MCS undergraduate Erik Schwartz worked on a novel computational method that uses a new approach to brain image analysis. Rather than analyzing single voxels, his method analyzes global features in the brain connectivity as imaged by structural MRI. Experimental results using a dataset of MRI images acquired from 416 subjects show that the method detected strong correlation between brain MRI images and basic physiological indicators such as age and gender, but also found significant correlation between the brain MRI images and the level of education or socio-economical status. This approach of brain image analysis can be used in population studies for detecting biomarkers, as well as correlation of structure of the brain with continuous physiological, environmental, or behavioral traits.
His research paper was peer-reviewed and accepted for publication in the Journal of Medical Imaging and Health Informatics.