Abstract
Current medical diagnosis and treatment methods for neurogenic lower urinary tract dysfunction (NLUTD) disorders are constrained by our limited understanding of how a set of the complex neural circuits that regulate the LUT function. Identifying robust biomarkers for perceived bladder sensation could be key to advancing diagnostic and therapeutic modalities for NLUTD. In this work, we applied a transfer learning approach to infer bladder fullness sensation from functional magnetic resonance imaging (fMRI) data. While the proposed approach effectively represented fMRI scans in the embedding space, it did not predict bladder fullness sensation significantly better than random chance.