Brain imaging studies using an MRI generate large volumes of raw data that need to be transformed into forms amenable to statistical analysis. Different types of image and studies require varied approaches. We have developed numerous software processing pipelines to process data from studies run by our group, such as the TASCOG, CDOT, METTS, and within international datasets (ADNI and the UKBiobank). We have also developed methods for analysis of images from preclinical studies.
The pipelines produced data for many different kinds of analysis, ranging from volumetric analysis of white matter hyperintensities, white and grey matter, to analysis of structural and functional connectivity and structural covariance. Structural covariance is an approach to generating population level networks from brain structural MRI data. This differs from the typical source of brain network information such as diffusion weighted imaging or functional imaging. This project explores sparse methods for generation of structural covariance networks.
For further information please contact Richard Beare – firstname.lastname@example.org