Tutorial: a computational framework for the design and optimization of peripheral neural interfaces.
Journal
Nature protocols
ISSN: 1750-2799
Titre abrégé: Nat Protoc
Pays: England
ID NLM: 101284307
Informations de publication
Date de publication:
10 2020
10 2020
Historique:
received:
24
07
2019
accepted:
15
06
2020
entrez:
29
9
2020
pubmed:
30
9
2020
medline:
4
11
2020
Statut:
ppublish
Résumé
Peripheral neural interfaces have been successfully used in the recent past to restore sensory-motor functions in disabled subjects and for the neuromodulation of the autonomic nervous system. The optimization of these neural interfaces is crucial for ethical, clinical and economic reasons. In particular, hybrid models (HMs) constitute an effective framework to simulate direct nerve stimulation and optimize virtually every aspect of implantable electrode design: the type of electrode (for example, intrafascicular versus extrafascicular), their insertion position and the used stimulation routines. They are based on the combined use of finite element methods (to calculate the voltage distribution inside the nerve due to the electrical stimulation) and computational frameworks such as NEURON ( https://neuron.yale.edu/neuron/ ) to determine the effects of the electric field generated on the neural structures. They have already provided useful results for different applications, but the overall usability of this powerful approach is still limited by the intrinsic complexity of the procedure. Here, we illustrate a general, modular and expandable framework for the application of HMs to peripheral neural interfaces, in which the correct degree of approximation required to answer different kinds of research questions can be readily determined and implemented. The HM workflow is divided into the following tasks: identify and characterize the fiber subpopulations inside the fascicles of a given nerve section, determine different degrees of approximation for fascicular geometries, locate the fibers inside these geometries and parametrize electrode geometries and the geometry of the nerve-electrode interface. These tasks are examined in turn, and solutions to the most relevant issues regarding their implementation are described. Finally, some examples related to the simulation of common peripheral neural interfaces are provided.
Identifiants
pubmed: 32989306
doi: 10.1038/s41596-020-0377-6
pii: 10.1038/s41596-020-0377-6
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
3129-3153Références
Grill, W. M. Modeling the effects of electric fields on nerve fibers: influence of tissue electrical properties. IEEE Trans. Biomed. Eng. 46, 918–928 (1999).
pubmed: 10431456
McNeal, D. R. Analysis of a model for excitation of myelinated nerve. IEEE Trans. Biomed. Eng. 23, 329–337 (1976).
pubmed: 1278925
Rattay, F. Analysis of models for external stimulation of axons. IEEE Trans. Biomed. Eng. 33, 974–977 (1986).
pubmed: 3770787
Coburn, B. Electrical stimulation of the spinal cord: two-dimensional finite element analysis with particular reference to epidural electrodes. Med. Biol. Eng. Comput. 18, 573–584 (1980).
pubmed: 7464280
Coburn, B. & Sin, W. K. A theoretical study of epidural electrical stimulation of the spinal cord—Part I: finite element analysis of stimulus fields. IEEE Trans. Biomed. Eng. 32, 971–977 (1985).
pubmed: 3877677
Coburn, B. A theoretical study of epidural electrical stimulation of the spinal cord—Part II: effects on long myelinated fibers. IEEE Trans. Biomed. Eng. 32, 978–986 (1985).
pubmed: 3877678
Bossetti, C. A., Birdno, M. J. & Grill, W. M. Analysis of the quasi-static approximation for calculating potentials generated by neural stimulation. J. Neural Eng. 5, 44–53 (2008).
pubmed: 18310810
Struijk, J. J., Holsheimer, J., van der Heide, G. G. & Boom, H. B. K. Recruitment of dorsal column fibers in spinal cord stimulation: influence of collateral branching. IEEE Trans. Biomed. Eng. 39, 903–912 (1992).
pubmed: 1335438
Rattay, F., Minassian, K. & Dimitrijevic, M. R. Epidural electrical stimulation of posterior structures of the human lumbosacral cord: 2. quantitative analysis by computer modeling. Spinal Cord. 38, 473–489 (2000).
pubmed: 10962608
Capogrosso, M. et al. A computational model for epidural electrical stimulation of spinal sensorimotor circuits. J. Neurosci. 33, 19326–19340 (2016).
