The aim of this study was to investigate the trajectory of the stria terminalis and develop a protocol for mapping the stria terminalis using multi‐shell diffusion images based tractography. The stria terminalis was reconstructed by combining one region of interest at the amygdala with another region of interest at the bed nucleus of stria terminalis. In addition, one region of avoidance was placed on the fornix at the interventricular foramen and another was set at the anterior perforated substance. The fiber‐tracking protocol was tested in a Human Connectome Project‐842 template, 35 healthy subjects from Massachusetts General Hospital, and 20 healthy subjects from the Human Connectome Project using generalized q‐sampling imaging based tractography. The stria terminalis was reconstructed in the Human Connectome Project‐842 template, 35 Massachusetts General Hospital healthy subjects, and 20 Human Connectome Project healthy subjects with our protocol. The stria terminalis originated from the amygdala and traveled parallel to the fornix. Then, the stria terminalis followed a C‐shaped trajectory around the inferior, posterior, and dorsal surfaces of the thalamus before projecting to the bed nucleus of stria terminalis between the thalamus and caudate nucleus. There were no significant differences in the quantitative anisotropy and fractional anisotropy values between the left and right stria terminalis. The stria terminalis was accurately visualized across subjects using multi‐shell diffusion images through generalized q‐sampling imaging based tractography. This method could be an important tool for the reconstruction and evaluation of the stria terminalis in various neurological disorders. One Sentence Summary The visualization of the stria terminalis through the multi‐shell diffusion images using generalized q‐sampling imaging based tractography.

The oculomotor nerve (OCN) is the main motor nerve innervating eye muscles and can be involved in multiple flammatory, compressive, or pathologies. The diffusion magnetic resonance imaging (dMRI) tractography is now widely used to describe the trajectory of the OCN. However, the complex cranial structure leads to difficulties in fiber orientation distribution (FOD) modeling, fiber tracking, and region of interest (ROI) selection. Currently, the identification of OCN relies on expert manual operation, resulting in challenges, such as the carries high clinical, time-consuming, and labor costs. Thus, we propose a method that can automatically identify OCN from dMRI tractography. First, we choose the multi-shell multi-tissue constraint spherical deconvolution (MSMT-CSD) FOD estimation model and deterministic tractography to describe the 3D trajectory of the OCN. Then, we rely on the well-established computational pipeline and anatomical expertise to create a data-driven OCN tractography atlas from 40 HCP data. We identify six clusters belonging to the OCN from the atlas, including the structures of three kinds of positional relationships (pass between, pass through, and go around) with the red nuclei and two kinds of positional relationships with medial longitudinal fasciculus. Finally, we apply the proposed OCN atlas to identify the OCN automatically from 40 new HCP subjects and two patients with brainstem cavernous malformation. In terms of spatial overlap and visualization, experiment results show that the automatically and manually identified OCN fibers are consistent. Our proposed OCN atlas provides an effective tool for identifying OCN by avoiding the traditional selection strategy of ROIs.

We evaluated the fiber bundles in mild traumatic brain injury (mTBI) patients using differential and correlational tractography in a longitudinal analysis. Diffusion MRI data were acquired in 34 mTBI patients at 7 days (acute stage) and 3 months or longer (chronic stage) after mTBI. Trail Making Test A (TMT‐A) and Digital Symbol Substitution Test changes were used to evaluate the cognitive performance. Longitudinal correlational tractography showed decreased anisotropy in the corpus callosum during the chronic mTBI stage. The changes in anisotropy in the corpus callosum were significantly correlated with the changes in TMT‐A (false discovery rate [FDR] = 0.000094). Individual longitudinal differential tractography found that anisotropy decreased in the corpus callosum in 30 mTBI patients. Group cross‐sectional differential tractography found that anisotropy increased (FDR = 0.02) in white matter in the acute mTBI patients, while no changes occurred in the chronic mTBI patients. Our study confirms the feasibility of using correlational and differential tractography as tract‐based monitoring biomarkers to evaluate the disease progress of mTBI, and indicates that normalized quantitative anisotropy could be used as a biomarker to monitor the injury and/or repairs of white matter in individual mTBI patients.