Written by: Jacqueline Haring
Edited by: Ryan Lee
Edited by: Ryan Lee
Neurotechnology describes a broad field of electrical devices and interfaces which are designed to monitor and modulate neural activity in the brain and spinal cord. It is a rapidly growing field with high potential for evolving our understanding of the brain and nervous system in functional states as well as being used as a tool for rehabilitation from neural injury and neurodegeneration. The central technologies which have progressed in development for clinical application include methods of electrical stimulation and recording as well as device implantation. This piece will outline the current technologies which have been implemented as treatments for neural injury and disorder as well as the prospects for novel methods being tested within translational clinical research.
Neurotechnology has greatly progressed in terms of its development, and as a result its application has grown within the field of clinical neuroscience throughout the past forty years. Its central objective in the clinical sense is to restore sensory, motor, and cognitive functions in patients with disruptions in these areas (Cometa et al. 2022). The main route of administration of these technologies within the brain include various types of electrodes for recording and stimulation. Different technologies vary in the depth and localization of cortical structures which are targeted. Some of the leading devices include Deep Brain Stimulation (DBS) leads for larger volume subcortical stimulation, microelectrode recording (MER) leads for deeper more localized stimulation, and microelectrode arrays (MEA’s) for cortical recording and intracortical microstimulation for targeted stimulation at the cortical level (Cometa et al. 2022).
DBS has been at the forefront of most exploratory and rehabilitative techniques after its initial rise for treatment of motor diseases in the 1990’s and use as a method to accurately map various regions of subcortical structures and their somatotopic organization. (Cometa et al. 2022). One of the most recent successes in elucidating brain functionality were the DBS recordings acquired from the hypothalamic region in patients with essential tremor syndrome by the Neudorfer group. This study provided evidence that stimulating subzones of the human thalamus has a significant connection with the primary motor cortex homunculus and has the ability to suppress hand tremors upon stimulation (Neurodorfer et al. 2022). DBS is an excellent tool for precise mapping of brain functionality in a noninvasive manner which foregoes the risks of neurosurgery procedures, allowing it to expand into wider areas of clinical research. Prominent novel studies have been directed toward connectivity studies, wherein neuromodulation of said brain networks have shown promise to ameliorate symptoms of disruptions associated with disorders such as Parkinson’s Disease, Tourette Syndrome, and even psychiatric disorders such as treatment resistant depression (Cometa et al. 2022).
Another emerging technology for treating neurological disorders resulting in cognitive, sensory, and motor dysfunction are Brain Machine Interface (BMI) systems. BMI’s are devices which are generally used to artificially generate electrical signals mimicking sensory inputs to the nervous system in those with sensory deficits (Shahdoost et al. 2018). More current BMI’s are being developed which receive the brain’s natural electrical signals as input in order to control additional devices which serve to produce movement in individuals with motor deficits (Shahdoost et al. 2018). Examples of such BMI approaches include Neurochip-2 and neural interface system-on-chips (SoCs), which have some exciting changes in structural approach. Neurochip-2 is an autonomous head mounted interface with external processing of neural and muscle activity, while SoCs are essentially microchips which contain all of the electrical circuitry and parts necessary for the entire BMI system to operate, both of which create a more user friendly device structure (Yoo et al. 2021). However, these devices still require modification to meet safety requirements for implantation, as this method for faster external signal processing has problematic heat dissipation which exceeds safe levels for human contact (Yoo et al. 2021). To rectify these issues, Yoo et al. has proposed a solution: low power closed loop systems and BMIs with Artificial Intelligence (AI) to best develop therapeutic technologies.
An example of a closed loop device currently being investigated is a neural interface with Machine Learning (ML) called On-chip ML. This device allows for real time predictions of disease symptoms in many types of neural disorders such as epileptic seizures, Parkinson’s tremors, and even cognitive changes involved in anxiety and initiates stimulation for therapeutic modification (Yoo et al. 2021). Technologies such as On-chip ML are particularly promising as they address the shortcomings of DBS as they are able to more efficiently target brain structures and do not rely on open-loop delivery methods (Yoo et al. 2021). Further, closed loop stimulation approaches which incorporate disease biomarkers and ML are currently being investigated as a more efficient, power friendly, and adaptable method. For instance, early studies have demonstrated that closed loop stimulation targeting vocal tic onset in individuals with Tourette’s syndrome has shown significantly improved treatment and decreased tic onset compared to methods implementing conventional DBS devices (Yoo et al. 2021). These various studies show great promise for the future of neurotechnology within clinical practice.
It is also important to consider the clinical applications of neurotechnology for disruptions in spinal cord and peripheral nervous system function. Early approaches led to the development of multifaceted neurotechnologies such as lower limb exoskeletons or functional electrical stimulation of muscles (Wagner et al. 2018). However, one of the most promising developments for the proper reorganization of neural pathways needed for rehabilitation after Spinal Cord Injury (SCI) is Epidural Electrical Stimulation (EES) (Wagner et al. 2018). This electrode device has great therapeutic potential, as it functions to directly innervate motor neurons communicating with proprioceptive circuits within the spinal cord with spatiotemporal accuracy in order to assist those with SCI to produce properly coordinated movements despite their chronic paralysis (Wagner et al. 2018).
In a study conducted by Wagner et al. on individuals with SCI resulting in either severe lower limb deficits or paralysis, EES was applied to the spinal cord to to stimulate motor neurons innervating hip, knee, and ankle joints in order to facilitate locomotion (Wagner et al. 2018). Results unveiled that EES induced muscle contraction and augmented excitability of spinal cord motor neurons to restore communication with associated lower limb muscles. Further, the application of a spatiotemporal EES with the assistance of body weight supports allowed these individuals to walk voluntarily under stimulation and even walk on a treadmill for a duration of one hour. Without stimulation they were unable to engage in any locomotive activity (Wagner et al. 2018). This study is tangible evidence supporting neurotechnology as a successful and minimally invasive method for both understanding the mechanism behind neuromuscular disorders and applying technology in order to rectify the dysfunction.
Neurotechnology is a versatile tool which has taken significant strides toward becoming a treatment option for various neurological disorders. The advancement of electrode based technologies targeting cortical regions from broader based DBS methods to more targeted BMI approaches has provided direct evidence of their increasing applicability for treatment of motor and cognitive based neurological diseases. Further, this stimulation can be directed to further extensions of the nervous system to treat the peripheral effects of motor dysfunction as has been shown with the results of EES for SCI. These findings support the argument that there may be a wide expansion in the use of neurotechnology, allowing it to be released from the currently limited experimental structures it currently operates under into general clinical practice to reach a wider population of individuals currently suffering from many neurological disorders.
References
Capogrosso, M., Rowald, A., Seáñez, I., Caban, M., Pirondini, E., Vat, M., McCracken, L. A., Heimgartner, R., Fodor, I., Watrin, A., Seguin, P., Paoles, E., Van Den Keybus, K., Eberle, G., . . . Courtine, G. (2018). Targeted neurotechnology restores walking in humans with spinal cord injury. Nature, 563 (7729), 65–71. https://doi.org/10.1038/s41586-018-0649-2
Cometa, A., Falasconi, A., Biasizzo, M., Carpaneto, J., Horn, A., Mazzoni, A., & Micera, S. (2022). Clinical neuroscience and neurotechnology: An amazing symbiosis. IScience, 25(10), 105124. https://doi.org/10.1016/j.isci.2022.10512.
