Magnetic Resonance Imaging (MRI)

Understanding the large-scale structural network formed by neurons is a major challenge in system neuroscience. A detailed connectivity map covering the entire brain would therefore be of great value. Based on diffusion MRI, we propose an efficient methodology to generate large, comprehensive and individual white matter connectional datasets of the living or dead, human or animal brain. This non-invasive tool enables us to study the basic and potentially complex network properties of the entire brain.

PURPOSE: To study the anatomical relationships involving the intrinsic and extrinsic myofiber populations of the human tongue employing diffusion tensor imaging (DTI) with tractography.

Knowledge of the electrical conductivity properties of excitable tissues is essential for relating the electromagnetic fields generated by the tissue to the underlying electrophysiological currents. Efforts to characterize these endogenous currents from measurements of the associated electromagnetic fields would significantly benefit from the ability to measure the electrical conductivity properties of the tissue noninvasively.

Existing magnetic resonance methods for diffusion imaging, including echo planar, are ineffective in the beating heart due to motion-induced signal attenuation. To overcome this problem, we used a diffusion-weighted stimulated echo-echo planar magnetic resonance imaging sequence. The two lobes of the diffusion-sensitizing gradient were synchronized to the same point in successive cardiac cycles in order to fix the cardiac position and avoid bulk motion effects.

In diffusion MRI, standard approaches for fibertract identification are based on algorithms that generate lines of coherent diffusion, currently known as tractography. A tract is then identified as a set of such lines selected on some criteria. In the present study, we investigate whether fibertract identification can be formulated as a segmentation task that recognizes a fibertract as a region where diffusion is intense and coherent.

Mapping complex crossing fibers using diffusion MRI techniques requires adequate angular precision and accuracy. Beyond diffusion tensor imaging (DTI), high angular resolution sampling schemes such as diffusion spectrum imaging (DSI) and q-ball imaging (QBI) were proposed to resolve crossing fibers. These schemes require hundreds of data approximately five to ten times more than DTI, offsetting their clinical feasibility.

A diffusion deconvolution method is proposed to apply deconvolution to the diffusion orientation distribution function (dODF) and calculate the fiber orientation distribution function (fODF), which is defined as the orientation distribution of the fiber spin density. The dODF can be obtained from q-space imaging methods such as q-ball imaging (QBI), diffusion spectrum imaging (DSI), and generalized q-sampling imaging (GQI), and thus the method can be applied to various diffusion sampling schemes.

Examination of the three-dimensional axonal pathways in the developing brain is key to understanding the formation of cerebral connectivity. By tracing fiber pathways throughout the entire brain, diffusion tractography provides information that cannot be achieved by conventional anatomical MR imaging or histology. However, standard diffusion tractography (based on diffusion tensor imaging, or DTI) tends to terminate in brain areas with low water diffusivity, indexed by low diffusion fractional anisotropy (FA), which can be caused by crossing fibers as well as fibers with less myelin.

A man with 25 years of mild left neck, arm, and leg paresthesias had initial MRI in 1996 identifying a left C3-4 dorsal horn cavernous hemangioma. In 1997, hemorrhage (C3-7) and resection induced left arm > leg proprioceptive loss and clumsiness.

High-resolution MR imaging and spectroscopic imaging were used to study differences in proton spectra between cortical gray matter and subcortical white matter in 23 normal volunteers using a 1.5 T scanner and surface coil receivers. A point-resolved spectroscopy (PRESS) volume with an 8 x 8 x 8 phase-encoding matrix was used to acquire over 1900 0.09-0.2 cc spectral voxels.