Reconstructing visual experiences from brain activity evoked by natural movies.
Wednesday 26th of October 2011 at 12:00pm
Quantitative modeling of human brain activity can provide crucial
insights about cortical representations and can form the basis for
brain decoding devices. Recent functional magnetic resonance imaging
(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 measured via fMRI
are very slow, so it has been difficult to model brain activity
elicited by dynamic stimuli such as natural movies. Here we present a
new motion-energy encoding model that largely overcomes this
limitation. The model describes fast visual information and slow
hemodynamics by separate components. 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.
560 Evans Hall
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