PROCEEDINGS of the23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Phenotyping and computational modelingof diverse forms of genetic hearing loss
Ian C. BRUCE1,a,; Michael R. WIRTZFELD1; Anne GRIFFIN2; Amanda K. MORGAN3;
Matthew B. LUCAS3, Jill LOWTHER2; Terry-Lynn YOUNG2; Susan G. STANTON3
12McMaster University, Canada

Memorial University Newfoundland, Canada
3

Western University, Canada

EXTENDED ABSTRACT
Computational models of auditory processing that can incorporate pathology (1, 2, 3) may be helpful inunderstanding the effects of hearing impairment (4, 5, 6) and in the development of improved devices for
those with hearing loss (7), such as hearing aids and cochlear implants. However, incorporating pathology
into computational models to explain a human subject’s experimental data is typically problematic due to alack of definitive knowledge about the pathology that they have.
A number of diverse forms of genetic hearing impairment have been molecularly characterizedfrom the genetically isolated human population of Newfoundland, Canada. Affected members of thesefamilies form a useful pool of subjects to target for computational modeling, as within each extended
family the specific genetic mutation leads to a similar pattern of pathology and resulting
pathophysiology and perceptual deficits, but the types of hearing loss are very different between
families due to different gene mutations.
A CLDN14 autosomal recessive mutation, affecting the protein Claudin14 that regulates theformation of tight cellular junctions, produces precipitous mid/high frequency hearing loss at around
4–6 years of age in 15 affected family members (8). A FOXL1 autosomal dominant mutation, affecting
the signaling protein FOXL1, gives rise to otosclerosis in 11 individuals (9). A KCNQ4 autosomal
dominant mutation affecting the Kv7.4 ion channel leads to progressive high frequency hearing loss inapproximately 13 affected family members (10). A WFS1 autosomal dominant mutation, affecting theprotein Wolframin that is involved in intracellular Ca 2+ regulation, gives rise to a nonsyndromic
low-frequency hearing loss in 28 affected individuals (11).
We are developing computational models incorporating the suspected cochlear pathology for eachaffected relative with a specific mutation, based on psychophysical tuning curves (12) anddistortion-product otoacoustic emission (DPOAE) growth functions (13), in addition to routine
audiometric measures. The model for each research subject will be validated via quantitative
predictions of advanced electrophysiological recordings, specifically auditory brainstem responses(ABRs) and electrocochleograms (ECochGs), and word perception in quiet and noise (14) for that
subject. The electrophysiological measurements were collected at a range of click rates using
continuous loop averaging deconvolution (CLAD) sequences, in order to take into considerationneural adaptation at high click rates (15). Model predictions of the ABRs and ECochG will use theunitary response function approach of (16), while word perception predictions will make use ofphysiologically-based speech intelligibility predictors (17).
Keywords: Auditory modeling; genetic hearing loss; human electrophysiology

a686

ibruce@ieee.org

ACKNOWLEDGEMENTS
Supported by Atlantic Canada Opportunities Agency- Innovative Communities Fund Project #204210 and Business Development Program Project # 205595, the Government of Newfoundland andLabrador Regional Development Fund Ref # 30-11044-003, Excite Corporation, and NSERC
Discovery Grant 261736.

REFERENCES
1. Bruce IC, Erfani Y, Zilany MSA. A phenomenological model of the synapse between the inner hair celland auditory nerve: Implications of limited neurotransmitter release sites. Hear Res. 2018; 360: 40–54.
2. Verhulst S, Altoè A, Vasilkov. Computational modeling of the human auditory periphery:
Auditory-nerve responses, evoked potentials and hearing loss. Hear Res. 2018; 360: 55–75.
3. Sumner CJ, O'Mard LP, Lopez-Poveda EA, Meddis R. A nonlinear filter-bank model of the guinea-pig
cochlear nerve: rate responses. J Acoust Soc Am. 2003; 113(6): 3264–3274.
4. Zilany MSA, Bruce IC. Predictions of speech intelligibility with a model of the normal and impaired
auditory-periphery. Proc 3rd International IEEE/EMBS Conference on Neural Engineering; May 2–5,
2007; Kohala Coast, HI, USA 2007. pp. 481–485.
5. Verhulst S, Jagadeesh A, Mauermann M, Ernst F. Individual differences in auditory brainstem response
wave characteristics: relations to different aspects of peripheral hearing loss. Trends Hear. 2016; 20:1–20.
6. Meddis R, Lecluyse W, Clark NR, Jürgens T, Tan CM, Panda MR, Brown GJ. A computer model of theauditory periphery and its application to the study of hearing. In: Moore BCJ, Patterson RD, Winter IM,
Carlyon RP, Gockel HE, editors. Basic Aspects of Hearing. Advances in Experimental Medicine andBiology, vol 787. New York, NY: Springer; 2013. p. 11–20.
7. Sachs MB, Bruce IC, Miller RL, Young ED. Biological basis of hearing-aid design. Ann Biomed Eng.
2002; 30(2):157–168.
8. Pater JA, Benteau T, Griffin A, Penney C, Stanton SG, Predham S, Kielley B, Squires J, Zhou J, Li Q,
Abdelfatah N, O’Rielly DD, Young T-L. A common variant in CLDN14 causes precipitous, prelingual
sensorineural hearing loss in multiple families due to founder effect. Hum Genet. 2017; 136(1):107–118.
9. Abdelfatah N. The Genetic Aetiology of Otosclerosis in the Population of Newfoundland and Labrador.
PhD Thesis, Memorial University Newfoundland 2014.
10.Abdelfatah N, McComiskey DA, Doucette L, Griffin A, Moore SJ, Negrijn C, Hodgkinson KA, King JJ,Larijani M, Houston J, Stanton SG, Young TL. Identification of a novel in-frame deletion in KCNQ4(DFNA2A) and evidence of multiple phenocopies of unknown origin in a family with ADSNHL. Eur J
Hum Genet. 2013;21(10):1112–1119.
11. Young TL, Ives E, Lynch E, Person R, Snook S, MacLaren L, Cator T, Griffin A, Fernandez B, Lee MK,
King MC. Non-syndromic progressive hearing loss DFNA38 is caused by heterozygous missense
mutation in the Wolfram syndrome gene WFS1. Hum Mol Gen. 2001; 10(22): 2509–2514.
12. Sek A, Moore BCJ. Implementation of a fast method for measuring psychophysical tuning curves. Int JAudiol. 2011; 50:237–242.
13. Kummer P, Janssen T, Arnold W. The level and growth behavior of the 2f1-f2 distortion productotoacoustic emission and its relationship to auditory sensitivity in normal hearing and cochlear hearing
loss. J Acoust Soc Am. 1998; 103(6):3431–3444.
14. Tillman TW, Carhart R. An expanded test for speech discrimination utilizing CNC monosyllabic words.
Northwestern University Auditory Test No. 6, USAF School of Aerospace Medicine Technical Report,Brooks Air Force Base, TX; 1966. p. 1–12
15. Delgado RE, Ozdamar O. Deconvolution of evoked responses obtained at stimulus rates. J Acoust Soc
Am. 2004; 115(3):1242–1251.
16.Rønne FM, Dau T. Modeling auditory evoked brainstem responses to transient stimuli. J Acoust Soc Am.
2012; 131(5):3903–3913.
17. Wirtzfeld MR, Ibrahim RA, Bruce IC. Predictions of speech chimaera intelligibility using auditory
nerve mean-rate and spike-timing neural cues. JARO. 2017; 18(5):687–710.

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