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

Brent C. Munsell, Chong Yaw Wee, Simon S. Keller, Bernd Weber, Christian Elger, Laura Angelica Tomaz da Silva, Travis Nesland, Martin Styner, Dinggang Shen, Leonardo Bonilha

Research output: Contribution to journalArticle

53 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)219-230
Number of pages12
JournalNeuroImage
Volume118
DOIs
Publication statusPublished - 2015 Sep 1

Keywords

  • Brain connectome
  • Brain network analysis
  • Diffusion tensor imaging (DTI)
  • Sparse machine learning
  • Support vector machine (SVM)
  • Temporal lobe epilepsy (TLE)
  • White matter fiber tractography

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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  • Cite this

    Munsell, B. C., Wee, C. Y., Keller, S. S., Weber, B., Elger, C., da Silva, L. A. T., Nesland, T., Styner, M., Shen, D., & Bonilha, L. (2015). Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data. NeuroImage, 118, 219-230. https://doi.org/10.1016/j.neuroimage.2015.06.008