F. Hoffmann-La Roche Ltd
University of Oxford

Andrei is investigating how deep learning and AI can be used to help model the relationship between the structure of the brain and its functional properties. In nature, the structure-function relationship is crucial, with an object’s shape enabling it to fulfil a job, as in the case of an animal’s body shape determining how fast it can run. Predicting the relationship between the structure and function of the brain is one of the primary goals of neuroscience. Achieving this will not only allow a deeper understanding of human neurobiology but also aid in the treatment of certain neurological conditions, such as Alzheimer’s or Parkinson’s. The raw data is obtained using magnetic resonance imaging (MRI) from the UK Biobank, a large long-term biomedical study and database containing information on tens of thousands of UK participants. MRI images and scans are invaluable for research, as they allow the study of the brain, its tissues, connections and activity in living subjects without the need for invasive operations or other medical procedures.

This AI model will learn the underlying relationship between the brain’s structure and its functions. Once working, the model can be expanded by adding other types of information, such as genetics and lifestyle data, not only to provide a better understanding of the structure-function relationship, but also to aid in practical applications such as neurosurgery and to gain a better understanding of rare neurological diseases, allowing for the development of new treatments. Not only does Andrei’s work have significant importance in the field of neuroscience, but also in the wider AI sphere, as the development of deep learning algorithms at this level of complexity could bring about contributions to both the fields of machine learning and medical imaging.