Nelson Trujillo Barreto

Principal Investigator

Research Fellow, University of Manchester, UK

Division of Neuroscience and Experimental Psychology, Faculty of Medical and Human Sciences, The University of Manchester, Manchester, UK


I have more than 20 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, including, Machine Learning methods and models, Non-linear Dynamical System theory, Ordinary and Stochastic Differential Equations, Time-Series Analysis, Stochastic Process Analysis, Inverse Problems, Bayesian and non-Bayesian (frequentist) Inference Theory, among others.

I am also involved in the provision of user-friendly licensed software (NEURONIC SA: CNEURO Spin-off Company) and toolboxes for the analysis of EEG and fMRI which are being used across Europe and Latin America, including:

  • Contributions to the Statistical Parametric Mapping (SPM) toolbox (, the industry standard in functional imaging analysis.
  • Development of MATLAB Toolboxes for the identification and characterisation of dynamic brain states based on recirded spatio-temporal brain signals (M/EEG, fMRI, LFP,…), such as:


My current research (initially funded by an EPSRC Intermediate Career Fellowship) focusses on the use of Hybrid Dynamical Systems models for the inference of time-resolved (dynamical) brain networks as recorded using functional Neuroimaging (M/EEG and fMRI) in naturalistic environments (e.g. resting-state or spontaneous activity), which can also be applied to other types of functional data at different spatial scales, such as LFP or multi-unitary recordings (to e.g. identify recurrent neuronal ensembles).

Areas of interest

  • Statistical modelling
  • Computational neuroscience
  • Statistical estimation methods
  • Probabilistic and biophysical models
  • Neuroimaging
  • Inverse problem
  • Electronencephalography (EEG)
  • Magnetoencephalography (MEG)
  • Functional Magnetic Resonance Imaging (fMRI)
  • Brain Electromagnetic Tomography (BET)
  • Diffusion Weighter Magnetic Resonance Imaging (DWMRI)
  • Neural Mass Models (NMM)
  • Bayesan models
  • Dynamical Causal Networks (DCNs)
  • Neurofeedback
  • Brain-Computer Interfaces (BCI)
  • Biophysical generative models