Facilitating early diagnosis of neurodegenerative diseases through normative modelling of healthy brain states

We are looking for an eager and enthusiastic PhD student to join our team at the University of Dundee and conduct ground-breaking research into diagnostic brain signatures of Parkinson’s disease (PD). The project will provide ample opportunities to work both in cognitive neuroscience and clinical environments and aims to develop a diverse skill set, including – but not limited to – neuroimaging brainwaves using EEG, neuro-cognitive assessment of Parkinson’s patients, computational modelling and data analysis, and the scientific dissemination of findings. The project is fully funded for 3 years, with a starting date of September/October 2024.

PD can have devastating effects on the quality of life of patients, as well as their loved ones and caretakers. Individuals suffering from neurodegenerative PD experience both motor (i.e., tremor) and non-motor psychological symptoms (i.e., depression, apathy, dysphoria, thought and memory impairments). Diagnosis of PD is complicated and often requires differential diagnosis, usually at an advanced stage of the disease, after observable symptoms present. However, early interventions can meaningfully delay neurodegeneration and help managing symptoms. Hence, it is critically important that widely applicable and accurate techniques for early diagnosis are developed.

Recent studies point to brainwaves, measured by electroencephalography (EEG), as a promising candidate, even in early stages of the disease. We will capitalise on the latest advances in the analysis of brain waves (EEG) and statistical modelling to sensitise and push the boundaries of EEG-biomarker based PD diagnosis. Using existing EEG data from healthy controls, we will build the most comprehensive model to date of resting-state EEG activity expected over the lifespan in the absence of disease. We will then compare how strongly EEG activity from PD patients differs from this normal distribution. This method has recently been successful in accurately mapping unique patterns of brain structure differences in various disorders.

Moreover, the patients will perform a reinforcement learning task whilst their EEG is recorded. This task tests decision-making abilities affected by the non-motor symptom apathy in PD. An age matched healthy control group will perform the same task for comparison. By comparing PD-patient and healthy-control EEG in a decision-making situation, we will cast a new light on altered neural activity underlying apathy in PD patients, opening new avenues of intervention for the often-overlooked non-motor symptoms of PD.

Given its prevalence, care for Parkinson’s patients represents a substantial portion of NHS and welfare costs in the UK. If we can tune EEG biomarkers optimally to facilitate early PD diagnosis and improve individual treatment options, this will significantly reduce the health and economic costs associated with the disease. Crucially, in the long term the approach developed in our project can also be extended to other disorders associated with abnormal EEG signatures including Traumatic Brain Injury, Alzheimer’s disease, and Schizophrenia.

The student will be based in the Division of Psychology within the School of Humanities, Social Sciences and Law (SHSL), situated in the accessible Scrymgeour building on the main city campus of the University of Dundee, and supervised by Dr Christopher Benwell, Dr Christian Keitel, Dr Tom Gilbertson (School of Medicine) and Professor Douglas Steele (School of Medicine). Patient data collection will involve traveling to Ninewells Hospital, Dundee, as required. City campus and Ninewells are well connected with frequent bus services

For informal enquiries about the project, contact Dr Christopher Benwell, by email, [email protected]

For general enquiries about the University of Dundee, contact [email protected]

Our research community thrives on the diversity of students and staff which helps to make the University of Dundee a UK university of choice for postgraduate research. We welcome applications from all talented individuals and are committed to widening access to those who have the ability and potential to benefit from higher education.

QUALIFICATIONS

Applicants must have obtained, or expect to obtain, a UK honours degree at 2.1 or above (or equivalent for non-UK qualifications), and/or a Masters degree in a relevant discipline. For international qualifications, please see equivalent entry requirements here: www.dundee.ac.uk/study/international/country/.

English language requirement: IELTS (Academic) overall score must be at least 6.5 (with not less than 5.5 in reading, listening, speaking and 6.0 in writing). The University of Dundee accepts a variety of equivalent qualifications and alternative ways to demonstrate language proficiency; please see full details of the University’s English language requirements here: www.dundee.ac.uk/guides/english-language-requirements.

 

APPLICATION PROCESS

Step 1: Email Dr Christopher Benwell ([email protected]) sending (1) a copy of your CV, (2) a cover letter explaining how you meet the essential (and desirable) criteria for the studentship and (3) contact information for 2 referees. The deadline for emailing this information to Dr Benwell is 14th May 2024, 11:59pm.

Step 2: Formal interviews will take place for shortlisted candidates via Microsoft Teams, likely on either the 23rd or 28th May 2024.

Step 3: The successful candidate (after interview) will be invited to submit a formal application for a PhD studentship via the University of Dundee direct application system. We will provide exact instructions.

Please select the study mode (full-time/part-time) and start date agreed with the lead supervisor.

In the Research Proposal section, please:

–         Enter the lead supervisor’s name in the ‘proposed supervisor’ box

–         Enter the project title listed at the top of this page in the ‘proposed project title’ box

In the ‘personal statement’ section, please outline your suitability for the project selected.