Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies

• Activity Number: 145
• Type: Invited
• Date/Time: Monday, July 30, 2012 : 10:30 AM to 12:20 PM
• Sponsor: International Chinese Statistical Association
• Author(s): Bin Yu*+ and Shinji Nishimoto and An T. Vu and Thomas Naselaris and Yuval Benjamini and Jack Gallant
• Companies: University of California at Berkeley and University of California at Berkeley and University of California at Berkeley and University of California at Berkeley and University of California at Berkeley and University of California at Berkeley
• Keywords: fMRI ; natural movie ; encoding ; decoding ; regularization ; Bayesian

Abstract

Quantitative modeling of human brain activity can provide crucial insights about cortical representations and can form the basis for brain decoding devices. Recent fMRI studies have modeled brain activity elicited by static visual patterns and have reconstructed these patterns from brain activity. However, blood oxygen level-dependent (BOLD) signals via fMRI are very slow, so it has been difficult to model brain activity elicited by dynamic stimuli such as natural movies. In this talk I present a new motion-energy encoding model that largely overcomes this limitation. We recorded BOLD signals in occipitotemporal visual cortex of human subjects who watched natural movies and fit the model separately to individual voxels. Visualization of the fit models reveals how early visual areas represent the information in movies. To demonstrate the power of our approach,we also constructed a Bayesian decoder by combining estimated encoding models with a sampled natural movie prior. The decoder provides remarkable reconstructions of the viewed movies. These results demonstrate that dynamic brain activity measured under naturalistic conditions can be decoded using current fMRI technology.