Neuroengineering as an interface for therapeutic development
DOI:
https://doi.org/10.55684/2024.82.e022Keywords:
Electric stimulation therapy, electrophysiology, neural prosthesis, neurological rehabilitation, neurodegenerative diseasesAbstract
Introduction: Nervous system’s cells, particularly neurons, communicate through neurotransmitters and ionic exchanges that generate electrical currents when receiving an external stimulus or when the system itself transmits information through nerve impulses. Considering the principle that this information can be captured, decoded, and used by devices to restore motor and sensory functions, the field of neuroengineering has significantly advanced in recent years. As a multidisciplinary study area, its development requires aligning knowledge of the electrical functioning of the nervous system with engineering and circuits in order to optimize neuroprosthetics to be increasingly efficient, durable, and safe.
Objectives: To map the state of the art in neuroengineering and its nuances based on scientific literature. To identify the main developments, challenges, and opportunities in the future of the field.
Methods: A systematic review of literature on the combination of engineering and neuroscience in therapeutic applications was conducted. English texts published between 2012 and 2022, that met pre-determined inclusion criteria, were considered/accepted using the following terms for the research: Robotic prosthesis, Neuroengineering, Electrophysiology, Robotic movement, Neural decodification, Nervous system engineering, Neurophysiology, Neural prosthesis, and Neuroanatomy.
Results: The review demonstrated that there are established therapeutic approaches based on neuroengineering, such as deep brain stimulation for alleviating Parkinson's symptoms. However, for some neurodegenerative diseases and nervous system injuries, therapeutic-focused neuroprostheses are still in experimental phases or require adjustments to meet user demands and thus achieving greater acceptance and accuracy.
Conclusion: Despite the numerous challenges faced in this early stage of the field’s development, advances in research are already observable due to technological developments allowing the implementation of artificial intelligence, more modern microelectrodes, and a better understanding of the system and adaptation between organism and machine.
References
Thakor NV. Translating the Brain-Machine Interface. Science Translational Medicine. 2013;5(210):210ps17–7. Doi: 10.1126/scitranslmed.
Andersen RA, Musallam S, Pesaran B. Selecting the signals for a brain–machine interface. Current Opinion in Neurobiology. 2004;14(6):720–6. Doi: 10.1016/j.conb.2004.10.005
Hochberg LR, Bacher D, Jarosiewicz B, Masse NY, Simeral JD, Vogel J, et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature. 2012; 485:372–5. Doi: 10.1038/nature11076
Chiappalone M, Cota VR, Carè M, Di Florio M, Beaubois R, Buccelli S, et al. Neuromorphic-Based Neuroprostheses for Brain Rewiring: State-of-the-Art and Perspectives in Neuroengineering. Brain Sciences. 2022;12(11):1578. Doi: 10.3390/brainsci12111578
Ienca M, Kressig RW, Jotterand F, Elger B. Proactive Ethical Design for Neuroengineering, Assistive and Rehabilitation Technologies: the Cybathlon Lesson. Journal of NeuroEngineering and Rehabilitation. 2017;14(1):115. Doi: 10.1186/s12984-017-0325-z
Lebedev MA, Tate AJ, Hanson TL, Li Z, O’Doherty JE, Winans JA, et al. Future developments in brain-machine interface research. Clinics. 2011;66(Suppl 1):25-32. Doi: 10.1590/S1807-59322011001300004
Patil PG, Turner DA. The development of brain-machine interface neuroprosthetic devices. Neurotherapeutics. 2008;5(1):137-46. Doi: 10.1016/j.nurt.2007.11.002
