Not Just Down and Out: Oculomotor Nerve Pathologic Spectrum

Condos, Amy; Story, Scott; Bahorik, Eric; Propokovich, Eric; Hawley, Daniel; Cho, Aaron; Cathey, Michael

doi:10.26226/morressier.5e5e709500701ceba4a26e21

Cited by 1 publication

(3 citation statements)

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“…We set the whole brainstem as the seed imaging (as shown in Figure 2). In terms of anatomical considerations, the anatomical pathway of the OCN originates from the ONC which is located in the central and dorsal midbrain (Brazis et al, 2012; Condos, 2021). Then OCN crosses the ventral midbrain and enters the cistern from the outer edge of the interpeduncular fossa (Park et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The oculomotor nerve (OCN) consists of somatic and parasympathetic nerves and plays a key role in the eye movements and pupil contractions by controlling extraocular muscles, ciliary muscles, and sphincter pupillae (Condos, 2021; Park, Rha, Lee, Chough, & Joo, 2017). The intramesencephalic segment of OCN extends from the oculomotor nuclear complex (ONC), which is ventral to the periaqueductal gray, and travels to the interpeduncular fossa (Brazis, Masdeu, & Biller, 2012; Condos, 2021). The cisternal segment of OCN travels outward and downward to the cavernous sinus and enters the orbits in the direction of the cavernous sinus parallel to the cavernous part of the internal carotid (Liang et al, 2009; as shown in Figure 1).…”

Section: Introductionmentioning

confidence: 99%

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Automatic oculomotor nerve identification based on data‐driven fiber clustering

Huang

Zeng

et al. 2022

Human Brain Mapping

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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.

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Section: Methodsmentioning

confidence: 99%