Disorders

Neurodegenerative Diseases - 2014

Associate Professor Stephen Bowden was the recipient of BF grant funding in 2014

Neurodegenerative Diseases

Neurodegenerative Diseases
Automating brain scan analysis to improve the diagnosis of Alheimer's disease in clinical practice
Associate Professor Stephen Bowden
University of Melbourne
Funded By Nina Hennessy Estate and Kathleen Toy Estates
Co-Investigators : Professor Steven Collins, Dr Catherine Meade, Mr Simon Vogrin

PROJECT SUMMARY:

Despite dramatic advances in technologies, accurate diagnosis of dementia remains a scientific challenge, particularly in the early stages of disease. The clinical diagnosis in a person suspected of suffering dementia still relies on observation of changes in behaviour, particularly changes in cognitive functions such as memory and reasoning abilities. Accurate diagnosis assists in patient care and lifestyle planning, as well as allowing more effective targeting of therapeutic agents that may slow the worsening of symptoms.

 

With the aim of improving the diagnosis of dementia, we plan to evaluate a diagnostic strategy that combines high-quality cognitive assessment with automated, quantitative neuroimaging. Despite enormous research interest, the cognitive assessments used to diagnose dementias often compromise on quality, trading-off diagnostic accuracy in the belief that brief assessments retain sufficient sensitivity to measure the effects of dementia.

 

In addition, quantitative neuroimaging has excellent potential to identify certain types of disease processes in the brain, but the best available techniques are labour intensive and require highly skilled technicians to manually trace target brain structures using the operator’s expert knowledge of neuroanatomy. Using such techniques, it has been hypothesised that volume loss in medial temporal-lobe structures may be an early warning sign of deterioration into Alzheimer’s disease. Advances in automated, quantitative neuroimaging promise high accuracy and inexpensive investigations, but accuracy that is yet to be proven against the best manual techniques.

 

In this study we will compare the accuracy of the newest automated neuroimaging techniques against the established manual techniques. Both quantitative techniques will be combined with high sensitivity cognitive assessments to measure change prospectively in people with an early diagnosis of Alzheimer’s. If the automated approach to quantitative neuroimaging proves to be as accurate as the manual approach, we will have an inexpensive, efficient and readily available technique to improve the early diagnosis of dementia.

Progress Report

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