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Scientia Professor of Neuropsychiatry, CHeBA Co-Director and Clinical Director of theÌýNeuropsychiatric Institute

Epidemiology

This work will be based on the three longitudinal studies of cognitive ageing and dementiaÌýbeing conducted at CHeBA –ÌýSydney Memory and Ageing Study (MAS),ÌýOlder AustralianÌýTwins Study (OATS)ÌýandÌýSydney Centenarian Study (SCS), and three internationalÌýconsortia of studies –ÌýCOSMIC,ÌýSTROKOGÌýandÌýICC-Dementia. Some examples of theÌýpotential topics:

• Ageing and cognitive decline in diverse ethnic and geographical groups
• Differential risk and protective factors for MCI and dementia in diverse internationalÌýcohorts
• The differential effects of vascular risk factors on cognitive impairment and decline inÌýEastern and Western countries
• The interaction of genetic and environmental factors in dementia risk
• Examining risk factor models for dementia across international cohorts Longitudinal trajectories of cognitive function and their trajectories

NeuropathologyÌý(in collaboration with Dr Claire Shepherd)

• The neuropathological basis of dementia in centenarians

NanotechnologyÌý(in collaboration with the Nanotechnology group)

• Using magnetic nanoparticles as imaging agents for brain disease, both for MRI and Magnetic Particle Imaging

Omics and Neurobiology of Ageing Group

• Plasma biomarkers of ageing, MCI and AD using normal population cohortsÌýSydney Memory and Ageing Study (MAS)ÌýandÌýSydney Centenarian Study (SCS)Ìýand specialisedÌýcohorts such as theÌýOlder AustralianÌýTwins Study (OATS), Dominantly InheritedÌýAlzheimer Network (DIAN) and Australian Imaging, Biomarker & Lifestyle StudyÌýofÌýAgeing (AIBL)
• Proteomic changes in plasma that associate with ageing and health status (frailty,ÌýMCI, APOE allele, etc) and disorders such as MCI and dementia.
• Explore novel superparamagnetic iron oxide nanoparticles thatÌýcan penetrate the blood-brain barrier (BBB) and provide aÌýÌýsuperparamagnetic signalÌýfor amyloid imaging using MRI with limited toxicity
• Promotion of cellular nicotinamide adenine dinucleotide (NAD+) anabolism as aÌýstrategy to improve cellular senescence and cognitive function
• In vitro and in vivo studies on polyphenols as an integral strategy in preventing andÌýtreating diseases associated with neurodegeneration

Genetics

• The genetics of exceptional longevity
• CNV, ageing and cognition
• DNA methylation and its relationship to cognitive function
• Rare genetic variants associated with healthy ageing and dementia, using wholeÌýgenome sequencing
• Gene expression and changes with ageing and dementia
• Cerebral microbleeds and their relationship to AD and SVD pathology and cognition
• Imaging microinfarcts and examining their significance
• The blood-brain barrier (BBB) in vascular dementia and Alzheimer’s disease

Neuroimaging

• The relative effects of vascular and Alzheimer’s pathology on cognitive impairment inÌýolder individuals

Neuropsychology

• Computerised neuropsychological testing – reliability and validity The cognitive profile of exceptionally old individuals
• Specific risk factors
• Homocysteine, brain abnormalities and cognitive impairment
• Diabetes, brain changes and cognitive impairment in diverse settings
• Nutrition and cognition in diverse populations
• The application of new analytical techniques such as machine learning

Further information can be found on theÌýNeuropsychiatry GroupÌýpage.Ìý

Scientia Professor of Ageing & Mental Health, CHeBA Co-Director and Consultant Old Age Psychiatrist & Head of the Memory Disorders Clinic, Prince of Wales Hospital

Sydney Memory and Ageing Study (MAS)

a) What is the psychological health over time of MAS participants aged 70-90? We have data longitudinally for up to 14 years on depression, anxiety, K10/PHQ9, apathy, positive mental health, satisfaction with life:

• Correlations
• Psychotropics
• Course
• Outcomes e.g. association with dementia and death

b)ÌýApathy and MRI

• Apathy vs depression
• Subdomains of apathy vs MRI
• Do inflammatory markers mediate the relationship between apathy and MRI findings?

c)ÌýMild behavioural impairment (MBI) (Ismail Z et al.)

