The PhD in Medical Sciences:
The University of Nicosia Medical School offers the degree PhD in Medical Sciences. The degree is awarded to students who successfully complete an independent research programme that breaks new ground in the chosen field of study. The PhD programme aspires to empower students to become independent researchers, thus advancing innovation and development.
The Research Project:
We are currently inviting application through a competitive process for high calibre candidates to apply for one PhD Scholarship in the field of Neuroscience. The successful candidate will enrol on the PhD programme in Medical Sciences and will work under the Supervision of Dr Avgis Hadjipapas, Professor of Neuroscience and Research Methods at the University of Nicosia Medical School. The project is based on an international collaboration between the University of Nicosia Medical School, (UN) the University Maastricht University Medical Center (MUMC), Maastricht University (MU) and McGill University (McGill U).
Characterization of circadian rhythm modulations in intracranial EEG and their relationship to seizure onsets in focal epilepsy
Background, rationale and objectives: Epilepsy affects roughly 1% of the population, and about a third of patients have unpredictable seizures which cannot be adequately controlled with medication (Kuhlmann et al., 2018). Therefore, better understanding of seizure generation and improving seizure predictability are central goals in epilepsy research to prevent seizures from occurring. Recent investigations by our own (Mitsis et al., 2020) and other groups (Leguia et al., 2021) have shown that seizure onsets exhibit a tight correlation to certain phases of circadian rhythms, which leads to improved seizure predictability (Karoly et al., 2017). However, our previous work (Mitsis et al., 2020) also raised questions as to the spatial-scale of the EEG state, which best captures this correlation. Interestingly, using surface EEG we found that global measures of brain states are more correlated to seizure onset than local EEG measures. However, this work utilized an indirect estimate of the local brain state obtained by surface EEG, which may have not been sensitive and specific enough to capture relevant epileptogenic activity sufficiently. In this project, and based on a collaboration formed between the University of Nicosia Medical School (UN), Maastricht University Medical Center (MUMC), Maastricht University (MU), and McGill University (McGill U), we will address this question by examining intracranial recordings provided by the MUMC partner, obtained directly from the area of the suspected epileptogenic focus. We will then employ a battery of time series analysis methods to obtain a faithful characterization of the circadian modulations of the local brain state. This will include measures obtained by singular spectrum decomposition, measures of dynamic complexity of the time series, as well as measures derived by graph theory. We will then characterize the phase coupling (phase correlation) of the such characterized local brain state to seizure onsets using circular statistics. This correlation will then be compared to the one obtained from the global brain state as estimated from surface EEG recordings. This will inform both important pathophysiological questions in terms of the extent of the functional seizure generating network but also practical questions related to what EEG recording modality is likely to be most useful in terms of seizure predictability. Further, analysis of this correlation at the level of individual patient recordings will inform the feasibility of seizure forecasting informed by circadian rhythms. Successful candidates will benefit from interacting with an international and interdisciplinary consortium of neuroscientists, neurologists and engineers throughout the duration of the project.
Karoly, P.J., Ung, H., Grayden, D.B., Kuhlmann, L., Leyde, K., Cook, M.J., Freestone, D.R., 2017. The circadian profile of epilepsy improves seizure forecasting. Brain 140, 2169–2182. https://doi.org/10.1093/brain/awx173
Kuhlmann, L., Lehnertz, K., Richardson, M.P., Schelter, B., Zaveri, H.P., 2018. Seizure prediction — ready for a new era. Nat. Rev. Neurol. https://doi.org/10.1038/s41582-018-0055-2
Leguia, M.G., Andrzejak, R.G., Rummel, C., Fan, J.M., Mirro, E.A., Tcheng, T.K., Rao, V.R., Baud, M.O., 2021. Seizure Cycles in Focal Epilepsy. JAMA Neurol. In press, 1–10. https://doi.org/10.1001/jamaneurol.2020.5370
Mitsis, G.D., Anastasiadou, M.N., Christodoulakis, M., Papathanasiou, E.S., Papacostas, S.S., Hadjipapas, A., 2020. Functional brain networks of patients with epilepsy exhibit pronounced multiscale periodicities, which correlate with seizure onset. Hum. Brain Mapp. hbm.24930. https://doi.org/10.1002/hbm.24930
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