Neuroscience and Reece Foundation PhD Studentships

Hyperactivation of mammalian target of rapamycin (mTOR) signalling in neurons is associated with aberrant axonal and dendritic connectivity, enlarged soma size, increased cellular stress, reduced myelination, synaptic dysfunction and neuronal hyperexcitability. Inhibiting mTOR with rapamycin can modulate neuronal function and reduces hyperexcitability, but may impair executive and recognition memory, and shows no benefit for neurocognitive or behavioural issues in patients with mTORC1 hyperactivation (Coburn et al., 2025).

We have previously used patient induced pluripotent stem cells (iPSCs)-derived neurons with Tuberous sclerosis (TSC) mutation to understand the effect of knocking down TSC function (associated with mTORC1 hyperactivity) on neuronal network behaviours (Alsaqati et al., 2021). This research showed that promoting autophagy can rescue the aberrant neuronal network behaviours seen in TSC patient neurons. We have then generated a homozygous TSC stable knockout (KO) human iPSC cell line using CRISPR-Cas9 technology.

We are now planning to use those cells to generate glutamatergic neurons and investigate the effect of a novel approach to inhibiting mTOR activity on neuronal network behaviours, with potential applications for patients with neurological disorders linked to mTORC1 hyperactivation. Specifically, we plan to employ peptide inhibitors to precisely disrupt the RAS/mTOR signalling pathways. This approach will utilise first-in-class peptides, a proprietary class developed by one of the Principal Investigators, Dr Al Musaimi. These multifunctional peptides will be used to effectively inhibit downstream Rheb, Deptor, and PTEN signalling. They can be engineered to specifically target neurons in a neuron–astrocyte co-culture system, making them ideal carriers for delivering these inhibitors to neurons in the human brain. In addition, they are capable of crossing cell membranes, have demonstrated a favourable safety profile, and show promise in the treatment of brain injuries.

Aim: To investigate the effects of a peptide inhibitor of mTORC1 pathways on the functional properties of neurons and astrocytes derived from TSC patient specific iPSCs.

Objectives:

1. To design and validate first-in-class peptide that selectively targets and inhibits mTORC1.
2. To evaluate the impact of the peptides on mTORC1 activity and its downstream effectors in TSC iPSC-derived neurons and astrocytes.
3. To assess how peptide-mediated modulation of mTORC1 affects functional abnormalities in TSC-derived neural cells, including electrophysiological properties and network behaviours.

This project represents a unique opportunity to gain in-depth training in neural stem cell and peptide engineering and synthesis. The appointed student will be trained in culturing human iPS cells and their differentiation into neurons and astrocytes.

Supervisors:

Dr Othman Al Musaimi: almusaimi@newcastle.ac.uk

Dr Mouhamed Alsaqati: alsaqati@newcastle.ac.uk

Background: Memory disruption is a defining feature of neurodegenerative diseases, including Dementia with Lewy Bodies (DLB). The large-scale anatomical organisation of memory-supporting circuits, and how these circuits fail in disease, is poorly understood. A key barrier has been the lack of scalable tools capable of analysing neuroanatomical data across species and disease states.

This project will use and adapt a state-of-the-art AI-driven neuroimaging analysis pipeline to process two large neuroanatomical datasets:

• Nonhuman primate (NHP) temporal lobe material generated using cutting-edge viral tracing to map networks supporting high-level visual memory.
• Post-mortem brain tissue from patients with (DLB) and age-matched controls.

The project will combine research on visual anatomical pathways in NHPs with human studies of neuronal loss and alpha-synuclein pathology. The goal is to identify memory-network nodes likely to underlie the visual–mnemonic deficits observed in patients.

Hypotheses:

1. NHP anatomical mapping will reveal bypass and interhemispheric temporal lobe projections not represented in current models of the ventral visual stream, identifying targets for the manipulation of information flow within the visual memory pathway.
2. Analysis of DLB tissue will define the relative representations of neuronal loss versus neuronal dysfunction, and their spatial distribution across human temporal lobe.
3. Integration of NHP and human datasets will reveal structures/pathways that can be targeted to model DLB pathology in nonhuman primates.

Methods: High-resolution histological images from 200 DLB cases and age-matched controls will be analysed using a custom convolutional neural network with human-level accuracy to detect neurons. The model will be optimised for the heterogeneity of human tissue and to automate detection of neuronal density and alpha-synuclein aggregates. Large-scale quantitative datasets will be generated across multiple temporal lobe regions and analysed using appropriate statistical models. The student will receive training in the preparation, imaging, and neuropathological assessment of human post-mortem brain tissue. Parallel AI-based analysis of NHP viral-tracing material will be used to map memory-network connectivity, integrating both datasets.

Timeliness and Impact: This project is timely due to advances in AI-based image analysis and the availability of Newcastle University’s renowned human brain tissue collection. It will deliver the first scalable, region-by-region quantification of neuronal loss and alpha-synuclein pathology in DLB, establishing a principled anatomical framework for future primate DLB modelling seeded with patient-derived alpha-synuclein. Outputs will support biomarker discovery, therapeutic evaluation, and comparative neurodegeneration research.

