Abnormal phase–amplitude coupling characterizes the interictal state in epilepsy

Significance 

Epilepsy is a growing concern, especially in countries with aging populations. Epilepsy diagnosis remains a big challenge for medical practitioners. First, the diagnosis requires a multidisciplinary approach incorporating semiology and different neurophysiological and neuroimaging tests conducted using various methods like magnetoencephalography (MEG) and electroencephalography (EEG). The second aspect involves the visual interpretation of the MEG/EEG waveforms by specialists for accurate diagnosis of epilepsy. However, visual interpretation is bound to errors that might lead to inaccurate and unquantifiable diagnoses.

Automatic epilepsy diagnosis that does not depend on visual interpretation of MEG/EEG signals has recently attracted significant research attention. To this end, various features of the MEG/EEG signals contributing to the detection of seizures have been explored and applied to machine learning for possible automatic diagnosis. These features include functional connectivity, relative power, entropy and spike in the interictal state, which has all been proved to be beneficial in diagnosing epilepsy. It is hypothesized that epilepsy patients in the interictal state and healthy people may have different resting-state phase-amplitude coupling (PAC) capable of improving the discrimination between the groups. Nevertheless, the usefulness of interictal PAC in facilitating epilepsy diagnosis remains poorly understood.

Compared with machine learning methods, deep convolutional neural networks (DCNN) have been applied to many EEG/MEG signals to identify epileptic waveforms and discriminate patients from healthy participants with higher accuracy. This can be partly attributed to new electrophysiological features learned by DCNN though it is still difficult to understand what these new features are. Thus, improving automatic diagnosis requires applying machine learning to many patients with different forms of epilepsy. In addition, whether PAC in the interictal state is abnormal in epilepsy patients and its applicability in discriminating patients from healthy participants is also yet to be examined.

Herein to address these limitations, a team of researchers from Osaka University: Dr. Yuya Fujita, Professor Takufumi Yanagisawa, Specially Appointed Associate Professor Ryohei Fukuma, Natsuko Ura, Associate Professor Satoru Oshino and Professor Haruhiko Kishima characterized the abnormal PAC in the interictal state in epilepsy. They hypothesized that PAC in the resting state is abnormal in epilepsy patients in the interictal states and could improve the discrimination between two groups alongside other relevant features, including those extracted by deep learning. In their approach, cortical currents estimated from MRI and MEG were used to determine power in different bands. PAC was determined using synchronization index for eight frequent band pairs. Their work is currently published in the journal, Journal of Neural Engineering.

The research team showed that the mean synchronization index values were significantly different for healthy participants and patients with epilepsy. Specifically, the resting-state PAC and the synchronization index value difference were higher for θ/low γ in the temporal lobe. Consequently, the discrimination accuracy of the DCNN was significantly improved by PAC, allowing further high accuracy discrimination of the estimated cortical currents. For example, using a combination of DL and PAC, a maximum discrimination accuracy of 90% was attained. Though its underlying physiological mechanisms remain unknown, PAC was considered appropriate for this study owing to its contribution to different cortical functions and association with various neurological diseases like autism spectrum disorder and Alzheimer’s disease.

In summary, this is the first study to demonstrate abnormal PAC in patients in the interictal state. The combination of DCNN and physiologically characteristic features played an important role in improving the accuracy of automated epilepsy diagnosis. In a joint statement to Advances in Engineering, the authors explained their study will contribute to developing highly effective and accurate automated epilepsy diagnosis techniques.

About the author

Haruhiko KISHIMA M.D., Ph.D. 
Professor and Chairman, Department of Neurosurgery, Osaka University Graduate School of Medicine.
Director of Epilepsy Center, Osaka University Hospital.

Kishima H. graduated from Osaka University, School of Medicine in 1991 and was awarded M.D. in May 1991. He was also awarded Ph.D. in March 1998.

He had trained at Osaka University Hospital and its affiliated hospitals. Then he was certified as a boarded neurosurgeon in Japan in 1998. He was in France for two years as a post-doctoral fellow to study stereotactic neurosurgery. Now his best concern is in epilepsy surgery and functional neurosurgery.

About the author

Dr. Yuya Fujita is currently a specially appointed researcher in Osaka University Graduate School of Medicine. Also, he is a board certificated neurosurgeon at Osaka University Hospital and a member of Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI) at Osaka University. He received the M.D. in Faculty of Medicine at Osaka University in 2013 and the Ph.D. in Osaka University Graduate School of Medicine in 2022.

His main research interests are related to quantification and standardization of epilepsy diagnosis, exploring the seizure onset zone of patients with epilepsy, and surgical treatments that preserve higher brain function. The goal is to enable more patients with epilepsy to lead their daily lives. To achieve it, he is working on the automated diagnosis of patients with epilepsy by combining magnetoencephalography with artificial intelligence. This research, entitled “Automated diagnosis of epilepsy using deep learning and exploring features for the deep neural network”, received the “Poster Award” by Japan Epilepsy Society in 2021.

About the author

Dr. Takufumi Yanagisawa is professor of Institute for Advanced Co-Creation Studies, Osaka University, Japan and a researcher of ATR computational neuroscience laboratories. He completed BA degrees in Physics in 1998, followed by his Master degrees in Physics at the Waseda University in Japan. Then, he completed BA degree in Medicine and obtained a license of Medical Doctor, followed by his PhD in Medicine in 2004 at the Osaka University. He has had extensive prior experience in neurophysiological evaluation of electrocorticographic (ECoG) signals and magnetoencephalographic (MEG) signals, and development of brain-machine interfaces (BMI) using these signals, application of the developed BMI for clinical purposes. Especially, he has developed a novel BMI training to control phantom limb pain by inducing the cortical plasticity on the sensorimotor cortex relating to the phantom hand movements. He also developed a deep learning model to diagnose various neurological diseases.

Reference

Fujita, Y., Yanagisawa, T., Fukuma, R., Ura, N., Oshino, S., & Kishima, H. (2022). Abnormal phase–amplitude coupling characterizes the interictal state in epilepsyJournal of Neural Engineering, 19(2), 026056.

Go To Journal of Neural Engineering

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