Anderson, D. J. et al. Intradural spinal cord stimulation: performance modeling of a new modality. Front. Neurosci. 13, 253 (2019).
pubmed: 30941012
pmcid: 6434968
McIntyre, C. C., Grill, W. M., Sherman, D. L. & Thakor, N. V. Cellular effects of deep brain stimulation: model-based analysis of activation and inhibition. J. Neurophysiol. 91, 1457–1469 (2003).
pubmed: 14668299
Chaturvedi, A., Butson, C. R., Lempka, S. F., Cooper, S. E. & McIntyre, C. C. Patient-specific models of deep brain stimulation: Influence of field model complexity on neural activation predictions. Brain Stimul. 3, 65–67 (2010).
pubmed: 20607090
pmcid: 2895675
McIntyre, C. C. & Foutz, T. J. Computational modeling of deep brain stimulation. Handb. Clin. Neurol. 116, 55–61 (2013).
pubmed: 24112884
pmcid: 5570759
Lowery, M. M. in Computational Models of Brain and Behavior (ed. Moustafa, A. A.) 109–123 (Wiley, 2017).
Li, M. et al. A simulation of current focusing and steering with penetrating optic nerve electrodes. J. Neural Eng. 10, 066007 (2013).
pubmed: 24140618
McIntyre, C. C. & Grill, W. M. Finite element analysis of the current-density and electric field generated by metal microelectrodes. Ann. Biomed. Eng. 29, 227–235 (2001).
pubmed: 11310784
Raspopovic, S., Carpaneto, J., Micera, S. & Navarro, X. Comparison of intraneural electrode geometries: Preliminary guidelines for electrode design. in Proc. 4th International IEEE/EMBS Conference on Neural Engineering (IEEE, Antalya, 2009).
Raspopovic, S., Capogrosso, M., Navarro, X. & Micera, S. Finite element and biophysics modelling of intraneural transversal electrodes: influence of active site shape. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2010, 1678–1681 (2010).
Schiefer, M. A., Triolo, R. J. & Tyler, D. J. A model of selective activation of the femoral nerve with a flat interface nerve electrode for a lower extremity neuroprosthesis. IEEE Trans. Neural Syst. Rehabil. Eng. 16, 195–204 (2008).
pubmed: 18403289
pmcid: 2920206
Raspopovic, S., Petrini, F. M., Zelechowski, M. & Valle, G. Framework for the development of neuroprostheses: from basic understanding by sciatic and median nerves models to bionic legs and hands. Proc. IEEE 105, 34–49 (2017).
Raspopovic, S. & Petrini, F. M. A computational model for the design of lower-limb sensorimotor neuroprostheses. in Proc of the 4th International Conference on NeuroRehabilitation, 49–53 (Springer, 2018).
Zelechowski, M., Valle, G. & Raspopovic, S. A computational model to design neural interfaces for lower-limb sensory neuroprostheses. J. Neuroeng. Rehabil. 17, 24 (2020).
pubmed: 32075654
pmcid: 7029520
Raspopovic, S., Capogrosso, M. & Micera, S. A computational model for the stimulation of rat sciatic nerve using a transverse intrafascicular multichannel electrode. IEEE Trans. Neural Syst. Rehabil. Eng. 19, 333–344 (2011).
pubmed: 21693427
Raspopovic, S., Capogrosso, M., Badia, J., Navarro, X. & Micera, S. Experimental validation of a hybrid computational model for selective stimulation using transverse intrafascicular multichannel electrodes. IEEE Trans. Neural Syst. Rehabil. Eng. 20, 395–404 (2012).
pubmed: 22481834
McIntyre, C. C. & Grill, W. M. Selective microstimulation of central nervous system neurons. Ann. Biomed. Eng. 28, 219–233 (2000).
pubmed: 10784087
Oddo, C. M. et al. Intraneural stimulation elicits discrimination of textural features by artificial fingertip in intact and amputee humans. eLife 8, e09148 (2016).
