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
Imaging genetics in schizophrenia and bipolar disorder: shared neurocognitive endophenotypes. Combined, schizophrenia and bipolar disorder afflict approximately 506,000 Australians at any one time, and are leading causes of disability and national economic burden. This study will delineate genetic underpinnings for these conditions in association with specific neurocognitive dysfunctions that are common to both disorders. These findings have important implications for the implementation of perso ....Imaging genetics in schizophrenia and bipolar disorder: shared neurocognitive endophenotypes. Combined, schizophrenia and bipolar disorder afflict approximately 506,000 Australians at any one time, and are leading causes of disability and national economic burden. This study will delineate genetic underpinnings for these conditions in association with specific neurocognitive dysfunctions that are common to both disorders. These findings have important implications for the implementation of personalised pharmaceutical treatments on the basis of genotype, and the development of therapeutic agents to target cognitive function. These results will also aid detection of premorbid psychotic illness in young individuals who may benefit from early intervention that may thwart the illness trajectory. Read moreRead less