Supervisory Team: Supervisors include experts in NHP neurobiology, human neuropathology, and AI-based biological imaging.

• Dr Mark Eldridge: eldridge@newcastle.ac.uk
• Professor Peter Taylor: Taylor@newcastle.ac.uk
• Dr Daniel Erskine: erskine@newcastle.ac.uk

Background: A fundamental aspect of cognition and language is creating internal models of the sensory world that can be used to replay past events and simulate what the future may hold. As universal as it is for neural systems to actively integrate sequential information from the sensory world with internal memories and forecasts, the significant impact when these functions fail is evident. However, how neuronal systems mechanistically achieve this integration remains an open question. We focus on the auditory system that requires urgent attention because there is substantially less work integrating auditory memory and prediction functions, despite the clear relevance to the universal auditory experience of animals and the specific human experience of speech and language.

Statistical-learning and working memory tasks can establish predictions of forthcoming sensory events across delays that engage the prefrontal cortex. The hippocampus has also recently been implicated in statistical learning and working memory. To advance integrative models of the brain, it is now crucial to use a combined working memory and prediction task during simultaneously recordings of prefrontal, hippocampal and auditory cortical interactions in an animal model when sensory predictions are instantiated across delays.

Hypotheses: We hypothesise that the frontal cortex manages the rules and the hippocampal memory system establishes the context. We will test the hypothesis with fronto-temporal recordings in the macaques during the auditory delayed sensory predictions task. We will examine neuronal (single unit) responses and local-field potentials (LFPs) from simultaneous array recordings of prefrontal, hippocampal and auditory cortical sites in macaque monkeys during the task.

Methods: The research will involve cutting-edge laminar neurophysiology of prefrontal, auditory and hippocampal circuits in awake behaving macaques and developing computational models.

Timelines: Although theoretical frameworks on memory and prediction are emerging, simultaneous recordings from key nodes in the frontal, hippocampal and auditory network in an animal model are a timely scientific need. The proposed research has the potential to show how PFC and HC signals for memory and prediction are distinct or complementary, including how they interact with auditory cortex.

Given that prior insights are largely from the visual spatial or olfactory domain, testing general principles of temporal coding with the auditory as a model system can identify temporal mechanisms that may apply more generally.

Potential impact: The potential impact for the broad cognitive neuroscience and auditory fields is substantial because the role of the hippocampus in audition and more generally in incidental statistical learning (as opposed to its established roles in episodic memory) remains controversial owing to a paucity of data.

Supervisory team: The student will join a multidisciplinary team at Newcastle University and undergo comprehensive training in primate electrophysiology and behavioural testing. Dr Kikuchi has extensive experience in primate cognitive and auditory neuroscience and Prof Griffiths will facilitate the translation of primate research findings to the study's clinical application potential. 

• Dr Yuki Kikuchi: kikuchi@ncl.ac.uk
• Professor Tim Griffiths: griffiths@newcastle.ac.uk

Background: Hereditary ataxia (HA) is a group of genetic disorders that result in cerebellar ataxia, which manifests as unsteady gait, limb incoordination, dysarthric speech, and abnormal eye movements. Recently, cerebellar contributions to cognitive and emotional functions have been recognized, whereby cerebellar ataxia patients may experience impairments in executive functions, visual-spatial skills, and language, along with emotional disturbances such as disinhibition, summarized as cerebellar cognitive affective syndrome (CCAS).

Currently, we lack validated tools to comprehensively assess non-motor, cognitive functions specific to cerebellar pathology, hindering our ability to develop effective screening tools that inform clinical management for patients with HA and their caregivers.

Theoretical underpinning: During motor control, the cerebellum predicts consequences of actions, helping steer our motor system towards desired endpoints. We propose that in the process of decision making, the cerebellum helps steer decisions towards desired outcomes. In that sense the ‘cognitive’ cerebellum predicts the consequences of actions in an abstract space, analogous to predicting the consequences of motor actions in 3-D space, an idea supported by cerebellar involvement in reward coding, attention, decision-making, and planning.

Aim: Develop new assessment methods and compare against existing tools to better characterise and understand CCAS, with a focus on decision making and impulsivity, in two adult patient cohorts of HA and age-matched controls.

Cohorts will comprise of those with pure cerebellar syndrome (spinocerebellar ataxia type 6, SCA6), and those with late-onset cerebellar ataxia (biallelic RFC1 expansion, causing cerebellar ataxia, neuropathy, and vestibular areflexia syndrome, or CANVAS). SCA6 is the most common form of autosomal dominant ataxia in the UK, while CANVAS is becoming recognized as the most prevalent cause of late-onset autosomal recessive ataxia in Western populations. These will be compared to matched healthy controls.

Proposed studies: We will employ probabilistic decision-making tasks [1], versions of the Iowa Gambling Task (IGT) [2], and a newly developed mental tracking task, which requires path reconstruction based on time interval/sequence information. These tasks allow isolate latent components such as subjective value, learning, working memory, impulsivity, and prediction based on mental sequence reconstruction.

