Diffusion-weighted MRI

In diffusion-weighted MRI, we collect a set of images that are weighted by the displacements of water molecules in a set of different directions. We use these images to determine the preferential direction of diffusion at each brain location. Following these diffusion vectors around the brain allows us to infer the shape of white-matter axon bundles.

Algorithms for reconstructing white-matter pathways

Parsing diffusion-weighted MRI scans to tease apart different brain pathways is time consuming and requires extensive neuroanatomical expertise. We develop algorithms to extract that information automatically from the images. This allows researchers who study brain pathways in different disease populations to process large amounts of data robustly. Our algorithms rely on the observation that anatomists define brain pathways based on the structures that each pathway goes through or next to. We incorporate this knowledge into our algorithms to automate pathway reconstruction. We have tools for cross-sectional and longitudinal studies, and for processing brain scans from infancy through the entire human lifespan. Our tools are open-source and can be downloaded as part the FreeSurfer package.

Brain connections in depression and anxiety in adolescence

We are responsible for MRI data acquisition, as well as diffusion and structural MRI data analyses, for the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA) project. This a consortium led by MIT in collaboration with 3 clinical sites (BU, McLean Hospital, MGH) and funded by a phase II Human Connectome Project (HCP) award. The goal is to perform detailed clinical characterization, as well as neuroimaging with HCP protocols, of a large number of adolescents with depression and anxiety disorders. We will analyze this data to investigate if measures of brain structure and function obtained from MRI can be used to predict disease progression. [Image: Structural and diffusion-weighted MRI data from the BANDA project.]

Ex vivo diffusion-weighted MRI

For in vivo neuroimaging, whole-brain scans must be acquired during the short period of time that a human subject can lie still inside a scanner. This places constraints on the resolution and overall quality of the images that we can collect. This means that, in some areas of the brain, the wiring is too complex to be reconstructed accurately from an in vivo scan. By scanning ex vivo brains, we can circumvent some of these issues. Ex vivo imaging allows us to perform much longer scans (lasting many hours or even days), to place receiver elements much closer to the brain area that we want to image, and to use scanners with much higher magnetic field strengths. [Image: Ex vivo diffusion-weighted MRI collected at 9.4 Tesla.]

Optical coherence tomography

Diffusion-weighted MRI gives us indirect measurements of the shape of white-matter axon bundles, by measuring the diffusion of water molecules along these bundles. Polarization-sensitive optical coherence tomography allows us to estimate the position and orientation of axons by detecting light that is backscattered from them. This can only be done in small brain samples, but it can serve as an independent source of measurements for the problem areas where axon configurations are too complex to resolve at the resolution that can be achieved with diffusion-weighted MRI. [Image: Optical data by Hui Wang and the Martinos Optics Core.]

Chemical tracing

Chemical tracing has been the method of choice for neuroanatomists studying the wiring of the brain, and it is still the only source of gold standard data on long-range brain connections. Chemical tracing data are very challenging to acquire and to annotate, and thus extensive collections of such data are only available in a handful of laboratories. In principle, chemical tracing could provide validation for and resolve ambiguities in diffusion-weighted MRI. However, the data is not in a form that can be easily compared to MRI scans. Our goal is to provide such a context, by performing ex vivo diffusion-weighted MRI scans in brains that have already received tracer injections. [Image: Tracer data by Suzanne Haber, University of Rochester Medical School.]