A machine learning approach to explore cognitive signatures in patients with temporo-mesial epilepsy

We aimed to identify cognitive signatures (phenotypes) of patients suffering from mesial temporal lobe epilepsy (mTLE) with respect to their epilepsy lateralization (left or right), through the use of SVM (Support Vector Machine) and XGBoost (eXtreme Gradient Boosting) machine learning (ML) algorithms. Specifically, we explored the ability of the two algorithms to identify the most significant scores (features, in ML terms) that segregate the left from the right mTLE patients. We had two versions of our dataset which consisted of neuropsychological test scores: a "reduced and working" version (n = 46 patients) without any missing data, and another one "original" (n = 57) with missing data but useful for testing the robustness of results obtained with the working dataset. The emphasis was placed on a precautionary machine learning (ML) approach for classification, with reproducible and generalizable results. The effects of several clinical medical variables were also studied. We obtained excellent predictive classification performances (>75%) of left and right mTLE with both versions of the dataset. The most segregating features were four language and memory tests, with a remarkable stability close to 100%. Thus, these cognitive tests appear to be highly relevant for neuropsychological assessment of patients. Moreover, clinical variables such as structural asymmetry between hippocampal gyri, the age of patients and the number of anti-epileptic drugs, influenced the cognitive phenotype. This exploratory study represents an in-depth analysis of cognitive scores and allows observing interesting interactions between language and memory performance. We discuss implications of these findings in terms of clinical and theoretical applications and perspectives in the field of neuropsychology.