Outcomes: The proposed study will yield unique insight which aspects of decision-making are affected in individuals with HA with the ultimate goal to develop clinical screening tools for early identification, better inform patients and care givers about likely day-to-day difficulties and help develop approaches of alleviating some of the symptoms through adequate behavioural interventions.

Supervisors: 

• Dr Yi Shiau Ng: ng@ncl.ac.uk
• Professor Alexander Thiele: thiele@ncl.ac.uk

Electrical stimulation of the spinal cord, while widely applied in clinical practice for the treatment of chronic pain, also provides a unique experimental tool for probing the organisation and dynamics of spinal pathways. Evoked compound action potentials (ECAPs), which reflect the summed synchronous activation of primary afferent fibres within the dorsal columns, have been successfully utilised as the primary biomarker in closed-loop spinal cord stimulation (SCS) systems, improving patient outcomes. Despite these advances highlighting the importance of accurately characterising SCS-evoked signals, the fundamental mechanisms by which electrical stimulation interacts with spinal circuits remain poorly understood.

Our recent work has identified an additional class of evoked responses recorded via epidural SCS electrodes that are distinct from conventional ECAPs. Termed secondary ECAPs, or doublets, these signals propagate unidirectionally along the spinal cord and exhibit faster conduction velocities than primary ECAPs, suggesting an origin in a neural population separate from that generating the primary ECAP. These findings indicate the involvement of alternative pathways, the nature of which remains unknown. Thus, this project aims to utilise SCS as a controlled perturbation to investigate the structure, function, and regulation of spinal pathways in both healthy and chronic pain conditions, thereby enhancing our understanding of how electrical stimulation engages spinal circuits.

To achieve these aims, the successful candidate will combine epidural electrophysiological recordings in rats and macaques with pharmacological and molecular approaches to investigate the origin of doublets. Three competing hypotheses will be tested: (1) that doublets arise from fibres within a secondary dorsal column pathway, (2) that they originate from the spinocerebellar tract, or (3) that they involve fibres of the spinothalamic pathway. By dissecting these possibilities, the project will provide fundamental insights into how electrical stimulation recruits distinct neural populations and how these responses propagate through the spinal network. Subsequently, a rat model of chronic pain will be used to determine whether doublets provide insight into pathological spinal processing, thus establishing clinical relevance and assessing their potential utility as control signals for closed-loop SCS systems. Ultimately, this work will establish a mechanistic foundation for interpreting SCS-evoked signals, enabling their use as physiological markers in next-generation SCS therapies and advancing our broader understanding of spinal cord function in health and disease.

The supervisory team leads successful experimental laboratories and brings together exceptional electrophysiological and pharmacological expertise across multiple species. This unique combination ensures the student receives world-class guidance to achieve the ambitious objectives of the project.

• Dr Ilona Obara: obara@ncl.ac.uk
• Dr Alexander (Sasha) Kraskov: kraskov@newcastle.ac.uk

Stress resilience is crucial to deal with unpredictable environments. This applies to humans and their mental health, but also to animals and their welfare. However, there is a lot we still don’t know about the neurobiological mechanisms that convey stress resilience. We also don’t know if these mechanisms are evolutionarily ancient or have evolved several times in different lineages. This project looks at the neurobiological correlates of stress resilience in birds, and compares them to the literature on mammals.

You will investigate the effect of a commercial feed supplement, derived from natural botanical secondary metabolites, on stress resilience in laying hens. In order to investigate stress resilience, you will measure both acute and chronic stress responses at different levels of analysis: from behavioural to physiological, molecular, and neuroanatomical. This will involve training in in-vivo experimental design and state-of-the-art neuroanatomical techniques. You will work primarily in a university setting to conduct the experiments on small groups of laying hens.

Study 1 will investigate whether the feed supplement reduces the acute physiological stress response and/or speeds up the recovery from this response. It will also investigate activity in different brain areas, using immediate early genes, in order to identify which parts of the brain are involved in increasing stress resilience.

Study 2 will look at the effects of chronic stress, with or without the feed supplement. You will investigate how chronic stress changes the circadian rhythm of corticosterone, and whether this is different when treated with the feed supplement. You will also measure the effects of chronic stress on behaviour, and in the brain. You will quantify the density of doublecortin-expressing neurons in the hippocampus. This cell population is known to be sensitive to chronic stress, and therefore a good indicator of stress resilience.

As part of your project, you will also spend time at the Lakes Free Range Egg Co, the largest packer of free-range eggs in the UK, to conduct field trials of the feed supplement in realistic commercial settings, and validate the laboratory brain findings in a commercial, real-world environment.

This is an ideal project for a student interested in comparative physiology and neuroscience, comparing the neural mechanisms underlying stress responses and resilience between birds and mammals.

Supervisors:

• Dr Tom V Smulders: smulders@ncl.ac.uk
• Professor Lucy Asher: asher@newcastle.ac.uk

Bimanual movements are central to daily life—from tool use and surgery to music— but are frequently disrupted in neurological disorders (such as stroke and Parkinson’s Disease) where impairments in inter-limb coordination and independent control are common and disabling. Despite this, the structure by which the nervous system coordinates muscles across both limbs remains poorly quantified.

