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.

我们的目的是通过使用SVM（支持向量机）和XGBoost（极限梯度增强）机器，从中度颞叶癫痫症（mTLE）的患者中识别颞下叶癫痫（mTLE）患者的认知特征（表型）学习（ML）算法。具体来说，我们探索了这两种算法识别最左侧得分与右侧mTLE患者最显着得分（以ML表示的特征）的能力。我们有两个版本的数据集，其中包括神经心理学测试分数：一个“精简和工作”版本（n = 46例患者），没有任何缺失数据，另一个版本“原始”版本（n = 57），其中有缺失数据但对测试有用使用工作数据集获得的结果的鲁棒性。重点放在用于分类的预防性机器学习（ML）方法上，其结果具有可重复性和可概括性。还研究了几种临床医学变量的影响。对于这两个版本的数据集，我们都获得了左右mTLE的出色的预测分类性能（> 75％）。最分离的功能是四种语言和内存测试，其稳定性接近100％。因此，这些认知测试似乎与患者的神经心理学评估高度相关。此外，诸如海马回旋结构不对称，患者年龄和抗癫痫药数量之类的临床变量也影响了认知表型。这项探索性研究代表了对认知得分的深入分析，并允许观察语言与记忆表现之间有趣的相互作用。我们根据神经心理学领域的临床和理论应用以及观点来讨论这些发现的含义。