Predictors Of Response To Antidepressants: Utility Of Behavioural, Neuroimaging And Genetics Data
Funder
National Health and Medical Research Council
Funding Amount
$310,071.00
Summary
Major depressive disorder (MDD) is projected to cause the second greatest global burden of disease by 2020, highlighting the urgent need for valid predictors of effective treatment response. Currently, there are no accurate predictors of response to antidepressants in MDD, and successful treatment relies greatly on 'trial and error'. This process is demanding on health resources, and may be a factor in the high suicide rates in depressed patients. Previous research on treatment response has been ....Major depressive disorder (MDD) is projected to cause the second greatest global burden of disease by 2020, highlighting the urgent need for valid predictors of effective treatment response. Currently, there are no accurate predictors of response to antidepressants in MDD, and successful treatment relies greatly on 'trial and error'. This process is demanding on health resources, and may be a factor in the high suicide rates in depressed patients. Previous research on treatment response has been limited by recruitment of small, heterogeneous patient samples, lack of placebo control, and a failure to examine task related activity in brain imaging studies. Perhaps one of the more troubling aspects of research that aims to predict treatment response to antidepressant medications is the use of commonly used outcome measures such as the Hamilton Rating Depression Scale (HAM-D), which were developed long before current classification systems of depression came into use. The US Federal Drug Administration has recently identified what they call a translational gap such that behavioural and biological measures are the most robust for detection of disorders such as depression, yet these measures remain to be translated into clinical tools that can be used to evaluate treatment. The aim of the current study therefore is to determine whether genetic variability is related to treatment outcome as defined by a more objective outcome measure (facial expression perception) using a randomised controlled design. The study will also determine whether brain measures (fMRI, EEG) enhance the prediction of SSRI response to both clinical and behavioural measures, over and above the genetic contribution.Read moreRead less
I am a translational, human physiologist which places me in a unique position to address important clinical questions. My current interests centre on: • Identification of novel predictors of unstable coronary heart disease • Novel treatment approaches in:
Engaging the forgotten public health workforce. This Fellowship project aims to provide the first in-depth, coordinated, critical public health examination and application of consumer behaviour-informed methodology to examine health promotion and complementary medicine. The project aims to build on novel analyses and critical engagement with community members, health professionals and policymakers to advance public health scholarship of health information-seeking and chronic illness prevention. ....Engaging the forgotten public health workforce. This Fellowship project aims to provide the first in-depth, coordinated, critical public health examination and application of consumer behaviour-informed methodology to examine health promotion and complementary medicine. The project aims to build on novel analyses and critical engagement with community members, health professionals and policymakers to advance public health scholarship of health information-seeking and chronic illness prevention. It seeks to identify challenges and opportunities to improve Australian health promotion initiatives; provide an evidence-base to inform coordinated implementation of the National Preventive Health Strategy; and optimise the primary care workforce to benefit health promotion for Australians.Read moreRead less