Sentence examples for primary eigenvector from inspiring English sources

To examine consistency of DTI data among individual subjects in this study and difference in diffusion indices between separate brain structures within each subject, averaged values of DTI indices (axial diffusivity, radial diffusivity, fractional anisotropy, and angular deviation of the primary eigenvector) were computed in 9 white matter structures in each brain.

In coherent, densely packed, white matter fiber bundles, the direction of fastest diffusion, given by the principal axis (or primary eigenvector) of DT, points along the main axis of the fiber bundle and is commonly used to map the trajectory of white matter fiber tracts in the brain [9] [11].

Multiple parameters have been derived, including fractional anisotropy, axial and radial diffusivity, and a color-coded orientation map of the primary eigenvector.

When compared to primary eigenvector fields extracted from noisy synthetic data, the denoised vector fields show greater similarity to the ground truth for signal-to-noise ratios ranging from 20 to 5. Similarity greater than 0.9 (in terms of fiber direction) is observed for all signal-to-noise ratios, for smoothing parameter values greater than or equal to 10 (larger values yield more smoothing).

In this case, the second primary eigenvector v2 of R ̂ u can be taken out to match with the beams included in the several selected subsets, until the final PMI1 is selected.

Under the DT model, the fiber orientation is given by a single delta function pointed in the direction of the primary eigenvector.

Fiber architectures were computed from human forearm diffusion tensor data using extracted primary eigenvectors and the denoised data.

When the number of individuals is large, variation between individuals from the same population is small compared to that of the different populations, so that the first eigenvector describes the primary population structure of the data.

Figure 3 shows the first two primary modes and the corresponding eigenvectors.

For evolutionary studies eigenvectors may identify primary trajectories of divergence.

The first eigenvector v 1 reflects the primary population structure.