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

E Roger,L Torlay,J Gardette,C Mosca,S Banjac,L Minotti,P Kahane,M Baciu

doi:10.1016/j.neuropsychologia.2020.107455

Abstract

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.

Highlights

Cognitive impairment recently became an integral part of the defi nition and classification of epilepsies adopted by the International League Against Epilepsy (ILAE; Fisher et al, 2005)
Two parallel analyses have been conducted on the two versions of the dataset (i.e., D and D’) using two different algorithms: (a) a classical Support Vector Machine (SVM) algorithm (Cortes and Vapnik, 1995) with a Radial Basis Function (RBF); and (b) a state-of-the-art XGBoost algorithm (Chen and Guestrin, 2016), previ ously and successfully used by our team (Torlay et al, 2017)
This high level of performance clearly shows the ability of the complete neuropsychological evaluation (NPE) to predict epilepsy later alization

Summary

Introduction

Cognitive impairment recently became an integral part of the defi nition and classification of epilepsies adopted by the International League Against Epilepsy (ILAE; Fisher et al, 2005). In terms of cognitive symptoms, the focal subtypes of epilepsies are more frequently associated with specific and restricted cognitive deficits than the generalized forms (Brissart and Maillard, 2018) and the observed im pairments are generally mild to moderate (for a review see Baciu and Perrone-Bertolotti, 2015). This suggests a continuous cerebral reorga nization through time, depending on neuroplasticity phenomena taking over the impaired cognitive function(s) (i.e. chronic plasticity; (Berg and Scheffer, 2011)

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