Information-Based Brain Markers of Consciousness and Cognition in Health and Disease Queen Mary University of London in United Kingdom

This PhD project at Queen Mary University of London investigates the neural basis of consciousness and cognition in both healthy individuals and those with neurodegenerative diseases, particularly Alzheimer’s disease. The research leverages advanced information-theoretic analyses of human neuroimaging data, including large fMRI and MEG datasets, to explore how synergistic information—information greater than the sum of its parts—relates to cognitive processes and consciousness. Recent studies suggest that such synergy is disrupted in early dementia, but it remains unclear which cognitive functions are most affected and how these changes relate to consciousness. The student will quantify markers based on Integrated Information Decomposition (ΦID) and other multivariate information measures to map the relationships between cognition and consciousness in both healthy and clinical populations.

The project also offers optional components in computational modelling to examine how learning mechanisms influence ΦID markers, and in method development to expand information-theoretic tools for brain imaging. This interdisciplinary research integrates neuroscience, information theory, data science, and clinical translation, aiming to uncover mechanistic, information-based neural markers that can serve as early diagnostic or stratification biomarkers for dementia. The research environment is highly collaborative, with supervisors Dr. Daniel Bor and Dr.

Pedro Mediano having published extensively and maintaining networks across leading institutions. The School of Biological and Behavioural Sciences at Queen Mary provides a multidisciplinary setting, access to high-performance computing, and support from experienced staff. Funding is available through a partnership with the China Scholarship Council (CSC), covering tuition fees, living expenses, and travel for eligible Chinese applicants. Candidates must have a strong academic background in neuroscience, computer science, mathematics, or physics, with solid coding skills in Python or Matlab.

Knowledge of information theory is advantageous. Applicants must meet English language requirements and submit all documentation by the deadline. The application process includes a formal online submission, identification as a CSC Scholar, and an interview for shortlisted candidates. Successful applicants will receive a conditional offer and then apply to CSC for the scholarship.