Large clinical and population cohort neuroimaging resources are increasingly coming online, forming a new field of imaging epidemiology. These offer a unified perspective that links brain connectional organization to behaviour and cognition. Currently, however, the full potential of these resources for understanding brain connectivity is not being realized. This is due to a lack of suitable analysis tools that explore relationships between and integrate across modalities, are sensitive to subtle changes in individual connectivity profiles and provide a means to move beyond simple case-control analysis towards understanding inter-individual differences in connectivity. In this talk I will outline novel approaches for charting the organisation of functional connectivity and introduce a ‘normative modelling’ strategy for utilising big cohort data for generating individualised predictions with application in clinical neuroimaging studies.
Prof Beckmann's research focus is on developing novel methods for imaging neurosciences.
Over the last two decades neuroimaging has made significant contributions to our understanding of human brain function. The indirect nature of the data requires sophisticated modeling and analysis approaches in order to infer interpretable quantities of interest.
Prof Beckmann leads a multi-disciplinary team with research foci that cover a range of subjects from the analysis of Neuroimaging data, via the development of novel tools for general image and signal processing, to the technology development for imaging biomarkers and advanced diagnosis systems for the neural system. He uses imaging techniques, including MRI, FMRI, MEG and EEG and integrates this with other types of data, such as Genetic or questionnaire information.