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
Teaching An Old Brain New Tricks: Optimising Cognitive Training Through Neuroplasticity
Funder
National Health and Medical Research Council
Funding Amount
$1,562,250.00
Summary
People with early dementia have the most to gain from brain training programs aimed at delaying deterioration. Yet, its power is under-realised, with improvements not generalising to everyday living. This research program will harness the power of neuroplasticity to optimise brain training so that the effects transfer to everyday life. The knowledge gained will transform the way that we design and deliver brain training programs and revolutionise our understanding of why and how people respond.
A new perspective on how we learn motor skills: two adaptation classes? The capacity to adapt and acquire movement skills is essential for success in almost every aspect of our lives. This project will test the idea that there are two fundamentally distinct classes of motor learning processes in the brain that are driven by different error types. Using brain recordings, robotic perturbation of movement, and novel variations of classical learning paradigms, the project aims to reveal the neurocom ....A new perspective on how we learn motor skills: two adaptation classes? The capacity to adapt and acquire movement skills is essential for success in almost every aspect of our lives. This project will test the idea that there are two fundamentally distinct classes of motor learning processes in the brain that are driven by different error types. Using brain recordings, robotic perturbation of movement, and novel variations of classical learning paradigms, the project aims to reveal the neurocomputational properties of these proposed adaptation classes across a range of sensorimotor learning paradigms. The knowledge gained from this project may identify new strategies for adapting movements that are widely applicable to industry, defence, sport, and health.Read moreRead less
Evaluating the Network Neuroscience of Human Cognition to Improve AI. This project will translate the brain’s inherent complexity into a set of explorable networks that will test the network theory of intelligence, and also be used to drive advances in next generation artificial neural networks. Our approach will catalyse new knowledge regarding how the complexity of the brain gives rise to cognition using innovative analyses inspired by physics and engineering. This fresh perspective on cogniti ....Evaluating the Network Neuroscience of Human Cognition to Improve AI. This project will translate the brain’s inherent complexity into a set of explorable networks that will test the network theory of intelligence, and also be used to drive advances in next generation artificial neural networks. Our approach will catalyse new knowledge regarding how the complexity of the brain gives rise to cognition using innovative analyses inspired by physics and engineering. This fresh perspective on cognition will accelerate understanding of normal cognitive function and also advance the development of advances in artificial neural network performance. Expected outcomes include methods to describe the computational signature of how cognition emerges from dynamic brain network activity and novel AI algorithms. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE230100608
Funder
Australian Research Council
Funding Amount
$457,810.00
Summary
Characterising brain networks of intelligence through information tracking. For intelligent behaviour, the human brain needs to engage several processes including sensory, memory and motor processes. How it does this is one of the most significant questions in cognitive neuroscience. This project characterises the neural networks of human intelligence by advancing and building on the most recent advances in neuroimaging analyses. It will determine the interaction of different brain processes by ....Characterising brain networks of intelligence through information tracking. For intelligent behaviour, the human brain needs to engage several processes including sensory, memory and motor processes. How it does this is one of the most significant questions in cognitive neuroscience. This project characterises the neural networks of human intelligence by advancing and building on the most recent advances in neuroimaging analyses. It will determine the interaction of different brain processes by developing novel connectivity methods that track the flow of information through the brain with high temporal and spatial accuracy. The outcomes will be fundamental insights into the mechanisms of human intelligence and new connectivity analysis software that will have wide application in brain research.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: