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Cognitive Ageing, Brain Ageing and Dementia

• Dementia Risk Factor Review Project
• Magnetic Resonance Imaging (MRI) studies
• Genetic correlates and risk factors
• Online self-assessment for dementia
• Cognition as a predictor of injury, functional impairment and depression

 

Dementia Risk Factor Review Project

A large focus of our research is on understanding cognitive ageing and dementia, and identifying strategies for the prevention of cognitive decline and for the promotion of healthy ageing. Epidemiological work conducted on the PATH Through Life Project and the Australian Longitudinal Study of Ageing has identified health and lifestyle factors that are associated with poorer cognitive function in both late and middle-adulthood.

To determine the impact of these risk factors, the Dementia Risk Factor Review Project was commenced in 2005. This project involves conducting a series of systematic reviews on modifiable risk factors for Alzheimer’s disease, vascular dementia, mild cognitive impairment and cognitive decline in normal ageing. We have completed reviews on smoking¹ and serum cholesterol, and are presently undertaking reviews in relation to alcohol consumption and body weight. This project is conducted in conjunction with the Dementia Collaborative Research Centre led by Associate Professor Marc Budge from Geriatric Medicine, ANU.

 

 

MRI studies

Researchers in ARU work on the MRI substudy of the PATH Through Life Project in collaboration with Professor Sachdev and Dr Wei Wen from UNSW. Recently graduated doctoral students have examined the hippocampus in relation to memory and cognition (Dr Jerome Maller)², and the association between hormone replacement therapy and brain structure (Dr Lee-Fay Low)³. Other recent studies have recently examined the association between alcohol consumption and brain structure and investigated relationships between intraindividual variability in cognitive test performance and corpus callosum area. ^{4, 5}

RESEARCH HIGHLIGHT
The above figures show the process of brain parcellation: firstly the skull is removed from the image, then a 3D model of the brain is created. This model is then decomposed into sub-structures that can be viewed and analysed.

 

Dr Nicolas Cherbuin is specialising in the analysis of cerebral MRI scans to assess how brain structures are affected by the ageing process and to identify the determinants of anatomical brain ageing. A new technique called brain parcellation is being used to process scans which are composed of images representing “slices” of individual brains. The software first removes the skull from the image and, using complex algorithms, recreates a 3D model of each scanned individual’s brain. This model is then decomposed into sub-structures that can individually be viewed in space and analysed to determine how they relate to other measures such as memory, health, and cognition.

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Genetic correlates and risk factors

In collaboration with Professor Simon Easteal from the John Curtin School of Medical Research, members of the ARU have been investigating the apolipoprotein E gene (APOE) and its relationship to Alzheimer’s, cardiovascular disease, and late onset dementia (non-familial). Recent cross-sectional findings from the PATH project showed no demonstrable difference in brain structure and cognitive performance between healthy older participants who carry this version of the gene and those who do not.⁶ Longitudinal analyses of these relationships are underway. These cross-sectional results suggest that APOE genotype is more important in the advanced pathological stages leading to dementia than in healthy ageing.

Previous studies have shown that serotonin is involved in learning and memory, however the role of serotonin in normal cognitive functioning is not well understood. We are analysing the association between functional polymorphisms of serotonin transporter gene (5-HTTLPR) and serotonin receptor gene (5-HT1A) in relation to cognitive performance over time for all PATH cohorts.

The investigation of other genetic factors, particularly those involved in pathways contributing to neurodegeneration such as inflammation and oxidative stress will also be the focus of planned research.

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Online self-assessment for dementia

As part of work concerned with development of the National Dementia Website, members of the ARU have also been investigating the use of screening tools for dementia.⁷ We have investigated ethical and practical concerns associated with dementia screening, as wells as conducting a systematic review of available instruments. Future research will focus on adapting and validating screening instruments for the digital world.

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Cognition as a predictor of injury, functional impairment and depression

Projects underway also focus on the impact of cognitive decline in later life. Our work has shown how cognitive decline is associated with increased risk of falling over an 8 year follow-up in community based older adults.⁸ In our work on the Australian Longitudinal Study of Ageing cognitive performance has also been associated with increased risk of developing late life depression. We are currently examining how cognitive deficits in executive function lead to errors on an on-road driving test.

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ARU staff working on Cognitive Ageing, Brain Ageing and Dementia

• Associate Professor Kaarin Anstey
• Dr Nicolas Cherbuin
• Dr Holly Mack
• Professor Helen Christensen

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References

1. Anstey, K.J., von Sanden, C., Salim, A., & O'Kearney, R. (2007). Somoking as a risk factor for dementia and cognitive decline: a meta-analysis of prospective studies. American Journal of Epidemiology. Published Online. doi: 10.1093/ajekwm116.

2. Maller, J. J., Anstey, K. J., Meslin, C., Christensen, H., & Sachdev, P. (accepted June 22, 2007). Hippocampus and amygdala volumes in random community-based sample of 60-64 year olds and their relationship to cognition. Psychiatric Research: Neuroimaging.

3. Low, L-F., Anstey, K. J., Maller, J. J., Kumar, R., Wen, W., Lux, O., Salonikas, C., Naidoo, D., & Sachdev., P. (2006). Hormone replacement therapy, brain volumes and white matter in postmenopausal women aged 60-64 years. Neuroreport, 17, 101-104.

4. Anstey, K. J., Jorm, A. F., Réglade-Meslin, C., Maller, J., Kumar, R., von Sanden, C., Windsor, T. D., Rodgers, B., Wen, W., & Sachdev, P. (2006). Weekly alcohol consumption, brain atrophy and white matter hyper intensities in a community based sample aged 60-64. Psychosomatic Medicine, 68, 778-785.

5. Anstey, K. J., Mack, H. A., Christensen, H., Li, S-C., Réglade-Meslin, C., Maller, J., Kumar, R., Dear, K., Easteal, S., & Sachdev, P. (2007). Corpus collosum size, reaction time speed and variability in mild cognitive disorders and in a normative sample. Neuropsychologia, 45, 1911-1920.

6. Cherbuin, N., Anstey, K. J., Sachdev, P., Maller, J.J., Meslin, C., Mack, H., Wen, W., Esteal, S. (accepted August 17, 2007). Total and regional grey matter volume is not related to APOE*E4 status in a community sample of middle-aged individuals. Journal of Gerontology: Medical Sciences.

7. Cherbuin, N., & Anstey, K. J. (in press). DIY dementia screening and online assessment tools. International Psychogeriatrics.

8. Anstey, K. J., von Sanden, C., & Luszcz, M. A. (2006). An 8-year prospective study of the relationship between cognitive performance and falling among very old adults. Journal of the American Geriatrics Society, 54, 1169-1176.

 

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