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We here take advantage of recent non-invasive Computed Tomography (CT) imaging to infer transport parameters in a 1-dimensional advection\u2013diffusion model of the cochlear aqueduct using a Bayesian approach.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Six male C56BL\/6 mice injected with the small molecule tracer iohexol in cisterna magna were scanned every 5 min for 30 min in CT under anesthesia. Using the CT data to set boundary conditions, we solve the advection\u2013diffusion equation for given advection and spatially varying diffusion parameters. The CT data is modeled as normally distributed around the model-predicted concentration. We specify priors for the model unknown parameters and infer their posterior distributions using Bayes\u2019 formula and the CUQIpy library. The statistical approach is validated using synthetic data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The evolution of the concentration of tracer in the cochlear aqueduct is well predicted by the advection\u2013diffusion model using inferred parameters. Though diffusion of the small CT tracer varies along the aqueduct, it is usually near the free diffusion and therefore not likely to be influenced much by membranes or flow. Advection is inferred near zero in most cases. We show how to use these results to calculate transport through the cochlear aqueduct for other animals and molecules.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Free diffusion dominates transport of small molecules in the cochlear aqueduct, which can therefore be effectively approximated using simple 1-dimensional (advection-)diffusion formulas.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s10162-026-01047-x","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T16:24:50Z","timestamp":1776097490000},"page":"555-567","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Cochlear Aqueduct Advection and Diffusion Inferred from Computed Tomography Imaging with a Bayesian Approach"],"prefix":"10.1007","volume":"27","author":[{"given":"Amal M. 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The procedures were performed in accordance with the European directive 2010\/63\/EU, with due care to minimize the number of animals included in the study.","order":1,"name":"Ethics","label":"Ethics Approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"M.N. and B.K.M. have a pending patent application for delivery of therapeutics to the inner ear via the cisterna magna (international patent application no. PCT\/US22\/82226, title: \u201cCompositions and methods for delivery of agents to the inner ear\u201d). M.N. is a paid advisor and stockholder in CNS2 Inc., from which her lab also receives sponsored research for work unrelated to the study. The other authors declare that they have no other competing interests.","order":2,"name":"Ethics","label":"Competing Interests","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Artificial intelligence was not used in making this manuscript.","order":3,"name":"Ethics","label":"Use of AI","group":{"name":"EthicsHeading","label":"Declarations"}}]}}