Incremental Full Correlation Matrix Analysis for Real-Time fMRI Studies

Abstract:

Real-time functional magnetic resonance
imaging (rtfMRI) is an emerging approach both for
studying the function of the human brain, and for neural
feedback-based training. To date, rtfMRI has relied
exclusively on the real-time analyses that treat activity
in different regions as independent of one another.
However, critical aspects of brain function may depend
on, and be revealed more sensitively by correlations
among regions. An exhaustive analysis of such correlations
is substantially more demanding computationally,
so full correlation matrix analysis (FCMA) of the entire
brain has not been carried out in real-time before. This
paper presents the algorithms and an implementation
of the first real-time system to perform whole brain
FCMA in real-time. Our system includes incremental
voxel selection, model training, and real-time classification.
We have implemented this system on an fMRI
scanner connected to a computer cluster over HTTP.
Experiments show that our system is able to achieve
real-time FCMA analysis of a stream of brain volumes
with neurofeedback with less than 200 ms of lag with
very few exceptions. The incremental FCMA algorithm
running on our real-time fMRI system performs about
1.8x-6.2x faster than using an offline FCMA toolbox in
the real-time context while getting comparable neurofeedback
results