Neudorfer, C., Kroneberg, D., Al‐Fatly, B., Goede, L., Kübler, D., Faust, K., van Rienen, U., Shahdoost, S., Frost, S. B., Guggenmos, D. J., Borrell, J. A., Dunham, C., Barbay, S., Nudo, R. J., & Mohseni, P. (2018). A brain-spinal interface (BSI) system-on-chip (SoC) for closed-loop cortically-controlled intraspinal microstimulation. Analog Integrated Circuits and Signal Processing, 95(1), 1–16. https://doi.org/10.1007/s10470-017-1093-1
Tietze, A., Picht, T., Herrington, T. M., Middlebrooks, E. H., Kühn, A., Schneider, G., & Horn, A. (2022). Personalizing Deep Brain Stimulation Using Advanced Imaging Sequences. Annals of Neurology, 91(5), 613–628. https://doi.org/10.1002/ana.26326
Wagner, F. B., Mignardot, J. B., Le Goff-Mignardot, C. G., Demesmaeker, R., Komi, S.,
Yoo, J., & Shoaran, M. (2021). Neural interface systems with on-device computing: machine learning and neuromorphic architectures. Current Opinion in Biotechnology, 72, 95–101. https://doi.org/10.1016/j.copbio.2021.10.012
Neurotechnology has greatly progressed in terms of its development, and as a result its application has grown within the field of clinical neuroscience throughout the past forty years. Its central objective in the clinical sense is to restore sensory, motor, and cognitive functions in patients with disruptions in these areas (Cometa et al. 2022). The main route of administration of these technologies within the brain include various types of electrodes for recording and stimulation. Different technologies vary in the depth and localization of cortical structures which are targeted. Some of the leading devices include Deep Brain Stimulation (DBS) leads for larger volume subcortical stimulation, microelectrode recording (MER) leads for deeper more localized stimulation, and microelectrode arrays (MEA’s) for cortical recording and intracortical microstimulation for targeted stimulation at the cortical level (Cometa et al. 2022).
DBS has been at the forefront of most exploratory and rehabilitative techniques after its initial rise for treatment of motor diseases in the 1990’s and use as a method to accurately map various regions of subcortical structures and their somatotopic organization. (Cometa et al. 2022). One of the most recent successes in elucidating brain functionality were the DBS recordings acquired from the hypothalamic region in patients with essential tremor syndrome by the Neudorfer group. This study provided evidence that stimulating subzones of the human thalamus has a significant connection with the primary motor cortex homunculus and has the ability to suppress hand tremors upon stimulation (Neurodorfer et al. 2022). DBS is an excellent tool for precise mapping of brain functionality in a noninvasive manner which foregoes the risks of neurosurgery procedures, allowing it to expand into wider areas of clinical research. Prominent novel studies have been directed toward connectivity studies, wherein neuromodulation of said brain networks have shown promise to ameliorate symptoms of disruptions associated with disorders such as Parkinson’s Disease, Tourette Syndrome, and even psychiatric disorders such as treatment resistant depression (Cometa et al. 2022).
Another emerging technology for treating neurological disorders resulting in cognitive, sensory, and motor dysfunction are Brain Machine Interface (BMI) systems. BMI’s are devices which are generally used to artificially generate electrical signals mimicking sensory inputs to the nervous system in those with sensory deficits (Shahdoost et al. 2018). More current BMI’s are being developed which receive the brain’s natural electrical signals as input in order to control additional devices which serve to produce movement in individuals with motor deficits (Shahdoost et al. 2018). Examples of such BMI approaches include Neurochip-2 and neural interface system-on-chips (SoCs), which have some exciting changes in structural approach. Neurochip-2 is an autonomous head mounted interface with external processing of neural and muscle activity, while SoCs are essentially microchips which contain all of the electrical circuitry and parts necessary for the entire BMI system to operate, both of which create a more user friendly device structure (Yoo et al. 2021). However, these devices still require modification to meet safety requirements for implantation, as this method for faster external signal processing has problematic heat dissipation which exceeds safe levels for human contact (Yoo et al. 2021). To rectify these issues, Yoo et al. has proposed a solution: low power closed loop systems and BMIs with Artificial Intelligence (AI) to best develop therapeutic technologies.
An example of a closed loop device currently being investigated is a neural interface with Machine Learning (ML) called On-chip ML. This device allows for real time predictions of disease symptoms in many types of neural disorders such as epileptic seizures, Parkinson’s tremors, and even cognitive changes involved in anxiety and initiates stimulation for therapeutic modification (Yoo et al. 2021). Technologies such as On-chip ML are particularly promising as they address the shortcomings of DBS as they are able to more efficiently target brain structures and do not rely on open-loop delivery methods (Yoo et al. 2021). Further, closed loop stimulation approaches which incorporate disease biomarkers and ML are currently being investigated as a more efficient, power friendly, and adaptable method. For instance, early studies have demonstrated that closed loop stimulation targeting vocal tic onset in individuals with Tourette’s syndrome has shown significantly improved treatment and decreased tic onset compared to methods implementing conventional DBS devices (Yoo et al. 2021). These various studies show great promise for the future of neurotechnology within clinical practice.
