Download 2015.03.25_eli merriam

CNIR Neuro@noon
2015. 3. 25, 12:00 PM
Room 85777, Sungkyunkwan University, Suwon
fMRI decoding : what does it reflect, and what can we learn
Elisha P. Merriam/ Ph.D.
Center for Neural Science, New York University
Abstract
To encode and make inferences about the world, the brain represents patterns -- visual,
auditory, cognitive -- using populations of neurons with diverse and complex forms of
selectivity. A coarse-scale method like functional magnetic resonance imaging (fMRI)
would, at first glance, appear poorly suited to studying these representations. Consider
the case of orientation representation in primary visual cortex (V1). A single fMRI voxel
pools responses from many orientation-tuned neurons. Because orientation tuning varies
at a fine, columnar spatial-scale, tuning should cancel at the level of fMRI voxels.
Surprisingly, results from multivariate decoding analyses imply that voxels in human V1
are weakly but reliably orientation selective. It is widely believed that these small biases
arise because of random spatial irregularities in the underlying columnar architecture, and
this interpretation, while untested, has been extended to the study of cognitive functions
throughout the brain. I will describe a set of experiments that test this hypothesis directly
by characterizing the cortical organization of orientation-selective fMRI responses in
human V1. We developed an approach for measuring orientation selectivity at multiple
spatial scales. Using this technique, we discovered a large-scale map of orientation
preference. The existence of this map is both necessary and sufficient for multivariate
decoding of orientation, demonstrating that random spatial irregularities do not contribute
to decoding. Our results thus imply a parsimonious, but sobering, explanation for why
decoding works, and help guide the interpretation of the rapidly growing number of
studies based on this technique.