You are viewing AANS Neurosurgeon Volume 27, Number 3, 2018. View our current issue, Volume 27, Number 4, 2018

AANS Neurosurgeon | Volume 27, Number 3, 2018

Advertisement

Predicting the Future of Neurosurgery and Neural Interfacing

The pace of innovation and the breadth of integration of technologies into neurosurgery have been accelerating with the field of functional neurosurgery poised at an inflection point. Patients can anticipate a significant increase in innovation and impact on a wide spectrum of diseases and conditions.

Functional neurosurgery lies at the intersection of a converging wave of technological and scientific clusters of innovation, including:

  • Miniaturization of microelectronics;
  • Advances in transcutaneous transmission of power and data;
  • Multimodality physiological biomarker sensing;
  • Closed-loop control;
  • Adaptive control and customized patient specific therapy;
  • Minimally invasive implantation neuromodulators
  • implants with improved mechanical properties which minimize neural trauma, and
  • Scientific advances in the understanding of neural control of a vast swath of cognitive and physiological functions.

There are several distinct areas in which this field will be transformed:

1. Cortical and sensorimotor interfacing;

2. Deep brain modulation;

3. Spinal cord modulation; and

4. Autonomic modulation.

Cortical and Sensorimotor Systems

Cortical and sensorimotor interfacing includes the brain computer interface (BCI) and other low and high channel count surface interfaces. Early examples of this are the retinal prosthesis1,2 as well as the cochlear prosthesis. Specialized recording using low density macroelectrodes has already been shown to be helpful in the treatment of epilepsy, seizure control3,4, seizure prediction5,6, and long term monitoring for planning of seizure resection.7 Development of safe and efficacious therapies using high channel count microelectrodes and their clinical adoption has been limited by the electrode tissue interface. Other aspects of implementing this technology have been established:

  • Safe and robust materials and stimulation parameters have been well characterized in animal8,9,10 and in human11 analyses, and
  • Many elegant algorithms have been developed for patterned stimulation and for ensemble recording and interpretation of neutrally initiated commands.12-15

The limiting technology has been the interface itself, however several technologies are poised to significantly advance this facet of the technology. Flexible electrode materials as well as flexible tethers and wireless links will allow implanted electrode arrays to more gently connect with and/or float on neural surfaces, thereby minimizing neural damage inherent with many past and present designs.16,17 With these advancements, we will see long standing implanted high channel count arrays performing a variety of useful functions. 

Restoring Function: Paralyzed, Blind, Aphasic and More

Paralyzed patients will be the target for one of the first applications. This will involve ensemble neuronal recording from regions of premotor and motor cortex for command signal extraction to restore function in paralyzed patients by controlling motorized exoskeletons and/or by controlling implanted or external functional electrical stimulation (FES) modules which may use adaptive control18 of muscle contraction to reanimate paralyzed limbs19,20. After over 40 years of development in preclinical and clinical research, we will likely see clinical approval in the near future of high density patterned visual cortex stimulation for vision restoration.21-25 We may also see multichannel recording with closed-loop stimulation of hippocampal and related structures using spatially and temporally patterned stimulation for memory augmentation.26-29 These are being evaluated to improve memory deficits for patients with medical conditions; however, we are already seeing discussions among clinicians and researchers in the field about both the plausibility as well as the ethical concerns regarding the application of this technology beyond disease treatment and to performance enhancement in nonmedical applications.

Extrapolating these advances forward, the notion of decoding and concatenating phonemes or other output modalities into words, sentences, and even thoughts and images, does not seem that far-fetched. This could be life changing for patients with locked-in syndromes, ALS, and some other conditions with communication deficits. There may also be nonmedical applications, such as interfaces with mobile communication devices; however, the safety and ethics of such forms of augmentation should be thoroughly vetted.

Next Gen DBS and SCS      

Deep brain stimulation (DBS)30 and Spinal cord stimulation (SCS) are well known among neurosurgeons, and these will continue to advance with higher channel counts and directional leads, enabling more sophisticated field shaping. Closed-loop and adaptive control31 is an area of active research and development and will facilitate new level of personalized medicine in which devices sense physiological biomarkers, determine neural and disease state, and respond with a specifically tailored therapeutic dose optimized for that patient at that particular instant in time. Predictive algorithms have been shown to be efficacious in estimating seizure likelihood and providing forewarning of impending seizures.5,6 Applying techniques in algorithm development and machine learning to multimodal sensing will extend these technologies to the prediction of affective states and perhaps to other time varying conditions including PTSD, bipolar disorder, schizophrenia, and other neuropsychiatric conditions, enabling devices to anticipate pathological neural states and to use feedforward and feedback control to deliver therapies to prevent and to stabilize their clinical manifestations.

