Back

Towards a differential diagnosis of cochlear synaptopathy and outer-hair-cell deficits in mixed sensorineural hearing loss pathologies

Vasilkov, V.; Verhulst, S.

2019-11-05 otolaryngology
10.1101/19008680 medRxiv
Show abstract

Damage to the auditory periphery is more widespread than predicted by the gold-standard clinical audiogram. Noise exposure, ototoxicity and aging can destroy cochlear inner-hair-cell afferent synapses and result in a degraded subcortical representation of sound while leaving hearing thresholds unaffected. Damaged afferent synapses, i.e. cochlear synaptopathy, can be quantified using histology, but a differential diagnosis in living humans is difficult: histology cannot be applied and existing auditory evoked potential (AEP) metrics for synaptopathy become insensitive when other sensorineural hearing impairments co-exist (e.g., outer-hair-cell damage associated with elevated hearing thresholds). To develop a non-invasive diagnostic method which quantifies synaptopathy in humans and animals with normal or elevated hearing thresholds, we employ a computational model approach in combination with human AEP and psychoacoustics. We propose the use of a sensorineural hearing loss (SNHL) map which comprises two relative AEP-based metrics to quantify the respective degrees of synaptopathy and OHC damage and evaluate to which degree our predictions of AEP alterations can explain individual data-points in recorded SNHL maps from male and female listeners with normal or elevated audiometric thresholds. We conclude that SNHL maps can offer a more precise diagnostic tool than existing AEP methods for individual assessment of the synaptopathy and OHC-damage aspect of sensorineural hearing loss. Significance StatementHearing loss ranks fourth in global causes for disability and risk factors include noise exposure, ototoxicity and aging. The most vulnerable parts of the cochlea are the inner-hair-cell afferent synapses and their damage (cochlear synaptopathy) results in a degraded subcortical representation of sound. While synaptopathy can be estimated reliably using histology, it cannot be quantified this way in living humans. Secondly, other co-existing sensorineural hearing deficits (e.g., outer-hair-cell damage) can complicate a differential diagnosis. To quantify synaptopathy in humans and animals with normal or elevated hearing thresholds, we adopt a theoretical and interdisciplinary approach. Sensitive diagnostic metrics for synaptopathy are crucial to assess its prevalence in humans, study its impact on sound perception and yield effective hearing restoration strategies.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

1
The Journal of Neuroscience
928 papers in training set
Top 0.1%
34.3%
2
Hearing Research
49 papers in training set
Top 0.1%
23.4%
50% of probability mass above
3
Trends in Hearing
12 papers in training set
Top 0.1%
5.0%
4
Journal of the Association for Research in Otolaryngology
11 papers in training set
Top 0.1%
5.0%
5
iScience
1063 papers in training set
Top 6%
3.2%
6
Scientific Reports
3102 papers in training set
Top 40%
3.2%
7
The Journal of the Acoustical Society of America
33 papers in training set
Top 0.1%
2.8%
8
PLOS ONE
4510 papers in training set
Top 44%
2.7%
9
Ear & Hearing
15 papers in training set
Top 0.1%
1.8%
10
Frontiers in Neuroscience
223 papers in training set
Top 4%
1.7%
11
Nature Communications
4913 papers in training set
Top 50%
1.7%
12
Communications Biology
886 papers in training set
Top 12%
1.4%
13
Communications Medicine
85 papers in training set
Top 0.4%
1.4%
14
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 40%
0.9%
15
Philosophical Transactions of the Royal Society B
51 papers in training set
Top 5%
0.8%
16
Frontiers in Immunology
586 papers in training set
Top 8%
0.7%
17
Biophysical Journal
545 papers in training set
Top 6%
0.7%
18
Frontiers in Neurology
91 papers in training set
Top 6%
0.7%
19
PLOS Genetics
756 papers in training set
Top 16%
0.7%
20
NeuroImage
813 papers in training set
Top 7%
0.5%
21
eLife
5422 papers in training set
Top 62%
0.5%
22
Journal of Clinical Medicine
91 papers in training set
Top 8%
0.5%
23
PLOS Computational Biology
1633 papers in training set
Top 28%
0.5%