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Multidimensional Analysis of Cochlear Pathology in Sensorineural Hearing Loss

Award Number: F32DC023799
ORGANIZATION: NATIONAL INSTITUTE ON DEAFNESS AND OTHER COMMUNICATION DISORDERS
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: FELLOWSHIP/SCHOLARSHIP/STUDENT LOANS
PERIOD OF PERFORMANCE START DATE: 04/16/2026
PERIOD OF PERFORMANCE END DATE: 04/15/2029

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Multidimensional Analysis of Cochlear Pathology in Sensorineural Hearing Loss - Project Summary Sensorineural hearing loss (SNHL) is a major public health concern and often originates with damage to cochlear hair cell (HC) stereocilia and the synapses between HCs and their afferent spiral ganglion neurons (SGNs). Current research quantifies these subcellular changes via post‑mortem imaging with manual or proprietary analysis, while clinical audiometry cannot resolve synaptic or stereocilia pathology in vivo. This gap limits mechanism‑based diagnostics, personalized treatment selection, and evaluation of novel therapeutics. This project leverages AI to automate high‑resolution quantification of HC synapses and stereocilia in fixed cochlear tissues, mine and harmonize legacy datasets of noise exposed and aged cochleae to characterize common and divergent patterns of structural change, and establish interpretable structure‑function correlations between imaging and noninvasive auditory brainstem response (ABR) data. Preliminary results show a 3D convolutional neural network segments presynaptic ribbons with >90% performance (F1 score) across data from five microscopes and four collaborating labs. Aim 1 extends these open-source pipelines for 3D segmentation and morphological analysis of synaptic markers across models of noise-induced and age-related hearing loss. Aim 2 quantifies synapse turnover in vivo using HaloTag pulse-chase labeling of AMPA receptors to develop dynamic measures of synaptic health across cochlear regions and hearing loss models. Aim 3 trains machine learning models to predict synapse and stereocilia structural features from paired auditory functional readouts. All code, trained models, and annotated datasets will be released under permissive open‑source licenses with comprehensive documentation and user tutorials. By linking noninvasive functional assays to subcellular cochlear structure, this work will pave the way for next-generation diagnostic biomarkers and accelerate preclinical screening of therapeutics. Training in both deep learning and auditory neuroscience will occur within a collaborative research environment with strengths in cochlear biology, 3D imaging, and computational neuroscience, preparing the fellow for an independent career at the intersection of structural and functional analysis of hearing.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $81,580 )
2026	2026	UNIVERSITY OF CALIFORNIA, SAN DIEGO	9500 GILMAN DR	LA JOLLA	CA	92093	SAN DIEGO	USA	Research Related to Deafness and Communication Disorders	001	1	4/16/2026	NEW	$0
2026	2026	UNIVERSITY OF CALIFORNIA, SAN DIEGO	9500 GILMAN DR	LA JOLLA	CA	92093	SAN DIEGO	USA	Research Related to Deafness and Communication Disorders	000	1	4/16/2026	NEW	$81,580
														Subtotal = $81,580

Grand Total All Awards = $81,580

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Multidimensional Analysis of Cochlear Pathology in Sensorineural Hearing Loss

Award Number: F32DC023799

ORGANIZATION: NATIONAL INSTITUTE ON DEAFNESS AND OTHER COMMUNICATION DISORDERS

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: FELLOWSHIP/SCHOLARSHIP/STUDENT LOANS

PERIOD OF PERFORMANCE START DATE: 04/16/2026

PERIOD OF PERFORMANCE END DATE: 04/15/2029

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