Gaillet, V. et al. Spatially selective activation of the visual cortex via intraneural stimulation of the optic nerve. Nat. Biomed. Eng. 4, 181–194 (2019).
pubmed: 31427779
Lubba, C. H. et al. PyPNS: multiscale simulation of a peripheral nerve in Python. Neuroinformatics 17, 63–81 (2019).
pubmed: 29948844
Tubbs, R. S. et al. Nerves and Nerve Injuries—History, Embriology, Anatomy, Imaging, and Diagnosis (Academic, Elsevier, 2015).
Hämäläinen, M., Riitta, H., Ilmoniemi, R. J., Knuutila, J. & Lounasmaa, O. V. Magnetoencephalography theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev. Modern Phys. 65, 413–497 (1993).
Grinberg, Y., Schiefer, M. A., Tyler, D. J. & Gustafson, K. J. Fascicular perineurium thickness, size, and position affect model predictions of neural excitation. IEEE Trans. Neural Syst. Rehabil. Eng. 16, 572–581 (2008).
pubmed: 19144589
pmcid: 2918421
Watchmaker, G. P., Gumucio, C. A., Crandall, R. E., Vannier, M. A. & Weeks, P. M. Fascicular topography of the median nerve: a computer based study to identify branching patterns. J. Hand Surg. 16A, 53–59 (1991).
Parent A. & Carpenter, M. B. Carpenter’s Human Neuroanatomy (Williams & Wilkins, 1996).
Delgado-Martínez, I., Badia, J., Pascual-Font, A., Rodríguez-Baeza, A. & Navarro, X. Fascicular topography of the human median nerve for neuroprosthetic surgery. Front. Neurosci. 10, 286 (2016).
pubmed: 27445660
pmcid: 4929846
Gustafson, K. J., Grinberg, Y., Joseph, S. & Triolo, R. J. Human distal sciatic nerve fascicular anatomy: implications for ankle control using nerve-cuff electrodes. J. Rehabilitation Res. Dev. 49, 309–321 (2012).
Sunderland, S. The intraneural topography of the radial, median and ulnar nerves. Brain 68, 243–249 (1945).
pubmed: 20982793
Sunderland, S. & Ray, L. J. The intraneural topography of the sciatic nerve and its popliteal divisions in man. Brain 71, 242–273 (1948).
pubmed: 18099549
Jabaley, M. E., Wallace, W. H. & Heckler, F. R. Internal topography of major nerves of the forearm and hand: a current view. J. Hand Surg. 5, 1–18 (1980).
Planitzer, U. et al. Median nerve fascicular anatomy as a basis for distal neural prostheses. Ann. Anat. 196, 144–149 (2014).
pubmed: 24374103
Bäumer, P., Weiler, M., Bendszus, M. & Pham, M. Somatotopic fascicular organization of the human sciatic nerve demonstrated by MR neurography. Am. Acad. Neurol. 84, 1782–1787 (2015).
Wurth, S. et al. Long-term usability and bio-integration of polyimide-based intraneural stimulating electrodes. Biomaterials 122, 114–129 (2017).
pubmed: 28110171
Brill, N. A. & Tyler, D. J. Quantification of human upper extremity nerves and fascicular anatomy. Muscle Nerve 56, 463–471 (2017).
pubmed: 28006854
pmcid: 5712902
Hallin, R. G. Microneurography in relation to intraneural topography: somatotopic organisation of median nerve fascicles in humans. J. Neurol. Neurosurg. Psychiatry 53, 736–744 (1990).
pubmed: 2246655
pmcid: 1014249
Stewart, J. D. Peripheral nerve fascicles: anatomy and clinical relevance. Muscle Nerve 28, 525–541 (2003).
pubmed: 14571454
McKinley, J. C. The intraneural plexys of fasciculi and fibers in the sciatic nerve. Arch. Neurol. Psychiatry, 6, 16–23 (1921).
Lumsden, D. B. et al. Topography of the distal tibial nerve and its branches. Foot Ankle Int. 24, 696–700 (2003).
pubmed: 14524520
Ugrenovic, S. et al. Morphological and morphometric analysis of fascicular structure of tibial and common peroneal nerves. Facta Univ. Ser. Med. Biol. 16, 18–22 (2014).
