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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. XX, NO. YY, JANUARY 2011 1 A Bayesian Hierarchical Correlation Model
 

Summary: IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. XX, NO. YY, JANUARY 2011 1
A Bayesian Hierarchical Correlation Model
for fMRI Cluster Analysis
Camille G´omez-Laberge, Andy Adler, Ian Cameron, Thanh Nguyen, and Matthew J. Hogan
Abstract--Data-driven cluster analysis is potentially suitable to
search for, and discriminate between, distinct response signals in
blood oxygenation level dependent functional magnetic resonance
imaging (BOLD fMRI), which appear during cerebrovascular
disease. In contrast to model-driven methods, which test for
a particular BOLD signal whose shape must be given before-
hand, data-driven methods generate a set of BOLD signals
directly from the fMRI data by clustering voxels into groups
with correlated time signals. Here we address the problem of
selecting only the clusters that represent genuine responses to
the experimental stimulus by modeling the correlation structure
of the clustered data using a Bayesian hierarchical model. The
model is empirically justified by demonstrating the hierarchical
organization of the voxel correlations after cluster analysis.
BOLD signal discrimination is demonstrated using i) simulations
that contain multiple pathological BOLD response signals and ii)

  

Source: Adler, Andy - Department of Systems and Computer Engineering, Carleton University

 

Collections: Computer Technologies and Information Sciences