Autonomic Modulation: The Next Horizon

Autonomic modulation represents a new frontier in the treatment of disease, beginning with vagus nerve stimulation for epilepsy32and expanding to a seemingly infinite set of possibilities with numerous permutations of branches, pathways, modulation modalities, temporal patterning, biomarker tracking, and organ system physiologies, incorporating chronic recording33 and stimulating34 interfaces. We will likely see neuromodulatory therapies in the foreseeable future for a spectrum of conditions, including heart failure, asthma, pulmonary hypertension, systemic hypertension, hemorrhage, inflammatory conditions, metabolism and many others, expanding the impact beyond the traditional boundaries of neurosurgery and to offer the possibility of physiologically and temporally focal therapy free of many of the limiting side effects of present-day pharmaceutical modalities.   

Safer, Cost-Effective Restorative Future

We are entering a new chapter of medicine, one which may realistically offer the combination of greater safety, truly individualized personalization of therapy, and substantially reduced morbidity and mortality. With clinical adoption will come economies of scale, which coupled with competition and technological innovations underway, will also realize an overall cost reduction and improved access to a multiplicity of therapies within our healthcare system.

References

1. Cruz, L. D., Dorn, J. D., Humayun, M. S., Dagnelie, G., Handa, J., Barale, P., . . . Greenberg, R. J. (2016). Five-Year Safety and Performance Results from the Argus II Retinal Prosthesis System Clinical Trial. Ophthalmology, 123(10), 2248-2254.

2. Ho, A. C., Humayun, M. S., Dorn, J. D., Cruz, L. D., Dagnelie, G., Handa, J., . . . Greenberg, R. J. (2015). Long-Term Results from an Epiretinal Prosthesis to Restore Sight to the Blind. Ophthalmology, 122(8), 1547-1554.

3. Heck, C. N., King-Stephens, D., Massey, A. D., Nair, D. R., Jobst, B. C., Barkley, G. L., . . . Morrell, M. J. (2014). Two-year seizure reduction in adults with medically intractable partial onset epilepsy treated with responsive neurostimulation: Final results of the RNS System Pivotal trial. Epilepsia, 55(3), 432-441.

4. DiLorenzo, D.J. (2009). Patent #: US7623928 B2 Controlling a subject’s susceptibility to a seizure.

5. Cook, M. J., Obrien, T. J., Berkovic, S. F., Murphy, M., Morokoff, A., Fabinyi, G., . . . Himes, D. (2013). Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: A first-in-man study. The Lancet Neurology, 12(6), 563-571.

6. DiLorenzo, D.J. (2007). Patent #: US 7,277,758 B2. Methods and systems for predicting future symptomatology in a patient suffering from a neurological or psychiatric disorder.

7. DiLorenzo, D. J., Mangubat, E. Z., Rossi, M. A., & Byrne, R. W. (2014). Chronic unlimited recording electrocorticography–guided resective epilepsy surgery: Technology-enabled enhanced fidelity in seizure focus localization with improved surgical efficacy. Journal of Neurosurgery, 1402-1414.

8. Agnew, W. F., Yuen, T. G., & Mccreery, D. B. (1983). Morphologic changes after prolonged electrical stimulation of the cats cortex at defined charge densities. Experimental Neurology, 79(2), 397-411.

9. Mccreery, D., Agnew, W., Yuen, T., & Bullara, L. (1990). Charge density and charge per phase as cofactors in neural injury induced by electrical stimulation. IEEE Transactions on Biomedical Engineering, 37(10), 996-1001.

10. Shannon, R. (1992). A model of safe levels for electrical stimulation. IEEE Transactions on Biomedical Engineering, 39(4), 424-426.

11. Dilorenzo, D. J., Jankovic, J., Simpson, R. K., Takei, H., & Powell, S. Z. (2014). Neurohistopathological Findings at the Electrode-Tissue Interface in Long-Term Deep Brain Stimulation: Systematic Literature Review, Case Report, and Assessment of Stimulation Threshold Safety. Neuromodulation: Technology at the Neural Interface, 17(5), 405-418.

12. Taylor, D. M. (2002). Direct Cortical Control of 3D Neuroprosthetic Devices. Science, 296(5574), 1829-1832.

13. Serruya, M. D., Hatsopoulos, N. G., Paninski, L., Fellows, M. R., & Donoghue, J. P. (2002). Instant neural control of a movement signal. Nature, 416(6877), 141-142.

14. Carmena, J. M., Lebedev, M. A., Crist, R. E., Odoherty, J. E., Santucci, D. M., Dimitrov, D. F., . . . Nicolelis, M. A. (2003). Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates. PLoS Biology, 1(2).