Weis, J., Brandner, S., Lammens, M., Sommer, C. & Vallat, J. M. Processing of nerve biopsies: a practical guide for neuropathologists. Clin. Neuropathol. 31, 7–23 (2012).
pubmed: 22192700
Reina, M. A. et al. Atlas of Functional Anatomy for Regional Anesthesia and Pain Medicine (Springer, 2013).
Caparso, A. V., Durand, D. M. & Mansour, J. M. A nerve cuff electrode for controlled reshaping of nerve geometry. J. Biomater. Appl. 24, 247–273 (2009).
pubmed: 18987020
Cutrone, A. et al. A three-dimensional self-opening intraneural peripheral interface (SELINE). J. Neural Eng. 12, 016016 (2015).
pubmed: 25605565
Grill, W. M. & Mortimer, J. T. Neural and connective tissue response to long-term implantation of multiple contact nerve cuff electrodes. J. Biomed. Mater. Res. 50, 215–226 (2000).
pubmed: 10679687
Grill, W. M. & Mortimer, J. T. Electrical properties of implant encapsulation tissue. Ann. Biomed. Eng. 22, 23–33 (1994).
pubmed: 8060024
Fitzhugh, R. Computation of impulse initiation and saltatory conduction in a myelinated nerve fiber. Biophys. J. 2, 11–21 (1962).
pubmed: 13893367
pmcid: 1366385
Sundt, D., Gamper, N. & Jaffe, D. B. Spike propagation through the dorsal root ganglia in an unmyelinated sensory neuron: a modeling study. J. Neurophysiol. 114, 3140–3153 (2015).
pubmed: 26334005
pmcid: 4686302
Sweeney, J. D., Mortimer, J. T. & Durand, D. M. Modeling of mammalian myelinated nerve for functional neuromuscular stimulation. in Proc. 9th IEEE Annual Conference of the Engineering in Medicine and Biology Society, 1577–1578 (1987).
Halter, J. A. & Clark, J. W. Jr. A distributed-parameter model of the myelinated nerve fiber. J. Theor. Biol. 148, 345–382 (1991).
pubmed: 2016898
McIntyre, C. C., Richardson, A. G. & Grill, W. M. Modeling the excitability of mammalian nerve fibers: influence of afterpotentials on the recovery cycle. J. Neurophysiol. 87, 995–1006 (2002).
pubmed: 11826063
Blight, A. R. Computer simulation of action potentials and afterpotentials in mammalian myelinated axons: the case for a lower resistance myelin sheath. Neuroscience 15, 13–31 (1985).
pubmed: 2409473
Berthold, C. H. & Rydmark, M. Electrophysiology and morphology of myelinated nerve fibers. VI. Anatomy of the paranode-node-paranode region in the cat. Experientia 39, 976–979 (1983).
Hodgkin, A. L. & Huxley, A. F. A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117, 500–544 (1952).
pubmed: 12991237
pmcid: 1392413
Richardson, A. G., McIntyre, C. C. & Grill, W. M. Modelling the effects of electric fields on nerve fibres: influence of the myelin sheath. Med. Biol. Eng. Comput. 38, 438–446 (2000).
pubmed: 10984943
Scholz, A., Reid, G., Vogel, W. & Bostock, H. Ion channels in human axons. J. Neurophysiol. 70, 1274–1279 (1993).
pubmed: 7693885
Schwarz, J. R., Reid, G. & Bostock, H. Action potentials and membrane currents in the human node of Ranvier. Pflug. Arch. Eur. J. Physiol. 430, 283–292 (1995).
Reid, G., Scholz, A., Bostock, H. & Vogel, W. Human axons contain at least five types of voltage-dependent potassium channel. J. Physiol. 518, 681–696 (1999).
pubmed: 10420006
pmcid: 2269457
Schwarz, J. R. & Eikhof, G. Na currents and action potentials in rat myelinated nerve fibres at 20 and 37 degrees C. Pflug. Arch. Eur. J. Physiol. 409, 569–577 (1987).
