  Estimated connectomes by the means of neuroimaging techniques have enriched
our knowledge of the organizational properties of the brain leading to the
development of network-based clinical diagnostics. Unfortunately, to date, many
of those network-based clinical diagnostics tools, based on the mere
description of isolated instances of observed connectomes are noisy estimates
of the true connectivity network. Modeling brain connectivity networks is
therefore important to better explain the functional organization of the brain
and allow inference of specific brain properties. In this report, we present
pilot results on the modeling of combined MEG and fMRI neuroimaging data
acquired during an n-back memory task experiment. We adopted a pooled
Exponential Random Graph Model (ERGM) as a network statistical model to capture
the underlying process in functional brain networks of 9 subjects MEG and fMRI
data out of 32 during a 0-back vs 2-back memory task experiment. Our results
suggested strong evidence that all the functional connectomes of the 9 subjects
have small world properties. A group level comparison using comparing the
conditions pairwise showed no significant difference in the functional
connectomes across the subjects. Our pooled ERGMs successfully reproduced
important brain properties such as functional segregation and functional
integration. However, the ERGMs reproducing the functional segregation of the
brain networks discriminated between the 0-back and 2-back conditions while the
models reproducing both properties failed to successfully discriminate between
both conditions. Our results are promising and would improve in robustness with
a larger sample size. Nevertheless, our pilot results tend to support previous
findings that functional segregation and integration are sufficient to
statistically reproduce the main properties of brain network.
