2015/07/15

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Design and application of user-specific models of

cochlear implants

Tania HanekomTiaan K Malherbe, Liezl Gross, Rene Baron, Riaze Asvat,

Werner Badenhorst & Johan J Hanekom

UP BioengineeringOur people

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Larry Schmidt, Tiaan Malherbe, Johannes Myburgh, Pieter Venter, Rene Baron, Dirk Oosthuizen, Johanie Roux, Werner Badenhorst, Liza Blignaut, Johan Hanekom, Liezl Gross, Heinrich Crous, Tania Hanekom, Alex Oloo. Insert: Riaze Asvat.

UP BioengineeringOur place

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www.up.ac.za/eece

UP Bioengineeringis part of

Electrical, Electronic and Computer Engineering

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http://www.ee.up.ac.za/main/emk310/index

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Agenda

• The development of our user-specific models– Human and guinea pig model generations

– What the models can do

– What the models show

• Translating models into tools– Research tools

– Model-predicted mapping (MPM)

– Model-based diagnostics (MBD)

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Progression of UP Bioengineering's volume conduction models

Human Generation 1

HG1: Generalised human cochlea extruded on analytical spiral from 2D section (1996-2001).

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User-specificity in cochlear implants…

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• Realised that models need to migrate to include user-specific characteristics

• Started to work on user-specific models in 2007• Micro-CT data from guinea-pig subject plus neural data from same subject

was made available through Russ Snyder/ Ben Bonham from Pat Leake’s lab (Epstein Labs)

Micro-CT of subject’s cochlea

Acoustic frequency response areas of the 16 electrode contacts implanted in the inferior colliculus

What qualifies subject-specificity in animals?

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• Subject-specificity characterised by, among others:

– duration of cochlear implantation and duration of deafness

– age of implantation

– stimulation mode (BP, MONO, CG)

– electrode insertion depth

– design of the electrode array

– position of the electrode array

– neural survival patterns

– cochlear morphometry

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Progression of UP Bioengineering's volume conduction models

Guinea pig Generation 1

GPG1: From CT to FEM. Subject-specific cochlear morphometry and electrode location included (2007-2009).

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Progression of UP Bioengineering's volume conduction models

Guinea pig Generation 2

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GPG2: Adding subject-specificity: bone capsule and hook area (cochlear morphometry), return electrode location (2007-2009).

What qualifies user-specificity in humans?

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• Speech perception variability caused by, among others:

– duration of cochlear implantation and duration of deafness

– age of implantation

– stimulation mode (BP, MONO, CG)

– electrode insertion depth

– design of the electrode array

– position of the electrode array

– neural survival patterns

– cochlear morphometry

– speech perception before implantation

– speech processing algorithm used

– unilateral or bilateral implantation

– …

From dead guinea pig to live human

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Challenge 1: Resolution of image data VC model

Challenge 2: Access to neural response data ANF model

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Progression of UP Bioengineering's volume conduction cochlear models

Human Generation 2

HG2: Realistic generalised human cochlea extruded on spiral derived from mid-modiolar section of cochlea (2009).

TEMPLATE MODEL USED AS BASE FOR SUBSEQUENT GENERATIONS

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HG3. Person-specific models based on CT data from live implantees (2010>>).

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Progression of UP Bioengineering's volume conduction cochlear models

Human Generation 3

HG1-3 & GPG1. Cochlea embedded into infinite bone volume (outer surface of sphere/cylinder modelled at infinity).

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Progression of UP Bioengineering's volume conduction cochlear models

Extra-cochlear volume: infinite homogeneous

HG4. Cochlea embedded into head-sized ellipsoid bone volume with accurate description of return electrode.

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Progression of UP Bioengineering's volume conduction cochlear models

Human Generation 4

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Progression of UP Bioengineering's volume conduction cochlear models

Human Generation 5

HG5. Cochlea embedded into skull with brain and scalp and accurate description of return electrode.

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HG5. Detail of return electrode placement.

Progression of UP Bioengineering's volume conduction cochlear models

Human Generation 5

Current status of VC model

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• User-specific cochlear morphometry (macro characteristics)

• User-specific electrode location

• Correct return electrode location

• Description of skull, brain and scalp

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2013: Initiated project to create morphometric library of inner structure templates

• collaborate with Dept Anatomy, Faculty of Health Sciences

• address low-res/soft tissue problem

• Improve user-specific model representation of morphometry

• 60 dry skulls imaged and digitized to date (micro-CT)

Progression of UP Bioengineering's volume conduction cochlear models

NEXT: Human Generation 6

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Progression of UP Bioengineering's auditory nerve fibre (ANF) models

ANF models integrate with VC models to predict neural excitation from spread of electrical activity.

We have used / are using• GSEF / Hodgkin-Huxley / Rattay (literature)

We have worked on• Smith-2·Hanekom model (own)

Problems• Single fibre instead of population• Electrical stimulation• Bottom-line: can't predict absolute thresholds; trends okay

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• Working to create physiologically-based neural models that can predict responses to electrical stimulation– Computationally INTENSIVE!