Neumcke, B. & Stampfli, R. Sodium currents and sodium-current fluctuations in rat myelinated nerve fibres. J. Physiol. 329, 163–184 (1982).
pubmed: 6292404
pmcid: 1224773
Zhu, K., Li, L., Wei, X. & Sui, X. A 3D computational model of transcutaneous electrical nerve stimulation for estimating Aβ tactile nerve fiber excitability. Front. Neurosci. 11, 250 (2017).
pubmed: 28559787
pmcid: 5432565
Gaines, J. L., Finn, K. E., Slopsema, J. P., Heyboer, L. A. & Polasek, K. H. A model of motor and sensory axon activation in the median nerve using surface electrical stimulation. J. Comput. Neurosci. 45, 29–43 (2018).
pubmed: 29946922
Howells, J., Trevillion, L., Bostock, H. & Burke, D. The voltage dependence of I_(h) in human myelinated axons. J. Physiol. 590, 1625–1640 (2012).
pubmed: 22310314
pmcid: 3413487
Joucla, S. & Yvert, B. The mirror estimate: an intuitive predictor of membrane polarization during extracellular stimulation. Biophys. J. 96, 3495–3508 (2009).
pubmed: 19413956
pmcid: 2711410
Sweeney, J. D., Ksienski, D. A. & Mortimer, J. T. A nerve cuff technique for selective excitation fo peripheral nerve trunk regions. IEEE Trans. Biomed. Eng. 37, 706–715 (1990).
pubmed: 2394459
Warman, E. N., Grill, W. M. & Durand, D. Modeling the effects of electric fields on nerve fibers: determination of excitation thresholds. IEEE Trans. Biomed. Eng. 39, 1244–1254 (1992).
pubmed: 1487287
Peterson, E. J., Izad, O. & Tyler, D. J. Predicting myelinated axon activation using spatial characteristics of the extracellular field. J. Neural Eng. 8, 046030 (2011).
pubmed: 21750371
pmcid: 3197268
Moffitt, M. A., McIntyre, C. C. & Grill, W. M. Prediction of myelinated nerve fiber stimulation thresholds: limitations of linear models. IEEE Trans. Biomed. Eng. 51, 229–236 (2004).
pubmed: 14765695
Gasser, H. S. The classification of nerve fibers. Ohio J. Sci. 41, 145–159 (1941).
Arbuthnott, E. R., Boyd, I. A. & Kalu, K. U. Ultrastructural dimensions of myelinated peripheral nerve fibers in the cat and their relation to conduction velocity. J. Physiol. 308, 125–157 (1980).
pubmed: 7230012
pmcid: 1274542
Veltink, P. H., van Alsté, J. A. & Boom, H. B. K. Multielectrode intrafascicular and extraneural stimulation. Med. Biol. Eng. Comput. 27, 19–24 (1989).
pubmed: 2779293
Rattay, F. Electrical Nerve Stimulation: Theory, Experiments and Applications (Springer, 1990).
Bégin, S. et al. Automated method for the segmentation and morphometry of nerve fibers in large-scale CARS images of spinal cord tissue. Biomed. Opt. Express 5, 4145–4161 (2014).
pubmed: 25574428
pmcid: 4285595
Zaimi, A. et al. AxonSeg: open source software for axon and myelin segmentation and morphometric analysis. Front. Neuroinform. 19, 37 (2016).
Zaimi, A. et al. AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks. Sci. Rep. 8, 3816 (2018).
pubmed: 29491478
pmcid: 5830647
Assaf, Y., Blumenfeld-Katzir, T., Yovel, Y. & Basser, P. J. AxCaliber: a method for measuring axon diameter distribution from diffusion MRI. Magn. Reson. Med. 59, 1347–1354 (2008).
pubmed: 18506799
pmcid: 4667732
Schellens, R. L. et al. A statistical approach to fiber diameter distribution in human sural nerve. Muscle Nerve 16, 1342–1350 (1993).
pubmed: 8232391
Sepehrband, F. et al. Parametric probability distribution functions for axon diameters of corpus callosum. Front. Neuroanat. 26, 59 (2016).