A fundamental question remains unresolved: when both hands act together, does the motor system implement a single, unified control strategy, or does it merely coordinate two largely independent controllers? Recent neural-manifold approaches suggest that biological control can be captured within low-dimensional latent spaces whose geometry constrains behaviour. However, it remains unclear how the underlying dynamical structure of shared neural control is expressed in muscle activity during natural behaviours (beyond muscle synergies).

This PhD will test whether the two hands are controlled by a single shared command or by two largely independent commands that are then coordinated during bimanual action. You will assess whether this generalises across behaviours, using laboratory paradigms in volunteers (e.g. symmetric versus asymmetric movements) alongside recordings during daily life with a wearable multi-EMG data logger to capture naturalistic “motor motifs”.

In the laboratory, you will collect high-density surface EMG together with kinematics. Crucially, non-invasive perturbations—via peripheral nerve stimulation and transcranial magnetic stimulation—will be used to probe the stability of extracted low-dimensional manifolds and test whether perturbation-evoked responses are constrained to the same latent subspaces as voluntary movements, or whether they recruit distinct control pathways. There will also be scope to analyse existing EMG and neural datasets from non-human primates performing bimanual behaviours, from parallel project strands.

A key translational component will apply these approaches to patient groups, to test if abnormal coupling, such as post-stroke mirror movements, reflects reduced manifold dimensionality, loss of limb-specific subspaces, or compensatory recruitment of additional dimensions. This could enable the development of quantitative biomarkers linked to impairment and recovery.

This project is timely, as recent advances in wearable EMG, decomposition methods, and manifold analysis allow detailed study of human motor control during naturalistic behaviour beyond simple synergies. This has implications for neurorehabilitation, brain–machine interfaces, and assistive technologies. The project is supervised by an interdisciplinary team spanning motor control, neurophysiology, and movement disorders, providing training in experimental techniques, data analysis, and clinically focused research.

This is an exciting opportunity to contribute to ground-breaking research at the intersection of neuroscience, motor control, and clinical rehabilitation.

Supervisors:

• Dr Demetris Soteropoulos: soteropoulos@ncl.ac.uk
• Professor Stuart N Baker: baker@ncl.ac.uk
• Professor Mark R Baker: baker@ncl.ac.uk

Background: Microglia, the resident immune cells of the brain, are critical regulators of neural health and central drivers of neurodegenerative pathology. However, the therapeutic potential of targeting these cells is limited by a lack of tools to precisely control their function. Current approaches often classify microglia into simplistic M1/M2 states, which fails to capture the true spectrum of phenotypic plasticity they exhibit in the complex brain environment. To effectively treat conditions like Alzheimer’s or Parkinson’s, we must move beyond blunt anti-inflammatories and develop targeted interventions that can "sculpt" immune states with spatial and contextual precision.

Hypothesis and Aims: We propose that high-content imaging combined with machine learning can reveal a continuous "state space" of microglial activation that is invisible to traditional bulk assays. The central hypothesis is that by mapping this morphological landscape, we can identify small molecules that drive microglia away from neurotoxic phenotypes and towards protective, pro-resolving states.

Methods Develop and utilize a novel phenotypic screening platform through the following objectives:

1. Assay Development: Establish a robust high-content imaging assay for microglia, optimizing fluorescent labeling to capture hundreds of morphological features (e.g., cytoskeletal and organelle structure).
2. The "Atlas": Apply automated image analysis and dimensionality reduction (UMAP) to generate a high-resolution "atlas" of microglial phenotypes.
3. Screening: Screen a curated library of ~1,000 small-molecule immunomodulators to quantify how specific compounds shift the distribution of cells on the atlas.
4. Validation: Confirm the mechanism and therapeutic effect of "hit" compounds using functional assays, suggesting new targets for drug discovery.

Timeliness and Impact This project represents a timely shift from target-based to phenotype-based drug discovery in neuroscience. By establishing the first quantitative map of the microglial morphological landscape, this work will generate a powerful new framework for understanding neuroinflammation, along with new proposed targets for future drug discovery

Supervisory Team This interdisciplinary project combines advanced quantitative biology with phenotypic assay development and chemical biology. AW is an expert in microscopy and image analysis who will guide the high-content profiling and computational aspects, and KH is a specialist in image-based phenotypic assay development and medicinal chemistry for neuroscience and immunology.

• Dr Adam J.M. Wollman: wollman@newcastle.ac.uk
• Dr Kate S Harris: harris@newcastle.ac.uk

Background: Amyotrophic lateral sclerosis (ALS), also known as motor neuron disease (MND), causes progressive skeletal motor unit degeneration leading to muscle weakness and paralysis. Surviving motor units compensate by re-establishing contact with muscle fibres which have lost their nerve supply, but we know remarkably little about this process of re-innervation. Specifically, we do not know whether this process occurs exclusively within the territory of the surviving motor unit or results in an increase in its spatial extent; whether all surviving motor units contribute to re-innervation or only a sub-group; whether re-innervating motor units are more or less likely to degenerate than their neighbours; and how re-innervation affects overall muscle function. Answering these questions is crucial for developing new therapies which increase re-innervation whilst protecting surviving motor units, but until now the tools to address this in patients has been lacking.

