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 Prof Avgis Hadjipapas, Professor for 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). The project predominantly involves data-analysis (signal processing), which means that a large part of the project can be conducted remotely. Project Description: Title of research project: 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. However, our previous work (Mitsis et al., 2020) only utilized surface EEG. 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 first characterize in detail the circadian variation of signal parameters extracted from the intracranial EEG. We will then examine whether seizure onsets are phase coupled (correlated) to these circadian modulations. This will inform both important pathophysiological questions in terms of the extent of the functional seizure generating network. 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. References 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 The Scholarship: The Scholarship will have a duration of three to four years and will cover: • The tuition fees for the PhD programme which are €13,500 in total for the first 3 years and €1,500 for year 4. • A monthly stipend of €1,000 for the duration of three to four years. Application for the PhD Scholarship: Candidates should submit an online application through this link and upload the following supporting documents: • A cover letter clearly stating that they apply for the PhD Scholarship in the field of Neuroscience for the PhD Research Project ‘Characterization of circadian rhythm modulations in intracranial EEG and their relationship to seizure onsets in focal epilepsy.’ • Copies of the applicant’s qualifications/degree(s) – the application can be assessed with scanned copies, but certified true copies must be provided if the candidate is successful and prior to enrolment on the PhD programme. • Copies of the applicant’s transcript(s) - the application can be assessed with scanned copies, but certified true copies must be provided if the candidate is successful and prior to enrolment on the PhD programme. • Proof of English language proficiency such as IELTS with a score of 7 overall and with a minimum score of 7 in writing or TOEFL iBT with a score of 94 overall and a minimum score of 27 in Writing. Other internationally recognized English language qualifications might be considered upon review. Students from the UK, Ireland USA, Canada (from English speaking provinces), Australia and New Zealand are exempt from the English language requirement. • Two reference letters, of which at least one should be from an academic. • A full Curriculum Vitae (CV). Applications should be submitted by Friday, July 29, 2022 at 5pm. Only fully completed applications, containing all necessary supporting documents will be reviewed. Only candidates who are shortlisted will be contacted and invited to an interview.

There are over 30 million people with drug-resistant epilepsy worldwide. When neuroimaging and non-invasive neural recordings fail to localise seizure onset zones (SOZ), intracranial recordings become the best chance for localisation and seizure-freedom in those patients. However, intracranial neural activities remain hard to visually discriminate across recording channels, which limits the success of intracranial visual investigations. In this presentation, I present methods which quantify intracranial neural time series and combine them with explainable machine learning algorithms to localise the SOZ in the epileptic brain. I present the potentials and limitations of our methods in the localisation of SOZ in epilepsy providing insights for future research in this area.

In most brains across the animal kingdom, brain dynamics can enter pathological states that are recognisable as epileptic seizures. Yet usually, brain operate within certain constraints given through neuronal function and synaptic coupling, that will prevent epileptic seizure dynamics from emerging. In this talk, I will bring together different approaches to identifying how networks in the broadest sense shape brain dynamics. Using illustrative examples from intracranial EEG recordings, disorders characterised by molecular disruption of a single neurotransmitter receptor type, to single-cell recordings of whole-brain activity in the larval zebrafish, I will address three key questions - (1) how does the regionally specific composition of synaptic receptors shape ongoing physiological brain activity; (2) how can disruption of this regionally specific balance result in abnormal brain dynamics; and (3) which cellular patterns underly the transition into an epileptic seizure.

Why do we make decisions impulsively blinded in an emotionally rash moment? Or caught in the same repetitive suboptimal loop, avoiding fears or rushing headlong towards illusory rewards? These cognitive constructs underlying self-control and compulsive behaviours and their influence by emotion or incentives are relevant dimensionally across healthy individuals and hijacked across disorders of addiction, compulsivity and mood. My lab focuses on identifying theory-driven modifiable biomarkers focusing on these cognitive constructs with the ultimate goal to optimize and develop novel means of neuromodulation. Here I will provide a few examples of my group’s recent work to illustrate this approach. I describe a series of recent studies on intracranial physiology and acute stimulation focusing on risk taking and emotional processing. This talk highlights the subthalamic nucleus, a common target for deep brain stimulation for Parkinson’s disease and obsessive-compulsive disorder. I further describe recent translational work in non-invasive neuromodulation. Together these examples illustrate the approach of the lab highlighting modifiable biomarkers and optimizing neuromodulation.

The development of circuit-based therapeutics to treat neurological and neuropsychiatric diseases require detailed localization and understanding of electrophysiological signals in the human brain. Electrodes can record and stimulate circuits in many ways, and we often rely on non-invasive imaging methods to predict the location to implant electrodes. However, electrophysiological and imaging signals measure the underlying tissue in a fundamentally different manner. To integrate multimodal data and benefit from these complementary measurements, I will describe an approach that considers how different measurements integrate signals across the underlying tissue. I will show how this approach helps relate fMRI and intracranial EEG measurements and provides new insights into how electrical stimulation influences human brain networks.

