Abstract
Abstract
Objective. Diagnosing epilepsy still requires visual interpretation of electroencephalography (EEG) and magnetoencephalography (MEG) by specialists, which prevents quantification and standardization of diagnosis. Previous studies proposed automated diagnosis by combining various features from EEG and MEG, such as relative power (Power) and functional connectivity (FC). However, the usefulness of interictal phase–amplitude coupling (PAC) in diagnosing epilepsy is still unknown. We hypothesized that resting-state PAC would be different for patients with epilepsy in the interictal state and for healthy participants such that it would improve discrimination between the groups. Approach. We obtained resting-state MEG and magnetic resonance imaging (MRI) in 90 patients with epilepsy during their preoperative evaluation and in 90 healthy participants. We used the cortical currents estimated from MEG and MRI to calculate Power in the δ (1–3 Hz), θ (4–7 Hz), α (8–13 Hz), β (13–30 Hz), low γ (35–55 Hz), and high γ (65–90 Hz) bands and FC in the θ band. PAC was evaluated using the synchronization index (SI) for eight frequency band pairs: the phases of δ, θ, α, and β and the amplitudes of low and high γ. First, we compared the mean SI values for the patients with epilepsy and the healthy participants. Then, using features such as PAC, Power, FC, and features extracted by deep learning (DL) individually or combined, we tested whether PAC improves discrimination accuracy for the two groups. Main results. The mean SI values were significantly different for the patients with epilepsy and the healthy participants. The SI value difference was highest for θ/low γ in the temporal lobe. Discrimination accuracy was the highest, at 90%, using the combination of PAC and DL. Significance. Abnormal PAC characterized the patients with epilepsy in the interictal state compared with the healthy participants, potentially improving the discrimination of epilepsy.
Funder
Exploratory Research for Advanced Technology
Core Research for Evolutional Science and Technology
Japan Agency for Medical Research and Development
Council for Science, Technology and Innovation
Japan Society for the Promotion of Science
Japan Science and Technology Agency
Precursory Research for Embryonic Science and Technology
Subject
Cellular and Molecular Neuroscience,Biomedical Engineering
Cited by
8 articles.
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