Diffusion MRI tractography provides a non-invasive modality to examine the human retinofugal projection, which consists of the optic nerves, optic chiasm, optic tracts, the lateral geniculate nuclei (LGN) and the optic radiations. However, the pathway has several anatomic features that make it particularly challenging to study with tractography, including its location near blood vessels and bone-air interface at the base of the cerebrum, crossing fibers at the chiasm, somewhat-tortuous course around the temporal horn via Meyer’s Loop, and multiple closely neighboring fiber bundles. To date, these unique complexities of the visual pathway have impeded the development of a robust and automated reconstruction method using tractography. To overcome these challenges, we develop a novel, fully automated system to reconstruct the retinofugal visual pathway from high-resolution diffusion imaging data. Using multi-shell, high angular resolution diffusion imaging (HARDI) data, we reconstruct precise fiber orientation distributions (FODs) with high order spherical harmonics (SPHARM) to resolve fiber crossings, which allows the tractography algorithm to successfully navigate the complicated anatomy surrounding the retinofugal pathway. We also develop automated algorithms for the identification of ROIs used for fiber bundle reconstruction. In particular, we develop a novel approach to extract the LGN region of interest (ROI) based on intrinsic shape analysis of a fiber bundle computed from a seed region at the optic chiasm to a target at the primary visual cortex. By combining automatically identified ROIs and FOD-based tractography, we obtain a fully automated system to compute the main components of the retinofugal pathway, including the optic tract and the optic radiation. We apply our method to the multi-shell HARDI data of 215 subjects from the Human Connectome Project (HCP). Through comparisons with post-mortem dissection measurements, we demonstrate the retinotopic organization of the optic radiation including a successful reconstruction of Meyer’s loop. Then, using the reconstructed optic radiation bundle from the HCP cohort, we construct a probabilistic atlas and demonstrate its consistency with a post-mortem atlas. Finally, we generate a shape-based representation of the optic radiation for morphometry analysis.

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Early research into neural correlates of obsessive compulsive disorder (OCD) has focused on individual components, several network-based models have emerged from more recent data on dysfunction within brain networks, including the the lateral orbitofrontal cortex (lOFC)-ventromedial caudate, limbic, salience, and default mode networks. Moreover, the interplay between multiple brain networks has been increasingly recognized. As the understanding of the neural circuitry underlying the pathophysiology of OCD continues to evolve, so will too our ability to specifically target these networks using invasive and noninvasive methods. This review discusses the rationale for and theory behind neuromodulation in the treatment of OCD.

BACKGROUND Dural sinus injuries are potentially serious complications associated with acute blood loss. It is imperative that neurosurgery trainees are able to recognize and manage this challenging scenario. OBJECTIVE To assess the feasibility of a novel perfusion-based cadaveric simulation model to provide the fundamentals of dural sinus repair to neurosurgical trainees. METHODS A total of 10 perfusion-based human cadaveric models underwent superior sagittal sinus (SSS) laceration. Neurosurgery residents were instructed to achieve hemostasis by any method in the first trial and then repeated the trial after watching the instructional dural flap technique video. Trials were timed until hemostasis and control of the region of injury was achieved. Pre- and post-trial questionnaires were administered to assess trainee confidence levels. RESULTS The high-flow extravasation of the perfusion-based cadaveric model mimicked similar conditions and challenges encountered during acute SSS injury. Mean ± standard deviation time to hemostasis was 341.3 ± 65 s in the first trial and 196.9 ± 41.8 s in the second trial (P < .0001). Mean trainee improvement time was 144.4 s (42.3%). Of the least-experienced trainees with longest repair times in the initial trial, a mean improvement time of 188.3 s (44.8%) was recorded. All participants reported increased confidence on post-trial questionnaires following the simulation (median pretrial confidence of 2 vs post-trial confidence of 4, P = .002). CONCLUSION A perfusion-based human cadaveric model accurately simulates acute dural venous sinus injury, affording neurosurgical trainees the opportunity to hone management skills in a simulated and realistic environment.

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