Research Aims: The project will track the process of motor unit loss and re-innervation in a longitudinal study of patients with ALS. Specifically, the project will image the size, shape, complexity and functional properties of defined populations of motor units over time to see how re-innervation affects motor unit function and survival.

Methods: Our group has developed a novel MRI-based imaging technique called motor unit MRI (MUMRI). When combined with in-scanner electrical stimulation this allows the 3-dimensional imaging of specific motor units along with measurement of their twitch profiles and resistance to fatigue. In year 1 you will further optimise this technique by developing an upper-limb protocol and a correlation-based method of motor unit visualisation. In years 2 and 3 you will recruit patients with recently diagnosed ALS (via NuTH clinics), and measure motor unit structure and function in the tibialis anterior and biceps brachii muscles at baseline, 6 and 12 months. You will validate the MUMRI results against functional outcome data (ALS FRS and MRC strength scales), high-density surface electromyography and muscle ultrasound.

Impact: This project will be the first to track the process of motor unit loss and re-innervation in patients. If successful this will provide unprecedented insight into the underlying biology, and will crucially answer the question of whether re-innervation maintains overall muscle function, or counter-intuitively hastens motor unit loss. The techniques developed in this project will also serve as future biomarkers of therapeutic trials aimed at manipulating this process.

Supervisory Team: The supervisory team invented MUMRI and are experts in clinical neurophysiology (Whittaker), MRI physics and translational research (Birkbeck & Blamire).

• Professor Roger G Whittaker: whittaker@newcastle.ac.uk
• Dr Matthew G Birbeck: birkbeck@newcastle.ac.uk
• Professor Andrew M Blamire: blamire@newcastle.ac.uk

Learning in the neocortex is not driven by synaptic connections alone but is powerfully shaped by neuromodulators such as dopamine and acetylcholine. These chemical signals act as masters of brain function, regulating shifts between behavioural states such as learning and prediction, attention and distraction, and exploration and exploitation by controlling the electrical oscillations in neural networks. Despite their central role, these dynamic modulatory effects are poorly captured in current computational models of learning, which typically rely on static architectures and fixed learning rules.

The main aim of this project is to design a neuromodulation-aware AI framework that enables adaptive, energy-efficient, and interpretable learning by incorporating principles derived from neuromodulators into computational models. By embedding mechanisms such as dynamic plasticity gating, oscillatory coordination, and context-dependent learning, the project will overcome the limitations of static AI architectures. Key questions include:

1. Which neuromodulator mechanisms can be abstracted to guide adaptive and interpretable learning in AI models?
2. How can these principles be implemented to enhance learning flexibility and efficiency?
3. How do neuromodulator-inspired mechanisms affect adaptiveness, interpretability, and energy efficiency of AI models?

This research will be carried out over a three-year programme:

• Year 1: Conduct literature review on neuromodulators and cortical circuits, identify and formalise key modulatory principles, and develop computational abstractions to guide AI framework design
• Year 2: Implement these principles in computational models (e.g. spiking and recurrent neural network), incorporating context-dependent plasticity, oscillatory coordination, and energy-efficient learning strategies for energy-efficient, adaptive, and interpretable learning.
• Year 3: Evaluate model performance, adaptability, and energy efficiency on benchmark learning tasks, and refine the models to ensure interpretability, biological plausibility, and computational effectiveness.

This project will deliver a neuromodulation-aware AI framework that addresses key limitations of current computational models, including limited adaptability to new contexts, poor interpretability, reliance on large datasets, and high energy consumption. By incorporating biologically inspired mechanisms such as dynamic plasticity gating and oscillatory coordination, the framework aims to improve learning efficiency, robustness, and energy usage while enabling more transparent, context-sensitive decision-making. Beyond AI, the project will generate computational predictions to guide neuroscience experiments on neuromodulator function.

The PhD will be supported by a diverse supervisory team with expertise in AI and neuroscience, particularly neuromodulators and cortical circuits, ensuring comprehensive interdisciplinary training and significant impact across both fields.

Supervisors:

• Dr Jingjing Zhang: Zhang@newcastle.ac.uk
• Dr Srikanth Ramaswamy: Ramaswamy@newcastle.ac.uk
• Professor Rishad Shafik: Shafik@newcastle.ac.uk

Parkinson’s disease (PD) is a progressive neurodegenerative disorder affecting 11.8 million worldwide. A slowness (bradykinesia) and reduced magnitude (hypokinesia) of movement as well as postural instability are among the most disabling features, arising from dynamic failures in brain–body coordination. Traditional neuroimaging and clinical gait assessments have largely been performed in isolation, limiting insight into the neural mechanisms that govern locomotion and postural control. PD disrupts not only motor circuits but also the large-scale metabolic and structural networks that support coordination between brain activity and whole-body movement. However, most neuroimaging has been performed at rest, isolated from actual gait. Understanding how brain metabolic activity (using a glucose tracer; FDG and Positron Emission Tomography; PET) and structural connectivity (diffusion weighted magnetic resonance imaging; dwMRI) relate to gait and postural control is critical for understanding the fundamental mechanisms underlying impairment.

