Stephen Smith
Speaker of Workshop 3
Will talk about: The contribution of resting-FMRI connectivity to the connectome
Steve Smith is Professor of Biomedical Engineering and the Associate Director at The Oxford University Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB). The Analysis Group, which he started in 1997, now comprises about 20 research fellows, postdocs, students and support staff, carrying out brain image analysis and statistics research: image reconstruction, artefact removal, motion correction, image and signal filtering, registration, 4-dimensional statistical modelling, diffusion morphometry and tractography, segmentation, structural brain change mapping and pathology analysis.
The Analysis Group also provides the tools, environment and training for FMRI and MRI analysis, supporting a large number of imaging applications researchers. It has produced the brain image analysis software package FSL (FMRIB
Software Library) which is widely used in many laboratories internationally.
Prior to taking up the position at FMRIB, Smith worked on a number of projects in the areas of computer vision, image processing, artificial intelligence, robotics and parallel processing for real-time systems, during his DPhil and at the Defence Research Agency.
Resting state functional MRI (R-fMRI) is a relatively new and powerful method for evaluating regional interactions that occur when a subject is not performing an explicit task. Low-frequency (<0.3 Hz) BOLD fluctuations often show strong correlations at rest even in distant grey matter regions. Fluctuations in spontaneous neural activity are presumed to underlie the BOLD fluctuations, though the exact mechanisms giving rise to the neural fluctuations remain unclear. The spatial patterns of R-fMRI correlations are stable, in that they are similar across multiple ‘resting’ states, such as eyes-open, eyes-closed, and fixation, and across individuals and sessions. From experiments in the macaque monkey, R-fMRI correlations often overlap with known anatomical pathways, but they sometimes involve regions that are not directly connected. Hence, functional connectivity (R-fMRI) and anatomical connectivity (tractography) are complementary yet related measures that together provide a powerful approach to analyzing brain circuitry. A major goal of the human connectome project is to find an optimal combination of methods to parcellate brain regions and understand relationships between them, for example, using both seed-based approaches and independent component analysis approaches. In this talk I will give a brief background to resting-FMRI connectivity, the major methods used for analysing the data, and some cutting-edge advances in data acquisition and analysis techniques that are allowing us to find dynamic resting networks with ever-improving detail and richness.
