I have an interest in using computational models derived from biophysical measures to study the complexity of the brain in terms of function and neuroanatomy. I am fascinated by the complexity of the human mind and by how neuroscience is transforming our understanding of what constitutes mental illness and the separation between the healthy and the abnormal brain.
I specialise in diffusion MRI tractography, a neuroimaging modality that allow us, to explore non-invasively, the trajectories of white matter axons in the living human brain. Like other neuroimaging techniques, diffusion MRI can provide us with large amounts of biophysical data that have the potential to reveal insightful information about the brain.
Nevertheless, this rich data will yield optimal results only when the best possible methods of analysis are also applied. An example of this is the data collected from hundredths of MRI scans on healthy individuals for the Biomedical Research Centre (BRC) project for a MRI atlas of the brain. Within my PhD project I am developing and applying such methods to data already collected by the BRC and by other studies at the IoPPN.
Qualifications and History
MSc Mathematics, University of Granada, Spain
MSc Neuroimaging, King’s College London
New strategies for group comparison and automatic analysis of large-scale tractography datasets
Over the years, several diffusion Magnetic Resonance Imaging (MRI) and tractography techniques have been developed for the study of the brain. With diffusion MRI and tractography we can investigate the microstructure properties of tissue and make inferences about structure connectivity between local or more distant regions in the brain.
Most tractography methods generate large amounts of data in the form of millions of "streamlines" representing the pathways of plausible neurite connections in the brain. Because of its size and complexity, tractography data remains difficult to analyse. For example, to obtain accurate anatomical reconstructions this data usually has to be segmented using dedicated and time consuming manual methods like virtual "dissections", as automatic tract-segmentation procedures do not provide yet optimal results. Therefore, the quantification and analysis of tractography datasets becomes a difficult task or a time-consuming process that is not easily translated to large studies.
Nevertheless, the information conveyed by tractography data expands that available through other neuroimaging modalities increasing our capacity to detect and to interpret differences between groups of subjects in neuroimaging studies.
In my PhD project, I study existing and emerging approaches for the group-level analysis of diffusion MRI tractography data. I identify and implement potential new metrics and methods for tractography analysis and then, I evaluate their application in the context of group studies.
Pedro is funded by the Maudsley NIHR BRC