  Major depressive disorder (MDD) requires study of brain functional
connectivity alterations for patients, which can be uncovered by resting-state
functional magnetic resonance imaging (rs-fMRI) data. We consider the problem
of identifying alterations of brain functional connectivity for a single MDD
patient. This is particularly difficult since the amount of data collected
during an fMRI scan is too limited to provide sufficient information for
individual analysis. Additionally, rs-fMRI data usually has the characteristics
of incompleteness, sparsity, variability, high dimensionality and high noise.
To address these problems, we proposed a multitask Gaussian Bayesian network
(MTGBN) framework capable for identifying individual disease-induced
alterations for MDD patients. We assume that such disease-induced alterations
show some degrees of similarity with the tool to learn such network structures
from observations to understanding of how system are structured jointly from
related tasks. First, we treat each patient in a class of observation as a task
and then learn the Gaussian Bayesian networks (GBNs) of this data class by
learning from all tasks that share a default covariance matrix that encodes
prior knowledge. This setting can help us to learn more information from
limited data. Next, we derive a closed-form formula of the complete likelihood
function and use the Monte-Carlo Expectation-Maximization(MCEM) algorithm to
search for the approximately best Bayesian network structures efficiently.
Finally, we assess the performance of our methods with simulated and real-world
rs-fMRI data.
