High Resolution Diffusion Tensor Atlas of the Mouse Brain

Abstract

Event Abstract Back to Event High Resolution Diffusion Tensor Atlas of the Mouse Brain Yi Jiang1* and G. A. Johnson1 1 Duke University Medical Center, Center for In Vivo Microscopy, United States Introduction Eight Diffusion tensor imaging (DTI) datasets of normal adult mouse brains were acquired at 43 µm isotropic resolution by using a streamlined protocol, including specimen fixation and staining, image acquisition, reconstruction, and normalization to a common reference brain (Waxholm Space). The normalization accuracy was evaluated by measuring landmarks displacement between individual brains and the reference brain. Mean values of DTI parameters, e.g. anisotropy and diffusivity, were computed in 9 white matter (WM) structures to determine if the current protocol is able to provide consistent data and distinguish anatomical difference between WMs. Method 8 normal adult male C57BL/6 mouse brains were actively stained. T1 and T2*, one b0, and 6 diffusion-weighted images were acquired on a 9.4 T magnet using a spin-echo sequence at 43 µm resolution. The T1 and T2* images were used with DiffeoMap to register each individual brain to a reference brain, using rigid transformation, affine transformation, and two-channel LDDMM. The transformation matrix was then applied to properly map and reorient the diffusion tensors. Eigenvalues, where the primary eigenvalue is axial diffusivity (AD), eigenvectors, fractional anisotropy (FA), radial diffusivity (RD), and color-coded orientation map of the primary eigenvector (ev0) were calculated in DTIStudio. 80 landmarks were manually selected on each brain and the reference brain. The landmarks on individual brains were mapped on to the reference coordinate with the corresponding transformation matrix. Displacements between landmarks mapped from individual brains and landmarks of the reference brain were measured to quantify registration accuracy. Nine WM structures including lateral lemniscus, anterior commissure, cerebral peduncle, internal capsule, optic tract, fimbria, corpus callosum, fornix, and spinal trigeminal tract were manually defined in the reference brain. The averaged values of FA, AD, RD, and angular difference of ev0 were calculated in the 9 WMs in each individual brain. One-way ANOVA and post hoc Tukey-Kramer tests were used to examine regional variations of each parameter across 8 brains and across 9 WMs. To decide if the quality of manual delineation has any influence on the statistical findings, a white matter probability atlas (WMPA) was constructed by averaging the 8 thresholded FA maps of individual brains. The same statistical tests were applied on the core regions (e.g., with WM probability of 0.5, 0.625, 0.75, 0.875, or 1) of the manually defined WMs. Results The figure shows group average of FA, AD, RD and colormap of 8 brains providing visual feedback on the normalization quality. Mean displacement of the 640 landmarks is only 1.5±1.0 pixels. The table lists p-values of the ANOVA test of FA, AD, RD, and ev0 across 8 brains or across 9 WMs. No significant difference was found across the 8 brains for any parameter. In contrast, there exists significant difference across the 9 WMs for all parameters. Subsequent Tukey-Kramer tests provide additional information, such as the lateral lemniscus was found to have significantly lowest anisotropy, while the cerebral peduncle exhibits the highest anisotropy. Analysis of core regions of WMs with probability level of 0.5, 0.625, 0.75, 0.875 or 1 does not change the statistical findings (data not shown). Conclusions This protocol is able to acquire high-resolution DTI data of mouse brain in a robust and repeatable fashion, allowing us to normalize individual brains onto a common reference with high accuracy, lending validity to atlas-based representation of DTI parameters. It will serve as a foundation to quantitatively study mouse brain integrity and white matter architecture, at what we believe to be the highest spatial resolution yet attained. Acknowledgement All work performed at the Duke CIVM, an NCRR National Resource (P41 RR005959).