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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.
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.
No abstract
Traumatic brain injury (TBI) results when external physical forces impact the head with sufficient intensity to cause damage to the brain. TBI can be mild, moderate, or severe and may have long-term consequences including visual difficulties, cognitive deficits, headache, pain, sleep disturbances, and post-traumatic epilepsy. Disruption of the normal functioning of the brain leads to a cascade of effects with molecular and anatomical changes, persistent neuronal hyperexcitation, neuroinflammation, and neuronal loss. Destructive processes that occur at the cellular and molecular level lead to inflammation, oxidative stress, calcium dysregulation, and apoptosis. Vascular damage, ischemia and loss of blood brain barrier integrity contribute to destruction of brain tissue. This review focuses on the cellular damage incited during TBI and the frequently life-altering lasting effects of this destruction on vision, cognition, balance, and sleep. The wide range of visual complaints associated with TBI are addressed and repair processes where there is potential for intervention and neuronal preservation are highlighted.
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