Multifactorial Modelling for Understanding Neurodegenerative Disorders and Implementing Personalized Medicine We apply mathematical models and control theory principles to integrate data from several imaging modalities (PET, MRI) to characterize neurodegenerative diseases and identify optimal personalized treatments. Selected Publication: Yasser Iturria-Medina et al., 2018. Neuroimage, Vol. 179, pages 40-50. 
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Artificial Intelligence for Tracking Disease Progression and Patients StratificationWe leverage state-of-the-art AI techniques and develop novel methods to identify patterns in data and track disease evolution. We have developed an unsupervised machine learning tool which can infer disease stage from gene expression data obtained from in vivo blood samples. Selected Publication: Yasser Iturria-Medina et al., 2020. Brain, 143(2):661-673. 
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Dynamic Models of Brain Activity for Treatment OptimizationBrain dynamics are altered in most neurological diseases. Understanding these dynamics in health and disease is important for developing treatments. Together with our collaborators, we apply dynamical systems principles to brain signals such as EEG/MEG & fMRI, to identify optimum targets for brain stimulation. Selected Publication: Sanchez-Rodriguez, L. M., et al., 2018. PLoS Comput Biol 14(5): e1006136. 
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Open-Access SoftwareAll our methods are implemented in a user-friendly open-access software (NeuroPM-box), which allows advanced integration of molecular, multimodal imaging, clinical and therapeutic data. 
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