This proposal leverages three complementary datasets: DynaMo-PD, FDG-PET/MR and the deeply phenotyped ICICLE-Gait – a longitudinal cohort study of early PD and older adults. In DynaMo-PD and FDG-PET/MR, FDG-PET was acquired following periods of standing and walking, alongside multi-modal MRI and instrumented gait and balance assessments, enabling direct interrogation of brain metabolic activity. ICICLE-Gait provides longitudinal structural MRI and dwMRI allowing investigation of microstructural integrity and basal ganglia–brainstem–cortical network organization, as well as measures of gait and balance from the laboratory and real-world. By integrating multimodal neuroimaging and movement analysis, this project will define neural signatures of impaired brain–body neural coupling in PD. We hypothesise that gait and balance impairments arise from specific patterns of cortical–subcortical hypometabolism, network disconnection, and dysfunctional coupling. This integrative approach addresses a critical gap in PD research by linking neuroimaging biomarkers to real-life motor behaviours.

The findings may inform more targeted and personalised interventions by identifying neural mechanisms underlying specific motor deficits. In the longer term, this work could guide optimisation of deep brain stimulation or non-invasive neuromodulation strategies (i.e. tDCS, tFUS, TMS) to boost cortical-muscular coherence. Collectively, these efforts will close critical knowledge gaps.

The supervisory team comprises experts in human movement science, musculoskeletal biomechanics, neuroimaging and Parkinson’s disease (Dr Alcock, Professor Rochester, Dr Firbank, Dr Sigurdsson). The project benefits from the support of a wider team with national (Professor Ray, Manchester Metropolitan University) and international (Dr Sigurdsson, Alicante, Spain) links. Provision for training is planned to include time with experts developing knowledge in MRI, diffusion imaging, FDG-PET and movement science.

• Dr Lisa Alcock: alcock@newcastle.ac.uk
• Professor Lynn Rochester: rochester@newcastle.ac.uk
• Dr Michael Firbank: firbank@newcastle.ac.uk

Background: People with Parkinson’s (PwP) experience fluctuating motor symptoms (e.g., mobility impairment, tremor) that are partially alleviated by complex medication schedules. Adhering to these schedules is challenging and often leads to ‘off’ periods and reduced quality of life. Clinical assessments typically occur only twice per year, providing limited insight into how symptoms evolve during everyday life.

Wearable technology enables continuous, objective monitoring of mobility, motor symptoms and medication adherence. However, raw data alone is insufficient. To generate meaningful, actionable insights, we need analytical models that can identify medication response, predict fluctuations and deliver personalised feedback to PwP.

This PhD provides a unique opportunity to contribute to the development of next-generation digital tools for real-time clinical decision support in Parkinson’s. You will work at the intersection of data science/AI, bioengineering and translational neuroscience to help build an integrated remote-monitoring system.

Research Question

Can data-driven predictive modelling and real-time analytics of real-world wearable data generate personalised, actionable insights to improve medication management in Parkinson’s?

Methods: The project leverages high-quality data from the CiC-PD study (https://www.isrctn.com/ISRCTN13156149), including demographic and clinical information alongside continuous real-world recordings of mobility, motor symptoms and medication intake using Axivity AX6 devices and smartwatches.

This PhD focuses on:

1. Developing predictive models to characterise individual medication-response patterns and group-level trends using validated digital mobility and motor outcomes. AI methods designed for high-density wearable data including hierarchical Bayesian models and hidden Markov models will identify underlying motor states and shifts in mobility following medication intake.
2. Creating a real-time analytics framework and proof-of-concept digital system capable of generating actionable insights to support self-management and personalised clinical care. This will involve translating offline models into efficient real-time pipelines using data-streaming and model-compression strategies.

Timeliness: This PhD aligns with the NHS 10-Year Health Plan, which emphasises digitally enabled, personalised and community-based care. This project directly intersects with national priorities, including expansion of remote monitoring, integration of patient-generated data and deployment of AI-driven decision support.

Potential Impact: By developing validated real-time predictive models for Parkinson’s, this project will accelerate the integration of wearables into routine care and advance next-generation digital clinical decision-support systems with the potential to transform Parkinson’s management.

Supervisory Team: An interdisciplinary team with expertise in Parkinson’s and digital health (Del Din), AI and time-series modelling (Kingston), and real-time systems (Noccaro) will guide you. You will benefit from close interaction with clinicians, PwP, and a highly supportive and translational training environment.

• Dr Silvia Del Din: del-din@newcastle.ac.uk
• Dr Andrew Kingston: kingston@newcastle.ac.uk
• Dr Alessia Noccaro: noccaro@newcastle.ac.uk

This interdisciplinary project involves applying the quantum formalism (QF) to model experimental observations of human emotional memory performance, and the neural systems that support it, to better understand the interaction between elements. The quantum formalism provides a natural mechanism for modelling combinations of difficult-to-reconcile memory effects, such as the question order effect (where changing question order may change the respondent’s answer) and the response replicability effect (where repeated presentations of a question produce the same response across contexts). Conversely, the paradoxical temporal effects often observed in human memory performance mean it may provide a useful theatre for observing analogies of quantum phenomena we cannot access directly.

