Machine Learning of DTI Structural Brain Connectomes for Lateralization of Temporal Lobe Epilepsy

Abstract

Background and Purpose: We analyzed the ability of a machine learning approach that uses diffusion tensor imaging (DTI) structural connectomes to determine lateralization of epileptogenicity in temporal lobe epilepsy (TLE).Materials and Methods: We analyzed diffusion tensor and 3-dimensional (3D) T 1 -weighted images of 44 patients with TLE (right, 15, left, 29; mean age, 33.0 « 11.6 years) and 14 age-matched controls. We constructed a whole brain structural connectome for each subject, calculated graph theoretical network measures, and used a support vector machine (SVM) for classification among 3 groups (right TLE versus controls, left TLE versus controls, and right TLE versus left TLE) following a feature reduction process with sparse linear regression.Results: In left TLE, we found a significant decrease in local efficiency and the clustering coefficient in several brain regions, including the left posterior cingulate gyrus, left cuneus, and both hippocampi. In right TLE, the right hippocampus showed reduced nodal degree, clustering coefficient, and local efficiency. With use of the leave-one-out crossvalidation strategy, the SVM classifier achieved accuracy of 75.9 to 89.7% for right TLE versus controls, 74.4 to 86.0% for left TLE versus controls, and 72.7 to 86.4% for left TLE versus right TLE.Conclusion: Machine learning of graph theoretical measures from the DTI structural connectome may give support to lateralization of the TLE focus. The present good discrimination between left and right TLE suggests that, with further refinement, the classifier should improve presurgical diagnostic confidence.

Automatic Lateralization of Temporal Lobe Epilepsy Based on MEG Network Features Using Support Vector Machines

“…However, in the classification of left TLE and right TLE, the metric has better classification accuracy (88.10%) than and , which is significant for determining lateralization of unilateral TLE in clinic. Our results can be compared with those from a recent study [10] that also utilized an SVM approach to determine lateralization of the TLE epileptogenic focus. In that study, the input vectors were four graph-theory metrics that were based on DTI signals.…”

“…SVM algorithms have been applied for measuring brain morphology [9], including 2 Complexity cortical thickness, volume, curvature, and identification of MTS in TLE patients. SVM approaches have been utilized to determine lateralization of the TLE epileptogenic focus with diffusion tensor imaging (DTI) structural connectomes [10]. Another study verified the use of SVM for voxel-based MRI classification, and TLE with MTS can be distinguished from TLE without MTS with over 88% accuracy [8].…”

“…Evidence indicates that unilateral TLE is a network disease rather than a disease with a single focal region [23][24][25][26]. Additionally, graph-theory metrics are able to summarize the network properties with less computational cost than the voxel-based and skeletonbased methods [8,10,59]. Additionally, for classification of TLE lateralization, MEG has higher temporal resolution than MRI [9,45], fMRI [33], or DTI [8,10,59].…”

“…Additionally, graph-theory metrics are able to summarize the network properties with less computational cost than the voxel-based and skeletonbased methods [8,10,59]. Additionally, for classification of TLE lateralization, MEG has higher temporal resolution than MRI [9,45], fMRI [33], or DTI [8,10,59]. Thus, method that can analyze the effects of different frequency bands, especially the frequency band in which epileptiform discharges, was generated.…”

“…First, similar to a previous study [10], our study used a graph-based approach in which the nodal properties of the brain-network were input into the SVM. Evidence indicates that unilateral TLE is a network disease rather than a disease with a single focal region [23][24][25][26].…”