Modulation: Fine Tuning and Resetting of Neurological Function

doi:10.1017/9781009597852.007

Chapter 4 - Modulation: Fine Tuning and Resetting of Neurological Function

from Section II - Ideas and Concepts in Modern Neurosurgical Innovation

Published online by Cambridge University Press: aN Invalid Date NaN

Peter Konrad and

Edited by

Rohan Ramakrishna and

Michael L. J. Apuzzo

Show author details

Benjamin Hartley: Affiliation:
Weill Cornell Medical Center
Philip E. Stieg: Affiliation:
Weill Cornell Medical College
Rohan Ramakrishna: Affiliation:
Weill Cornell Medical College
Michael L. J. Apuzzo: Affiliation:
Adjunct of Yale Medical School and Weill Cornell Medical College

Book contents

Get access

Summary

Neurological disorders are the leading cause of disability worldwide. Restoring function through the modulation of brain networks has been a cornerstone in the field of functional restoration. Deep brain stimulation (DBS) along with neuroprosthetics such as cochlear implants have significantly improved the quality of life for patients with functional restoration. However, there remains a large population of patients who cannot benefit from existing approved medical technologies. Brain–machine interfaces (BMI) show great promise in addressing the unmet need in diagnostic and functional needs for patients with neurological disorders and disabilities. To date, more humans have received clinical benefit from the Utah Array than from any other BMI, but this also had several limitations. Recent advances in BMI address these limitations, showing improvements in invasiveness, longevity, signal quality, and usability. This chapter provides an overview of BMI and discusses the evolving technology in the field of BMI, which provides a novel dimension to the existing neurosurgical armamentarium modulating neurological function beyond the conventional neurosurgical treatment.

Keywords

Neurosurgery deep brain stimulation brain machine interface functional neurosurgery treatment

Information

Type: Chapter
Information: Neurosurgery
Beyond the Cutting Edge
, pp. 43 - 57

DOI: https://doi.org/10.1017/9781009597852.007 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2025

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Book purchase

Temporarily unavailable

References

Feigin, VL, Vos, T, Nichols, E, Owolabi, MO, Carroll, WM, Dichgans, M, et al. The Global Burden of Neurological Disorders: Translating Evidence Into Policy. Lancet Neurol. 2020;19:255–65.10.1016/S1474-4422(19)30411-9CrossRef Google Scholar PubMed

GBD 2017 US Neurological Disorders Collaborators. Burden of Neurological Disorders Across the US from 1990–2017: A Global Burden of Disease Study. JAMA Neurol. 2021;78:165–76.Google Scholar

del, R. Millan, J, Galan, F, Vanhooydonck, D, Lew, E, Philips, J, Nuttin, >M. Asynchronous Non-invasive Brain-actuated Control of an Intelligent Wheelchair. 2009 Annu Int Conf IEEE Eng Med Biol Soc. 2009:3361–4.10.1109/IEMBS.2009.5332828CrossRef M.+Asynchronous+Non-invasive+Brain-actuated+Control+of+an+Intelligent+Wheelchair.+2009+Annu+Int+Conf+IEEE+Eng+Med+Biol+Soc.+2009:3361–4.>Google Scholar

Birbaumer, N, Ghanayim, N, Hinterberger, T, Iversen, I, Kotchoubey, B, Kübler, A, et al. A Spelling Device for the Paralysed. Nature. 1999;398:297–8.10.1038/18581CrossRef Google Scholar PubMed

Scherer, R, Muller, GR, Neuper, C, Graimann, B, Pfurtscheller, G. An Asynchronously Controlled EEG-based Virtual Keyboard: Improvement of the Spelling Rate. IEEE Trans Biomed Eng. 2004;51:979–84.10.1109/TBME.2004.827062CrossRef Google Scholar PubMed

Sellers, EW, Krusienski, DJ, McFarland, DJ, Vaughan, TM, Wolpaw, JR. A P300 Event-related Potential Brain–Computer Interface (BCI): The Effects of Matrix Size and Inter Stimulus Interval on Performance. Biol Psychol. 2006;73:242–52.10.1016/j.biopsycho.2006.04.007CrossRef Google Scholar PubMed

Bensch, M, Karim, AA, Mellinger, J, Hinterberger, T, Tangermann, M, Bogdan, M, et al. Nessi: An EEG-Controlled Web Browser for Severely Paralyzed Patients. Comput Intell Neurosci. 2007;2007:e71863.10.1155/2007/71863CrossRef Google Scholar PubMed

