Alzheimer's disease (AD) is often mis-perceived as a disorder largely or solely of memory. However the disease also affects the visual areas of the brain leading to problems seeing what and where things are. Dementia-related visual impairment tends to be neglected, partly because people assume any problems are due to the eyes rather than the brain, and because it occurs at a point when language and other skills are too impaired for the person with dementia to explain the perceptual problems they are having. Visual problems are also often mis-attributed to poor memory (e.g. a person with AD failing to recognize a family member in a photo may be thought to have "forgotten" the person, when in fact they may simply be unable to perceive the face clearly). Visual impairment in AD has received increased attention recently with the identification of the syndrome Posterior Cortical Atrophy (PCA) which is typically caused by AD but presents with dramatic impairment of vision not memory, as experienced and described by the author Terry Pratchett in his documentary Living with Alzheimer's. Very few studies have explored the effect of impaired vision upon people with dementia or their caregivers. A motivation for improving our understanding of how people with AD see the world is that the limited number of small studies which have been conducted suggest that even simple changes to the environment (e.g. changing the colour of tableware from white to red) can compensate for vision problems in people with AD and lead to improved functioning and health (e.g. better eating and drinking). The project objective is to demonstrate that helping AD patients to interact more successfully with their visual environment at home can have a significant positive impact upon the wellbeing and quality of life of both patients and carers. The project will involve 50 people with PCA, 150 with typical Alzheimer's disease and 100 healthy volunteers. The impact of visual aids and strategies will be measured at three time-points over the course of one year, with a staggered start to enable comparisons of quality of life in those with and without the intervention. The success of the project will be judged primarily using established measures of quality of life, caregiver burden, everyday abilities, and behavioural and psychological wellbeing. However, the design of the visual aids and compensatory strategies themselves will be based upon a combination of patient/carer interviews (qualitative evidence) and cutting-edge scientific understanding of the nature of visual impairments caused by conditions such as Alzheimer's disease (quantitative evidence). This quantitative evidence will be gathered through studies of patient's visual skills and eye movements, and their ability to move around a purpose-built laboratory environment, before the main study commences in patients' own homes. Another important aspect of the project is the involvement of people with PCA, who experience AD-related visual loss but without the loss of memory and insight seen in typical AD. These individuals with PCA offer a new and unique perspective on the AD patient's view of the world. Their experiences of care homes and day hospitals draws attention to the fact that many current social and behavioural interventions for people with dementia may be limited in their effectiveness by over-reliance upon visual information and by a systemic failure to recognize visual impairment in many service users. The research brings together experts in the fields of dementia, engineering, social science, social work, occupational therapy and ophthalmology. This interdisciplinary research team will work closely with the DeNDRoN ENRICH scheme and project advisors in the 3rd sector and industry specializing in dementia and vision loss (e.g. Thomas Pocklington Trust, Dementia and Sight Loss Interest Group, ARUP, CDRAKE) to improve the study and implement its findings.

SAMS is a proposed 3-year project to that will investigate the potential for novel data and text mining techniques for detecting subtle signs of Cognitive Dysfunction that may indicate the early stages of Alzheimer's disease. Promoting self-awareness of change in cognitive function is will investigate the potential for novel data and text mining techniques for detecting subtle signs of change in cognition that may indicate the early stages of Alzheimer's disease. Promoting self-awareness of change in cognitive function is a key step in encouraging people to self-refer for clinical evaluation. A key motivation for SAMS, therefore, is to provide a non-invasive tool that helps develop such self-awareness. An increasing number of older people, the group most at risk of cognitive dysfunction and dementia, regularly use the Internet to keep in touch with their families, particularly grandchildren. This Internet activity presents an opportunity to harness rich, routinely available information that may contain indications of changes in the linguistic, executive and motor speed abilities in older people. Development work is needed to develop the software to harness this opportunity, to establish the optimal thresholds for flagging up important changes in cognition and the optimal methods for feeding this information back to individuals. SAMS will validate thresholds by examining changes in performance in people with established cognitive dysfunction and mild Alzheimer's disease and begin to explore the potential for technology-enhanced detection of early cognitive dysfunction. Patterns of computer use and content analysis of e-mails, such as forgetting topics, expressions of concern, emotion, etc., will be analysed and coupled to feedback mechanisms to enhance users' cognitive self awareness, empowering them self administer follow up tests and decide when to self refer themselves for expert medical advice. Tackling cognitive change detection requires the novel pooling of knowledge and integration of techniques from different sub-disciplines within a Computer Science. In addition to developing techniques for MCI detection and supporting self-referral, an explicit goal of the research is to develop a generic sense making and user-centred feedback architecture. This could be applied to a wide range of problems where interpreting computer use may be appropriate, e.g. mental health, social loneliness, privacy and social exploitation.

The increasing occurrence of dementia within our ageing population is one of the pressing challenges facing society. Successful management of patients with dementia is significantly aided by early and accurate diagnosis. Imaging methods such as magnetic resonance imaging (MRI) and positron emission tomography (PET) are already used in the diagnostic process; we believe that there is substantial scope for both methods to be improved to provide more precise and sensitive diagnostic information, and to do so in a way that is easily tolerated by patients. If we are correct in this belief, then the methods we develop within this project will not only help in early diagnosis, but may also help in the discovery of new therapies and in the longer term with helping doctors select the best therapeutic strategies for patients with different forms of dementia. Imaging methods such as MRI and PET can tell us a lot more about brains than simply providing a picture of brain shape and size. We will focus on improving MRI and PET to be sensitive to two important microscopic aspects of dementia. Firstly, we will develop and validate new methods for measuring the loss of brain cells due to the condition; this loss is the cause of many of the symptoms of dementia, such as memory problems, and we hope to be able to detect these changes earlier than has previously been possible. Secondly we will develop and validate new methods for measuring changes in blood delivery to the brain and how this can affect oxygen delivery. These changes are thought to be part of one of the important processes involved in causing cell death and tissue loss, and are likely to be particularly relevant to vascular dementia. We will also spend considerable time checking that the measurements we develop are both accurate and practical for application in dementia patients in the future. We will optimise the way in which the scanning processes take place so that the time required for patients to lie in the scanner(s) is minimised. This will be important for future adoption of these methods in the clinical environment.