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Results: 11

1.
Figure 7

Figure 7. From: ConTrack: Finding the most likely pathways between brain regions using diffusion tractography.

Score histograms for pathways connecting left DOC to CC (left column) and right DOC to CC (right column) for four subjects (rows) in a log-log plot. There are far fewer pathways returned by the STT algorithm (open circles) as compared to the ConTrack algorithm (open triangles).

Anthony J. Sherbondy, et al. J Vis. ;8(9):15.1-1516.
2.
Figure 8

Figure 8. From: ConTrack: Finding the most likely pathways between brain regions using diffusion tractography.

ConTrack pathways between left and right MT+ (blue) and between left and right DOC (green). The pathways shown are the top scoring 1% pathways connecting the specific ROIs. The red object is the lateral ventricle, shown for reference. The four panels show estimates from four subjects. Crosshair: 1 × 1 cm.

Anthony J. Sherbondy, et al. J Vis. ;8(9):15.1-1516.
3.
Figure 4

Figure 4. From: ConTrack: Finding the most likely pathways between brain regions using diffusion tractography.

Scoring function parameters. (A) The sigmoidal dependence of δ on CL when η = 0.175 (Equation 4). (B) These four spheres plot four extremes of the local distribution on the pathway tangent to the diffusion data. The conditions are prolate (λ2 = λ3) and oblate (λ2 > λ3) tensors with either low or high linearity values.

Anthony J. Sherbondy, et al. J Vis. ;8(9):15.1-1516.
4.
Figure 3

Figure 3. From: ConTrack: Finding the most likely pathways between brain regions using diffusion tractography.

Pathway scoring variables. The diffusion tensor (D(si)) is labeled for each node (si). Pathway tangent vectors at interior nodes (t2) are computed as the average of the line segments from the two connecting nodes. Tangent vectors at endpoints (t1, t3) are in the direction of the one connecting segment. The angle between adjacent pathway segments is πθ2 or θ2 if the segments are aligned tail to tail.

Anthony J. Sherbondy, et al. J Vis. ;8(9):15.1-1516.
5.
Figure 2

Figure 2. From: ConTrack: Finding the most likely pathways between brain regions using diffusion tractography.

Independence and symmetry in pathway scoring. Glyphs represent tensor data. The shape varies from isotropic and round (low FA) to thin cylinders (high FA). In this figure, the tensors are generally isotropic and randomly oriented (gray) apart from a row of tensors between R1 and R2 (green) and a column of tensors rising from R1 (orange). Symmetry asserts that the green pathway should have the same score whether measured from R1 to R2 or from R2 to R1. Independence asserts that the green pathway score should be the same whether the orange pathway is present or replaced by a random set of tensors.

Anthony J. Sherbondy, et al. J Vis. ;8(9):15.1-1516.
6.
Figure 9

Figure 9. From: ConTrack: Finding the most likely pathways between brain regions using diffusion tractography.

Callosal projection zones from MT+ measured with ConTrack. (A) Locations of the three regions of interest: Left and right MT+ (blue) and the corpus callosum (cyan). (B) Callosal projection zones from left (dark yellow), right (medium yellow), and both (bright yellow) for four subjects are shown. The intersection of the DOC crossing fiber positions in Figure 6 is shown for each subject (red). Scale bar: 1 cm. Cross hair: 1 × 1 cm.

Anthony J. Sherbondy, et al. J Vis. ;8(9):15.1-1516.
7.
Figure 1

Figure 1. From: ConTrack: Finding the most likely pathways between brain regions using diffusion tractography.

Example outputs of the three stages of the ConTrack algorithm. (A) ROI selection: One ROI was placed to cover the corpus callosum (cyan) and a second ROI covers a region of occipital cortex (green). (B) Pathway sampling: Pathways (yellow curves) are generated that have an endpoint in each ROI. (C) Pathway selection: The highest scoring 1% of the sampled pathways is retained as representing the most likely pathways connecting the two regions. Crosshair: 1 × 1 cm.

Anthony J. Sherbondy, et al. J Vis. ;8(9):15.1-1516.
8.
Figure 6

Figure 6. From: ConTrack: Finding the most likely pathways between brain regions using diffusion tractography.

Callosal projection zones from dorsal occipital cortex (DOC) measured with ConTrack and STT. (A) Locations of the three regions of interest: Left and right DOC (green) and the corpus callosum (cyan). (B) The four images in this column show the callosal projection zones from left and right DOC in four subjects. Colors distinguish regions containing projections from left DOC only (dark yellow), right DOC only (medium yellow), and both (bright yellow). (C) The callosal projection zones from DOC estimated with STT are shown in the same four subjects. Projections from left DOC only (dark green), right DOC only (medium green), and both (bright green) are shown. Scale bar: 1 cm. Crosshair: 1 × 1 cm.

Anthony J. Sherbondy, et al. J Vis. ;8(9):15.1-1516.
9.
Figure 10

Figure 10. From: ConTrack: Finding the most likely pathways between brain regions using diffusion tractography.

Comparison of methods for identifying pathways between MT+ and the CC within subject S1. (A) Maximum intensity projections of FDT pathway estimates shown superimposed on T1 axial image. The MT+ ROIs are shown as blue and the CC ROI as cyan. Purple pathway connects left MT+ and the CC (only one found) and truncated just right of CC; light blue represent right (MT+)–CC pathways (any voxel with FDT probability of connectivity >0.01) and truncated just left of CC. (B) Three-dimensional visualization of the FDT pathways intersecting right MT+ and the CC only. These pathways are not truncated after the CC as was done in A. (C) ConTrack estimates of the top 1% scoring pathways intersecting right MT+ and the CC. Crosshairs: 1 × 1 cm.

Anthony J. Sherbondy, et al. J Vis. ;8(9):15.1-1516.
10.
Figure 11

Figure 11. From: ConTrack: Finding the most likely pathways between brain regions using diffusion tractography.

Comparison of methods for identifying the optic radiation. (A) The estimated course of the optic radiation (OR) using FDT (from Behrens, Johansen-Berg, et al., 2003). These fibers do not connect to the medial wall of occipital lobe or the occipital pole, where V1 is located in this slice. (B) Maximum intensity projection from a more recent algorithm based on FDT (from Jbabdi et al., 2007). These pathways connect to the expected location of the far-peripheral representation of V1 and do not appear to reach the occipital pole. (C) Three-dimensional visualization of the top scoring 1% of ConTrack pathways connecting the LGN (red) and V1 in subject S3. Pathways viewed from the bottom of the brain. Crosshair: 1 × 1 cm.

Anthony J. Sherbondy, et al. J Vis. ;8(9):15.1-1516.
11.
Figure 5

Figure 5. From: ConTrack: Finding the most likely pathways between brain regions using diffusion tractography.

Scoring algorithm sensitivity to parameters. The dotted light gray line summarizes pathways connecting dorsal occipital cortex with the corpus callosum (CC) and the solid black line summarizes (MT+)–CC pathways. The correlation coefficient is described in the text. (A) Varying the length penalty λ between e−4 and e1 per node of pathway. (B) Varying the smoothness dispersion parameter, σc, from 10° to 45°. (C) Varying the position of the sigmoid mediating the effect of the diffusion tensor on the score, η, between 0.05 and 0.35. Both η and σc have little effect on the relative pathway scoring, while altering λ produces a smooth difference across a large range of values. (D) The effect of the number of initial samples on the similarity of the output pathway set for η = 0.175, σc = 14°, and λ = e−2. All pathway sets were compared to the set retained after collecting 100,000 pathways.

Anthony J. Sherbondy, et al. J Vis. ;8(9):15.1-1516.

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