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Machine learning–XGBoost analysis of language networks to classify patients with epilepsy

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Machine learning–XGBoost analysis of
language networks to classify patients with

L. Torlay, M. Perrone-Bertolotti,
E. Thomas & M. Baciu

Abstract Our goal was to apply a statistical approach to
allow the identification of atypical language patterns and to
differentiate patients with epilepsy from healthy subjects,
based on their cerebral activity, as assessed by functional
MRI (fMRI). Patients with focal epilepsy show reorganization
or plasticity of brain networks involved in cognitive
functions, inducing ‘atypical’ (compared to ‘typical’ in
healthy people) brain profiles. Moreover, some of these
patients suffer from drug-resistant epilepsy, and they
undergo surgery to stop seizures. The neurosurgeon should
only remove the zone generating seizures and must preserve
cognitive functions to avoid deficits. To preserve
functions, one should know how they are represented in the
patient’s brain, which is in general different from that of
healthy subjects. For this purpose, in the pre-surgical stage,
robust and efficient methods are required to identify atypical
from typical representations. Given the frequent location
of regions generating seizures in the vicinity of
language networks, one important function to be considered
is language. The risk of language impairment after
surgery is determined pre-surgically by mapping language
networks. In clinical settings, cognitive mapping is classically
performed with fMRI. The fMRI analyses allowing
the identification of atypical patterns of language networks
in patients are not sufficiently robust and require additional
statistic approaches. In this study, we report the use of a
statistical nonlinear machine learning classification, the
Extreme Gradient Boosting (XGBoost) algorithm, to
identify atypical patterns and classify 55 participants as
healthy subjects or patients with epilepsy. XGBoost analyses
were based on neurophysiological features in five
language regions (three frontal and two temporal) in both
hemispheres and activated with fMRI for a phonological
(PHONO) and a semantic (SEM) language task. These
features were combined into 135 cognitively plausible
subsets and further submitted to selection and binary
classification. Classification performance was scored with
the Area Under the receiver operating characteristic curve
(AUC). Our results showed that the subset SEM_LH
BA_47-21 (left fronto-temporal activation induced by the
SEM task) provided the best discrimination between the
two groups (AUC of 91 ± 5%). The results are discussed
in the framework of the current debates of language reorganization
in focal epilepsy.
Keywords Language - Epilepsy - Atypical - Machine
learning - ML - Extreme Gradient Boosting - XGBoost