The view of human brain function has drastically shifted over the last decade, owing to the observation that the majority of brain activity is intrinsic rather than driven by external stimuli or cognitive demands. Specifically, all brain regions continuously communicate in spatiotemporally organized patterns that constitute the functional connectome, with consequences for cognition and behavior. In this talk, I will argue that another shift is underway, driven by new insights from synergistic interrogation of the functional connectome using different acquisition methods. The human functional connectome is typically investigated with functional magnetic resonance imaging (fMRI) that relies on the indirect hemodynamic signal, thereby emphasizing very slow connectivity across brain regions. Conversely, more recent methodological advances demonstrate that fast connectivity within the whole-brain connectome can be studied with real-time methods such as electroencephalography (EEG). Our findings show that combining fMRI with scalp or intracranial EEG in humans, especially when recorded concurrently, paints a rich picture of neural communication across the connectome. Specifically, the connectome comprises both fast, oscillation-based connectivity observable with EEG, as well as extremely slow processes best captured by fMRI. While the fast and slow processes share an important degree of spatial organization, these processes unfold in a temporally independent manner. Our observations suggest that fMRI and EEG may be envisaged as capturing distinct aspects of functional connectivity, rather than intermodal measurements of the same phenomenon. Infraslow fluctuation-based and rapid oscillation-based connectivity of various frequency bands constitute multiple dynamic trajectories through a shared state space of discrete connectome configurations. The multitude of flexible trajectories may concurrently enable functional connectivity across multiple independent sets of distributed brain regions.

Given the complex and dynamic nature of our social relationships, the human brain needs to quickly learn and adapt to new social situations. The breakdown of any of these computations could lead to social deficits, as observed in many psychiatric disorders. In this talk, I will present our recent neurocomputational and intracranial work that attempts to model both 1) how humans dynamically adapt beliefs about other people and 2) how individuals can exert influence over social others through model-based forward thinking. Lastly, I will present our findings of how impaired social computations might manifest in different disorders such as addiction, delusion, and autism. Taken together, these findings reveal the dynamic and proactive nature of human interactions as well as the clinical significance of these high-order social processes.

An emerging hypothesis in the field of Alzheimer’s disease (AD) is that neuronal hyperexcitability and other forms of aberrant network activity play an important role in shaping the clinical course of AD. In this talk, Dr. Lam will highlight the close and bi-directional relationships between epilepsy and AD, noting recent advances in our understanding of this topic spanning from animal models to humans. She will describe recent intracranial electrode recordings in humans that have revealed silent hippocampal epileptiform activity occurring in early stages of AD. Finally, she will discuss machine learning approaches that her laboratory has been developing to non-invasively diagnose and quantify silent hippocampal epileptiform activity from scalp EEG recordings.

General anesthesia is a drug-induced, reversible condition comprised of five behavioral states: unconsciousness, amnesia (loss of memory), antinociception (loss of pain sensation), akinesia (immobility), and hemodynamic stability with control of the stress response. Our work shows that a primary mechanism through which anesthetics create these altered states of arousal is by initiating and maintaining highly structured oscillations. These oscillations impair communication among brain regions. We illustrate this effect by presenting findings from our human studies of general anesthesia using high-density EEG recordings and intracranial recordings. These studies have allowed us to give a detailed characterization of the neurophysiology of loss and recovery of consciousness due to propofol. We show how these dynamics change systematically with different anesthetic classes and with age. As a consequence, we have developed a principled, neuroscience-based paradigm for using the EEG to monitor the brain states of patients receiving general anesthesia. We demonstrate that the state of general anesthesia can be rapidly reversed by activating specific brain circuits. Finally, we demonstrate that the state of general anesthesia can be controlled using closed loop feedback control systems. The success of our research has depended critically on tight coupling of experiments, signal processing research and mathematical modeling.

Electrophysiology of eye and visual pathway is useful tool in ophthalmology and neurology. It covers a few examinations to find out if defect of vision is peripheral or central. Visual evoked potentials (VEP) are most frequently used in neurology and neuroophthalmology. VEP are evoked by flash or pattern stimulations. The combination of these both examinations gives more information about the visual pathway. It is very important to remember that VEP originate in the retina and reflect its function as well. In many cases not only VEP but also electroretinography (ERG) is essential for diagnosis. The seminar presents basic electrophysiological procedures used for diagnosis and follow-up of optic neuropathies and some of central nervous system diseases which affect vision (mostly multiple sclerosis, CNS tumors, stroke, traumas, intracranial hypertension).