Abstract #0960

Advanced brain aging in patients with right mesial temporal lobe epilepsy: A machine learning approach based on white matter tract integrity

Chang-Le Chen¹, Yao-Chia Shih^1,2, Horng-Huei Liou^3,4, Yung-Chin Hsu⁵, Fa-Hsuan Lin², and Wen-Yih Isaac Tseng^1,4,6

¹Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, Taipei, Taiwan, ²Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan, ³Department of Neurology, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan, ⁴Graduate Institute of Brain and Mind Sciences, College of Medicine, National Taiwan University, Taipei, Taiwan, ⁵AcroViz Technology Inc., Taipei, Taiwan, ⁶Molecular Imaging Center, National Taiwan University, Taipei, Taiwan

It is unclear whether left and/or right side lesions of mesial temporal lobe epilepsy (MTLE) exhibit different degrees of brain aging. Therefore, we developed machine-learning-based brain age models to quantify the brain aging of patients with unilateral MTLE and of healthy controls. The significantly overestimated brain age was found in the right but not left MTLE patients. Also, the degree of overestimated brain age was correlated with the clinical factors. Moreover, the right uncinate fasciculus was the most contributing feature to the overestimated brain age. This study uncovered the underpinning of advanced brain aging in right MTLE patients.

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