Titterington, D. M., Smith, A. F. M. & Makov, U. E. Statistical Analysis of Finite Mixture Distributions (Wiley, 1985).
Prodanov, D. & Feirabend, H. K. P. Morphometric analysis of the fiber populations of the rat sciatic nerve, its spinal roots, and its major branches. J. Comp. Neurol. 503, 85–100 (2007).
pubmed: 17480027
Schwarz, G. Estimating the dimension of a model. Ann. Stat. 6, 138–145 (1978).
Jacobs, J. M. & Love, S. Qualitative and quantitative morphology of human sural nerve at different ages. Brain 108, 897–924 (1985).
pubmed: 4075078
Navarro, X. et al. A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems. J. Peripher. Nerv. Syst. 10, 229–258 (2005).
pubmed: 16221284
Tyler, D. J., Polasek, K. H. & Schiefer, M. A. in Nerves and Nerve Injuries—History, Embriology, Anatomy, Imaging, and Diagnosis (eds Tubbs, R. S. et al.) (Academic, Elsevier, 2015).
Boretius, T. et al. A transverse intrafascicular multichannel electrode (TIME) to interface with the peripheral nerve. Biosens. Bioelectron. 26, 62–69 (2010).
pubmed: 20627510
Tyler, D. J. & Durand, D. M. Functionally selective peripheral nerve stimulation with a flat interface nerve electrode. IEEE Trans. Neural Syst. Rehabil. Eng. 10, 294–303 (2002).
pubmed: 12611367
Joucla, S., Glière, A. & Yvert, B. Current approaches to model extracellular electrical neural microstimulation. Front. Comput. Neurosci. 8, 13 (2014).
pubmed: 24600381
pmcid: 3928616
Heuschkel, M. O., Fejtl, M., Raggenbass, M., Bertrand, D. & Renauda, P. A three-dimensional multi-electrode array for multi-site stimulation and recording in acute brain slices. J. Neurosci. Methods 114, 135–148 (2002).
pubmed: 11856564
Joucla, S. & Yvert, B. Improved focalization of electrical microstimulation using microelectrode arrays: a modeling study. PlosONE 4, e4828 (2009).
Joucla, S., Branchereau, P., Cattaert, D. & Yvert, B. Extracellular neural microstimulation may activate much larger regions than expected by simulations: a combined experimental and modeling study. PlosONE 7, e41324 (2012).
Günter, C., Delbeke, J. & Ortiz-Catalan, M. Safety of long-term electrical peripheral nerve stimulation: review of the state of the art. J. Neuroeng. Rehabil. 16, 13 (2019).
pubmed: 30658656
pmcid: 6339286
Meijs, J. W. H., Weier, O. W., Peters, M. J. & Oosterom, A. V. On the numerical accuracy of the boundary element method. IEEE Trans. Biomed. Eng. 36, 1038–1049 (1989).
pubmed: 2793196
Schimpf, P. H., Ramon, C. & Haueisen, J. Dipole models for the EEG and MEG. IEEE Trans. Biomed. Eng. 49, 409–418 (2002).
pubmed: 12002172
Koole, P., Holsheimer, J., Struijk, J. J. & Verloop, A. J. Recruitment characteristics of nerve fascicles stimulated by a multigroove electrode. IEEE Trans. Rehabil. Eng. 5, 40–50 (1997).
pubmed: 9086384
Polasek, K. H., Hoyen, H. A., Keith, M. W. & Tyler, D. J. Human nerve stimulation thresholds and selectivity using a multi-contact nerve cuff electrode. IEEE Trans. Neural Syst. Rehabil. Eng. 15, 76–82 (2007).
pubmed: 17436879
Hees, J. V. & Gybels, J. M. Pain related to single afferent C fibers from human skin. Brain Res. 48, 397–400 (1972).
pubmed: 4645215
Gorman, P. H. & Mortimer, J. T. The effect of stimulus parameters on the recruitment characteristics of direct nerve stimulation. IEEE Trans. Biomed. Eng. 30, 407–414 (1983).
pubmed: 6604691
McNeal, D. R., Baker, L. L. & Symons, J. T. Recruitment data for nerve cuff electrodes: implications for design of implantable stimulators. IEEE Trans. Biomed. Eng. 36, 301–308 (1989).
