Research statement

During the last 10 years, I have been fascinated by the brain. It gives us the capacity for speech reflective thoughts, fine movement coordination, attention, judgment, emotion and social behavior. It keeps our memories, enables us to feel emotions and pain, and gives us our identity. I want to understand as much as possible about the brain and this is why my research focuses in the study of how different regions in the brain interact with each other. These regions can be connected structurally (thanks to the white matter of the brain) or functionally (as neuronal activation patterns of anatomically separated brain regions). To study the brain, I apply advanced computational methods (such as graph theory, machine learning and pattern recognition techniques) to extract patterns that differentiate a healthy brain from a brain with a neurological disorder.

Past research

My PhD project focused on the application of graph theory to study brain functional connectivity using resting-state functional MRI (rs-fMRI) with several purposes: (i) computational, to test the reproducibility of the brain graph representations, (ii) biological, to study the reorganization of the brain after a stroke and, (iii) clinical, to assess the effect of stem cells based therapies in stroke. A graph representation is composed of nodes, identifying different regions of the brain, and edges, which establish links among these regions. This abstract representation permits to visualize the networks of the brain and describe their non-trivial topological properties. In addition to this line of computational research, I have also been involved in the application of several computational methods and machine learning techniques to perform variable selection, reduce data dimensionality and classify diseased patients, such as Alzheimer disease, bipolar disorder or cocaine addicted people, from healthy subjects. From the biological point of view, I showed that stroke induces a network-wide pattern of reorganization in the contralesional hemisphere whatever the side of the lesion. I also tried to identify where in the brain are located the main differences between healthy subjects and patients with the neurological disorders mentioned above. My primary objective from a clinical perspective has been to obtain MRI biomarkers of stroke recovery to assess the efficacy of stem cells based therapies.

Future research

I would like to focus my future work on studying the structural and functional brain connectivity of healthy people in order to extract common patterns of healthy brains. To extract these patterns I will need to deal with big databases with hundreds of subjects comprising multiple MRI acquisitions per subject: structural T1 and T2, diffusion tensor images (DTI) and rs-fMRI. I will apply computational methods to analyze both intra- and inter-hemispheric connectivity measures and to extract common functional networks of the healthy brains. According to the literature, there are several characteristics that may influence the connectivity of the brain, such as: the age, the level of education, the gender, left vs right handed people, bilingualism, among others. I would like to quantify as many of these characteristics as possible and to extract the common characteristics among them. There are multiple factors affecting the computation of the brain functional connectivity (e.g. the rs-fMRI scan duration or the connectivity measure used to relate the different regions of the brain). Among them, the way the brain is parcellated into different regions highly influences the computation of the functional connectivity and it is an open debate in the literature. I would like to study the conditions for optimal parcellation among the different available parcellation schemes.