Postgraduate Projects

Neurobiology of consciousness (PhD/MPhil)

A significant challenge of contemporary neuroscience is to understand how the neurobiology and function of the human brain give rise to conscious experience. One way to address this question is to identify changes in functional architecture that accompany changes in conscious state. Consciousness alterations may occur through pharmacological interventions such as anaesthetic drugs (e.g. Varley et al., 2020; Pappas et al., 2019; Stamatakis et al., 2010) and psychedelics (e.g. Luppi et al., 2021; Varley et al., 2020) but also through trauma such as hypoxic-ischemic injuries or localised traumatic brain injuries (e.g. Varley et al., 2020; Luppi et al., 2019). In order to identify neurobiological signatures of loss of consciousness that are generalisable, we investigate commonalities in functional architecture changes and network dynamics, across a broad range of altered states of consciousness (Luppi et al., 2023). We have shown that, brain networks (particularly the Default Mode Network) exhibit altered connectivity and complexity and we have proposed these to be critical features of any mechanistic description for loss of consciousness (e.g. Luppi et al., 2019; Varley et al., 2020). We are also interested in quantifying complexity using fractal properties as a measure of proximity to a point of criticality between consciousness and unconsciousness (e.g. Luppi et al., 2021; Varley et al., 2020; Varley et al., 2020). Within these emerging empirical frameworks, projects in this area will utilise fMRI/EEG data to focus on the hierarchical organisation of large-scale brain networks at rest but also importantly during tasks, in patient and healthy volunteer cohorts. Experience in neuroimaging (fMRI or EEG) is required for these projects. Skills in scientific computing and programming (e.g, Linux, Matlab, Python) are essential.

Neuromodulatory effects of monoaminergic brainstem nuclei on large scale brain networks - Ultra-High-Field MRI (PhD/MPhil)

Preclinical work has revealed that projections from brainstem nuclei to the cortex are involved in awareness and wakefulness. Despite this, there are no precise accounts of the modulatory relationships between these nuclei and cortical/subcortical networks relevant for consciousness and associated disorders. Our findings so far support an important role for the dopaminergic system in consciousness. Specifically, we have shown that the functional relationship between the ventral tegmental area (VTA - the origin of the dopaminergic cell bodies of the mesocorticolimbic dopamine system) and posterior Default Mode Network is disrupted both under anaesthesia and in disorders of consciousness. Importantly, this connectivity re-emerges after recovery from anaesthesia and is upregulated by methylphenidate, a dopaminergic (and noradrenergic) agonist (Spindler et al., 2021). Next, we will focus on modulatory influences of cortical networks from other monoaminergic nuclei (e.g. locus coeruleus). The proposed work will develop high spatial/temporal resolution, ultra-high field 7T MRI protocols to overcome limitations in existing 3T MRI imaging of the human brainstem. This work provides an in vivo translational bridge between preclinical and clinical research and can potentially aid the identification of specific neurotransmitter systems as viable therapeutic targets for pharmacological or electro-stimulatory interventions. Experience in neuroimaging, in particular MRI analysis, is required for these projects. Skills in scientific computing and programming (e.g., Linux, Matlab, Python) are essential.

Neurobiology of Traumatic Brain Injury (PhD/MPhil)

CENTER-TBI is a large European project that aims to improve the care of patients with Traumatic Brain Injury (TBI). The work undertaken in Cambridge examines the role of MRI in routine TBI imaging and our projects focus on the role of resting state functional MRI in TBI imaging. There is now abundant evidence from functional MRI suggesting that the resting brain is organised in coherent long-range networks. The functionality of these networks is easily inferred since they typically track the functional anatomy of networks identified during activation studies. For this reason, the translational potential of resting networks, particularly in patients unable to participate in stimulus driven experiments (e.g. TBI), seems immense but is mostly unrealised. Projects in this research theme will evaluate the usefulness of resting state functional MRI imaging in TBI for understanding and tracking disease processes and prognosticating cognitive recovery. Specifically, we will aim to relate functional connectivity patterns to cognitive measures, vulnerability for depression (Pappas et al., 2020 -preprint; Moreno-López et al., 2016) and to functional outcome in general (e.g. Woodrow et al., 2023; Woodrow et al., 2024). Experience in neuroimaging, in particular MRI analysis, is required for these projects. Skills in scientific computing and programming (e.g, Linux, Matlab, Python) are essential.

Biomarkers for Risk and Clinical Outcome Prediction of Postoperative Cognitive Dysfunction (PhD/MPhil)

Postoperative cognitive impairment is increasingly prevalent in our aging society. This condition often has an acute phase of Postoperative Delirium (POD) which may then be followed by a more chronic phase of Postoperative Cognitive Dysfunction (POCD), which tends to persist over time. BIOCOG, a European FP7 funded project, aims to establish biomarkers for risk and clinical outcome prediction of POD/POCD. The study population are patients aged 65 to 80 undergoing major elective surgery. The work undertaken in Cambridge will focus on MRI imaging and will investigate whether brain network properties obtained from MRI diffusion tensor imaging (DTI) measures, relate to those obtained from resting state functional MRI data. Structural and functional network properties will be related to neuropsychological and clinical measures (e.g. Fislage et al., 2022; Fislage et al., 2024). Experience in neuroimaging, in particular MRI analysis, is required for these projects. Skills in scientific computing and programming (e.g, Linux, Matlab, Python) are essential.

How to apply

Department of Clinical Neurosciences 

PhD in Clinical Neurosciences

MPhil in Medical Science (Clinical Neurosciences) 

Clinical Neurosciences MPhil/PhD projects 

Funding guidance

Postgraduate Funding Search 

External funding