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School of Biological and Behavioural Sciences

Using information theory to diagnose Alzheimer’s disease and predict disease progression

Research environment

The School of Biological and Behavioural Sciences at Queen Mary is one of the UK’s elite research centres. We offer a multi-disciplinary research environment and have approximately 150 PhD students working on projects in the biological and psychological sciences. Our students have access to a variety of research facilities supported by experienced staff, as well as a range of student support services.

Dr Daniel Bor and Dr Valdas Noreika have a strong research track record, having published nearly 100 articles between them, including in journals such as Science, Neuron, Nature: Scientific Reports and Physical Review Letters. They have a broad network of collaborators in Cambridge, Imperial College, UCL and beyond. The psychology department is very well equipped with cutting-edge facilities and computing resources.

Training and development

Our PhD students become part of Queen Mary’s Doctoral College which provides training and development opportunities, advice on funding, and financial support for research. Our students also have access to a Researcher Development Programme designed to help recognise and develop key skills and attributes needed to effectively manage research, and to prepare and plan for the next stages of their career.

The PhD student will also be individually trained by lab members in developing coding, neuroimaging and cognitive neuroscience knowledge and skills.

Project description

Despite having a global cost of one trillion dollars and affecting 50 million people, Alzheimer’s disease (AD) is poorly diagnosed and treated. One key component in treatment failures is that symptoms often only appear many years after disease onset, while another is the heterogeneity of the disease. Therefore, early diagnosis and proper stratification is critical for effective pharmacological treatment. Here we propose to improve AD early diagnosis, stratification and prognosis, via new information theory tools applied to brain-scanning data.

Any project in the areas of supervisors Daniel Bor and Valdas Noreika is possible - please consult the supervisors to discuss your ideas. One possibility is the following: Information theory is the study of the quantification, storage and transfer of information. Given the brain is fundamentally an information processing organ, information theory has very successfully been applied to a wide range of neural data. A recent development in information theory is partial information decomposition (PID), which decomposes information into synergistic, redundant, and unique components.

We have recently shown using PID and fMRI on a large (n~650) cohort of normal adults that ageing and cognitive decline is associated with changes in both synergy and redundancy, specific to particular brain networks. This PID-driven ageing “signature” is a powerful new route to understand the precursors to dementia.

This PhD project will extend the above results in two key ways. First, it will continue to develop the PID framework, to generate a more fine-grained and powerful neural information theory toolset, which can be applied to an array of normal and clinical neuroscience questions. Second, critically, it will focus the current and extended PID methods on various big data fMRI and M/EEG longitudinal datasets relevant to AD, including pre-clinical cases at high risk, Mild Cognitive Impairment (a precursor to AD), as well as early AD cases and matched controls. This wide range of datasets will enable the project to find PID signatures of early AD diagnosis and prognosis, as well as key stratification features.


This studentship is open to Mexican students applying for CONACyT funding. CONACyT will provide a contribution towards your tuition fees each year and Queen Mary will waive the remaining fee. CONACyT will pay a stipend towards living costs to its scholars.

Eligibility and applying

Applications are invited from outstanding candidates with or expecting to receive a first or upper-second class honours degree  in an area relevant to the project. A masters degree is desirable, but not essential. Experience in python or Matlab coding is highly desirable, as would be a firm mathematical background.

Applicants are required to provide evidence of their English language ability. Please see our English language requirements page for details.

Applicants will need to complete an online application form by this date to be considered, including a CV, personal statement and qualifications. Shortlisted applicants will be invited for a formal interview by the project supervisor. Those who are successful in their application for our PhD programme will be issued with an offer letter which is conditional on securing a CONACyT scholarship (as well as any academic conditions still required to meet our entry requirements).

Once applicants have obtained their offer letter from Queen Mary they should then apply to CONACyT for the scholarship as per their requirements and deadlines, with the support of the project supervisor.

Only applicants who are successful in their application to CONACyT can be issued an unconditional offer and enrol on our PhD programme.

Apply Online


  • Luppi AI, Mediano PAM, Rosas FE, Allanson J, Pickard JD, Carhart-Harris RL, Williams GB, Craig MM, Finoia P, Owen AM, Naci L, Menon DK, Bor D, Stamatakis EA (2020) A Synergistic Workspace for Human Consciousness Revealed by Integrated Information Decomposition bioRxiv 2020.11.25.398081; doi:

  • Rosas FE, Mediano PAM, Jensen HJ, Seth AK, Barrett AB, Carhatt-Harris RL, Bor D (2020) Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate data. PLOS Computational Biology 16(12): e1008289.

  • Mediano, P., Ikkala, A., Kievit, R. A., Jagannathan, S. R., Varley, T. F., Stamatakis, E. A., ... & Bor, D. (2021). Fluctuations in Neural Complexity During Wakefulness Relate To Conscious Level and Cognition. bioRxiv.

  • Luppi, A. I., Mediano, P. A., Rosas, F. E., Harrison, D. J., Carhart-Harris, R. L., Bor, D., & Stamatakis, E. A. (2021). What it is like to be a bit: an integrated information decomposition account of emergent mental phenomena. Neuroscience of Consciousness, 2021(2), niab027.

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