A Neuroeconomics Approach to Biomarker Discovery in Depression

Students

Wharton

Faculty

Project Summary

During this summer, I have been working in Professor Platt’s neuroscience lab on a study which is to investigate the neuroeconomics approach to diagnose depression. As depression is becoming increasingly prevalent among people of all ages, the traditional methods of using questionnaires and subjective self-reports don’t seem accurate enough for the diagnosis and thus can’t help effectively preventing depression at its early phase.

Depression is often characterized by the negativity bias, which suggests that people with depression tend to focus more on the negative information. Therefore, we hypothesize that people’s loss aversion level – a neuroeconomics measure – is highly informative for depression diagnosis. We use both mouse tracking and eye tracking paradigms to measure participants’ unconscious attention, valuation, and memory biases towards negative stimulus. We then associated the data with the results from a group of depression scale tests to see whether the neuroeconomics biomarkers are correlated with depression level. The result shows that the severity of depression can be predicted by inherent bias to reject a gamble (memory bias), relative time spent viewing potential loss over gain(attention bias), and relative information accumulation rate for potential loss over gain(valuation bias).

I’ve been involved in the research from the experiment design to the data analysis, giving me a well-rounded research experience that helps me gain insight into how it is like to work as a scientist. For the experiment part, I functioned independently to carry out the eye tracking studies. Through this process, I gained firsthand experience of what data collecting and dealing with human subjects were like. I also got to know the full potential of this relatively affordable technique to measure people’s attention and arousal level. Through the data analysis, I greatly honed my coding and analytical skills as I had to deal with a great amount of data. Our research is highly interdisciplinary, which is a perfect match for me as an M&T student. I really appreciate to have this chance to witness how science can be applied in business(neuroeconomics) occasions.