I am a lecturer at the School of Psychology, University of Sussex, UK. I have a PhD in cognitive neuroscience and neuroimaging and I am interested in the functional hierarchy of the human brain, information flow, neural trait-state interactions, as well as their associations with behaviour and ongoing experience.
Born in Veria, Greece, I received my bachelor’s degree in physics from the department of Physics at the Aristotle University of Thessaloniki, Greece. Following several years of physics tutoring in Greece, I changed my career path and moved to the UK, where I obtained my master’s degree in cognitive neuroscience at the department of Psychology at the University of York, under the supervision of Prof. Gary Green.
Upon graduating my master’s programme, I worked as a research assistant in Prof. Jonathan Smallwood’s lab at the University of York. A year later, I was awarded a studentship for a PhD under the joint supervision of Prof. Smallwood and Prof. Elizabeth Jefferies, from the department of Psychology at the University of York. After a 5-month break in my 2nd PhD year, in order to work on a pilot neuroimaging study investigating experiences in virtual/augmented reality led by Prof. Alex Wade, I completed my PhD and then worked as a postdoctoral research associate for 3 years (1st post - Prof. Smallwood’s lab, University of York, UK, 2nd post - a collaborative neuroimaging project funded by the York Maastricht Partnership, with Profs Antony Morland and Alex Wade (YNiC), and Profs Elia Formisano and Rainer Goebel (M-BIC), until I started my current post.
My PhD thesis investigated the neural correlates of ongoing experience. We utilised different neuroimaging modalities (DWI, resting state fMRI) and analysis techniques (static connectivity and dynamic hidden Markov modelling) in order to explore how the structural and functional organisation of the brain at different temporal scales can help us identify the cognitive processes that support different types of unconstrained ongoing experience at different moments in time.
PhD in Cognitive Neuroscience and Neuroimaging, 2019
University of York, UK
MSc in Cognitive Neuroscience, 2014
University of York, UK
BSc in Physics
Aristotle University of Thessaloniki, Greece
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Undergraduate & postgraduate level
When unoccupied by an explicit external task, humans engage in a wide range of different types of self-generated thinking. These are often unrelated to the immediate environment and have unique psychological features. Although contemporary perspectives on ongoing thought recognise the heterogeneity of these self-generated states, we lack both a clear understanding of how to classify the specific states, and how they can be mapped empirically. In the current study, we capitalise on advances in machine learning that allow continuous neural data to be divided into a set of distinct temporally re-occurring patterns, or states. We applied this technique to a large set of resting state data in which we also acquired retrospective descriptions of the participants’ experiences during the scan. We found that two of the identified states were predictive of patterns of thinking at rest. One state highlighted a pattern of neural activity commonly seen during demanding tasks, and the time individuals spent in this state was associated with descriptions of experience focused on problem solving in the future. A second state was associated with patterns of activity that are commonly seen under less demanding conditions, and the time spent in it was linked to reports of intrusive thoughts about the past. Finally, we found that these two neural states tended to fall at either end of a neural hierarchy that is thought to reflect the brain’s response to cognitive demands. Together, these results demonstrate that approaches which take advantage of time-varying changes in neural function can play an important role in understanding the repertoire of self-generated states. Moreover, they establish that important features of self-generated ongoing experience are related to variation along a similar vein to those seen when the brain responds to cognitive task demands.
The capacity to imagine situations that have already happened or fictitious events that may take place in the future is known as mental time travel (MTT). Studies have shown that MTT is an important aspect of spontaneous thought, yet we lack a clear understanding of how the neurocognitive architecture of the brain constrains this element of human cognition. Previous functional magnetic resonance imaging (MRI) studies have shown that MTT involves the coordination between multiple regions that include mesiotemporal structures such as the hippocampus, as well as prefrontal and parietal regions commonly associated with the default mode network (DMN). The current study used a multimodal neuroimaging approach to identify the structural and functional brain organisation that underlies individual differences in the capacity to spontaneously engage in MTT. Using regionally unconstrained diffusion tractography analysis, we found increased diffusion anisotropy in right lateralised temporo-limbic, corticospinal, inferior fronto-occipital tracts in participants who reported greater MTT. Probabilistic connectivity mapping revealed a significantly higher connection probability of the right hippocampus with these tracts. Resting-state functional MRI connectivity analysis using the right hippocampus as a seed region revealed greater functional coupling to the anterior regions of the DMN with increasing levels of MTT. These findings demonstrate that the interactions between the hippocampus and regions of the cortex underlie the capacity to engage in MTT, and support contemporary theoretical accounts that suggest that the integration of the hippocampus with the DMN provides the neurocognitive landscape that allows us to imagine distant times and places.