I have more than 25 years of experience in the analysis of Brain Dynamics, particularly in the development of probabilistic and biophysical generative models of neuroimaging (fMRI and electromagnetic (M/EEG)) data, and their identification (inversion) based on recorded data. In order to do this I have developed expertise in a wide range of mathematical, statistical and modelling methodologies and tools for the modelling and analysis of multimodal brain data. I am also actively involved in the translation of my research developments into different clinical and non-clinical domains, as well as the provision of user-friendly licensed software and toolboxes for the analysis of EEG and fMRI.

Along years, I have been involved in collaborative efforts with different laboratories such as the Functional Imaging Laboratory at UCL, the MRC Cognition and Brain Science Unit (University of Cambridge), the Montreal Neurological Institute (McGill University), the School of Computing & Mathematical Sciences (John Moores University), the Institut für Allgemeine Psychologie (University of Leipzig), the Max Planck Institute for Biological Cybernetics (Tubingen, Germany), the Department of Psychology (University of Osnabruck), among others.

I received a B.Sc. with honours in Nuclear Physics from the Higher Institute  for Nuclear Science and Technology in 1995 and a PhD in Physical Sciences from the Havana University, Cuba in 2006. Since 1995 until 2014 I worked at the Cuban Neuroscience Centre where I was the Head of the Department for Brain Dynamics. In 2014 I moved to The University of Manchester where I was the recipient of an EPSRC Research Fellowship in 2016. Since January 2023 I am a Lecturer in Computational Neuroscience at the School of Health Sciences of The University of Manchester.

My research activities have been focused on the development of statistical estimation methods as well as probabilistic and biophysical models for the analysis of different Neuroimaging modalities and their integration. This entails bridging the gap between data produced by recording machines and the actual underlying neuronal activity by solving the forward (from neuronal activity to data) and inverse problems (from data to neuronal activity) that arise in each case. I have played out this goal to different extents in functional techniques like Electro and Magneto encephalography (EEG/MEG) and functional Magnetic Resonance Imaging (fMRI). Particularly, I have worked intensively in the development of the so called Brain Electromagnetic Tomography (BET). This allows reconstructing 3D images of electric activity inside the brain from EEG/MEG measurements with a high temporal resolution. BET can also benefit from the high spatial resolution of fMRI and the detailed anatomical connectivity information from Diffusion Weighted Magnetic Resonance Imaging (DWMRI) data. To achieve this, I have used Neural Mass Models and information from DWMRI to study the interactions between electric, metabolic and haemodynamic variables and their coupling with the common underlying excitatory and inhibitory neuronal activity.

It is now well established that it is not spatial (where) or temporal (when) localization of neural activity per se that causes brain function, but rather the way (how) the neural masses are dynamically (causally) interconnected as time evolves. Therefore, I am currently interested in the development of Bayesian Models to estimate fluctuations of the time varying (non-stationary) Dynamical Connectivity underlying on-going EEG/MEG and resting state fMRI. These fluctuations reflect changes in the dynamical regimes of the brain, which can be interpreted as fluctuations in its cognitive/physiological state. The tracking and prediction of brain state changes by means of such dynamical models can be applied to address important problems in a wide range of biomedical applications, such as the detection of changes in the physiological state of a patient (diagnosis), or the follow-up of the response to a therapy (treatment). As such, I am also interested in using these advances to develop and optimise state-of-the-art therapeutic interventions such as Neurofeedback, Brain-Computer Interfaces (BCI) and Non-Invassive Brain Stimulations (NIBS). Due to the high complexity of the above models (many parameters vs. scarce data) a key aspect of this enterprise is multimodal integration. I am particularly interested in the use of information about the space-time structure of interregional coupling as provided by DWMRI, to inform Bayesian as well as Biophysical generative models of EEG and fMRI. These models can then be inverted to obtain robust estimates of the time-varying brain connectivity that underlies brain function in both healthy and disease states.

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• Prof Karl J Friston, FRS (University College London - Wellcome Trust Centre for Neuroimaging)
• Prof Will Penny (University of East Anglia)
• Prof Dr Thomas Gruber (Universität Osnabrück - Institut für Psychologie)
• Prof Wael El-Deredy (The University of Manchester - School of Psychological Sciences)
• Dr Jean Daunizeau (Pitié-Salpêtrière Hospital - Institut du Cerveau et de la Moelle épinière)
• Prof Matthias M. Müller (Universität Leipzig - Institut für Allgemeine Psychologie)
• Prof Erich Schröger (Universität Leipzig - Institut für Allgemeine Psychologie)
• Prof Matthew Lambon Ralph (Cambridge University - MRC Cognition and Brain Science Unit)
• Prof Nicola Jane Ray (Manchester Metropolitan University)
• Prof Monty Silverdale (Salford Royal NHS Foundation Trust and The University of Manchester)

Member of the EPSRC Peer Review College

Member of the European Commission College of Experts

Grant Reviewer for the Royal Academy of Engineering (RAEng)

Grant Reviewer for thr Netherlands Organisation for Scientific Research (NWO)

Member of the Organisation for Human Brain Mapping

Member of the Latin American Brain Mapping Network (LABMAN)

Associate Editor of Frontiers in Brain Imaging Methods

Associate Editor of Frontiers in Computational Neuroimaging

Forward and inverse problems of EEG, MEG and fMRI, functional Neuroimaging techniques, Multimodal Integration, Computational Neuroscience, Bayesian Probability Theory, Machine Learning, Image Processing and analysis, Causality Theory, Deterministic and Stochastic Nonlinear Dynamics, Complex Systems, Time Series Analysis, Markov Chain Monte Carlo Methods, Differential Equations, Neuronal Network Modelling