Premature white matter aging in patients with right mesial temporal lobe epilepsy: A machine learning approach based on diffusion MRI data

Brain age prediction based on machine learning has been applied to various neurological diseases to discover its clinical values. By this innovative approach, it has been reported that the patients with refractory epilepsy had premature brain aging. Of refractory epilepsy, right and left subtypes of mesial temporal lobe epilepsy (MTLE) are the most common forms and exhibit distinct patterns in white matter alterations. So far, it is unclear whether these two subtypes of MTLE would have di ff erence in white matter aging due to distinct white matter alterations. To address this issue, a machine learning based brain age model using di ff usion MRI data was established to investigate biological age of white matter tracts. All di ff usion MRI datasets were obtained from the same 3-Tesla MRI scanner. To build the brain age prediction model, di ff usion MRI datasets of 300 healthy participants were processed to extract age-relevant di ff usion indices from 76 major white matter