Diffusion MRI of complex neural architecture.

While functional brain imaging methods can locate the cortical regions subserving particular cognitive functions, the connectivity between the functional areas of the human brain remains poorly understood. Recently, investigators have proposed a method to image neural connectivity noninvasively using a magnetic resonance imaging method called diffusion tensor imaging (DTI). DTI measures the molecular diffusion of water along neural pathways. Accurate reconstruction of neural connectivity patterns from DTI has been hindered, however, by the inability of DTI to resolve more than a single axon direction within each imaging voxel. Here, we present a novel magnetic resonance imaging technique that can resolve multiple axon directions within a single voxel. The technique, called q-ball imaging, can resolve intravoxel white matter fiber crossing as well as white matter insertions into cortex. The ability of q-ball imaging to resolve complex intravoxel fiber architecture eliminates a key obstacle to mapping neural connectivity in the human brain noninvasively.

A primer on diffusion tensor imaging of anatomical substructures.

In this article, the authors review the application of diffusion tensor (DT) magnetic resonance (MR) imaging to demonstrate anatomical substructures that cannot be resolved by conventional structural imaging. They review the physical basis of DT imaging and provide illustrative anatomical examples. The DT imaging technique measures the self-diffusion, or random thermal motion, of the endogenous water in nerve tissue. Because of the preferred diffusion of water molecules along the nerve fiber direction, DT imaging can measure the orientation of the neural fiber structure within each voxel of the MR image. The fiber orientation information yielded by DT imaging provides a new contrast mechanism that can be used to resolve images of anatomical substructures that cannot otherwise be visualized using conventional structural imaging. The authors illustrate how DT imaging can resolve individual pathways in the brainstem as well as individual nuclei of the thalamus and conclude by describing potential applications in neurosurgery.

Automatic segmentation of thalamic nuclei from diffusion tensor magnetic resonance imaging.

The nuclei of the thalamus have traditionally been delineated by their distinct cyto/myeloarchitectural appearance on histology. Here, we show that diffusion tensor magnetic resonance imaging (DTI) can noninvasively resolve the major thalamic nuclei based on the characteristic fiber orientation of the corticothalamic/thalamocortical striations within each nucleus. Using an automatic clustering algorithm, we extracted the Talairach coordinates for the individual thalamic nuclei. The center-of-mass coordinates for the segmented nuclei were found to agree strongly with those obtained from a histological atlas. The ability to resolve thalamic nuclei with DTI will allow for morphometric analysis of specific nuclei and improved anatomical localization of functional activation in the thalamus.

High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity.

Magnetic resonance (MR) diffusion tensor imaging (DTI) can resolve the white matter fiber orientation within a voxel provided that the fibers are strongly aligned. However, a given voxel may contain a distribution of fiber orientations due to, for example, intravoxel fiber crossing. The present study sought to test whether a geodesic, high b-value diffusion gradient sampling scheme could resolve multiple fiber orientations within a single voxel. In regions of fiber crossing the diffusion signal exhibited multiple local maxima/minima as a function of diffusion gradient orientation, indicating the presence of multiple intravoxel fiber orientations. The multimodality of the observed diffusion signal precluded the standard tensor reconstruction, so instead the diffusion signal was modeled as arising from a discrete mixture of Gaussian diffusion processes in slow exchange, and the underlying mixture of tensors was solved for using a gradient descent scheme. The multitensor reconstruction resolved multiple intravoxel fiber populations corresponding to known fiber anatomy. Ma