Jacqueline is a PhD student at the University of Alberta, working with the Alberta Machine Intelligence Institute, under the supervision of Dr. Russell Greiner. After finishing her Master’s degree at the University of Western Ontario in Medical Biophysics she went on to work with Dr. Glenda MacQueen, at the University of Calgary, and the Ontario Brain Institute’s Canadian Biomarker Integration Network in Depression (CAN-BIND). Working with Dr. MacQueen, Jacqueline became interested in the challenges faced by those suffering with mental health disorders and hopes to improve patient care through the use of artificial intelligence.
Functional magnetic resonance imaging (fMRI) is proving to be a promising medium for investigating psychiatric disorders; however, it remains challenging to find patterns within this data that may distinguish people into clinically relevant groups using current machine learning techniques. Capturing what is essentially a video of neuronal activity over time, fMRI requires additional processing to find the essence of the data, in a way that a machine learning algorithm could use to differentiate patients. With the support of CONP I will investigate new approaches to process fMRI for use in machine learning with the ultimate goal of improving the accuracy of models used to predict treatment outcomes in depression.