Neural Correlates of Vulnerability to Depression using Brain Functional and Structural Imaging Data
Principal Investigator: Professor Jiang Qiu
Approved Research ID: 30036
Approval date: March 20th 2018
Depression is a common mental disorder. We would like to know about the cognitive model of depression from the factors which contributed to the onset of depression. It is necessary to investigate the neuroanatomical and functional correlates of human behaviors, (especially about cognitive control and emotion) which are associated with depression, using novel methods, like machine learning and VBM. It would be useful if we can predict depression and human behaviors using imaging data. After that, using structural equation modeling, we might be able to predict the onset of depression with these behaviors and its related brain networks. It is a basic question about the relation of human brain and behaviors. In vivo, it might be the best way to using the imaging data, because of its high spatial resolution. Using the dataset, we might show some interesting results about the formation of depression. At first, we would calculate each individual's brain using some softwares. For example, we can measure the thickness or volume of a brain. Of course, activity of human brain can also be measured using imaging data. Then, we would relate the behaviors to the brain. The simplest way is to calculate the correlation. We can also use machine learning to automatically find the best features of each behaviors. the more the better. The biobank dataset has about 10000 subjects with full cohort with behaviors (cognitive function) and imaging data(T1 structural brain MRI, Resting functional brain MRI and Task functional brain MRI). It would be very good if Biobank can share them with us.