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Estimated connectomes by the means of neuroimaging techniques have enrichedour knowledge of the organizational properties of the brain leading to thedevelopment of network-based clinical diagnostics. Unfortunately, to date, manyof those network-based clinical diagnostics tools, based on the meredescription of isolated instances of observed connectomes are noisy estimatesof the true connectivity network. Modeling brain connectivity networks istherefore important to better explain the functional organization of the brainand allow inference of specific brain properties. In this report, we presentpilot results on the modeling of combined MEG and fMRI neuroimaging dataacquired during an n-back memory task experiment. We adopted a pooledExponential Random Graph Model (ERGM) as a network statistical model to capturethe underlying process in functional brain networks of 9 subjects MEG and fMRIdata out of 32 during a 0-back vs 2-back memory task experiment. Our resultssuggested strong evidence that all the functional connectomes of the 9 subjectshave small world properties. A group level comparison using comparing theconditions pairwise showed no significant difference in the functionalconnectomes across the subjects. Our pooled ERGMs successfully reproducedimportant brain properties such as functional segregation and functionalintegration. However, the ERGMs reproducing the functional segregation of thebrain networks discriminated between the 0-back and 2-back conditions while themodels reproducing both properties failed to successfully discriminate betweenboth conditions. Our results are promising and would improve in robustness witha larger sample size. Nevertheless, our pilot results tend to support previousfindings that functional segregation and integration are sufficient tostatistically reproduce the main properties of brain network.

Combined MEG and fMRI Exponential Random Graph Modeling for inferring functional Brain Connectivity