Keywords: Diagnosis/Prediction, Brain, neuroanatomical
Motivation: Emerging evidence suggests that Ménière's disease (MD) may extend beyond the confines of the inner ear, and involved the central nervous system.
Goal(s): To investigate the neuroanatomical alterations associated with MD and to develop a machine learning classification model to effectively discriminate between MD patients and HC.
Approach: A case-control morphometry study was performed to examine potential brain structural changes and delineate the diagnostic utility of these identified brain alterations.
Results: Distinctive alterations in gray matter volume and cortical thickness were identified in regions implicated in emotional processing and sensory integration. The classification model showcased a discriminative power with an impressive AUC value(0.92).
Impact: MD patients showed distinctive morphometry alterations, and were leveraged as potential biomarkers, facilitating the discrimination between MD and HC.These findings provide critical insights into the intricate neuroanatomical alterations in MD and highlight the diagnostic potential of advanced neuroimaging techniques.
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