  Brain decoding algorithms form an important part of the arsenal of analysis
tools available to neuroscientists, allowing for a more detailed study of the
kind of information represented in patterns of cortical activity. While most
current decoding algorithms focus on estimating a single, most likely stimulus
from the pattern of noisy fMRI responses, the presence of noise causes this
estimate to be uncertain. This uncertainty in stimulus estimates is a
potentially highly relevant aspect of cortical stimulus processing, and
features prominently in Bayesian or probabilistic models of neural coding.
Here, we focus on sensory uncertainty and how best to extract this information
with fMRI. We first demonstrate in simulations that decoding algorithms that
take into account correlated noise between fMRI voxels better recover the
amount of uncertainty (quantified as the width of a probability distribution
over possible stimuli) associated with the decoded estimate. Furthermore, we
show that not all correlated variability should be treated equally, as modeling
tuning-dependent correlations has the greatest impact on decoding performance.
Next, we examine actual noise correlations in human visual cortex, and find
that shared variability in areas V1-V3 depends on the tuning properties of fMRI
voxels. In line with our simulations, accounting for this shared noise between
similarly tuned voxels produces important benefits in decoding. Our findings
underscore the importance of accurate noise models in fMRI decoding approaches,
and suggest a statistically feasible method to incorporate the most relevant
forms of shared noise.
