The Burden Of Late Preterm Birth On Brain Development And 2 Year Outcomes – A Prospective, Longitudinal Cohort Study
Funder
National Health and Medical Research Council
Funding Amount
$838,690.00
Summary
80% of preterm babies are born from 32-36 weeks’ gestation, and are late preterm (LPT). LPT children have more learning problems, but why this occurs is unknown. This study aims to understand the effect of LPT birth on brain development. We will do brain scans at term and assess development at 2 years of age of 200 LPT and 200 full-term children. We expect LPT babies will have subtle alterations in brain development compared with term controls which will be associated with delayed development.
Motor problems, ranging from clumsiness to cerebral palsy, are one of the most common adverse outcomes in children born early. This study will investigate the motor development of children born <30 weeks’ gestation compared with peers born at term from birth to 5 years. We will determine whether early clinical evaluations or neuroimaging in the newborn period can predict later motor impairment at 5 years to be able to identify those who will benefit most from early intervention.
Modelling TRPV4 Skeletal Disorders Using Human IPSCs
Funder
National Health and Medical Research Council
Funding Amount
$1,171,187.00
Summary
Inherited skeletal disorders are a significant disease burden. Many gene mutations have been defined but we only have limited understanding about how they cause the disease. We will use patient skin cells and new in vitro re-programing technology to induce them to form cartilage cells to produce “disease in a dish” models of human skeletal disorders. These models will allow us to answer questions about how specific mutations cause disease and identify potential therapies
A Dimensional Approach To Mapping The Risk Mechanisms Of Mental Illness
Funder
National Health and Medical Research Council
Funding Amount
$1,677,975.00
Summary
There is ongoing debate about whether current definitions of mental disorders are accurate. We will use statistical techniques to identify the core dimensions of liability for mental illness, and map how genes and brain organization drive differences between people along each dimension.