• Does MBI in cognitively normal older people predict cognitive decline over time? Examine data from 1037 MAS participants had Neuropsychiatric Inventory ratings at T1 and have been followed up over 14 years

d)ÌýProgression of behavioural symptoms in population of cognitively normal people

• What is the natural history of behaviours and psychological symptoms associated with dementia (BPSD) over 14 years?Ìý

*c) & d) could be separate or combined into one project

COGNISANCEÌý– Codesigning Diagnostic process and post-diagnostic care

a) Interviewing participants (people diagnosed with dementia in last 12 months and their family members) about their experience with receiving the diagnosis and about what advice they had about how to live well with diagnosis

b) Qualitative study of Sydney older people with dementia and their family members and ?their doctors/health care practitioners

Maintain Your Brain

a) Use of medications (generally or specific Rxx e.g. PPIs, vitamins) and cognition

b) Hospitalisations over time (self-report)

c) Qualitative study interviewing people who have participated in MYB to determine:

• Their experience in using online intervention
• What worked and what did not
• What their computer experience was during the trial

Why do this study? Online medicine is increasingly being used. This is a large trial and if successful will be scalable nationally and internationally. The qualitative study will be useful to know what works and does not and how to improve online interventions generally and for cognition specifically.Ìý

Omics and Neurobiology of Ageing Group

Biomarker discovery in autosomal dominant Alzheimer’s disease using proteomicsÌýand metabolomics techniques

The most common cause of dementia is Alzheimer’s disease (AD), which currently has no cure.ÌýEarly diagnosis of AD is still challenging but would allow for therapeuticÌý intervention beforeÌýextensive damage to the brain has occurred, resulting in the clinical symptoms of AD. BiomarkersÌýcould prove invaluable for assisting with diagnosisÌý and monitoring the effects of new drugs andÌýtherapeutic strategies. Studying the early pathological changes that take place in AD before clinicalÌýsymptoms are evident is difficult as it cannot be predicted who will develop sporadic late onset ADÌýin the future. By contrast, the rare autosomal dominant form of AD has near absolute certainty ofÌýonset in mutation carriers and the age at onset is also predictable based on family history. ThisÌýenables well-informed prospective studies to identify early changes associated with AD pathology.ÌýFurthermore, the relatively young age of these patients minimises age-related changes and comorbidities, which can interfere with biomarker discovery in older adults.

The PhD student working on this project will apply proteomic and metabolomic techniques toÌýplasma samples from people carrying mutations causing the autosomal dominant form of AD toÌýidentify proteins and metabolites that could serve as biomarkers for early AD pathology.

Metabolomics for biomarker discovery in neurodegenerative disease

Blood in the form of plasma or serum contains a multitude of low molecular weight metabolites thatÌýbear a wealth of information about the physiological state of theÌý individual. The composition of theÌýmetabolome is influenced by disease, drugs, genetics, diet and lifestyle. Hence it is not surprisingÌýthat metabolite signatures can be used to diagnose diseases or predict progression of a disease.ÌýUsing NMR-based as well as GC- and LC-MS methods, this project will identify metaboliteÌýsignatures specific for neurodegenerative diseases such as Alzheimer’s disease or VascularÌýdementia.

Validation of protein biomarker candidates for Alzheimer’s disease and their potentialÌýrole in AD pathology

Using proteomics methods, we have previously identified a number of proteins that areÌýdifferentially abundant in plasma from patients with Alzheimer’s disease (AD) or mild cognitiveÌýimpairment, a prodrome of AD, and healthy elderly individuals. These proteins are promisingÌýbiomarker candidates and we are looking for a PhD student to validate these findings in largerÌýnumbers of samples and investigate the potential involvement of these proteins in AD pathology.

