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Neural Degeneration in Normal-Aging Human Cochleas: Machine-Learning Counts and 3D Mapping in Archival Sections

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Abstract

Quantifying the survival patterns of spiral ganglion cells (SGCs), the cell bodies of auditory-nerve fibers, is critical to studies of sensorineural hearing loss, especially in human temporal bones. The classic method of manual counting is tedious, and, although stereology approaches can be faster, they can only be used to estimate total cell numbers per cochlea. Here, a machine-learning algorithm that automatically identifies, counts, and maps the SGCs in digitized images of semi-serial human temporal-bone sections not only speeds the analysis, with no loss of accuracy, but also allows 3D visualization of the SGCs and fine-grained mapping to cochlear frequency. Applying the algorithm to 62 normal-aging human ears shows significantly faster degeneration of SGCs in the basal than the apical half of the cochlea. Comparison to fiber counts in the same ears shows that the fraction of surviving SGCs lacking a peripheral axon steadily increases with age, reaching more than 50% in the apical cochlea and almost 66% in basal regions.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Code Availability

The Code that support the findings of this study are available from GitHub.com, (https://github.com/PeizheWu/SGC-counting-and-3D-mapping). Further support is available from the corresponding author upon reasonable request.

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Funding

Research supported by grants from the National Institute on Deafness and other Communicative Disorders: P50 DC 015857 and U24 DC 020849.

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Contributions

PZW, JTO, and MCL conceived the study. PZW and JTO carried out the data collection. PZW created the computer algorithm and carried out the data analysis and the statistical analyses. MCL and PZW wrote the manuscript, and JTO edited it.

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Correspondence to Pei-zhe Wu.

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Wu, Pz., O’Malley, J.T. & Liberman, M.C. Neural Degeneration in Normal-Aging Human Cochleas: Machine-Learning Counts and 3D Mapping in Archival Sections. JARO 24, 499–511 (2023). https://doi.org/10.1007/s10162-023-00909-y

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