Brain Connectivity Imaging Markers To Confirm Diagnosis For Bipolar Vs. Unipolar Depression – A Connectome Approach.
Funder
National Health and Medical Research Council
Funding Amount
$434,369.00
Summary
Differentiating Bipolar disorders from Unipolar Depression is a major clinical challenge. This misdiagnosis hinders optimal clinical care and has many deleterious consequences such self-harm, increased chances of suicide, poor prognosis, and greater health care costs related to this disorder. This project will provide urgently-needed advance in accurate identification of Bipolar disorders using Magnetic Resonance Imaging and remove one of the key obstacles to accurate diagnosis.
Sudden Cardiac Arrest: Improving Detection Of Patients At Risk
Funder
National Health and Medical Research Council
Funding Amount
$838,845.00
Summary
Sudden cardiac death accounts for ~10% of deaths in our community. Many of these deaths occur in people who could otherwise have had many more years of productive life ahead of them. The aim of our research is to determine the underlying mechanisms so that we can develop better tools for detecting underlying problems before they become life threatening and potentially develop new treatments to modify the underlying causes.
Developmental Schizotypy In The General Population: Early Risk Factors And Predictive Utility.
Funder
National Health and Medical Research Council
Funding Amount
$830,952.00
Summary
This study will determine early childhood risk factors for psychosis-proneness in children aged 11 years, and emerging signs and symptoms of mental health disorders of these children, using population data from the NSW Child Development Study. Determining risk for psychosis as early as possible in the life course will enable the provision of preventative interventions to children at critical points in development.
A Multi-national Trial To Predict Treatment Response In Subtypes Of Depression
Funder
National Health and Medical Research Council
Funding Amount
$387,489.00
Summary
Treatment of MDD using trial and error can have serious consequences. It can prolong the patient’s suffering (depression is associated with substantial morbidity, and mortality), prolong their absence from work and other productive activity and increase the burden on their family-carers. This multi-national study will collect genetics, brain function and behavioural data from a large number of participants, allowing for sensitive predictors of response to be determined.
The Biology Of Risk For Bipolar Disorder: Genetic Effects In A High-risk Longitudinal Study
Funder
National Health and Medical Research Council
Funding Amount
$856,412.00
Summary
Bipolar disorder is a severe mood disorder affecting over 350,000 Australians. Some children of bipolar disorder patients will also become ill, although currently we have no tools to predict which of these genetically at-risk young individuals will eventually develop symptoms. This study will use genetic information plus brain structural changes to predict which at-risk individuals are likely to become ill. This study will help elucidate early clinical and biological markers of bipolar disorder.
Scalability Of The Transform-Us! Program To Promote Children's Physical Activity And Reduce Prolonged Sitting In Victorian Primary Schools
Funder
National Health and Medical Research Council
Funding Amount
$549,823.00
Summary
Transform-Us! is an innovative primary school program that has been found to substantially increase children’s physical activity levels, reduce sitting time and benefit health. With simple changes to the school and classroom environments and teaching practices (eg, standing lessons) we will work with partners in the education and health systems to translate this program across Victorian primary schools to determine the real-world implementation and impact of this program over 5 years.
Improved seasonal rainfall prediction for grain growers using farm level data and novel modelling. Successful grain production, a key export commodity for Australia, depends heavily on reliable seasonal forecasts. However, the highly variable climate means that for Australia’s 25,000 grain growers current forecasts lack detail in space and time. Using a combination of fuzzy classification and artificial neural networks, this project will develop a locally detailed continuously updating data-driv ....Improved seasonal rainfall prediction for grain growers using farm level data and novel modelling. Successful grain production, a key export commodity for Australia, depends heavily on reliable seasonal forecasts. However, the highly variable climate means that for Australia’s 25,000 grain growers current forecasts lack detail in space and time. Using a combination of fuzzy classification and artificial neural networks, this project will develop a locally detailed continuously updating data-driven seasonal forecast system using high density climate data from the 17,000 Grain Growers Association members and climate drivers such as sea surface temperature from the Bureau of Meteorology. After validation against observed data, the forecasts will be delivered via a web-based portal to users.Read moreRead less
Examining The Metabolic And Cognitive Deficits Caused By Insulin Resistance In The Ventral Striatum
Funder
National Health and Medical Research Council
Funding Amount
$400,372.00
Summary
Brain insulin resistance is thought to cause metabolic and cognitive deficits, but the underlying neural mechanisms remain elusive. This project addresses this gap in our knowledge by examining how brain insulin resistance disrupts the metabolic regulation of food intake and the cognitive control of actions. The outcomes will provide new insights in disorders characterised by brain insulin resistance such as obesity and dementia.
(When) should organisations employ over/underqualified individuals? The aim of the project is to better understand how employee qualifications (ie the extent to which an employee is qualified for the job they hold) influences their voluntary turnover decisions and job performance. Demand and supply in the labour market are frequently out of sync; around half of Australian employees have a level of qualification that does not match the position in which they are currently working. The project aim ....(When) should organisations employ over/underqualified individuals? The aim of the project is to better understand how employee qualifications (ie the extent to which an employee is qualified for the job they hold) influences their voluntary turnover decisions and job performance. Demand and supply in the labour market are frequently out of sync; around half of Australian employees have a level of qualification that does not match the position in which they are currently working. The project aims to establish under what conditions organisations and employees can get the performance benefits from employee qualifications while avoiding fast rates of voluntary turnover. The goal of the project is to develop insights about how to make full use of the benefits of qualifications while avoiding the pitfalls. Insights from this research may be of potential benefit to both workers and to employing organisations.Read moreRead less
Flexible methods for latent variable models applied to Health Economics. This project aims to develop flexible and powerful methods for estimating models containing variables that are unobserved, that is, latent. Such models are often used to capture individual heterogeneity and time dependence in data collected on individuals, with each individual observed for several time periods. Latent variables can also infer group membership, where such membership is unavailable from the data. The intended ....Flexible methods for latent variable models applied to Health Economics. This project aims to develop flexible and powerful methods for estimating models containing variables that are unobserved, that is, latent. Such models are often used to capture individual heterogeneity and time dependence in data collected on individuals, with each individual observed for several time periods. Latent variables can also infer group membership, where such membership is unavailable from the data. The intended methodology is Bayesian and based on new particle methods that allow users to select between models and predict future observations even in complex situations. The research aims to inform decision making through improved use of data in health economics and related fields.Read moreRead less