Temporally ordering the complex set of overlapping emotional events that comprise our lives is a demanding task, yet one our brains constantly undertake, providing shape and story to our lives. This ordering can enhance the detail, context, and accuracy of our memories - or be susceptible to distortions.

Behavioural Analysis: Initially, this project will use the QF to explore and model the role of emotional content and context on the temporal ordering and asynchronous retention of memories. Experiments will vary the proportion of negative, neutral, and positive stimuli and their relative temporal position to test predictions derived from this model. Behavioural outcomes measures will include memory performance and reaction time.

Implicit measures of memory encoding: We will use eye tracking to measure dependent variables (e.g., eye movement, pupil dilation, point of gaze, and blinking) to explore factors occurring during encoding of events containing different emotional content and occurring in different emotional contexts. We will apply the QF to these measurements to create a model that predicts the impact of emotional content  on the order and duration of memory for events. Seeing how this model encodes and represents these dependent variables will allow us to identify them as analogues to experimentally-unobservable mathematical elements of the QF, allowing us to more directly intuit how they these elements of the formalism manifest.

By integrating these disciplines, we aim to develop a sophisticated model that explains how emotional events influence memory accuracy and perception of time. The quantum formalism, typically used in physics, provides a natural mechanism for modelling psychological phenomena such as the question order effect and response replicability effect, and critically, perceptual responses to these.

Supervisory Team: Dr Barbara-Anne Robertson (Lecturer, Psychology), Dr Jonte R Hance (Lecturer, Applied Quantum Foundations), Dr Quoc Vuong (Senior Lecturer, Psychology).

• Dr Barbara-Anne Robertson: a.robertson@newcastle.ac.uk
• Dr Jonte Hance: hance@newcastle.ac.uk
• Dr Quoc C Vuong: vuong@newcastle.ac.uk

Background: Chronobiological disruptions are implicated in all major disease groups, particularly mood disorders. Individuals with bipolar disorder and treatment-resistant depression show altered circadian rhythms, sleep architecture, and diurnal patterns of mood and activity. However, our understanding of these disruptions in real-world settings remains limited. Current research relies predominantly on actigraphy or subjective reports, missing the rich multimodal physiological signals that could reveal how mood disorders manifest across neural and peripheral systems in daily life.

Hypothesis: We hypothesize that mood disorders involve characteristic dysregulation patterns across multiple chronobiological and physiological systems (neural rhythms, autonomic function, temperature regulation, activity patterns) that can be captured through continuous multimodal monitoring in naturalistic conditions.

Approach: This interdisciplinary project combines neuroscience with advanced machine learning to understand chronobiology and physiology in mood disorders. You will:

• Multimodal Data Collection: Collect synchronized EEG and wearable device data (actigraphy, heart rate variability, core body temperature, ambient light exposure) from individuals with mood disorders and healthy controls during their everyday lives. This multimodal approach represents a significant advance over current actigraphy-only standards, enabling investigation of neural-physiological interactions.
• Advanced Computational Analysis: Develop and apply machine learning methods to analyze circadian and ultradian rhythms across multiple signals simultaneously. You'll characterize how different physiological systems interact and whether these interactions differ between patient groups and controls.
• Mechanistic Understanding: Investigate fundamental questions about chronobiological dysregulation in mood disorders: How do neural oscillations relate to peripheral physiological rhythms? What environmental factors (light, activity) influence these relationships? Are disruptions specific to certain systems or reflect system-wide dysregulation?
• Clinical Context: Work with clinical collaborators, potentially including tertiary services like the Regional Affective Disorders Service (RADS), to relate physiological findings to clinical phenotypes and real-world functioning.

Timeliness & Impact: This project addresses critical knowledge gaps in psychiatry and chronobiology. Recent advances in portable EEG technology and wearable sensors now enable previously impossible investigations of neural-physiological dynamics in naturalistic settings. Your work will provide fundamental insights into the pathophysiology of mood disorders, moving beyond symptom descriptions to mechanistic understanding of how circadian and physiological regulation go awry. These insights could ultimately inform novel interventions targeting specific dysregulated systems.

Supervisory Team: Prof Yujiang Wang (UKRI Future Leaders Fellow) leads the CNNP lab specializing in computational neuroscience. Drs David Cousins and Peter Gallagher bring clinical expertise in mood disorders and clinical psychology. You'll join a vibrant, interdisciplinary research environment with strong clinical links, state-of-the-art computational facilities, and ongoing collaborations with wearable device manufacturers.

• Dr Yujiang Wang: wang@ncl.ac.uk
• Dr David Cousins: cousins@newcastle.ac.uk
• Dr Peter Gallagher: gallagher@newcastle.ac.uk

You must have, or expect to achieve, at least a 2:1 Honours degree, or international equivalent, in a subject relevant to the project (please contact the supervisors if you are unsure). A further qualification such as an MRes, or other research experience, is advantageous.

Please read these instructions carefully before you begin your application, and ensure you complete all the steps required.

To apply for the Neuroscience Fund Scheme

Choose which project you wish to apply for (you may apply for one project only). 

Complete and submit the Neuroscience Fund Scheme application form to apply.

