Research articleA challenge of predicting seizure frequency in temporal lobe epilepsy using neuroanatomical features
Introduction
Machine learning has been applied to predict particular disease states, including the presence and severity of a disease, in individuals. In temporal lobe epilepsy (TLE), machine learning has been employed, for instance, to discriminate TLE patients from healthy individuals [15,33] and to predict surgical treatment outcomes in TLE patients [2,6,23,26]. In such machine learning-based predictive models, features from neuroimaging data are frequently employed as predictors, primarily due to their massiveness and superior predictive values compared with those from other clinical data [9]. Features of brain function can be acquired from EEG [11,33] and functional MRI [35] data, whereas features of brain structure can be acquired from structural MRI (sMRI) [6,20,29] and diffusion-weighted MRI (dMRI) [1,26] data. In particular, since TLE often involves neuroanatomical damage beyond the epileptogenic zone [16], neuroanatomical features are likely to be useful as predictors.
In clinical applications of predictive models, although the detection of TLE, that is, the discrimination of TLE patients from healthy individuals appears to be a primary concern, the subdivision of TLE, that is, the classification between different clinical subgroups of TLE patients could be also a significant concern. The lateralization of the epileptogenic side is crucial for pre-surgical evaluation and the lateralization of TLE patients has been determined by adopting grey matter (GM) anatomical features from sMRI data [20] and white matter (WM) anatomical features from dMRI data [1]. Seizure frequency is another critical seizure characteristic that potentially contributes to the clinical severity of TLE, but the prediction of seizure frequency on the basis of neuroanatomical damage has not been thoroughly sought.
In this study, we examined whether subdivisions of TLE according to seizure frequency could be predicted using neuroimaging features of GM and WM anatomical damage in the framework of machine learning-based predictive modelling. Since there have been conflicting findings regarding the relationship between seizure frequency and neuroanatomical damage [21,24], we hypothesized that such neuroanatomical features may not be as promising for the subdivision of TLE as for the detection of TLE. In addition, we checked how closely seizure frequency-based subgroups would be matched with neuroanatomy-based subgroups when we clustered TLE patients on the basis of the neuroanatomical features.
Section snippets
Participants
Forty-two patients with TLE (age: 40.83 ± 10.06 years, 25 females) and 45 age- and sex-matched healthy controls (age: 41.18 ± 13.48 years, 23 females) participated in the study. The clinical diagnosis of TLE was made according to the criteria of the International League Against Epilepsy [13]. Age at epilepsy onset was 23.07 ± 12.15 years and the duration of epilepsy was 17.98 ± 12.82 years. The healthy controls had no history of neurological disorders and no abnormalities in brain sMRI. Written
Clinical characteristics
All the TLE patients underwent anti-epileptic drug (AED) treatment. The TLE patients with high seizure frequency were drug-resistant, and thus they were considered for epilepsy resective surgery although their surgical procedures and outcomes have not been thoroughly followed up. The number of AEDs differed depending on seizure frequency (p < 0.01), but not on seizure lateralization, at the significance level of a p value of 0.05 or less. According to the sMRI findings of the TLE patients,
Discussion
In this study, we employed machine learning, specifically the random forests method, for the subdivision of TLE, that is, the separation between the TLE patients with low and high seizure frequency as well as for the detection of TLE, that is, the discrimination of the TLE patients from the healthy controls, using the neuroanatomical features as predictors. We demonstrated that classification performance in TLE subdivision was not as adequate as in TLE detection. Furthermore, when we determined
Author contributions
S.H.O. was responsible for the study concept and design. C.P. analyzed the data and drafted the manuscript. All authors critically reviewed content and approved final version for publication.
Funding
This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT in Korea [2015R1C1A1A01052438].
Conflict of interest
The authors have no conflict of interest to declare.
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