Accounting for interindividual variability may improve prognosis of patients with drug-resistant temporal lobe epilepsy (TLE).
Why this matters
TLE can be characterized by mesiotemporal sclerosis, neuronal loss, gliosis, demyelination, and axonal degradation; however, in vivo magnetic resonance imaging (MRI) studies have shown widespread heterogeneity in patients with TLE.
The “one-size-fits-all” analytical approach for patients with TLE does not consider interindividual variations, making drug response, surgical outcome, and cognitive dysfunction difficult to predict. To address MRI heterogeneity among patients, machine learning may be useful to identify disease factors (i.e., structural pathologies) in patients with TLE.