Visual signs and symptoms of multiple system atrophy

Multiple system atrophy (MSA) is a rare movement disorder and a member of the 'parkinsonian syndromes', which also include Parkinson's disease (PD), progressive supranuclear palsy (PSP), dementia with Lewy bodies (DLB) and corticobasal degeneration (CBD). Multiple system atrophy is a complex syndrome, in which patients exhibit a variety of signs and symptoms, including parkinsonism, ataxia and autonomic dysfunction. It can be difficult to separate MSA from the other parkinsonian syndromes but if ocular signs and symptoms are present, they may aid differential diagnosis. Typical ocular features of MSA include blepharospasm, excessive square-wave jerks, mild to moderate hypometria of saccades, impaired vestibular-ocular reflex (VOR), nystagmus and impaired event-related evoked potentials. Less typical features include slowing of saccadic eye movements, the presence of vertical gaze palsy, visual hallucinations and an impaired electroretinogram (ERG). Aspects of primary vision such as visual acuity, colour vision or visual fields are usually unaffected. Management of the disease to deal with problems of walking, movement, daily tasks and speech problems is important in MSA. Optometrists can work in collaboration with the patient and health-care providers to identify and manage the patient's visual deficits. A more specific role for the optometrist is to correct vision to prevent falls and to monitor the anterior eye to prevent dry eye and control blepharospasm.

Quantitative pathological changes in the cerebellum of multiple system atrophy

Multiple system atrophy (MSA) is a rare neurodegenerative disorder associated with parkinsonism, ataxia, and autonomic dysfunction. Its pathology is primarily subcortical comprising vacuolation, neuronal loss, gliosis, and α-synuclein-immunoreactive glial cytoplasmic inclusions (GO). To quantify cerebellar pathology in MSA, the density and spatial pattern of the pathological changes were studied in α-synuclein-immunolabelled sections of the cerebellar hemisphere in 10 MSA and 10 control cases. In MSA, densities of Purkinje cells (PC) were decreased and vacuoles in the granule cell layer (GL) increased compared with controls. In six MSA cases, GCI were present in cerebellar white matter. In the molecular layer (ML) and GL of MSA, vacuoles were clustered, the clusters exhibiting a regular distribution parallel to the edge of the folia. Purkinje cells were randomly or regularly distributed with large gaps between surviving cells. Densities of glial cells and surviving neurons in the ML and surviving cells and vacuoles in the GL were negatively correlated consistent with gliosis and vacuolation in response to neuronal loss. Principal components analysis (PCA) suggested vacuole densities in the ML and vacuole density and cell losses in the GL were the main source of neuropathological variation among cases. The data suggest that: (1) cell losses and vacuolation of the GCL and loss of PC were the most significant pathological changes in the cases studied, (2) pathological changes were topographically distributed, and (3) cerebellar pathology could influence cerebral function in MSA via the cerebello-dentato-thalamic tract.

The integration of multi-source data to improve the classification of remotely sensed images

The number of remote sensing platforms and sensors rises almost every year, yet much work on the interpretation of land cover is still carried out using either single images or images from the same source taken at different dates. Two questions could be asked of this proliferation of images: can the information contained in different scenes be used to improve the classification accuracy and, what is the best way to combine the different imagery? Two of these multiple image sources are MODIS on the Terra platform and ETM+ on board Landsat7, which are suitably complementary. Daily MODIS images with 36 spectral bands in 250-1000 m spatial resolution and seven spectral bands of ETM+ with 30m and 16 days spatial and temporal resolution respectively are available. In the UK, cloud cover may mean that only a few ETM+ scenes may be available for any particular year and these may not be at the time of year of most interest. The MODIS data may provide information on land cover over the growing season, such as harvest dates, that is not present in the ETM+ data. Therefore, the primary objective of this work is to develop a methodology for the integration of medium spatial resolution Landsat ETM+ image, with multi-temporal, multi-spectral, low-resolution MODIS \Terra images, with the aim of improving the classification of agricultural land. Additionally other data may also be incorporated such as field boundaries from existing maps. When classifying agricultural land cover of the type seen in the UK, where crops are largely sown in homogenous fields with clear and often mapped boundaries, the classification is greatly improved using the mapped polygons and utilising the classification of the polygon as a whole as an apriori probability in classifying each individual pixel using a Bayesian approach. When dealing with multiple images from different platforms and dates it is highly unlikely that the pixels will be exactly co-registered and these pixels will contain a mixture of different real world land covers. Similarly the different atmospheric conditions prevailing during the different days will mean that the same emission from the ground will give rise to different sensor reception. Therefore, a method is presented with a model of the instantaneous field of view and atmospheric effects to enable different remote sensed data sources to be integrated.