Karim, AA, Hinterberger, T, Richter, J, Mellinger, J, Neumann, N, Flor, H, et al. Neural Internet: Web Surfing with Brain Potentials for the Completely Paralyzed. Neurorehabil Neural Repair. 2006;20:508–15.10.1177/1545968306290661CrossRef Google Scholar PubMed

Bayliss, JD. Use of the Evoked Potential P3 Component for Control in a Virtual Apartment. IEEE Trans Neural Syst Rehabil Eng. 2003;11:113–16.10.1109/TNSRE.2003.814438CrossRef Google Scholar

Leeb, R, Friedman, D, Müller-Putz, GR, Scherer, R, Slater, M, Pfurtscheller, G. Self-Paced (Asynchronous) BCI Control of a Wheelchair in Virtual Environments: A Case Study with a Tetraplegic. Comput Intell Neurosci. 2007;2007:e79642.10.1155/2007/79642CrossRef Google Scholar PubMed

Leeb, R, Lee, F, Keinrath, C, Scherer, R, Bischof, H, Pfurtscheller, G. Brain–Computer Communication: Motivation, Aim, and Impact of Exploring a Virtual Apartment. IEEE Trans Neural Syst Rehabil Eng. 2007;15:473–82.10.1109/TNSRE.2007.906956CrossRef Google Scholar PubMed

del R, Millán J, Rupp, R, Mueller-Putz, G, Murray-Smith, R, Giugliemma, C, Tangermann, M, et al. Combining Brain–Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges. Front Neurosci [Internet]. 2010 [cited 2023 Jul 14];4. Available from: https://www.frontiersin.org/articles/10.3389/fnins.2010.00161 Google Scholar

Chaudhary, U, Vlachos, I, Zimmermann, JB, Espinosa, A, Tonin, A, Jaramillo-Gonzalez, A, et al. Spelling Interface Using Intracortical Signals in a Completely Locked-in Patient Enabled via Auditory Neurofeedback Training. Nat Commun. 2022;13:1236.10.1038/s41467-022-28859-8CrossRef Google Scholar

Saha, S, Mamun, KA, Ahmed, K, Mostafa, R, Naik, GR, Darvishi, S, et al. Progress in Brain Computer Interface: Challenges and Opportunities. Front Syst Neurosci [Internet]. 2021 [cited 2023 Jun 1];15. Available from: https://www.frontiersin.org/articles/10.3389/fnsys.2021.57887510.3389/fnsys.2021.578875CrossRef Google Scholar PubMed

Gupta, A, Vardalakis, N, Wagner, FB. Neuroprosthetics: From Sensorimotor to Cognitive Disorders. Commun Biol. 2023;6:1–17.10.1038/s42003-022-04390-wCrossRef Google Scholar PubMed

Kim, GH, Kim, K, Lee, E, An, T, Choi, W, Lim, G, et al. Recent Progress on Microelectrodes in Neural Interfaces. Materials. 2018;11:1995.10.3390/ma11101995CrossRef Google Scholar PubMed

Kawala-Sterniuk, A, Browarska, N, Al-Bakri, A, Pelc, M, Zygarlicki, J, Sidikova, M, et al. Summary of Over Fifty Years with Brain-Computer Interfaces – A Review. Brain Sci. 2021;11:43.10.3390/brainsci11010043CrossRef Google Scholar PubMed

Mudry, A, Mills, M. The Early History of the Cochlear Implant: A Retrospective. JAMA Otolaryngol Neck Surg. 2013;139:446–53.Google Scholar PubMed

Rubin, DB, Ajiboye, AB, Barefoot, L, Bowker, M, Cash, SS, Chen, D, et al. Interim Safety Profile From the Feasibility Study of the BrainGate Neural Interface System. Neurology. 2023;100:e1177–92.10.1212/WNL.0000000000201707CrossRef Google Scholar PubMed

Ranjan, M, Ranjan, N, Deogaonkar, M, Rezai, A. Deep Brain Stimulation for Refractory Depression, Obsessive-Compulsive Disorder and Addiction. Neurol India. 2020;68:S282–7.Google Scholar PubMed

Biran, R, Martin, DC, Tresco, PA. The Brain Tissue Response to Implanted Silicon Microelectrode Arrays is Increased When the Device is Tethered to the Skull. J Biomed Mater Res A. 2007;82:169–78.Google Scholar PubMed

Woeppel, K, Hughes, C, Herrera, AJ, Eles, JR, Tyler-Kabara, EC, Gaunt, RA, et al. Explant Analysis of Utah Electrode Arrays Implanted in Human Cortex for Brain–Computer-Interfaces. Front Bioeng Biotechnol [Internet]. 2021 [cited 2023 May 31];9. Available from: https://www.frontiersin.org/articles/10.3389/fbioe.2021.75971110.3389/fbioe.2021.759711CrossRef Google Scholar

