Regularized Brain Reading with Shrinkage and Smoothing

• Activity Number: 230
• Type: Topic Contributed
• Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Leila Wehbe* and Aaditya Ramdas and Rebecca Steorts and Cosma Shalizi
• Companies: and Carnegie Mellon University and Duke University and Carnegie Mellon University
• Keywords: shrinkage ; spatial smoothing ; fMRI ; small-area estimation ; ridge regression ; elastic net

Abstract

Functional neuroimaging measures how the brain responds to complex stimuli. However, sample sizes are modest, noise is substantial, and stimuli are high-dimensional. Hence, direct estimates are inherently imprecise and call for regularization. We compare a suite of approaches which regularize via shrinkage: ridge regression, the elastic net (a generalization of ridge regression and the lasso), and a hierarchical Bayesian model based on small-area estimation (SAE) ideas. The SAE approach draws heavily on borrowing strength from related areas as to make estimates more precise. We contrast regularization with spatial smoothing and combinations of smoothing and shrinkage. All methods are tested on functional magnetic resonance imaging data from multiple subjects participating in two different experiments related to reading, for both predicting neural response to stimuli and decoding stimuli from responses. Surprisingly, all the regularization methods work equally well, suggesting that beating basic smoothing and shrinkage will take not just clever methods, but careful modeling.