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 difference in white matter aging due to distinct white matter alterations. To address this issue, a machine learning based brain age model using diffusion MRI data was established to investigate biological age of white matter tracts. All diffusion MRI datasets were obtained from the same 3-Tesla MRI scanner. To build the brain age prediction model, diffusion MRI datasets of 300 healthy participants were processed to extract age-relevant diffusion indices from 76 major white matter tracts. The extracted diffusion indices underwent Gaussian process regression to build the prediction model for white matter brain age. The model was validated in an independent testing set (N = 40) to ensure no overfitting of the model. The model was then applied to patients with right and left MTLE and matched controls (right MTLE: N = 17, left MTLE: N = 18, controls: N = 37), and predicted age difference (PAD) was obtained by calculating the difference between each individual's predicted brain age and chronological age. The higher PAD score indicated older brain age. The results showed that right MTLE exhibited older predicted brain age than the other two groups (PAD of right MTLE = 10.9 years [p < 0.05 against left MTLE; p < 0.001 against control]; PAD of left MTLE = 2.2 years [p > 0.1 against control]; PAD of controls = 0.82 years). Patients with right and left MTLE showed strong correlations of the PAD scores with age of onset and duration of illness, but both groups showed opposite directions of correlations. In right MTLE, positive correlation of PAD with seizure frequency was found, and the right uncinate fasciculus was the most attributable tract to the increase in PAD. In conclusion, the present study found that patients with right MTLE exhibited premature white matter brain aging and their PAD scores were correlated with seizure frequency. Therefore, PAD is a potentially useful indicator of white matter impairment and disease severity in patients with right MTLE.
['Chen|Chang-Le|CL|', 'Shih|Yao-Chia|YC|', 'Liou|Horng-Huei|HH|', 'Hsu|Yung-Chin|YC|', 'Lin|Fa-Hsuan|FH|', 'Tseng|Wen-Yih Isaac|WI|']
D000293:Adolescent D000328:Adult D017668:Age of Onset D000368:Aged D000369:Aged, 80 and over D019588:Aging, Premature D002648:Child D038524:Diffusion Magnetic Resonance Imaging D004833:Epilepsy, Temporal Lobe D005260:Female D007839:Functional Laterality D006801:Humans D007091:Image Processing, Computer-Assisted D000069550:Machine Learning D008297:Male D008875:Middle Aged D008959:Models, Neurological D016011:Normal Distribution D012640:Seizures D066127:White Matter D055815:Young Adult
Mesial temporal lobe epilepsy Machine learning Diffusion MRI White matter brain age Diffusion spectrum imaging