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Neuroimage. 2015 Sep;118:219-30. doi: 10.1016/j.neuroimage.2015.06.008. Epub 2015 Jun 6.

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

Author information

1
Department of Computer Science, College of Charleston, Charleston, SC, USA. Electronic address: munsellb@cofc.edu.
2
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
3
Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, UK.
4
Department of Epileptogy, University of Bonn, Germany.
5
Department of Computer Science, College of Charleston, Charleston, SC, USA.
6
Department of Neurology, Medical University of South Carolina, Charleston, SC, USA.
7
Department of Psychiatry, University of North Carolina at Chapel Hill, NC, USA.
8
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA. Electronic address: dinggang_shen@med.unc.edu.

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.

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

PMID:
26054876
PMCID:
PMC4701213
DOI:
10.1016/j.neuroimage.2015.06.008
[Indexed for MEDLINE]
Free PMC Article

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