Barha CK, Nagamatsu LS, Liu-Ambrose T. Basics of neuroanatomy and neurophysiology. Handbook of clinical neurology. 2016;138:53-68. Doi: 10.1016/B978-0-12-802973-2.00004-5
Warwick K. Neuroengineering and neuroprosthetics. Brain and Neuroscience Advances. 2018;2: 2398212818817499. Doi: 10.1177/2398212818817499
Potter SM, El Hady A, Fetz EE. Closed-loop neuroscience and neuroengineering. Frontiers in Neural Circuits. 2014;8:115. Doi: 10.3389/fncir.2014.00115
Cheng G, Ehrlich SK, Lebedev M, Nicolelis MAL. Neuroengineering challenges of fusing robotics and neuroscience. Science Robotics. 2020;5(49). Doi: 10.1126/scirobotics.abd1911
Thakor NV. In the Spotlight: Neuroengineering. IEEE Reviews in Biomedical Engineering. 2009; 2:18-20. Doi: 10.1109/RBME.2008.2008231
Dorsey ER, Sherer T, Okun MS, Bloem BR. The Emerging Evidence of the Parkinson Pandemic. Journal of Parkinson’s Disease. 2018;8(s1):S3–8. Doi: 10.3233/JPD-181474
Neumann WJ, Turner RS, Blankertz B, Mitchell T, Kühn AA, Richardson RM. Toward Electrophysiology-Based Intelligent Adaptive Deep Brain Stimulation for Movement Disorders. Neurotherapeutics. 2019;16(1):105–18. Doi: 10.1007/s13311-018-00705-0
Seppich N, Tacca N, Chao KY, Akim M, Hidalgo-Carvajal D, Pozo Fortunić E, et al. CyberLimb: a novel robotic prosthesis concept with shared and intuitive control. Journal of NeuroEngineering and Rehabilitation. 2022;19(1):41. Doi: 10.1186/s12984-022-01016-4
Neumann WJ, Köhler RM, Kühn AA. A practical guide to invasive neurophysiology in patients with deep brain stimulation. Clinical Neurophysiology. 2022;140:171–80. Doi: 10.1016/j.clinph.2022.05.004
Cota VR, Moraes MFD. Editorial: Engineered neuromodulation approaches to treat neurological disorders. Frontiers in Neuroscience. 2022;16. Doi: 10.3389/fnins.2022.1038215
Fiorin FS, e Silva MA, Rodrigues AC. Electrical stimulation in animal models of epilepsy: A review on cellular and electrophysiological aspects. Life Sciences. 2021;285:119972. Doi: 10.1016/j.lfs.2021.119972
Chen S, Wu J, Cai A, Gonzalez N, Yin R. Towards minimally invasive deep brain stimulation and imaging: A near-infrared upconversion approach. Neuroscience Research. 2020;152:59–65. Doi: 10.1016/j.neures.2020.01.005
Moxon KA, Foffani G. Brain-machine interfaces beyond neuroprosthetics. Neuron. 2015;86(1):55–67. Doi: 10.1016/j.neuron.2015.03.036
Adewole DO, Serruya MD, Harris JP, Burrell JC, Petrov D, Chen HI, et al. The Evolution of Neuroprosthetic Interfaces. Critical reviews in biomedical engineering. 2016;44(1-2):123–52. Doi: 10.1615/CritRevBiomedEng.2016017198
Cavanagh JF. Electrophysiology as a theoretical and methodological hub for the neural sciences. Psychophysiology. 2019;56(2). Doi: 10.1111/psyp.13314
Coscia M, Wessel MJ, Chaudary U, Millán J del R, Micera S, Guggisberg A, et al. Neurotechnology-aided interventions for upper limb motor rehabilitation in severe chronic stroke. Brain. 2019;142(8):2182–97. Doi: 10.1093/brain/awz181
Shokoueinejad M, Park DW, Jung YH, Brodnick SK, Novello J, Dingle A, et al. Progress in the Field of Micro-Electrocorticography. Micromachines. 2019;10(1):62. Doi: 10.3390/mi10010062
Formica D, Schena E. Smart Sensors for Healthcare and Medical Applications. Sensors. 2021;21(2):543. Doi: 10.3390/s21020543
Johson MD, Lim HH, Netoff TI, Conolly AT, Johnson N, Abhrajeet R, et al. Neuromodulation for Brain Disorders: Challenges and Opportunities. IEEE Transactions on Biomedical Engineering. 2013;60(3):610-24. Doi: 10.1109/TBME.2013.2244890
Steinmetz NA, Koch C, Harris KD, Carandini M. Challenges and opportunities for large-scale electrophysiology with Neuropixels probes. Current Opinion in Neurobiology. 2018; 50:92-100. Doi: 10.1016/j.conb.2018.01.009
