Analyzing functional MRI Neuroimaging data

Pamela Douglas from UCLA introduces the problem of classifying functional MRI data. An FMRI scan records signals over time from 100,000 voxels covering the brain region, which creates a huge 4-dimensional dataset. The ADHD2000 machine learning competition is to predict a subject as either “Typically developing (TD)” or “Attention deficit hyperactivity disorder (ADHD)” using data from 1000 subjects that includes both demographic and structural neuroimaging features. Pamela’s team calculated 100,000 functional neuroimaging attributes from the raw data, and was placed 3rd using a voted perceptron learning algorithm. Ironically, the winning team ignored the neuroimaging features and used demographic data only! The video also introduces Haxby’s classic FMRI dataset, collected while subjects viewed images from 8 object categories. You will use these in the Quiz that follows.