Research articleMapping the convergent temporal epileptic network in left and right temporal lobe epilepsy
Introduction
Mesial temporal lobe epilepsy (mTLE) with hippocampal sclerosis (HS), which is often associated with cognitive impairment [1], has been considered as a focal disease centered on a lateralized focus for a long time [2]. However, previous magnetic resonance imaging (MRI) studies have demonstrated widespread abnormalities in various cortical regions and networks [3], [4], [5], [6], suggesting that mTLE is a brain disease that involves network dysfunction [7], [8].
Pioneering studies indicated that the left and right mTLE involved distinct underlying pathological and etiological substrates [9]. In our last study, we found that the left mTLE could be distinguished from the right mTLE. However, the left mTLE partly exhibited a comparable connectivity pattern to the right mTLE [10]. Furthermore, similar functional connectivity reductions were found in both left and right mTLE [11]. Left mTLE patients even exhibit similar clinical dysfunctions to right mTLE patients, such as depressive mood, emotional dysregulation, memory deficits [12] and even major depression [13]. More importantly, some studies failed to detect significant white matter differences in direct comparisons between left and right mTLE [14]. The neuropathology underlying these phenomena remains unclear. One hypothesis is that white matter abnormalities reflect a secondary effect of ongoing seizure activity, representing downstream axonal degeneration [15], such that left and right mTLE may share a common extra-temporal network disruption.
In the current study, we investigated whether left and right mTLE share a common disrupted anatomical network; to address this question, we aimed to characterize the convergent disruptions of the anatomical networks in left and right mTLE using machine learning approaches. Whereas mass-univariate methods consider each individual variable separately, machine learning approaches take into account patterns of information that may be presented across multiple variables [16]. Thus, machine learning approaches may provide increased sensitivity for extracting stable patterns from neuroimaging data and for detecting subtle and spatially distributed differences in the brain [17]. First, we performed diffusion tensor imaging (DTI) probabilistic tractography to extract whole-brain anatomical networks. Then, machine learning approaches were used to extract the most discriminating connections and to investigate the convergent anatomical network disruptions between left and right mTLE.
Section snippets
Methods
This study was approved by the Research Ethics Review Board of the Institute of Mental Health of Southern Medical University. Each participant was informed of the details of the project, and written informed consent was obtained from all participants in accord with the standards of the Declaration of Helsinki. We confirmed that all potential participants who declined to participate or otherwise did not participate were eligible for treatment (if applicable) and were not disadvantaged in any way
Whole-brain classification
Using the LOOCV strategy, the SVM classifier achieved 93.4% accuracy for the left mTLE versus controls and 90.0% accuracy for the right mTLE versus controls (Table 2). Because the training data differed for each LOOCV, the selected features varied slightly in each LOOCV. However, 97 and 94 discriminating features, referred to as the consensus features [22], were detected in every LOOCV for the left mTLE versus controls and for the right mTLE versus controls, respectively. These two sets of
Discussion
In this study, we adopted a probabilistic diffusion tracking method and machine learning approaches to investigate the convergent connectivity patterns between the left and right mTLE. The left mTLE partly displayed convergent abnormal connectivity patterns with the right mTLE. These shared discriminating connections were primarily located in the limbic-frontal and temporo-occipital subsystems. Our results suggest that left and right mTLE share a convergent dysregulated anatomical network, in
Conclusions
In conclusion, the present study is the first study to investigate the common anatomical network disturbances between left and right mTLE which had been long neglected in mTLE using machine learning approaches. The results showed that left and right mTLE patients could be differentiated from healthy controls with high classification accuracy, indicating that the most discriminating connections could be viewed as potential biomarkers for mTLE with HS. The most discriminating connections’
Acknowledgements
The authors thank Olaf Sporns from Indiana University Bloomington for his modification of this paper and constructive suggestions. This study was supported by the National Science Foundation of China (61503397, 61420106001, 61375111, 81271389, and 81471251). Peng Fang is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. P.F. and J.A. wrote the manuscript and
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These authors contributed equally to the manuscript.