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;485:372–5.10.1038/nature11076CrossRef Google Scholar PubMed

Neuralink, Musk E. An Integrated Brain–Machine Interface Platform with Thousands of Channels. J Med Internet Res. 2019;21:e16194.Google Scholar

Towle, VL, Pytel, P, Lane, F, Plass, J, Frim, DM, Troyk, PR. Postmortem Investigation of a Human Cortical Visual Prosthesis that was Implanted for 36 years. J Neural Eng. 2020;17:045010.10.1088/1741-2552/ab9d11CrossRef Google Scholar PubMed

Ho, E, Hettick, M, Papageorgiou, D, Poole, AJ, Monge, M, Vomero, M, et al. The Layer 7 Cortical Interface: A Scalable and Minimally Invasive Brain–Computer Interface Platform [Internet]. bioRxiv; 2022 [cited 2023 May 31]. p. 2022.01.02.474656. Available from: https://www.biorxiv.org/content/10.1101/2022.01.02.474656v110.1101/2022.01.02.474656CrossRef Google Scholar

Precision Neuroscience Begins First-in-Human Study of its Neural Interface Technology [Internet]. financialpost. [cited 2023 Jun 10]. Available from: https://financialpost.com/globe-newswire/precision-neuroscience-begins-first-in-human-study-of-its-neural-interface-technology Google Scholar

Soldozy, S, Young, S, Kumar, JS, Capek, S, Felbaum, DR, Jean, WC, et al. A Systematic Review of Endovascular Stent-Electrode Arrays, A Minimally Invasive Approach to Brain–Machine Interfaces. Neurosurg Focus. 2020;49:E3.10.3171/2020.4.FOCUS20186CrossRef Google Scholar PubMed

Oxley, TJ, Yoo, PE, Rind, GS, Ronayne, SM, Lee, CMS, Bird, C, et al. Motor Neuroprosthesis Implanted with Neurointerventional Surgery Improves Capacity for Activities of Daily Living Tasks in Severe Paralysis: First In-human Experience. J NeuroInterventional Surg. 2021;13:102–8.10.1136/neurintsurg-2020-016862CrossRef Google Scholar PubMed

Mitchell, P, Lee, SCM, Yoo, PE, Morokoff, A, Sharma, RP, Williams, DL, et al. Assessment of Safety of a Fully Implanted Endovascular Brain–Computer Interface for Severe Paralysis in 4 Patients: The Stentrode with Thought-Controlled Digital Switch (SWITCH) Study. JAMA Neurol. 2023;80:270–8.10.1001/jamaneurol.2022.4847CrossRef Google Scholar

Wellman, SM, Eles, JR, Ludwig, KA, Seymour, JP, Michelson, NJ, McFadden, WE, et al. A Materials Roadmap to Functional Neural Interface Design. Adv Funct Mater. 2018;28:1701269.10.1002/adfm.201701269CrossRef Google Scholar PubMed

El-Atab, N, Shaikh, SF, Hussain, MM. Nano-scale Transistors for Interfacing with Brain: Design Criteria, Progress and Prospect. Nanotechnology. 2019;30:442001.10.1088/1361-6528/ab3534CrossRef Google Scholar

Liu, J, Fu, T-M, Cheng, Z, Hong, G, Zhou, T, Jin, L, et al. Syringe-injectable Electronics. Nat Nanotechnol. 2015;10:629–36.10.1038/nnano.2015.115CrossRef Google Scholar PubMed

Dural Venous Sinus Stenting for Idiopathic Intracranial Hypertension: An Updated Review – ClinicalKey [Internet]. [cited 2023 Jun 4]. Available from: https://www-clinicalkey-com.wvu.idm.oclc.org/#!/content/playContent/1-s2.0-S0150986118302025 Google Scholar

Skarpaas, TL, Jarosiewicz, B, Morrell, MJ. Brain-responsive Neurostimulation for Epilepsy (RNS® System). Epilepsy Res. 2019;153:68–70.10.1016/j.eplepsyres.2019.02.003CrossRef Google Scholar PubMed

Brandman, DM, Cash, SS, Hochberg, LR. Review: Human Intracortical Recording and Neural Decoding for Brain Computer Interfaces. IEEE Trans Neural Syst Rehabil Eng Publ IEEE Eng Med Biol Soc. 2017;25:1687–96.Google Scholar PubMed

