Organisation:
Keywords:
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?
Research Themes:
I am part of a RSAT project led by Dr. V. Vuksanović, 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.
Key Publications:
Small-worldness favours network inference in synthetic neural networks, Scientific Reports 10, 2296 (2020).
Decreased electrocortical temporal complexity distinguishes sleep from wakefulness, Scientific Reports 9, 18457 (2019).
Entropy-based Generating Markov Partitions for Complex Systems, Chaos 28(3), 033611 (2018).
Dynamical detection of network communities, Scientific Reports 6, 25570 (2016).
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
Prof. Arturo C. Martí, Universidad de la República, Instituto de Física de Facultad de Ciencias, Montevideo 11400, Uruguay
Dr. Cecilia Cabeza, Universidad de la República, Instituto de Física de Facultad de Ciencias, Montevideo 11400, Uruguay
Dr. Victoria Gradín, Universidad de la República, Centro de Investigaciones Básicas en Psicología de Facultad de Psicología, Montevideo 11200, Uruguay
Dr. Pablo Torterolo, Universidad de la República, Laboratorio de Neurobiología del Sueno de Facultad de Medicina, Montevideo 11800, Uruguay
Prof. Marcelo Barreiro, Universidad de la República, Departamento de Ciencias de la Atmósfera de Facultad de Ciencias, Montevideo 11400, Uruguay