pubmed: 2921067
Henneman, E., Somjen, G. & Carpenter, D. O. Functional significance of cell size in spinal motoneurons. J. Neurophysiol. 28, 560–580 (1965).
pubmed: 14328454
Solomonow, M. External control of the neuromuscular system. IEEE Trans. Biomed. Eng. 31, 752–763 (1984).
pubmed: 6335484
Petrofsky, J. S. & Phillips, C. A. Impact of recruitment order on electrode design for neural prosthetics of skeletal muscle. Am. J. Phys. Med. 60, 243–253 (1981).
Peterson, B. E., Healy, M. D., Nadkarni, P. M., Miller, P. L. & Shepherd, G. M. ModelDB: an environment for running and storing computational models and their results applied to neuroscience. J. Am. Med. Inform. Assoc. 3, 389–398 (1996).
pubmed: 8930855
pmcid: 116323
McDougal, R. A. et al. Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience. J. Comput. Neurosci. 42, 1–10 (2017).
pubmed: 27629590
Birmingham, K. et al. Bioelectronic medicines: a research roadmap. Nat. Rev. Drug Discov. 13, 399–400 (2014).
pubmed: 24875080
Craig, A. D. How do you feel? Interoception: the sense of the physiological condition of the body. Nat. Rev. Neurosci. 3, 655–666 (2002).
pubmed: 12154366
Sharma, K. R. et al. Demyelinating neuropathy in diabetes mellitus. Arch. Neurol. 59, 758–765 (2002).
pubmed: 12020257
Hoeijmakers, J. G., Faber, C. G., Lauria, G., Merkies, I. S. & Waxman, S. G. Small-fibre neuropathies—advances in diagnosis, pathophysiology and management. Nat. Rev. Neurol. 8, 369–379 (2012).
pubmed: 22641108
Lang, G. E., Stewart, P. S., Vella, D., Waters, S. L. & Goriely, A. Is the Donnan effect sufficient to explain swelling in brain tissue slices? J. Roy. Soc. Int. 11, 20140123 (2014).
Valle, G. et al. Biomimetic intraneural sensory feedback enhances sensation naturalness, tactile sensitivity, and manual dexterity in a bidirectional prosthesis. Neuron 100, 37–45.e7 (2018).
pubmed: 30244887
Jehenne, B., Raspopovic, S., Capogrosso, M., Arleo, A. & Micera, S. Recording properties of an electrode implanted in the peripheral nervous system: a human computational model. in Proc. of the 7th International IEEE/EMBS Conference on Neural Engineering (IEEE, 2015).
Koh, R. G. L., Nachman, A. I. & Zariffa, J. Use of spatiotemporal templates for pathway discrimination in peripheral nerve recordings: a simulation study. J. Neural Eng. 14, 016013 (2016).
pubmed: 28000616
Garai, P., Koh, R. G. L., Shuettler, M., Stieglitz, T. & Zariffa, J. Influence of anatomical detail and tissue conductivity variations in simulations of multi-contact nerve cuff recordings. IEEE Trans. Neural Syst. Rehabil. Eng. 25, 1653–1662 (2017).
pubmed: 27898383
Choi, A. Q., Cavanaugh, J. K. & Durand, D. M. Selectivity of multiple-contact nerve cuff electrodes: a simulation analysis. IEEE Trans. Biomed. Eng. 48, 165–172 (2001).
pubmed: 11296872
Weerasuriya, A., Spangler, R. A., Rapoport, S. I. & Taylor, R. E. AC impedance of the perineurium of the frog sciatic nerve. Biophys. J. 46, 167–174 (1984).
pubmed: 6332648
pmcid: 1435024
Ranck, J. B. & BeMent, S. L. The specific impedance of the dorsal columns of cat: an anisotropic medium. Exp. Neurol. 11, 451–463 (1965).
pubmed: 14278100
Geddes, L. A. & Baker, L. E. The specific resistance of biological material—a compendium of data for the biomedical engineer and physiologist. Med. Biol. Eng. 3, 271–293 (1967).