Lateralization of Temporal Lobe Epilepsies with and without Hippocampal Atrophy Based on Whole-Brain Automatic MRI Segmentation

Brain images contain information suitable for automatically sorting subjects into categories such as healthy controls and patients. We sought to identify morphometric criteria for distinguishing controls (n = 28) from patients with unilateral temporal lobe epilepsy (TLE), 60 with and 20 without hippocampal atrophy (TLE-HA and TLE-N, respectively), and for determining the presumed side of seizure onset. The framework employs multi-atlas segmentation to estimate the volumes of 83 brain structures. A kernel-based separability criterion was then used to identify structures whose volumes discriminate between the groups. Next, we applied support vector machines (SVM) to the selected set for classification on the basis of volumes. We also computed pairwise similarities between all subjects and used spectral analysis to convert these into persubject features. SVM was again applied to these feature data. After training on a subgroup, all TLE-HA patients were correctly distinguished from controls, achieving an accuracy of 96 6 2% in both classification schemes. For TLE-N patients, the accuracy was 86 6 2% based on structural volumes and 91 6 3% using spectral analysis. Structures discriminating between patients and controls were mainly localized ipsilaterally to the presumed seizure focus. For the TLE-HA group, they were mainly in the temporal lobe; for the TLE-N group they included orbitofrontal regions, as well as the ipsilateral substantia nigra. Correct lateralization of the presumed seizure onset zone was achieved using hippocampi and parahippocampal gyri in all TLE-HA patients using either classification scheme; in the TLE-N patients, lateralization was accurate based on structural volumes in 86 6 4%, and in 94 6 4% with the spectral analysis approach. Unilateral TLE has imaging features that can be identified automatically, even when they are invisible to human experts. Such morphometric image features may serve as classification and lateralization criteria. The technique also detects unsuspected distinguishing features like the substantia nigra, warranting further study. Citation: Keihaninejad S, Heckemann RA, Gousias IS, Hajnal JV, Duncan JS, et al. (2012) Classification and Lateralization of Temporal Lobe Epilepsies with and without Hippocampal Atrophy Based on Whole-Brain Automatic MRI Segmentation. PLoS ONE 7(4): e33096. doi:10.1371/journal.pone.0033096 Editor: Yong He, Beijing Normal University, Beijing, China Received September 3, 2011; Accepted February 9, 2012; Published April 16, 2012 Copyright: 2012 Keihaninejad et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: SK and AH were supported by the Medical Research Council (grant number G108/585 and core funding from MRC Clinical Sciences Centre). RH was supported by a research grant from the Dunhill Medical Trust. Authors affiliated with Imperial are grateful for support from the National Institute for Health Research (NIHR) Biomedical Research Centre Funding Scheme. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: alexander.hammers@fondation-neurodis.org ¤ Current address: Dementia Research Centre, UCL Institute of Neurology, London, United Kingdom