Sahasrabuddhe, K, Khan, AA, Singh, AP, Stern, TM, Ng, Y, Tadić, A, et al. The Argo: A High Channel Count Recording System for Neural Recording In Vivo. J Neural Eng. 2021;18:015002.10.1088/1741-2552/abd0ceCrossRef Google Scholar

Epilepsy | Ad-Tech Medical [Internet]. [cited 2023 Jun 10]. Available from: https://adtechmedical.com/epilepsy#subdural-electrodes Google Scholar

Oxley, TJ, Opie, NL, John, SE, Rind, GS, Ronayne, SM, Wheeler, TL, et al. Minimally Invasive Endovascular Stent-Electrode Array for High-fidelity, Chronic Recordings of Cortical Neural Activity. Nat Biotechnol. 2016;34:320–7.10.1038/nbt.3428CrossRef Google Scholar PubMed

Medtronic, SenSight – DBS Directional Lead [Internet]. [cited 2023 Jun 10]. Available from: https://www.medtronic.com/us-en/healthcare-professionals/products/neurological/deep-brain-stimulation-systems/sensight-lead.html Google Scholar

Zhuang, J, Truccolo, W, Vargas-Irwin, C, Donoghue, JP. Decoding 3-D Reach and Grasp Kinematics From High-Frequency Local Field Potentials in Primate Primary Motor Cortex. IEEE Trans Biomed Eng. 2010;57:1774–84.Google Scholar PubMed

Sharma, G, Friedenberg, DA, Annetta, N, Glenn, B, Bockbrader, M, Majstorovic, C, et al. Using an Artificial Neural Bypass to Restore Cortical Control of Rhythmic Movements in a Human with Quadriplegia. Sci Rep. 2016;6:33807.10.1038/srep33807CrossRef Google Scholar

Branco, MP, Pels, EGM, Sars, RH, Aarnoutse, EJ, Ramsey, NF, Vansteensel, MJ, et al. Brain–Computer Interfaces for Communication: Preferences of Individuals with Locked-in Syndrome. Neurorehabil Neural Repair. 2021;35:267–79.10.1177/1545968321989331CrossRef Google Scholar PubMed

Chari, A, Budhdeo, S, Sparks, R, Barone, DG, Marcus, HJ, Pereira, EAC, et al. Brain–Machine Interfaces: The Role of the Neurosurgeon. World Neurosurg. 2021;146:140–7.10.1016/j.wneu.2020.11.028CrossRef Google Scholar PubMed

Zhang, S, Tagliati, M, Pouratian, N, Cheeran, B, Ross, E, Pereira, E. Steering the Volume of Tissue Activated with a Directional Deep Brain Stimulation Lead in the Globus Pallidus Pars Interna: A Modeling Study with Heterogeneous Tissue Properties. Front Comput Neurosci. 2020;14:561180.10.3389/fncom.2020.561180CrossRef Google Scholar PubMed

Vercise^TM DBS Leads Directions for Use [Internet]. [cited 2023 Jun 10]. Available from: https://www.bostonscientific.com/content/dam/Manuals/us/current-rev-en/92104398-01_Vercise%E2%84%A2_DBS_Leads_DFU_en-USA_s.pdf Google Scholar

Thakor, NV. Translating the Brain–Machine Interface. Sci Transl Med. 2013;5:210ps17-210ps17.10.1126/scitranslmed.3007303CrossRef Google Scholar PubMed

Accessibility standard: Inaccessible, or known limited accessibility

Why this information is here

This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.

Accessibility Information

The PDF of this book is known to have missing or limited accessibility features. We may be reviewing its accessibility for future improvement, but final compliance is not yet assured and may be subject to legal exceptions. If you have any questions, please contact accessibility@cambridge.org.

Content Navigation

Table of contents navigation
Allows you to navigate directly to chapters, sections, or non‐text items through a linked table of contents, reducing the need for extensive scrolling.

Index navigation
Provides an interactive index, letting you go straight to where a term or subject appears in the text without manual searching.

Reading Order & Textual Equivalents

Single logical reading order
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.

Short alternative textual descriptions
You get concise descriptions (for images, charts, or media clips), ensuring you do not miss crucial information when visual or audio elements are not accessible.

Visual Accessibility

Use of colour is not sole means of conveying information
You will still understand key ideas or prompts without relying solely on colour, which is especially helpful if you have colour vision deficiencies.

Structural and Technical Features

ARIA roles provided
You gain clarity from ARIA (Accessible Rich Internet Applications) roles and attributes, as they help assistive technologies interpret how each part of the content functions.