People

Research for patient benefit: MRI physics, MRI safety, image analysis and clinical application of AI

Extraction of morphological networks based on structural MRI, analysing network measures that can help in providing neuroimaging makers for dementia.

My interests lie in the development of new technology and methods in MRI to solve problems in bio-medicine. Together with my group I have worked on methods of imaging free radicals by MRI, and on techniques for imaging solid materials with ultra-short T2 relaxation times. In recent years, my research has focussed on fast field-cycling (FFC) MRI , where the magnetic field is switched rapidly during the imaging procedure. In this way, FFC can access contrast mechanisms which are invisible to conventional MRI. My group has built a variety of MRI scanners over the years, including whole-body sized FFC-MRI scanners. We are currently investigating the use of FFC-MRI in a range of biological and medical research applications.

See our research group's web page at https://www.abdn.ac.uk/research/ffc-mri/ 

My research interests are focused on Complexity issues. In general, the complex systems that I research involve Coupled Dynamical Systems, namely, many non-trivially interacting sub-systems. I try to measure, explain, and/or predict their collective behaviour (for example, the emergence of synchronization or chaotic dynamics) in terms of how they are inter-connected, namely, in terms of the topological features of the underlying network topology (i.e., Graph Theory).

In particular, I am fascinated by Network Neuroscience research, where Complexity challenges abound -- neurons in the brain create a myriad of dynamical behaviours due to their intricate connectivity and complex substrate and our observations can only access these behaviours by indirect measurements. Hence, I am intereseted in questions such as, how do we manage to infer the brain's connectivity from indirect measurements (e.g., EEGs or MRIs)? how do particular diseases (e.g., Alzheimer's disease or chronic depression) affect the brain's connectivity? what data-driven conclusions can we draw from studying different states of consciousness (e.g., REM sleep)? and how can we develop/improve methods (both, in data acquisition and analysis) to increase our unserstanding of these issues?