tractography

Diffusion tensor imaging data can be used to perform tractography within white matter.

The first, when combined with tractography allows reconstruction of the major fiber bundles in the brain.

Only one axis is needed because the interest is in the vectorial property of axon direction to accomplish tractography.

In addition the directional information can be exploited at a higher level of structure to select and follow neural tracts through the brain-a process called tractography.

Many groups then paid attention to the possibility of using tensor based diffusion anisotropy imaging for neural tract tracing, beginning to optimize tractography.

Early in the development of DTI based tractography, a number of researchers pointed out a flaw in the diffusion tensor model.

A related technique for imaging neural tracts in the brain and spinal cord is called magnetic resonance tractography or diffusion tensor imaging.

Given the principal direction of diffusion at each location in the volume, it is possible to estimate the global pathways of diffusion through a process known as tractography.

Further advances in the development of tractography can be attributed to Mori, Pierpaoli, Lazar, Conturo, Poupon, and many others.

In neuroscience, tractography is a 3D modeling technique used to visually represent neural tracts using data collected by diffusion tensor imaging (DTI).

He has published in human brain imaging using positron emission tomography, magnetic resonance imaging, diffusion tensor imaging tractography techniques, and the new field of imaging genetics.

The Q-Ball method of tractography is an implementation of the HARDI approach in which David Tuch provides a mathematical alternative to the tensor model.

In some cases, the full set of tensor properties is of interest, but for tractography it is usually necessary to know only the magnitude and orientation of the primary axis or vector.

This anisotropy itself is the fundamental principle underlying the modern method of MRI tractography and structural connectomics (the in vivo visualization the axonal fibers that connect neurons in the brain) .

Moreover, the principal direction of the diffusion tensor can be used to infer the white-matter connectivity of the brain (i.e. tractography; trying to see which part of the brain is connected to which other part).

Though invasive tracer studies are largely not possible in humans, diffusion tensor imaging (DTI) tractography studies have also been used to map the connectivity of the orbitofrontal cortex to cortical and subcortical brain structures.

Its fibers are near to but can be distinguished by MRI tractography from adjacent fiber bundles such as the uncinate fasciculus, the external capsule, the arcuate fascicle, and the medial, inferior and superior longitudinal fascicles.

This enables researchers to make brain maps of fiber directions to examine the connectivity of different regions in the brain (using tractography) or to examine areas of neural degeneration and demyelination in diseases like multiple sclerosis.

Parcellation of localized areas of cortex have been accomplished using diffusion tractography (Beckmann et al. 2009) and functional connectivity (Nelson et al. 2010) to non-invasively measure connectivity patterns and define cortical areas based on distinct connectivity patterns.

Both vector and tensor methods provide a "rotationally invariant" measurement-the magnitude will be the same no matter how the tract is oriented relative to the gradient axes-and both provide a three dimensional direction in space, however the tensor method is more efficient and accurate for carrying out tractography.

Techniques range from high-resolution 3D MRI studies of brain morphology and localized proton MRS of brain metabolism to fiber tractography of the axonal connectivity via diffusion tensor imaging and mapping of the functional architecture of cortical networks by functional MRI.

We can still use tensor math to use the maxima to select groups of gradients to package into several different tensor ellipsoids in the same voxel, or use more complex higher rank tensors analyses, or we can do a true "model free" analysis that just picks the maxima and goes on about doing the tractography.