
Figure 1: Example visualization of a neurosurgical case using SlicerDMRI, with tracts from the UKF twotensor tractography method. The patient presented with a history of right side paresthesia and aphasia, leading to the diagnosis of a left insular glioblastoma. A, The corticospinal tract (blue) wraps around the medial and superior aspect of the lesion and the inferior frontooccipital fasciculus (pastel green) is relatively close to the inferior pole of the tumor. B, The arcuate fasciculus (green) fibers spread along the superior surface of the tumor, lateral to the corticospinal tract, and the uncinate fasciculus (white) is distant from the lesion. 


Figure 1: (A) Quantification of the linear model’s ability to predict cortical atrophy extent at 6 months after injury. For each gyrus and sulcus, the null hypothesis that there is no statistically significant correlation between the predictor variables and the response variable (cortical thinning, in millimeters) was tested. Values of the F2,30 statistic for each statistical test are encoded on the cortical surface, subject to the false discovery rate correction for multiple comparisons. Darker red hues indicate higher significance of the statistical test and, consequently, stronger ability to predict cortical thinning for the areas in question. Regions where the null hypothesis was not tested because less than 90% of cortical thickness data were available (see text) are drawn in black. Regions where the test statistic was lower than the threshold F statistic of the reliability analysis permutation test are drawn in white. (B) Statistical significance of the correlation between relative cortical atrophy and the GOSE. Values of the t31 statistic for each statistical test are encoded on the cortical surface, as in panel (A). Note that all values of this statistic are negative, which confirms that greater regional atrophy is associated with lower GOSE values (i.e., poorer functional outcome), as expected. The values of F and t statistics in (A) and (B), respectively, are associated with different statistical tests and different degrees of freedom and, therefore, they should not be compared to one another. 


Figure 1: Common genetic variants associated with infant brain volumes. Manhattan plots colored with a scheme that matches the corresponding tissue volume (middle) are shown (yellow is white matter (WM); green is gray matter (GM); blue is cerebrospinal fluid (CSF)). Genomewide significance is shown for the threshold of P=1.25 × 10−8 (gray dotted line). The green line indicates the threshold of P=1.25 × 10−6. The most significant singlenucleotide polymorphism (SNP) for each phenotype is labeled. 


Figure 3: Snapshots from the normative 4D shape atlas (top) and their medial surfaces with coloring of correspondence labels at 23, 44, 65 and 80 years of age. The 4D normative atlas is a continuous shape trajectory represented by the deformation ϕ0,T. Object boundaries in the atlas are diffeomorphic to each other by the spatiotemporal modeling bt ϕti,tj. A medial surface at each time point is estimated independently from its object boundary at time point ti via HamiltonJacobin skeletonization ψti. Medial surfaces are enhanced for a visualization purpose. The strcuture’s surface at the initial time point (age 23) is overlayed in grey to show how the structures deform and are pushed outwards by normal aging over time. 


Figure 1: White matter fiber pathways defined through tractography of DTI data. Targeted pathways include ATR anterior thalamic radiation, CST corticospinal tract, genu genu of corpus callosum, MCP midcerebellar peduncle, SCP superior cerebellar peduncle 


Figure 2: Cortical regions showing significant expansion in surface area from 612 months in HRASD. Map of significant group differences in surface area from 6 to 12 months. Exploratory analyses were conducted with a 78 region of interest surface map (see Supplementary Information), using an adaptive Hochberg method of p <0.05. The colored areas show the group effect for the HRASD versus LR subjects. Compared to the LR group, the HRASD group had significant expansion in cortical surface area in the left/right middle occipital gyrus and right cuneus (A), right lingual gyrus (B), and to a lesser extent the left inferior temporal gyrus (C), and middle frontal gyrus (D). HRASD, n = 34; LR, n = 84. 


Figure 8: AutoSeg GM parcellation (top row) and subcortical structures (bottom row) in a representative subject at 12 months, with axial (left) and sagittal slice (middle), and 3D rendering (right) views. 


Figure 1: ADNI data analysis: raw FA curves measured at 83 grid points (upperleft panel), the estimated index function with the broken red lines representing 95% simultaneous Confidence bands (upperright panel), the estimated accumulative proportion of estimated eigenvalues (lowerleft panel) and estimated eigenfunctions (lowerright panel) corresponding to the five largest eigenvalues. 


Figure 4: Case I (transverse slice). Material properties (shear modulus) assignment using the FCM algorithm at integration points. The shear modulus magnitude is represented by a colour scale. Note that the integration points belonging to the same tissue class (indicated by the same colour) match the areas where the image intensity is similar. Only local tissue misclassification is present. This can be seen as a local variation in the integration point colour (where the adjacent integration points have a different colour and, consequently, different shear modulus assigned) at the boundaries between different tissue classes. 


Figure 9: Axial view of manual segmentation by three human experts for acute nonhemorrhagic lesions (NHL) of Subject III. 
