Mri Fiber Tracking
Mri Fiber Tracking - Related Videos
11:38
In vivo Imaging of Optic Nerve Fiber Integrity by Contrast-Enhanced MRI in Mice
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Cited by 7 •
2014
This video illustrates a method, using a clinical 3 T scanner, for contrast-enhanced MR imaging of the naïve mouse visual projection and for repetitive and longitudinal in vivo studies of optic nerve degeneration associated with acute optic nerve crush injury and chronic optic nerve degeneration in knock-out mice (p50KO).
10:05
In vivo 19F MRI for Cell Tracking
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Cited by 21 •
2013
We describe a general protocol for in vivo cell tracking using MRI in a mouse model with ex vivo labeled cells. A typical protocol for cell labeling, image acquisition processing and quantification is included.
16:23
Fiber Connections of the Supplementary Motor Area Revisited: Methodology of Fiber Dissection, DTI, and Three Dimensional Documentation
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Cited by 32 •
2017
The purpose of this study is to show each step of the fiber dissection technique on human cadaveric brains, the 3D documentation of these dissections, and the diffusion tensor imaging of the anatomically dissected fiber pathways.
06:06
Optogenetic Functional MRI
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Cited by 15 •
2016
This protocol describes the steps and data analysis required to successfully perform optogenetic functional magnetic resonance imaging (ofMRI). ofMRI is a novel technique that combines high-field fMRI readout with optogenetic stimulation, allowing for cell type-specific mapping of functional neural circuits and their dynamics across the whole living brain.
04:33
Three-Dimensional Ultrasonic Needle Tip Tracking with a Fiber-Optic Ultrasound Receiver
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Cited by 7 •
2018
Accurate and efficient visualization of invasive medical devices is extremely important in many ultrasound-guided minimally invasive procedures. Here, a method for localizing the spatial position of a needle tip relative to the ultrasound imaging probe is presented.
FAQs
The key signal comes from how water movement varies with direction inside brain tissue. Diffusion-weighted MRI captures this directional behavior, providing information that mathematical models can use to estimate the local orientation of white-matter pathways. By linking these estimates across neighboring voxels, computational algorithms generate three-dimensional trajectories for studying structural brain organization.
Tractography reconstructs pathways indirectly from diffusion measurements and model-based calculations. The resulting lines represent computationally inferred trajectories through voxels, not photographs or direct anatomical views of individual axons. This distinction matters when interpreting findings: a displayed pathway can support analysis of structural connectivity, but it should not be treated as a literal image of every underlying fiber.
Mathematical models translate direction-dependent diffusion measurements into local pathway estimates, while computational algorithms connect those estimates from voxel to voxel. Together, they convert imaging data into a three-dimensional representation of possible white-matter courses. Their role makes tractography useful for examining large-scale organization, while also emphasizing that the output depends on inference rather than direct observation.
A typical workflow begins by acquiring diffusion-weighted MRI data, which measures direction-dependent water movement in brain tissue. Mathematical modeling then estimates pathway directions, and computational algorithms follow those estimates from voxel to voxel. The final product is a three-dimensional reconstruction that researchers can examine as evidence of structural connectivity between brain regions.
Neuroscientists apply the method to investigate brain organization, development, and neurological disease. It can also help examine how structural connections relate to functional regions and how white-matter changes correspond with behavior or cognition. These uses allow researchers to study connectivity patterns across different conditions without directly observing individual axons.
Researchers can use tractography findings to relate estimated pathway patterns or changes in white-matter integrity to behavior, cognition, injury, and treatment outcomes. In this context, the technique provides a structural-connectivity perspective that can be compared with meaningful neurological or behavioral measures. Its value lies in linking brain pathway estimates with changes observed across people or conditions.