Original Contributions
Mechanisms of Electrical Stimulation with Neural Prostheses

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ABSTRACT

Individual electric and geometric characteristics of neural substructures can have surprising effects on artificially controlled neural signaling. A rule of thumb approved for the stimulation of long peripheral axons may not hold when the central nervous system is involved. This is demonstrated here with a comparison of results from the electrically stimulated cochlea, retina, and spinal cord. A generalized form of the activating function together with accurate modeling of the neural membrane dynamics are the tools to analyze the excitation mechanisms initiated by neural prostheses. Analysis is sometimes possible with a linear theory, in other cases, simulation of internal calcium concentration or ion channel current fluctuations is needed to see irregularities in spike trains. Spike initiation site can easily change within a single target neuron under constant stimulation conditions of a cochlear implant. Poor myelinization in the soma region of the human cochlear neurons causes firing characteristics different from any animal data. Retinal ganglion cells also generate propagating spikes within the dendritic tree. Bipolar cells in the retina are expected to respond with neurotransmitter release before a spike is generated in the ganglion cell, even when they are far away from the electrode. Epidural stimulation of the lumbar spinal cord predominantly stimulates large sensory axons in the dorsal roots which induce muscle reflex responses. Analysis with the generalized activating function, computer simulations of the nonlinear neural membrane behavior together with experimental and clinical data analysis enlighten our understanding of artificial firing patterns influenced by neural prostheses.

Section snippets

INTRODUCTION

Which neural elements are activated by electrical stimulation in different parts of the nervous system? What are the principles to control complex neural processes with neural prostheses? We analyze local responses and network influences to handle these questions.

The first response of long homogeneous nerve or muscle fibers to an electric field is proportional to the second derivative of the electric potential along the fiber (1., 2., 3., 4.). This theory of the activating function is based on

Computer Simulation

The direct influence of a neuro-prosthetic device on a target neuron can be modeled in a two-step procedure. First we have to compute the electric potential Ve along the neural structures. This can be done with finite element (28,36,38), finite difference (40), boundary element (41), or Galerkin methods (42); all these methods are described in Ref. 43. In basic investigations authors often follow (1., 2., 3.,32,34,37) an assumption of the pioneering McNeal model (44) where for a point source in

Cochlear Neuron

For a given electrode position spike initiation regions in a target neuron depend on polarity and stimulus strength. Small variations in the pulse amplitude may cause essentially different arrival times at the axon terminal. This is demonstrated with a bipolar neuron close to a stimulating electrode of a cochlear implant (Fig. 3). Extracellular resistivity Ve = 0.3 kOhm.cm and calculation of potential from a 1 mA point source results in 1 V at r = 0.24 mm (Eq. 1), which is assumed as electrode

DISCUSSION

Fortunately, it is not necessary to generate an exact copy of the natural firing pattern when a neuroprosthetic device is used. The surprising amount of plasticity of the brain was demonstrated by successfully applying quite different stimulating strategies in cochlear implants or by a four-electrode cuff around the optic nerve that—after training—allows a blind patient to recognize different shapes, line orientations, and even letters depending on duration, amplitude, and repetition rate of

ACKNOWLEDGMENT

This work was supported by the Austrian Ministry of Transport, Innovation and Technology, the Austrian Science Fund (FWF), research project No. P15469; and a grant from the Kent Waldrep National Paralysis Foundation in Addison, TX.

REFERENCES (83)

  • JavelE et al.

    Electrical stimulation of the auditory nerve. III. Response initiation sites and temporal fine structure

    Hear Res

    (2000)
  • RubinsteinJT.

    Threshold fluctuations in an N sodium channel model of the node of Ranvier

    Biophys J

    (1995)
  • RubinsteinJT et al.

    Pseudospontaneous activity: stochastic independence of auditory nerve fibers with electrical stimulation

    Hear Res

    (1999)
  • McIntyreCC et al.

    Excitation of central nervous system neurons by nonuniform electric fields

    Biophys J

    (1999)
  • StettA et al.

    Electrical multisite stimulation of the isolated chicken retina

    Vision Res

    (2000)
  • HumayunMS et al.

    Pattern electrical stimulation of the human retina

    Vision Res

    (1999)
  • HeJ et al.

    Perception threshold and electrode position for spinal cord stimulation

    Pain

    (1994)
  • RattayF.

    Analysis of models for external stimulation of axons

    IEEE Trans Biomed Eng

    (1986)
  • RattayF.

    Analysis of models for extracellular fiber stimulation

    IEEE Trans Biomed Eng

    (1989)
  • RattayF.

    Electrical Nerve Stimulation: Theory Experiments and Applications

    (1990)
  • RauscheckerJP et al.

    Sending sound to the brain

    Science

    (2002)
  • CraeliusW.

    The bionic man: restoring mobility

    Science

    (2002)
  • ShealyCN et al.

    Electrical inhibition of pain by stimulation of the dorsal columns: preliminary clinical report

    Anesth Analg

    (1967)
  • LongDM et al.

    Stimulation of the posterior columns of the spinal cord for relief of intractable pain

    Surg Neurol

    (1975)
  • BarolatG.

    Spinal cord stimulation for persistent pain management

  • CookAW et al.

    Chronic dorsal column stimulation in multiple sclerosis. Preliminary report

    N Y State J Med

    (1973)
  • BarolatG et al.

    Effects of spinal cord stimulation on spasticity and spasms secondary to myelopathy

    Appl Neurophysiol

    (1988)
  • GybelsJ et al.

    Spinal cord stimulation for spasticity

  • DimitrijevicMR.

    Chronic spinal cord stimulation for spasticity

  • PinterMM et al.

    Epidural electrical stimulation of posterior structures of the human lumbosacral cord: 3. Control of spasticity

    Spinal Cord

    (2000)
  • GildenbergPL.

    Treatment of spasmodic torticollis by dorsal column stimulation

    Appl Neurophysiol

    (1978)
  • SiegfriedJ et al.

    Electrical spinal cord stimulation for spastic movement disorders

    Appl Neurophysiol

    (1978)
  • WaltzJM.

    Chronic stimulation for motor disorders

  • RosenfeldJE et al.

    Evidence of a pattern generator in paralyzed subjects with spinal cord injury during spinal cord stimulation

    Soc Neurosci Abstr

    (1995)
  • GerasimenkoY et al.

    Stepping movements in paraplegic patients induced by spinal cord stimulation

    Soc Neurosci Abstr

    (1996)
  • DimitrijevicMR et al.

    Effect of reduced afferent input on lumbar CPG in spinal cord injury subjects

    Soc Neurosci

    (1998)
  • PinterMM et al.

    Effect of motor task on externally induced stepping movement in spinal cord subjects

    Soc Neurosci

    (1998)
  • DimitrijevicMR et al.

    Study of locomotor capabilities induced by spinal cord stimulation (SCS) of the human lumbar cord isolated from the brain control by post traumatic spinal cord injury

    Soc Neurosci

    (2001)
  • MinassianK et al.

    Effective spinal cord stimulation (SCS) train for evoking stepping locomotor movement of paralyzed human lower limbs due to SCI elicits a late response additionally to the early monosynaptic response

    Soc Neurosci

    (2001)
  • DimitrijevicMR et al.

    Evidence for a spinal central pattern generator in humans. In: Kiehn O, Harris-Warrik RM, Jordan LM, Hultborn H, Kudo N eds. Neural Mechanisms for Generating Locomotor Activity.

    Ann N Y Acad Sci

    (1998)
  • CoburnB.

    Neural modeling in electrical stimulation

    Crit Rev Biomed Eng

    (1989)
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