Neuroimaging Group

• Connectome of older brains – both structural and functional descriptions
• Longitudinal studies of the brain in older brains – atrophy, connectivity and functionality
• Predicting the brain ageing trajectory using imaging, genetics and clinical data
• Mapping genetic influences on brain structures and functions using the twins design
• Functional and structural connectivity and its cognitive relevance
• Development of computational algorithms to segment brain lesions
• Development of a pipeline for automatic lesion detection and computation
• Construction of a MRI index for measuring cerebrovascular disease (CVD) burden: a computational approach (together with Dr. Anbu Thalamuthu)

Leader

Genomics and EpigenomicsÌýGroup

My research focuses on gaining a better understanding of the genetic and epigenetic factorsÌýinvolved in healthy ageing and age-related decline and disease. TheseÌýÌýgenetic factors includeÌývariation at the nucleotide level, epigenetic variation such as DNA methylation, and theÌýtranscriptome including non-coding RNA such as miRNAs, epistasis and gene- environmentalÌýinteractions. Current research is being undertaken using large population cohorts of olderÌýAustralians. Potential students are more than welcome to come and discuss possible projects withÌýme. Data available will include whole genome sequencing, gene expression data including RNAÌýsequencing, DNA methylation and genome-wide genotyping. There may be opportunities to workÌýin the laboratory.

Potential projects include:

• The genetics and epigenetics of exceptional longevity
• The relationship of the epigenome, particularly DNA methylation, and non-coding RNAs to cognitive function and dementia, the environment and other age-related phenotypes
• The transcriptome and ageing

Post-Doctoral Fellow

Dr Samtani's researchÌýfocuses on understanding the psychological, social and biological factors involved in ageing and dementia. Using longitudinal datasets, we can uncover the answers to important questions about healthy ageing. What is the interplay between our lifestyle, physical health, and cognitive processes? Do people have different trajectories depending on their combination of risk factors? What interventions do we need to improve the psychological, social and cognitive health of older adults?

Potential topics:

• Social connections: Which connections are protective for mental, physical and cognitive health in older adults? Which interventions can help older adults to feel more socially connected and less lonely?
• Social determinants of health: What is the link between socioeconomic factors, neighbourhood characteristics and health?
• Social cognition: What are ecologically valid ways to measure social cognitive skills such as reading emotions and responding appropriately in social situations? How can we help preserve or improve social cognition for older adults experiencing changes in their ability to interact with others?
• Mental health of older adults: What are the risk and protective factors associated with anxious and depressive symptoms? What interventions can help improve the mental health of older adults experiencing anxiety or depression?

Statistician

Genetics and Epigenomics Group

Identifying genes and environmental factors responsible for complex traits or disease phenotypesÌýis the focus in human genetics research. The genetic contribution to phenotype variability can beÌýstudied based on the data from several types of biological experiments such as DNA, RNA,ÌýmicroRNA and methylation. Environmental factors may include nutrition and behavioural traits.ÌýGreater insight and statistical power can be gained though integrated analysis of data from multipleÌýbiological experiments together with environmental factors.

Several statistical methodological projects can be developed based on various types of data setsÌýfrom the genetics and neuroimaging groups in CHeBA.

Some of the potential statistical genetics projects include:

• Integrated analysis of data sets from multiple genomic experiments.
• Joint association analysis of multiple phenotypes and multivariate genomic data.
• Comparative analysis of Centenarian genomes.
• Genetic basis of structural, functional imaging and brain networks: These include heritability,Ìýgenetic correlations and cluster analysis of structuralÌýÌýandÌýfunctional brain metrics andÌýnetworks. Association of analysis of network works modules to multiple age relatedÌýphenotypes such as memory and cognitive functions.
• Statistical methods for copy number variant (CNV) calling using sequence data and tests forÌýCNV associations with multiple age related phenotypes​​​​​.