Voitiuk K, Geng J, Keefe MG, Parks DF, Sanso SE, Hawthorne N, et al. Light-weight electrophysiology hardware and software platform for cloud-based neural recording experiments. Journal of Neural Engineering. 2021;12;18(6). Doi: 10.1088/1741-2552/ac310a
Opris I, Lebedev MA, Pulgar VM, Vidu R, Enachescu M, Casanova MF. Editorial: Nanotechnologies in Neuroscience and Neuroengineering. Frontiers in Neuroscience. 2020;14:33. Doi: 10.3389/fnins.2020.00033
Brunton BW, Beyeler M. Data-driven models in human neuroscience and neuroengineering. Current Opinion in Neurobiology. 2019; 58:21-9. Doi: 10.1016/j.conb.2019.06.008
Ghezzi D. Retinal prostheses: progress toward the next generation implants. Frontiers in Neuroscience. 2015; 9:290. Doi: 10.3389/fnins.2015.00290
Monaco AM, Giugliano M. Carbon-based smart nanomaterials in biomedicine and neuroengineering. Beilstein Journal of Nanotechnology. 2014;5:1849-63. Doi: 10.3762/bjnano.5.196
Fiani B, Reardon T, Ayres B, Cline D, Sitto SR. An Examination of Prospective Uses and Future Directions of Neuralink: The Brain-Machine Interface. Cureus. 2021;13(3). Doi: 10.7759/cureus.14192
He F, Lycke R, Ganji M, Xie C, Luan L. Ultraflexible Neural Electrodes for Long-Lasting Intracortical Recording. iScience. 2020;23(8). Doi: 10.1016/j.isci.2020.101387
Steinmetz NA, Aydin C, Lebedeva A, Okun M, Pachitariu M, Bauza M, et al. Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings. Science. 2021;372(6539). Doi: 10.1126/science.abf4588
Bassett DS, Khambhati AN, Grafton ST. Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity. Anual Review of biomedical engineering. 2017;19:327–52. Doi: 10.1146/annurev-bioeng-071516-044511
Cuevas-López A, Pérez-Montoyo E, López‐Madrona VJ, Canals S, Moratal D. Low-Power Lossless Data Compression for Wireless Brain Electrophysiology. Sensors. 2022;22(10):3676. Doi: 10.3390/s22103676
Santaniello S, Gale JT, Sarma SV. Systems approaches to optimizing deep brain stimulation therapies in Parkinson’s disease. Wiley Interdisciplinary Reviews: Systems Biology and Medicine. 2018;10(5):e1421. Doi: 10.1002/wsbm.1421
Bandara DSV, Gopura RARC, Hemapala KTMU, Kiguchi K. Development of a multi-DoF transhumeral robotic arm prosthesis. Medical Engineering & Physics. 2017; 48:131-41. Doi: 10.1016/j.medengphy.2017.06.034
Lum PS, Godfrey SB, Brokaw EB, Holley RJ, Nichols D. Robotic Approaches for Rehabilitation of Hand Function After Stroke. American Journal of Physical Medicine & Rehabilitation. 2012;91(11 suppl 3):S242–54. Doi: 10.1097/PHM.0b013e31826bcedb
Jeong JW, Shin G, Park SI, Yu KJ, Xu L, Rogers JA. Soft Materials in Neuroengineering for Hard Problems in Neuroscience. Neuron. 2015;86(1):175–86. Doi: 10.1016/j.neuron.2014.12.035
Raspopovic S, Valle G, Petrini FM. Sensory feedback for limb prostheses in amputees. Nature Materials. 2021;20:925–39. Doi: 10.1038/s41563-021-00966-9
Won SM, Cai L, Gutruf P, Rogers JA. Wireless and battery-free technologies for neuroengineering. Nature Biomedical Engineering. 2021;7:405-23. Doi: 10.1038/s41551-021-00683-3
Eapen BC, Murphy DP, Cifu DX. Neuroprosthetics in amputee and brain injury rehabilitation. Experimental Neurology. 2017;287:479–85. Doi: 10.1016/j.expneurol.2016.08.004
Ivanenko Y, Ferris DP, Lee K, Sakurai Y, Beloozerova IN, Lebedev M. Editorial: Neural Prostheses for Locomotion. Frontiers in Neuroscience. 2021;15. Doi: 10.3389/fnins.2021.788021
Panuccio G, Semprini M, Natale L, Buccelli S, Colombi I, Chiappalone M. Progress in Neuroengineering for brain repair: New challenges and open issues. Brain and Neuroscience Advances. 2018;2:2398212818776475. Doi: 10.1177/2398212818776475
Kim CK, Adhikari A, Deisseroth K. Integration of optogenetics with complementary methodologies in systems neuroscience. Nature Reviews Neuroscience. 2017;18(4):222–35. Doi: 10.1038/nrn.2017.15
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