Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data



Munsell, Brent C, Wee, Chong-Yaw, Keller, Simon ORCID: 0000-0001-5247-9795, Weber, Bernd, Elger, Christian, da Silva, Laura Angelica Tomaz, Nesland, Travis, Styner, Martin, Shen, Dinggang and Bonilha, Leonardo
(2015) Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data. NeuroImage, 118. pp. 219-230.

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Abstract

The objective of this study is to evaluate machine learning algorithms aimed at predicting surgical treatment outcomes in groups of patients with temporal lobe epilepsy (TLE) using only the structural brain connectome. Specifically, the brain connectome is reconstructed using white matter fiber tracts from presurgical diffusion tensor imaging. To achieve our objective, a two-stage connectome-based prediction framework is developed that gradually selects a small number of abnormal network connections that contribute to the surgical treatment outcome, and in each stage a linear kernel operation is used to further improve the accuracy of the learned classifier. Using a 10-fold cross validation strategy, the first stage in the connectome-based framework is able to separate patients with TLE from normal controls with 80% accuracy, and second stage in the connectome-based framework is able to correctly predict the surgical treatment outcome of patients with TLE with 70% accuracy. Compared to existing state-of-the-art methods that use VBM data, the proposed two-stage connectome-based prediction framework is a suitable alternative with comparable prediction performance. Our results additionally show that machine learning algorithms that exclusively use structural connectome data can predict treatment outcomes in epilepsy with similar accuracy compared with "expert-based" clinical decision. In summary, using the unprecedented information provided in the brain connectome, machine learning algorithms may uncover pathological changes in brain network organization and improve outcome forecasting in the context of epilepsy.

Item Type: Article
Uncontrolled Keywords: Brain connectome, Sparse machine learning, Support vector machine (SVM), Temporal lobe epilepsy (TLE), Brain network analysis, White matter fiber tractography, Diffusion tensor imaging (DTI)
Depositing User: Symplectic Admin
Date Deposited: 15 Nov 2016 17:22
Last Modified: 19 Jan 2023 07:25
DOI: 10.1016/j.neuroimage.2015.06.008
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3004517