Modular organization of resting state networks reveals differences between normal and chronic pain patients

PABLO BALENZUELA; ARIEL CHERNOMORETZ; DANIEL FRAIMAN; I. CIFRE; C. SITGES; PEDRO MONTOYA; DANTE CHIALVO

Chicago, Illinois, EEUU

Conferencia; Meeting of Neuroscience 2009; 2009

Society for Neuroscience, USA.

          Alterations in the brain resting state networks (RSN) of chronic pain patients have been already hinted by previous findings of disrupted correlations in chronic back pain patients (Baliki et al, J.N. 2008). In addition, our preliminary results in fibromyalgia patients show that the RSN obtained using PICA (Probabilistic Independent Component Analysis) are splitted into sub-components. To better quantify these important alterations, we analyze topological properties of brain networks in chronic pain patients using a global measure of modularity. Modularity is calculated from fMRI functional networks, which are constructed from thresholded correlations between BOLD time series collected during 20 minutes of resting conditions. A network module is a group of nodes more densely connected among them than with the rest of the network, and its number can be estimated computationally by a modularity index. We found that maximum modularity is larger in chronic pain patients compared with a matched control group, which is consistent with the observed “splitting” of the components of resting state networks. The observation that networks subdivide and modularity increases suggest that the characterization of the modules as well as the identification of provincial and connector hubs, will provide a deeper understanding of how these dynamical changes proceed during these chronic diseases.