PET is Wonderful Annual Meeting 2020 Oct 27, 2020 02:00 PM - 05:40 PM — Virtual Meeting (online)
Through the Looking Glass: Breaking Barriers in STEM Oct 28, 2020 12:00 PM - 03:30 PM — Virtual Meeting (online)
NRS Mental Health Network Annual Scientific Meeting 2020 Nov 04, 2020 09:00 AM - 05:30 PM — Virtual Meeting (online)
Scottish Radiological Society Annual General Meeting 2020 Nov 06, 2020 09:30 AM - 03:30 PM — Virtual Meeting (online)

eLearning

SINAPSE experts from around Scotland have developed ten online modules designed to explain medical imaging. They are freely available and are intended for non-specialists.


Edinburgh Imaging Academy at the University of Edinburgh offers the following online programmes through a virtual learning environment:

Neuroimaging for Research MSc/Dip/Cert

Imaging MSc/Dip/Cert

PET-MR Principles & Applications Cert

Applied Medical Image Analysis Cert

Online Short Courses

Dr Nicolás Rubido

Home page: Go to homepage
Position: Research Fellow

Interests:

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?

Institute: Institute of Medical Sciences

Department: Aberdeen Biomedical Imaging Centre


Research Themes

I am part of a RSAT project, focused on developing a model of brain impaired functions in Alzheimer’s disease from large-scale brain networks, rather than on existing diagnostic categories. The objective of the overall project is to develop novel neuroimaging-informed ways to classify Alzheimer’s disease and mild cognitive impairment. Neuroimaging-defined connectomes will be linked to specific behavioural and cognitive scores, aiming to help in early diagnosis.

  • Neuroimaging Research
  • Alzheimer's and Dementia Research
  • Functional Connectivity Research
  • Image Analysis
  • Time-series Analysis
Key Publications

Collaborators
  • Dr. Vesna Vuksanovic, Aberdeen Biomedical Imaging Centre, Aberdeen AB25 2ZD, United Kingdom
  • Dr. Murilo S. Baptista, University of Aberdeen, Institute for Complex Systems and Mathematical Biology, Aberdeen AB24 3UE, United Kingdom
  • Prof. Celso Grebogi, University of Aberdeen, Institute for Complex Systems and Mathematical Biology, Aberdeen AB24 3UE, United Kingdom
  • Dr. Chris Antonopoulos, University of Essex, Department of Mathematical Sciences, Colchester CO4 3SQ, United Kingdom