To apply for the Reece Foundation Scheme

Choose which project you wish to apply for (you may apply for one project only).

Complete and submit the Reece Foundation Scheme application form to apply.

You must also submit the following documents by email in a single .zip file attachment:

1. Your CV (including contact details for at least two academic (or other relevant) referees)
2. A covering letter – this should explain your particular interest in your chosen project. It should also include any additional information you feel is pertinent to your application.
3. Copies of your undergraduate degree transcripts and certificates.
4. A copy of your passport (photo page).
5. Your English language certificate (IELTS or TOEFL certificate, where applicable).

Documents should be submitted as .PDF files.

Do not submit photos of certificates.

Do not combine all the documents into one .PDF.

Each document type listed should be included in the .zip file and names as follows:

[candidate surname candidate name document type].

For example Jones Anna CV

Please zip the separate documents into a .zip file. Name the zip file:

Surname_name_[project number]

Email your zip file to centreforneuroscience@newcastle.ac.uk

The subject line of your email must include:

• Neuroscience Fund PhD 2026 or Reece Foundation PhD 2026 
• The number of your chosen project
• The surname of the lead supervisor
• E.g. Neuroscience Fund PhD 2026; Project 1; Anand

Applications not meeting these criteria may be rejected.

You will receive confirmation by email that your application has been received.  If we require any further information from you about your application, we will be in touch.

You may apply for both schemes only if you have the appropriate qualifications and experience for the projects concerned.  If you apply to both schemes, you must indicate which scheme is your first preference.  You can do this by writing 'first preference' next to the project number on the application form.  We may also ask candidates to transfer from one scheme to the other.  Where possible, we aim to place top candidates irrespective of the scheme to which they have applied.

You will need two referees, one of which must be an academic reference.  This could be:

• An undergraduate or master’s project/dissertation supervisor
• Personal tutor
• A module director/organiser
• Someone you have worked for in an academic context from your university.

If you are applying for a position with your current (or past) supervisors, it is not advisable to use them as a referee.  Supervisors are also competing for funding, so there is a conflict of interest.  In such cases your chosen supervisor can provide guidance on the most suitable referee to include.

Applications will be open 9th February 2026 and should be submitted by midnight on 6th March 2026.

What happens to my application after the closing date?

All completed applications will be screened by the Centre for Transformative Neuroscience for eligibility.  Following this:

• Applications will be scored by the supervisors and selection panel to arrive at a short list.
• The highest-ranking candidates in the short list will be invited to meet the supervisor of their selected project and to a panel interview. Meetings and interviews may take place in person or via Teams depending on circumstances.

If I am invited to interview, what does the interview process involve?

The interview will last approximately 30-45 minutes.  The interview format will involve:

• A short presentation by you describing a previous research project you’ve worked on. This includes a succinct description of how you contributed to that research (you will use screen share mode on the online platform if used).
• Around 30 minutes of questions from a panel of academics.

What happens after the interview?

Following the interview, candidates are scored by each panel member based on their performance.  The scores are collated, and a final ranking is decided, taking all factors into account.  Offers are made to candidates according to the rankings.

You will not be judged for having been out of academia, whether it is for work, caring duties, illness or anything else. Like everyone else, you will need a degree – however, there is no time limit on when this was awarded. We appreciate that experiences outside of academia can be a rich source of key skills that you would need for a PhD so be sure to think carefully about skills this experience has given you and make sure you tell us about them. It is likely that the supervisor or interview panel might want to know what drew you back to academia, so use this time to show how passionate you are about research.

You will not be penalised for not having a master’s degree. PhD studentships are highly competitive, and most successful applicants will have a master’s qualification. This is because of the experience a master’s degree provides rather than the certificate. However, experience can equally come from many other sources, such as work, both academic and non-academic.

If you are offered an interview, a standard email will be sent from the Centre team to your referees requesting a reference prior to your interview. We would advise that you contact your referees to advise them that they may receive a reference request.

The cover letter should explain your interest in your chosen projects and should include any additional information you feel is important to your application. You may wish to add why you are choosing Newcastle University. There are no formal word limits for your CV or cover letter, but we recommend you keep them concise.

Each student has a different set of strengths and weaknesses. That said, a passion for the project is an essential part of being a successful PhD student. It is a basic requirement that any supervisor will look for in selecting a student. Do remember that there may be some (but not endless) flexibility in what you actually do within the PhD project.

This will be dependent on the project supervisor. Our funding does not dictate any work schedule. It does ask that any difference from standard working patterns be agreed with your supervisor. It would be sensible to discuss this with them before you apply. Most supervisors will support a student's requirements (for example, to accommodate caring responsibilities), but the project may have specific requirements. E.g., where a particular type of lab work is necessary to complete the project.

Students may take on teaching or demonstration work, where this is compatible with their training in addition to a full-time studentship. This needs to be approved by their supervisors.  Other paid work would need the consent of the supervisor and should not delay or interfere with your research training. You may ask primary supervisors about flexibility of the PhD; this varies depending on the PhD project. Part time study is usually available; we advise that you discuss this further with the project supervisor.

Applications should be submitted by midnight on 6th March 2026.