have been identifying and manipulating components of the intra- and extracellular milieu to alter the regenerative potential of neurons, neuro-engineers have been producing brain-machine and neural interfaces that circumvent lesions to restore functionality, and neurorehabilitation experts have been developing new ways to revitalize the nervous system even in chronic disease. While each of these areas holds promise, their individual paths to clinical relevance remain difficult. Nonetheless, these methods are now able to synergistically enhance recovery of native motor function to levels which were previously believed to be impossible. Furthermore, such recovery can even persist after training, and for the first time there is evidence of functional axonal regrowth and rewiring in the central nervous system of animal models. To attain this type of regeneration, rehabilitation paradigms that pair cortically-based intent with activation of affected circuits and positive neurofeedback appear to be required-a phenomenon which raises new and far reaching questions about the underlying relationship between conscious action and neural repair. For this reason, we argue that multi-modal therapy will be necessary to facilitate a truly robust recovery, and that the success of investigational microscopic techniques may depend on their integration into macroscopic frameworks that include task-based neurorehabilitation. We further identify critical components of future neural repair strategies and explore the most updated knowledge, progress, and challenges in the fields of cellular neuronal repair, neural interfacing, and neurorehabilitation, all with the goal of better understanding neurological injury and how to improve recovery
Central nervous system microstimulation: Towards selective micro-neuromodulation
Electrical stimulation technologies capable of modulating neural activity are well established for neuroscientific research and neurotherapeutics. Recent micro-neuromodulation experimental results continue to explain neural processing complexity and suggest the potential for assistive technologies capable of restoring or repairing of basic function. Nonetheless, performance is dependent upon the specificity of the stimulation. Increasingly specific stimulation is hypothesized to be achieved by progressively smaller interfaces. Miniaturization is a current focus of neural implants due to improvements in mitigation of the body's foreign body response. It is likely that these exciting technologies will offer the promise to provide large-scale micro-neuromodulation in the future. Here, we highlight recent successes of assistive technologies through bidirectional neuroprostheses currently being used to repair or restore basic brain functionality. Furthermore, we introduce recent neuromodulation technologies that might improve the effectiveness of these neuroprosthetic interfaces by increasing their chronic stability and microstimulation specificity. We suggest a vision where the natural progression of innovative technologies and scientific knowledge enables the ability to selectively micro-neuromodulate every neuron in the brain
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Developing Next-generation Brain Sensing Technologies - A Review.
Advances in sensing technology raise the possibility of creating neural interfaces that can more effectively restore or repair neural function and reveal fundamental properties of neural information processing. To realize the potential of these bioelectronic devices, it is necessary to understand the capabilities of emerging technologies and identify the best strategies to translate these technologies into products and therapies that will improve the lives of patients with neurological and other disorders. Here we discuss emerging technologies for sensing brain activity, anticipated challenges for translation, and perspectives for how to best transition these technologies from academic research labs to useful products for neuroscience researchers and human patients
Low-frequency local field potentials in primate motor cortex and their application to neural interfaces
PhD ThesisFor patients with spinal cord injury and paralysis, there are currently very limited options for
clinical therapy. Brain-machine interfaces (BMIs) are neuroprosthetic devices that are being
developed to record from the motor cortex in such patients, bypass the spinal lesion, and use
decoded signals to control an effector, such as a prosthetic limb.
The ideal BMI would be durable, reliable, totally predictable, fully-implantable, and have
generous battery life. Current, state-of-the-art BMIs are limited in all of these domains; partly
because the typical signals used—neuronal action potentials, or ‘spikes’—are very susceptible
to micro-movement of recording electrodes. Recording spikes from the same neurons over
many months is therefore difficult, and decoder behaviour may be unpredictable from day-today. Spikes also need to be digitized at high frequencies (~104 Hz) and heavily processed. As
a result, devices are energy-hungry and difficult to miniaturise. Low-frequency local field
potentials (lf-LFPs; < 5 Hz) are an alternative cortical signal. They are more stable and can be
captured and processed at much lower frequencies (~101 Hz).
Here we investigate rhythmical lf-LFP activity, related to the firing of local cortical neurons,
during isometric wrist movements in Rhesus macaques. Multichannel spike-related slow
potentials (SRSPs) can be used to accurately decode the firing rates of individual motor
cortical neurons, and subjects can control a BMI task using this synthetic signal, as if they
were controlling the actual firing rate. Lf-LFP–based firing rate estimates are stable over time
– even once actual spike recordings have been lost. Furthermore, the dynamics of lf-LFPs are
distinctive enough, that an unsupervised approach can be used to train a decoder to extract
movement-related features for use in biofeedback BMIs. Novel electrode designs may help us
optimise the recording of these signals, and facilitate progress towards a new generation of
robust, implantable BMIs for patients.Research Studentship from the MRC, and Andy Jackson’s laboratory
(hence this work) is supported by the Wellcome Trust
Biointegrated and wirelessly powered implantable brain devices: a review
Implantable neural interfacing devices have added significantly to neural engineering by introducing the low-frequency oscillations of small populations of neurons known as local field potential as well as high-frequency action potentials of individual neurons. Regardless of the astounding progression as of late, conventional neural modulating system is still incapable to achieve the desired chronic in vivo implantation. The real constraint emerges from mechanical and physical diffierences between implants and brain tissue that initiates an inflammatory reaction and glial scar formation that reduces the recording and stimulation quality. Furthermore, traditional strategies consisting of rigid and tethered neural devices cause substantial tissue damage and impede the natural behaviour of an animal, thus hindering chronic in vivo measurements. Therefore, enabling fully implantable neural devices, requires biocompatibility, wireless power/data capability, biointegration using thin and flexible electronics, and chronic recording properties. This paper reviews biocompatibility and design approaches for developing biointegrated and wirelessly powered implantable neural devices in animals aimed at long-term neural interfacing and outlines current challenges toward developing the next generation of implantable neural devices
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
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