POSTER W26

Development of a voltage dependent current noise algorithm for conductance based stochastic modelling of auditory nerve fibre populations in compound models

Werner Badenhorst

Progression of UP Bioengineering's auditory nerve fibre (ANF) models

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How big is the influence of person-specificity on the periphery?

• Neural threshold profiles of – five cochlear models – inserted with identical medial and lateral arrays – stimulated on electrode 4 – inserted at the same angle in all the models.

• Mean medial-lateral difference in thresholds: 6.4 dB

• Mean medial-lateral difference in CFs: 2988 Hz.

Isti

m [

dB

re

1

A]

S13RS13LS3RS3LS25R

65

70

75

80

85 Lateral Array

S13RS13LS3RS3LS25R

Medial Array

Isti

m [

dB

re

1

A]

apex base

4000 8000 12000 1600065

70

75

80

85

Frequency along Organ of Corti [Hz]

a) Non-degenerate Neurons

b) Degenerate Neurons

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Inter-user variability of 3.93 dB (both)

Inter-user variability of 2535 Hz (medial) and 1992 Hz (lateral).

But are the models useful?

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– duration of cochlear implantation and duration of deafness

– age of implantation

– speech perception before implantation

– speech processing algorithm used

– stimulation mode used

– unilateral or bilateral implantation

– electrode insertion depth

– design of the electrode array

– position of the electrode array

– cochlear morphometry

– neural survival patterns

– complications

e.g. Scar tissue

e.g. Bone impedance

??

Predict characteristics of peripheral neural excitation using a specific speech algorithm and a specific stimulation mode

(e.g. frequency matching between ears)

Integral part of the VC models; affect neural excitation.

Probe the effect of neural survival on excitation characteristics

e.g. FNS

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Translating models into tools

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• Research tools

– Underlying functioning of auditory system

• Clinical tools

– Visualization

– Model-predicted mapping (MPM)

– Models-based diagnostics (MBD)

1. Find ways to build models quickly

2. Augment images to improve representation of inner structures (HG6)

3. Define what we need from clinicians to enable us to do this as a routine procedure– Pre-op & post-op scans, neurophysiological data,

psychoacoustic data, etc.

– Challenge in SA: User records very difficult to find, e.g. can’t find user records of imaging data older than a couple of months.

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Research Tools

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• Probe the fundamentals that underpin the functioning of the auditory system

• Model parameters

– Quantify characteristics of the system so that we can describe it with mathematics

– Example: bone impedance

Research Tools

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• From our models we know bone impedance affect spread of electrical activity, i.e. neural excitation

Equipotential surfaces at 0.5 dB below the electrode potential.

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Clinical ToolsVisualization

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Original CT image

Image derived from model

Scala Vestibule insertion

Scala Tympani insertion

Possible damage to cochlear wall

Clinical ToolsModel-Predicted Mapping (MPM)

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Example: tool to estimate spread of excitation • Commonly accepted 3 dB/mm decay for monopolar stimulation does not

hold.• Depends on

o location in cochlea (basal vs apical)o intrascaler location

• Derive equation to give a “quick-and-dirty” estimation of real current decay.o Useful for mappingo Also useful as a research tool in models that use current decay, e.g.

acoustic models.Scala Vestibuli

Reissner’s membrane

Stria Vascularis

Scala MediaOrgan of Corti

Buffer areaScala tympani

Basilar membrane

Spiral ligament

Axonal nerve

Sourcepositions

Clinical ToolsModel-Based Diagnostics (MBD)

Example: A case of facial nerve stimulation

Potential of MBD• Investigate the factors that may cause FNS

o e.g. current paths• Investigate the potential effectiveness of interventions

based on the current implanted systemo e.g. optimal electrode configuration

• Investigate the potential effectiveness of customized interventions designed for the individualo e.g. alternative stimulation strategies

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For details about model-based tools in development

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POSTER W28Model-based interventions in cochlear implants

Potential distributions as a result of stimulation with different electrodes

Nerve fibre plane

Electrode array

Electrode array

Scala vestibule insertion

Auditory nerveSpiral ganglion

Red area indicates location of electrode contact

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Other work

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Neurophysiology Perception

Computationalmodels

Parameters

1. Model of central processing

2. Acoustic Models 3. Volume

conduction model

4. Nerve fibre model

Models of the physical situationModels of perception

1. Psychoacoustics

2. Speech perception

Poster R13 NEW APPROACHES TO FEATURE INFORMATION TRANSMISSION ANALYSIS (FITA)

Dirk JJ Oosthuizen, Johan J HanekomPoster R58

RATE PITCH WITH MULTI-ELECTRODE STIMULATION PATTERNS: CONFOUNDING CUESPieter J Venter, Johan J Hanekom

Thank you

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For postdoc positions in our group, speak to

Prof Johan HanekomHead of UP BioengineeringJohan.hanekom@up.ac.za

or

Prof Tania Hanekomtania.hanekom@up.ac.za