Neuroimage Analysis Center

Neuroimage Analysis Center
"understanding the human brain through imaging"

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Figure 2: Distribution and frequency of WMHs in the normal (A), intermediate (B), and impaired (C) mobility groups. WMH frequency maps are overlaid on the corresponding anatomical slice of the ICBM atlas. WMH frequency is color-coded in percent (color bar). Panel D shows a 3D model reconstruction of the most affected structure, the corona radiata (green), within the white matter (transparent gray), and with ventricles (blue) also shown.
Figure 2: Distribution and frequency of WMHs in the normal (A), intermediate (B), and impaired (C) mobility groups. WMH frequency maps are overlaid on the corresponding anatomical slice of the ICBM atlas. WMH frequency is color-coded in percent (color bar). Panel D shows a 3D model reconstruction of the most affected structure, the corona radiata (green), within the white matter (transparent gray), and with ventricles (blue) also shown.
Figure 3: (A, B) Segmentation of corpus callosum, and selection of subdivisions. Tracts are grouped into clusters according to similarity of shape and location and are colour coded accordingly (A). Clusters are then selected (B), red highlighted from left to right, as genu, premotor and supplementary motor projections, motor projections, sensory projections, and finally parietal temporal and occipital projections.
Figure 3: (A, B) Segmentation of corpus callosum, and selection of subdivisions. Tracts are grouped into clusters according to similarity of shape and location and are colour coded accordingly (A). Clusters are then selected (B), red highlighted from left to right, as genu, premotor and supplementary motor projections, motor projections, sensory projections, and finally parietal temporal and occipital projections.
Figure 5: Arc length parameterizations (Bundle arc length parameterization section) in color in AF, in the entire bundle and in a zoomed region (dashed square). A sample of fibers from all subjects is shown. In gray, the leftmost DM image gives an example of regions that were excluded during matching (due to the Euclidean distance threshold and to restricted matching past the prototype endpoint). Note the PL method does not respect the curved tract geometry, and the DM method produces unexpected results at lower size scales.
Figure 5: Arc length parameterizations (Bundle arc length parameterization section) in color in AF, in the entire bundle and in a zoomed region (dashed square). A sample of fibers from all subjects is shown. In gray, the leftmost DM image gives an example of regions that were excluded during matching (due to the Euclidean distance threshold and to restricted matching past the prototype endpoint). Note the PL method does not respect the curved tract geometry, and the DM method produces unexpected results at lower size scales.
Figure 3: Clustering results across participants. The brain is partitioned into 5 clusters using SC/KM, and various seed are selected for SCA. The color indicates the proportion of participants for whom the voxel was included in the detected system.
Figure 3: Clustering results across participants. The brain is partitioned into 5 clusters using SC/KM, and various seed are selected for SCA. The color indicates the proportion of participants for whom the voxel was included in the detected system.
Figure 9: Segmentation of a synthetic example. Reorienting diffusion information based on the representative streamline (top left) result in almost uniform tensor distributions interior and exterior to the fiber bundle. While segmentation for the original data is difficult and leads to unsatisfactory results, segmentation of the reoriented data is much easier leading to a faithful segmentation with the proposed approach. For both segmentations, k = 10, θ = 0.01 λ = 0.7.
Figure 9: Segmentation of a synthetic example. Reorienting diffusion information based on the representative streamline (top left) result in almost uniform tensor distributions interior and exterior to the fiber bundle. While segmentation for the original data is difficult and leads to unsatisfactory results, segmentation of the reoriented data is much easier leading to a faithful segmentation with the proposed approach. For both segmentations, k = 10, θ = 0.01 λ = 0.7.
Figure 5: Reconstructions obtained using the ℓ1ℓ2-norm solver with different multi-resolution schemes. The top row presents the three simulated activation patches (color is used to label patches and does not indicate current directions). The detected results (row two to four) are shown in hot or cold colors corresponding to current flowing outward and inward, respectively. The time courses of the highlighted areas are shown in column (a). Solid curves in (a) are the simulated time courses. The dashed curves in row one are the dipole fitting results. The dashed curves in row two to four are the reconstructed time courses from ℓ1ℓ2-norm, which are scaled by a factor of four for illustration purposes.
Figure 5: Reconstructions obtained using the ℓ1ℓ2-norm solver with different multi-resolution schemes. The top row presents the three simulated activation patches (color is used to label patches and does not indicate current directions). The detected results (row two to four) are shown in hot or cold colors corresponding to current flowing outward and inward, respectively. The time courses of the highlighted areas are shown in column (a). Solid curves in (a) are the simulated time courses. The dashed curves in row one are the dipole fitting results. The dashed curves in row two to four are the reconstructed time courses from ℓ1ℓ2-norm, which are scaled by a factor of four for illustration purposes.
Figure: These data were acquired on a Siemens 3T scanner, using a multi-array head coil, in a healthy, 42 year old male.
Figure: These data were acquired on a Siemens 3T scanner, using a multi-array head coil, in a healthy, 42 year old male.
Figure 1: <b>Image atlas of the brain.</b> Image atlases represent spatial information by providing a parcellation of the anatomic structures contained in the brain (left). Each structure is represented as a spatial region of uniform color. Other anatomic knowledge about the structure, such as functional information, is not represented. Image atlases are generally used to infer the anatomic localization of brain structures in individual subjects by registering their images to the atlas. For example, the anatomic identity of areas of activity in fMRI are identified in this manner (right).
Figure 1: Image atlas of the brain. Image atlases represent spatial information by providing a parcellation of the anatomic structures contained in the brain (left). Each structure is represented as a spatial region of uniform color. Other anatomic knowledge about the structure, such as functional information, is not represented. Image atlases are generally used to infer the anatomic localization of brain structures in individual subjects by registering their images to the atlas. For example, the anatomic identity of areas of activity in fMRI are identified in this manner (right).
Figure 7: Four different views (at 90 deg increments.) of time averaged axial WSS (N /m<sup>2.</sup>) for (a) Simulation 1 and (b) Simulation 2.
Figure 7: Four different views (at 90 deg increments.) of time averaged axial WSS (N /m2.) for (a) Simulation 1 and (b) Simulation 2.
Figure 6: a. The somal region of the spiny neuron in Figure 5b is enlarged, the neuroWlament-distribution (blue) within the soma is inhomogeneous. b. The spiny neuron from Figure 5k is enlarged and seen in abluminal view, and the position of the nucleus is seen as a trough.
Figure 6: a. The somal region of the spiny neuron in Figure 5b is enlarged, the neuroWlament-distribution (blue) within the soma is inhomogeneous. b. The spiny neuron from Figure 5k is enlarged and seen in abluminal view, and the position of the nucleus is seen as a trough.
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