Machine learning–XGBoost analysis of language networks to classify patients with epilepsy
Open accessAbstract
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.
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XGBoost has proven successful in several diagnostic applications.
“This algorithm has proven successful in several diagnostic applications (Behravan et al., 2018; Torlay et al., 2017; Zheng et al., 2017).”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Torlay et al., 2017
The XGBoost algorithm provides parallel computation tree boosting, enabling fast and accurate predictions and advanced regularization techniques to avoid overfitting.
“The XGBoost algorithm is a gradient boosting machine implementation that provides parallel computation tree boosting, enabling fast and accurate predictions and advanced regularization techniques to avoid overfitting (Torlay et al., 2017).”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Torlay et al., 2017
When compared to other GBM algorithms, XGBoost provides regularized boosting, helping to reduce overfitting with more generalizable results.
“When compared to other GBM algorithms, XGBoost provides regularized boosting, helping to reduce overfitting with more generalizable results (Torlay et al., 2017; Xuan et al., 2019).”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Torlay et al., 2017
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- Authors
- Torlay, L., Perrone-Bertolotti, M., Thomas, E., & Baciu, M.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Inserm (FR)
- Université Pierre Mendès France (FR)
- Corresponding author
- Baciu, M.
- Venue
- Brain Informatics
- Published
- 2017-04-22
- Volume
- 4
- Issue
- 3
- Pages
- 159-169
- ISSN
- 2198-4018 (ISSN-L)
- 2198-4026
- PMC
- PMC5563301
- OpenAlex
- W2606665849
- Cited by
- 483
- References
- 61
- Citations by year
- 2026: 28 · 2025: 58 · 2024: 77 · 2023: 59 · 2022: 72 · 2021: 76 · 2020: 56 · 2019: 36 · 2018: 18 · 2017: 3
- FWCI
- 25.39
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Brain Informatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- HAL (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- ANR Grant Infrastructure d’Avenir en Biologie Santé (ANR-11-INBS-0006)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Epilepsy research and treatment
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- focal epilepsy, language network, XGBoost, functional MRI, language mapping, drug-resistant epilepsy, surgical planning, language reorganization, neuroplasticity, semantic tasks
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