This study used magnetoencephalography (MEG) to measure brain activity as subjects viewed different visual stimuli to understand how visual information is dynamically represented in the brain. Seven subjects performed a depth perception task while MEG measured brain activity. Data analysis involved pattern classification to predict stimulus types from brain activity patterns over time. Results showed maps of information flow between brain regions based on correlation strength between MEG sensors, and how this differed when stimuli contained depth cues versus no depth cues. The study developed analysis tools to further examine communication between brain areas during visual processing.

1. Dynamic representation of visual information in the human brain
1Myers CL, 1Bergstrand DB, 2Yonas A, 1,3Crowe DA, 3,4Georgopoulos AP
1 Augsburg College, Minneapolis, MN; 2 Institute of Child Development, University of Minnesota, Minneapolis, MN; 3 Brain Sciences Center, VA Medical Center, Minneapolis, MN; 4 Department of Neuroscience,
University of Minnesota, Minneapolis, MN
Data Acquisition
• Previously, seven subjects performed the depth
perception task while brain activity was measured
using MEG
• MEG instrument used has 248 sensors
• Data was collected at 1017 Hz from 1- 400 Hz
blink break
fixation
(2 sec)
stimulus
(6 sec)
blink break
(2 sec)
Monocular depth cues
5 cue types
6 second duration
8 repetitions each
Abstract
We applied a novel analysis in which the flow of information, rather than
activity, within the brain was measured in a task in which subjects
passively viewed visual stimuli. Our goal was to test whether the transfer
of information within the human brain would differ depending on various
aspects of the visual stimuli. We used statistical classification, correlation
and regression techniques in order to map out the transfer of information
associated with visual perception. Though the original questions about
the transfer of information under different visual conditions remain, the
work presented here represents a set of tools that will be able to answer
these questions in the future.
Data Analysis
Pattern classification
• Algorithm determined
distributions of MEG data
grouped by cue type (see
above) using portion of
data within a time bin
• Distributions were used to
predict cue types of
remaining data in that time
(brain activity 
information content).
Results
Task
Conclusions
• Flow of information
between 11 sensor
groups based on the
strength of their
correlations
• Shows all 6 different
maps according to the
threshold of correlation
used and if the data
contained all cues, depth
cues or non depth cues
• Development of a series of tools that can be used to
further probe into communication between brain
areas
• More time needed independently with cues
containing depth and cues containing none to
observe different types of processing utilized by the
brain
Static Cues Motion Cues
Snapshot of single lag regression video. A single lag regression was
applied to the data set in order to examine more probable
correlations. The strength of correlation between sensor groups is
indicated by thickness of arrow (arrow threshold f-vaulue > 10).
A
B
A. Plot of total number of arrows displayed throughout the single lag
regression video in each time frame. This can be seen as a global
representation of the amount of information flowing between sensor
groups. B. Plot of arrows going between the front of the brain to the
back and vice versa.
Cross correlations at 0 lag of each sensor group with the remaining 10.
These were performed for data containing all cues, data containg
depth cues, and data containing non depth cues.