It is also important to consider the clinical applications of neurotechnology for disruptions in spinal cord and peripheral nervous system function. Early approaches led to the development of multifaceted neurotechnologies such as lower limb exoskeletons or functional electrical stimulation of muscles (Wagner et al. 2018). However, one of the most promising developments for the proper reorganization of neural pathways needed for rehabilitation after Spinal Cord Injury (SCI) is Epidural Electrical Stimulation (EES) (Wagner et al. 2018). This electrode device has great therapeutic potential, as it functions to directly innervate motor neurons communicating with proprioceptive circuits within the spinal cord with spatiotemporal accuracy in order to assist those with SCI to produce properly coordinated movements despite their chronic paralysis (Wagner et al. 2018).
In a study conducted by Wagner et al. on individuals with SCI resulting in either severe lower limb deficits or paralysis, EES was applied to the spinal cord to to stimulate motor neurons innervating hip, knee, and ankle joints in order to facilitate locomotion (Wagner et al. 2018). Results unveiled that EES induced muscle contraction and augmented excitability of spinal cord motor neurons to restore communication with associated lower limb muscles. Further, the application of a spatiotemporal EES with the assistance of body weight supports allowed these individuals to walk voluntarily under stimulation and even walk on a treadmill for a duration of one hour. Without stimulation they were unable to engage in any locomotive activity (Wagner et al. 2018). This study is tangible evidence supporting neurotechnology as a successful and minimally invasive method for both understanding the mechanism behind neuromuscular disorders and applying technology in order to rectify the dysfunction.
Neurotechnology is a versatile tool which has taken significant strides toward becoming a treatment option for various neurological disorders. The advancement of electrode based technologies targeting cortical regions from broader based DBS methods to more targeted BMI approaches has provided direct evidence of their increasing applicability for treatment of motor and cognitive based neurological diseases. Further, this stimulation can be directed to further extensions of the nervous system to treat the peripheral effects of motor dysfunction as has been shown with the results of EES for SCI. These findings support the argument that there may be a wide expansion in the use of neurotechnology, allowing it to be released from the currently limited experimental structures it currently operates under into general clinical practice to reach a wider population of individuals currently suffering from many neurological disorders.
References
Capogrosso, M., Rowald, A., Seáñez, I., Caban, M., Pirondini, E., Vat, M., McCracken, L. A., Heimgartner, R., Fodor, I., Watrin, A., Seguin, P., Paoles, E., Van Den Keybus, K., Eberle, G., . . . Courtine, G. (2018). Targeted neurotechnology restores walking in humans with spinal cord injury. Nature, 563 (7729), 65–71. https://doi.org/10.1038/s41586-018-0649-2
Cometa, A., Falasconi, A., Biasizzo, M., Carpaneto, J., Horn, A., Mazzoni, A., & Micera, S. (2022). Clinical neuroscience and neurotechnology: An amazing symbiosis. IScience, 25(10), 105124. https://doi.org/10.1016/j.isci.2022.10512.
Neudorfer, C., Kroneberg, D., Al‐Fatly, B., Goede, L., Kübler, D., Faust, K., van Rienen, U., Shahdoost, S., Frost, S. B., Guggenmos, D. J., Borrell, J. A., Dunham, C., Barbay, S., Nudo, R. J., & Mohseni, P. (2018). A brain-spinal interface (BSI) system-on-chip (SoC) for closed-loop cortically-controlled intraspinal microstimulation. Analog Integrated Circuits and Signal Processing, 95(1), 1–16. https://doi.org/10.1007/s10470-017-1093-1
Tietze, A., Picht, T., Herrington, T. M., Middlebrooks, E. H., Kühn, A., Schneider, G., & Horn, A. (2022). Personalizing Deep Brain Stimulation Using Advanced Imaging Sequences. Annals of Neurology, 91(5), 613–628. https://doi.org/10.1002/ana.26326
Wagner, F. B., Mignardot, J. B., Le Goff-Mignardot, C. G., Demesmaeker, R., Komi, S.,
Yoo, J., & Shoaran, M. (2021). Neural interface systems with on-device computing: machine learning and neuromorphic architectures. Current Opinion in Biotechnology, 72, 95–101. https://doi.org/10.1016/j.copbio.2021.10.012
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