Unifying the analyses of anatomical and diffusion tensor images using volume‐preserved warping

Abstract

Purpose: To introduce a framework that automatically identifies regions of anatomical abnormality within anatomical MR images and uses those regions in hypothesisdriven selection of seed points for fiber tracking with diffusion tensor (DT) imaging (DTI). Materials and Methods:Regions of interest (ROIs) are first extracted from MR images using an automated algorithm for volume-preserved warping (VPW) that identifies localized volumetric differences across groups. ROIs then serve as seed points for fiber tracking in coregistered DT images. Another algorithm automatically clusters and compares morphologies of detected fiber bundles. We tested our framework using datasets from a group of patients with Tourette's syndrome (TS) and normal controls. Results:Our framework automatically identified regions of localized volumetric differences across groups and then used those regions as seed points for fiber tracking. In our applied example, a comparison of fiber tracts in the two diagnostic groups showed that most fiber tracts failed to correspond across groups, suggesting that anatomical connectivity was severely disrupted in fiber bundles leading from regions of known anatomical abnormality. Conclusion:Our framework automatically detects volumetric abnormalities in anatomical MRIs to aid in generating a priori hypotheses concerning anatomical connectivity that then can be tested using DTI. Additionally, automation enhances the reliability of ROIs, fiber tracking, and fiber clustering. THE GENERAL RUBRIC of magnetic resonance imaging (MRI) subsumes various modalities of data acquisition (e.g., T1-and T2-weighted anatomical imaging, magnetic resonance spectroscopy, and diffusion tensor (DT) imaging (DTI)), each of which provides unique but complementary information about brain structure and function. Combining data from multiple MRI modalities can provide a more comprehensive view of a subject or group of subjects than can any single MRI modality. For example, anatomical T1-or T2-weighted MR images can help to identify structural boundaries within the gray matter and white matter of the human cerebrum, whereas DTI and its derived measures (e.g., fractional anisotropy [FA], apparent diffusion coefficient [ADC]) provide information about the directional organization of brain tissue that can be used to track nerve-fiber pathways. Moreover, a map of the principal directions for the diffusion of water, generated using DTI, can help to more accurately parcellate the anatomy of the corpus callosum (CC) and other white matter structures (e.g., cingulum, external capsule, and anterior thalamic radiation) (1-4) that appear homogeneous in their contrast and signal intensities in T1-or T2-weighted images.Researchers have long desired to integrate information from both modalities to provide a more comprehensive view of anatomical structure and connectivity in the human brain. The integration of T1-weighted and DT imaging data, however, faces at least two major obstacles: 1) the accurate coregistration of datasets from the two modaliti...

Cerebellar morphology in Tourette syndrome and obsessive‐compulsive disorder

“…We used volume preserved warping (VPW) 28,29 to assess local expansion or reduction of tissue volumes at the cerebellar surface.…”

Multimodal magnetic resonance imaging: The coordinated use of multiple, mutually informative probes to understand brain structure and function

“…We corrected eddy-current spatial distortions along the phase-encoding direction [Haselgrove and Moore, 1996]. We computed the diffusion tensor at each voxel by fitting an ellipsoid to the DWI data acquired along 25 gradient directions and three baseline images [Xu et al, 2007]. To ensure that a tensor D was positive definite, we first decomposed it into the product D ¼ A Â A T , estimated the matrix A, and computed the tensor D from the product A Â A T .…”

“…We calculated the Pearson's correlation coefficient r to measure the strength of the pair-wise linear association of two imaging measures at each voxel. These imaging measures included (1) the concentration of NAA (a marker of neuronal density) from MPCSI data, (2) fractional anisotropy (FA, a measure of the directional constraint on the diffusion of water) from DTI data, (3) the BOLD signal amplitude of brain activations (a measure of task-induced neural responsivity) from fMRI data, and (4) an index of local volume expansion or compression from anatomical MRI data, calculated using volume-preserved-warping (VPW) [Xu et al, 2007]. VPW preserves during spatial normalization the intensity weighted volume (i.e., intensity  volume of the voxel) of each voxel.…”

Identification of a circuit-based endophenotype for familial depression

“…Thus regions of high VPW intensity are those that have higher volume in the participant’s brain than in the template brain, and regions of low VPW intensity are those that have lower volume (or “hypoplasia,” a term used throughout this paper, consistent with our hypothesis that the lower volumes reflect brain underdevelopment) in the participant’s relative to the template brain.. We then perform voxel-wise statistical analyses to test hypotheses that risk group status, current symptoms of depression or anxiety, and combined symptoms of ADHD correlate with VPW measures. These methods have been described extensively elsewhere and were implemented using software developed by Dr. Dongrong Xu at Columbia (Xu et al, 2007). …”