Co-lead

Social Determinants of Ageing and Dementia Research Group

Social determinants of health can be broadly defined as the conditions in which people are born, grow, live, work and age, and people’s access to choices, opportunities, socioeconomic and other resources. Examples include access to education and quality of education, access to health care and quality of health care, characteristics of the neighborhood and built environment, the social and community context, as well as economic stability. Social determinants have a major impact on health, well-being, and quality of life as we age. Research has shown that about 45% of the risk for dementia may be explained by modifiable risk factors, and there is increasing evidence that many of these are either rooted in social contexts (e.g., social isolation, excessive alcohol use) or are social determinants directly linked to dementia (e.g., low education in early life, air pollution). There is growing interest in identifying and creating the evidence base for further social and structural determinants of health, e.g., social mobility, housing quality and stability, climate vulnerability, experience of discrimination and racism, digital exclusion, workplace conditions, access to nature, access to culturally safe care, gendered life course roles and exposures.Ìý

Our Research Group aims to assess and address such social determinants of health in ageing and dementia to inform effective and targeted population-level dementia risk reduction and prevention strategies, and improve health outcomes and quality of life across populations, taking a brain health equity approach.Ìý

PhD projects can be negotiated based on the students’ preferences and skills. Potential PhD projects include:

• Data linkage studies of social determinants of dementia and brain health outcomes using the Australian Person Level Integrated Data Asset (PLIDA).

• Longitudinal studies of social determinants of dementia and brain health outcomes (e.g., cognitive trajectories and incident dementia) using local cohort studies (MAS, SCS, OATS) and international cohort studies (COSMIC).

• Prevalence and incidence studies of modifiable dementia risk factors according to social determinants of health.

• Neighbourhood mapping of risk and protective factors and related outcomes such as prevalence and incidence of dementia and brain pathology.

• The development and validation of appropriate measurement tools on social determinants to help with comparing, sharing, and combining data.Ìý

• Understanding associations between social determinants and dementia in low- and middle-income countries.

• Gender-differences in social determinants of health, modifiable dementia risk and cognitive outcomes.Ìý

• Social policy impacts on dementia risk and dementia occurrence.Ìý

• Intervention studies to lower exposure to social determinants of dementia and improve brain health outcomes in communities.

Co-lead

Social Determinants of Ageing and Dementia Research Group

Social determinants of health can be broadly defined as the conditions in which people are born, grow, live, work and age, and people’s access to choices, opportunities, socioeconomic and other resources. Examples include access to education and quality of education, access to health care and quality of health care, characteristics of the neighborhood and built environment, the social and community context, as well as economic stability. Social determinants have a major impact on health, well-being, and quality of life as we age. Research has shown that about 45% of the risk for dementia may be explained by modifiable risk factors, and there is increasing evidence that many of these are either rooted in social contexts (e.g., social isolation, excessive alcohol use) or are social determinants directly linked to dementia (e.g., low education in early life, air pollution). There is growing interest in identifying and creating the evidence base for further social and structural determinants of health, e.g., social mobility, housing quality and stability, climate vulnerability, experience of discrimination and racism, digital exclusion, workplace conditions, access to nature, access to culturally safe care, gendered life course roles and exposures.Ìý

Our Research Group aims to assess and address such social determinants of health in ageing and dementia to inform effective and targeted population-level dementia risk reduction and prevention strategies, and improve health outcomes and quality of life across populations, taking a brain health equity approach.Ìý

PhD projects can be negotiated based on the students’ preferences and skills. Potential PhD projects include:

• Data linkage studies of social determinants of dementia and brain health outcomes using the Australian Person Level Integrated Data Asset (PLIDA).

• Longitudinal studies of social determinants of dementia and brain health outcomes (e.g., cognitive trajectories and incident dementia) using local cohort studies (MAS, SCS, OATS) and international cohort studies (COSMIC).

• Prevalence and incidence studies of modifiable dementia risk factors according to social determinants of health.

• Neighbourhood mapping of risk and protective factors and related outcomes such as prevalence and incidence of dementia and brain pathology.

• The development and validation of appropriate measurement tools on social determinants to help with comparing, sharing, and combining data.Ìý

• Understanding associations between social determinants and dementia in low- and middle-income countries.

• Gender-differences in social determinants of health, modifiable dementia risk and cognitive outcomes.Ìý

• Social policy impacts on dementia risk and dementia occurrence.Ìý

• Intervention studies to lower exposure to social determinants of dementia and improve brain health outcomes in communities.