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

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.

Fig. 1

Example symmetric 82 × 82 connectivity map constructed using method outlined in the Presurgical connectome reconstruction section for normal control, seizure-free, and not seizure-free patients, respectively. The brain structures are numbered from 1 to 82 in accordance with the atlas provided in Supplementary Table 1. Regions 1 to 42 represent the hemisphere contralateral to seizure onset, and 43 to 82 represent the hemisphere ipsilateral to seizure onset. Within each hemisphere, the regions are grouped as follows: frontal lobe, temporal lobe, basal nuclei, parietal lobe, and occipital lobe.

Fig. 2

Block diagram that illustrates the basic design and operation of the proposed two-stage connectome-based prediction framework. The framework defines two different prediction pipelines, specifically a Stage-1 prediction pipeline, and a Stage-2 prediction pipeline. Each prediction pipeline has three trained components: 1) connectome feature selection, 2) linear kernel operation, and 3) linear SVM classifier. Note that the superscript value identifies the stage.

Fig. 3

Training of deep learning (DL) network includes an unsupervised and a supervised training procedure. In particular, (a) in the unsupervised training step each auto-encoder (i.e., AE1 and AE2) is trained separately, and each AE only defines two layers (visible and hidden). Once training is completed, the hidden layer of the current auto-encoder (AE1) becomes the visible layer of the next auto-encoder (AE2), and the unsupervised training step repeats itself with AE2. (b)When each AE has been trained, they are stacked to form a deep network. At this point a training label layer (that defines the known diagnosis labels) is added and the supervised training step is initialed to create a fine-tuned deep network.

Fig. 4

Single-stage connectome-based prediction framework that only has one pipeline trained to predict the surgical treatment outcome of a patient with TLE. In general, the pipeline includes three trained components: 1) connectome feature selection, 2) linear kernel operation, and 3) linear SVM classifier.

Fig. 5

The top 15 connected regions with the smallest p-value (i.e., the network connections with the greatest difference between patients with TLE and normal controls). The p-values are calculated using a two-sample t-test. Note that the brain regions (defined using the Lausanne anatomical atlas) are represented by the red nodes, and the edge connecting two brain regions represents a network connection in the connectome.

Fig. 6

The top 15 connected regions with the smallest p-value (i.e., the network connections with the greatest difference between the patients that are seizure-free after surgery and the patients that are not seizure-free after surgery). The p-values are calculated using a two-sample t-test. Note that the brain regions (defined using the Lausanne anatomical atlas) are represented by the red nodes, and the edge connecting two brain regions represents a network connection in the connectome.