Automated Surface-Based Segmentation of Deep Gray Matter Regions Based on Diffusion Tensor Images Reveals Unique Age Trajectories Over the Healthy Lifespan.

Many studies have demonstrated unique trajectories of deep gray matter (GM) volumes over development and aging, suggesting but not measuring microstructural alterations. Only a few studies have measured diffusion tensor imaging (DTI) parameters in deep GM or reported these values across a wide age range in a large cohort. Because many clinical protocols do not acquire T1-weighted images and registration between T1-weighted and diffusion images is error-prone, an automated segmentation technique is proposed that works solely on parametric maps calculated from DTI, enabling efficient DTI studies of deep GM in large cohorts without the need for additional structural imaging. The algorithm segments five subcortical GM structures by deforming 3D models to their boundaries visible on diffusion-weighted images and DTI maps. This DTI-only method is compared against standard T1-weighted segmentation using a 1.5-mm isotropic test-retest diffusion data (n = 24). Inter-method Dice coefficients were high (> 0.7) for 5 of 10 structures but were low for the left/right globus pallidus, left/right amygdala, and right hippocampus. The proposed DTI-only segmentation qualitatively appeared more accurate and yielded smaller volumes than T1w for all 10 structures. The segmentation method was then applied to a "lifespan" cohort (n = 357, 5-90 years, 203 females) to assess age changes in volume, fractional anisotropy (FA), and mean diffusivity (MD). For all five structures, MD trajectories were quadratic, decreasing and then increasing after ~30-35 years. For the globus pallidus and hippocampus, FA trajectories remained flat from 5 to ~25 years and then started to decrease over 5-90 years; FA decreased linearly for amygdala, increased linearly for striatum, and remained constant for the thalamus. Notably, the trajectories for DTI were distinct from those of the deep GM volumes. The proposed automated deep GM segmentation method on DTI-only will facilitate the analysis of deep GM DTI (not typically measured in most studies) and will be advantageous for studies without a T1-weighted scan, as in many clinical populations.