Measuring vision using innate behaviours in mice with intact and impaired retina function.

Measuring vision in rodents is a critical step for understanding vision, improving models of human disease, and developing therapies. Established behavioural tests for perceptual vision, such as the visual water task, rely on learning. The learning process, while effective for sighted animals, can be laborious and stressful in animals with impaired vision, requiring long periods of training. Current tests that that do not require training are based on sub-conscious, reflex responses (e.g. optokinetic nystagmus) that don't require involvement of visual cortex and higher order thalamic nuclei. A potential alternative for measuring vision relies on using visually guided innate defensive responses, such as escape or freeze, that involve cortical and thalamic circuits. In this study we address this possibility in mice with intact and degenerate retinas. We first develop automatic methods to detect behavioural responses based on high dimensional tracking and changepoint detection of behavioural time series. Using those methods, we show that visually guided innate responses can be elicited using parametisable stimuli, and applied to describing the limits of visual acuity in healthy animals and discriminating degrees of visual dysfunction in mouse models of retinal degeneration.

Subluxation-related ulnar neuropathy (SUN) syndrome related to distal radioulnar joint instability.

Ulnar neuropathy coexistent with distal radioulnar joint (DRUJ) instability has previously been observed in our practice. The aim of this study was to define this phenomenon and investigate the hypothesis that the cause of this intermittent, positional ulnar neuropathy is related to kinking of the ulnar nerve about the DRUJ. Ulna neuropathy was present in 10/51 (19.6%) of a historical cohort of patients who presented with DRUJ instability. Nine subsequent patients with DRUJ instability and coexistent ulnar neuropathy underwent 3-T magnetic resonance imaging to better understand the mechanism of the observed syndrome. Both 3D qualitative and quantitative analyses were used to assess the presence of nerve 'kinking', displacing the nerve from its normal course and causing nerve compression/distraction in the distal forearm and Guyon's canal. Results of the quantitative analysis were statistically significant (p < 0.05). The clinical features of the condition have been delineated and termed subluxation-related ulnar neuropathy or SUN syndrome. The imaging study was a level II diagnostic study.

3D brain segmentation using active appearance models and local regressors.

We describe an efficient and accurate method for segmenting sets of subcortical structures in 3D MR images of the brain. We first find the approximate position of all the structures using a global Active Appearance Model (AAM). We then refine the shape and position of each structure using a set of individual AAMs trained for each. Finally we produce a detailed segmentation by computing the probability that each voxel belongs to the structure, using regression functions trained for each individual voxel. The models are trained using a large set of labelled images, using a novel variant of 'groupwise' registration to obtain the necessary image correspondences. We evaluate the method on a large dataset, and demonstrate that it achieves results comparable with some of the best published.

Robust tracking and posture description for laboratory rodents using active shape models.

We are in the process of developing an automated image analysis system, which uses deformable models of shape, learned from image examples, to interpret video images of rodents. Active shape models provide a compact description of the shape of the animal in a way that enables the postures the differentiate various behaviors to be distinguished. They also model the image profile across the shape boundary. We show how these features allow automatic, robust segmentation of the explicit object of interest. Rather than just detecting movement or changes from background in the image, the system can focus on objects that are of the correct shape and appearance. The modeling of the image profiles also allows the system to distinguish between the actual animal and image artifacts. We show how these techniques are being extended to extract postural information, which can then be integrated with positional data to produce a model of behavior.