Pathologies of the optic nerve and orbit impact millions of Americans and quantitative assessment of the orbital structures on 3-D imaging would provide objective markers to enhance diagnostic accuracy, improve timely intervention and eventually preserve visual function. Recent studies have shown that the multi-atlas methodology is suitable for identifying orbital structures, but challenges arise in the identification of the individual extraocular rectus muscles that control eye movement. This is increasingly problematic in diseased eyes, where these muscles often appear to fuse at the back of the orbit (at the resolution of clinical computed tomography imaging) due to inflammation or crowding. We propose the use of Kalman filters to track the muscles in three-dimensions to refine multi-atlas segmentation and resolve ambiguity due to imaging resolution, noise, and artifacts. The purpose of our study is to investigate a method of automatically generating orbital metrics from CT imaging and demonstrate the utility of the approach by correlating structural metrics of the eye orbit with clinical data and visual function measures in subjects with thyroid eye disease. The pilot study demonstrates that automatically calculated orbital metrics are strongly correlated with several clinical characteristics. Moreover, the superior, inferior, medial and lateral rectus muscles obtained using Kalman filters are each correlated with different categories of functional deficit. These findings serve as foundation for further investigation in the use of CT imaging in the study, analysis and diagnosis of ocular diseases, specifically thyroid eye disease.

Facial vibrissae (whiskers) are thin, tapered, flexible, hair-like structures that are an important source of tactile sensory information for many species of mammals. In contrast to insect antennae, whiskers have no sensors along their lengths. Instead, when a whisker touches an object, the resulting deformation is transmitted to mechanoreceptors in a follicle at the whisker base. Previous work has shown that the mechanical signals transmitted along the whisker will depend strongly on the whisker’s geometric parameters, specifically on its taper (how diameter varies with arc length) and on the way in which the whisker curves, often called “intrinsic curvature.” Although previous studies have largely agreed on how to define taper, multiple methods have been used to quantify intrinsic curvature. The present work compares and contrasts different mathematical approaches towards quantifying this important parameter. We begin by reviewing and clarifying the definition of “intrinsic curvature,” and then show results of fitting whisker shapes with several different functions, including polynomial, fractional exponent, elliptical, and Cesàro. Comparisons are performed across ten species of whiskered animals, ranging from rodents to pinnipeds. We conclude with a discussion of the advantages and disadvantages of using the various models for different modeling situations. The fractional exponent model offers an approach towards developing a species-specific parameter to characterize whisker shapes within a species. Constructing models of how the whisker curves is important for the creation of mechanical models of tactile sensory acquisition behaviors, for studies of comparative evolution, morphology, and anatomy, and for designing artificial systems that can begin to emulate the whisker-based tactile sensing of animals.

Nearly all mammals have a vibrissal system specialized for tactile sensation, composed of whiskers growing from sensor-rich follicles in the skin. When a whisker deflects against an object, it deforms within the follicle and exerts forces on the mechanoreceptors inside. In addition, during active whisking behavior, muscle contractions around the follicle and increases in blood pressure in the ring sinus will affect the whisker deformation profile. To date, however, it is not yet possible to experimentally measure how the whisker deforms in an intact follicle or its effects on different groups of mechanoreceptors. The present study develops a novel model to predict vibrissal deformation within the follicle sinus complex. The model is based on experimental results from a previous ex vivo study on whisker deformation within the follicle, and on a new histological analysis of follicle tissue. It is then used to simulate whisker deformation within the follicle during passive touch and active whisking. Results suggest that the most likely whisker deformation profile is “S-shaped,” crossing the midline of the follicle right below the ring sinus. Simulations of active whisking indicate that an increase in overall muscle stiffness, an increase in the ratio between deep and superficial intrinsic muscle stiffness, and an increase in sinus blood pressure will all enhance tactile sensitivity. Finally, we discuss how the deformation profiles might map to the responses of primary afferents of each mechanoreceptor type. The mechanical model presented in this study is an important first step in simulating mechanical interactions within whisker follicles.

Understanding neural function requires quantification of the sensory signals that an animal's brain evolved to interpret. These signals in turn depend on the morphology and mechanics of the animal's sensory structures. Although the house mouse (Mus musculus) is one of the most common model species used in neuroscience, the spatial arrangement of its facial sensors has not yet been quantified. To address this gap, the present study quantifies the facial morphology of the mouse, with a particular focus on the geometry of its vibrissae (whiskers). The study develops equations that establish relationships between the three-dimensional (3D) locations of whisker basepoints, whisker geometry (arclength, curvature), and the 3D angles at which the whiskers emerge from the face. Additionally, the positions of facial sensory organs are quantified relative to bregma-lambda. Comparisons with the Norway rat (Rattus norvegicus) indicate that when normalized for head size, the whiskers of these two species have similar spacing density. The rostral-caudal distances between facial landmarks of the rat are a factor of ∼2.1 greater than the mouse, while the scale of bilateral distances is larger and more variable. We interpret these data to suggest that the larger size of rats compared to mice is a derived (apomorphic) trait. As rodents are increasingly important models in behavioral neuroscience, the morphological model developed here will help researchers generate naturalistic, multimodal patterns of stimulation for neurophysiological experiments and allow the generation of synthetic datasets and simulations to close the loop between brain, body, and environment.

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