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Alejo J Nevado-Holgado

PhD

Senior Research Associate

I am the lead of the informatics team in TNDR (https://www.psych.ox.ac.uk/research/dementia-research-group) laboratory. Said very plainly: I am interested on any machine learning related approach that could successfully be applied to understanding neurodegenerative diseases.

I did my PhD in the University of Bristol, Department of Computer Science, under the supervision of Dr Rafal Bogacz and Dr John Terry. During these very interesting years, I used mathematical modelling, signal analysis and machine learning to the study of the basal ganglia and Parkinson’s disease. With these techniques we investigated which anomalies were generating the patterns of neuronal activity recorded by experimental groups, like our collaborator Dr Peter Magill and his team.

After some experimental training in Cambridge, now I am again applying machine learning and bioinformatics to the study of neurodegeneration, but this time to investigate biomarkers and the metabolic network in these diseases. It is known that Alzheimer's and Parkinson's disease have a very long prodromal course, which means that some underlying cause gradually destroys brain tissue, not being the condition diagnosed until 20 years later, when most of the brain is lost and unrecoverable. Therefore, any technique aiming at stopping the advance of these diseases, needs first to be able to detect it in the prodromal stage. Traditional analysis approaches haven't been able to do so yet, although recent technological developments may change this luck.

For instance, a very large amount of medical and biological data has been produced during these decades of research, but this data is scattered across many institutes and hospitals, and its size makes it impossible to be analysed by a person in the classical way. We are aiming at first linking all these data together, and then thoroughly analysing it with machine learning and artificial intelligence approaches, which can make sense of data when it is beyond human interpretation due to size and complexity. We think this approach, which is proving of great success in high tech industry, has the best chances at detecting neurodegeneration in its prodromal stage, and helping us understand how to modify its course and avoid brain damage.