15. Ajiboye, A. B., Willett, F. R., Young, D. R., Memberg, W. D., Murphy, B. A., Miller, J. P., . . . Kirsch, R. F. (2017). Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: A proof-of-concept demonstration. The Lancet, 389(10081), 1821-1830.

16. Jorfi, M., Skousen, J. L., Weder, C., & Capadona, J. R. (2014). Progress towards biocompatible intracortical microelectrodes for neural interfacing applications. Journal of Neural Engineering, 12(1), 011001.

17. Lecomte, A., Descamps, E., & Bergaud, C. (2018). A review on mechanical considerations for chronically-implanted neural probes. Journal of Neural Engineering, 15(3), 031001.

18. Durfee, W. K., & Dilorenzo, D. J. (1990). Linear and Nonlinear Approaches to Control of Single Joint Motion by Functional Electrical Stimulation. 1990 American Control Conference.

19. Kilgore, K. L., Hoyen, H. A., Bryden, A. M., Hart, R. L., Keith, M. W., & Peckham, P. H. (2008). An Implanted Upper-Extremity Neuroprosthesis Using Myoelectric Control. The Journal of Hand Surgery, 33(4), 539-550.

20. Ajiboye, A. B., Willett, F. R., Young, D. R., Memberg, W. D., Murphy, B. A., Miller, J. P., . . . Kirsch, R. F. (2017). Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: A proof-of-concept demonstration. The Lancet, 389(10081), 1821-1830.

21. Dobelle, W. H., Mladejovsky, M. G., & Girvin, J. P. (1974). Artificial Vision for the Blind: Electrical Stimulation of Visual Cortex Offers Hope for a Functional Prosthesis. Science, 183(4123), 440-444.

22. Schmidt, E. M., Bak, M. J., Hambrecht, F. T., Kufta, C. V., Orourke, D. K., & Vallabhanath, P. (1996). Feasibility of a visual prosthesis for the blind based on intracortical micro stimulation of the visual cortex. Brain, 119(2), 507-522.

23. Brindley, G. S., & Lewin, W. S. (1968). The sensations produced by electrical stimulation of the visual cortex. The Journal of Physiology, 196(2), 479-493.

24. Troyk, P., Bak, M., Berg, J., Bradley, D., Cogan, S., Erickson, R., . . . Towle, V. (2003). A Model for Intracortical Visual Prosthesis Research. Artificial Organs, 27(11), 1005-1015.

25. Greenberg, R.J., McClure, K.H. & Roy, A. (2011). Patent #: US 8,000,000 B2, Visual prosthesis.

26. Berger, T. W., Song, D., Chan, R. H., Marmarelis, V. Z., Lacoss, J., Wills, J., . . . Granacki, J. J. (2012). A Hippocampal Cognitive Prosthesis: Multi-Input, Multi-Output Nonlinear Modeling and VLSI Implementation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20(2), 198-211.

27. Kucewicz, M. T., Berry, B. M., Miller, L. R., Khadjevand, F., Ezzyat, Y., Stein, J. M., . . . Worrell, G. A. (2018). Evidence for verbal memory enhancement with electrical brain stimulation in the lateral temporal cortex. Brain, 141(4), 971-978.

28. Ezzyat, Y., Wanda, P. A., Levy, D. F., Kadel, A., Aka, A., Pedisich, I., . . . Kahana, M. J. (2018). Closed-loop stimulation of temporal cortex rescues functional networks and improves memory. Nature Communications, 9(1).

29. Raghavachari, S., Kahana, M. J., Rizzuto, D. S., Caplan, J. B., Kirschen, M. P., Bourgeois, B., . . . Lisman, J. E. (2001). Gating of Human Theta Oscillations by a Working Memory Task. The Journal of Neuroscience, 21(9), 3175-3183.

30. Benabid, A., Pollak, P., Louveau, A., Henry, S., & Rougemont, J. D. (1987). Combined (Thalamotomy and Stimulation) Stereotactic Surgery of the VIM Thalamic Nucleus for Bilateral Parkinson Disease. Stereotactic and Functional Neurosurgery, 50(1-6), 344-346.

31. DiLorenzo, D.J. (2002). Patent #: US 6,366,813. Apparatus and method for closed-loop intracranical stimulation for optimal control of neurological disease 2002.

32. Zabara, J. (1987). Patent #: US 4,702,254 A. Neurocybernetic prosthesis.

33. Edell, D.J. (1980). Development of a chronic neuroelectronic interface. PhD Thesis, UC Davis, Davis, Calif.

34. DiLorenzo, D.J. (1999). Development of a Chronically Implanted Microelectrode Array for Intraneural Electrical Stimulation for Prosthetic Sensory Feedback. S.M. Thesis, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School and Massachusetts Institute of Technology, Cambridge, Mass.

Leave a Reply

Be the first to reply using the above form.