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

P. BALENZUELA; A. CHERNOMORETZ; D. FRAIMAN; I. CIFRE; C. SITGES; P. MONTOYA; D. CHIALVO

Chicago, Illinois, USA

Congreso; Neuroscience 2009; 2009

Abstract: Alterations in the brain resting state networks (RSN) of chronic pain patients have been already hinted byprevious findings of disrupted correlations in chronic back pain patients (Baliki et al, J.N. 2008). In addition, ourpreliminary results in fibromyalgia patients show that the RSN obtained using PICA (Probabilistic IndependentComponent Analysis) are splitted into sub-components. To better quantify these important alterations, we analyzetopological properties of brain networks in chronic pain patients using a global measure of modularity. Modularity iscalculated from fMRI functional networks, which are constructed from thresholded correlations between BOLD timeseries collected during 20 minutes of resting conditions. A network module is a group of nodes more denselyconnected among them than with the rest of the network, and its number can be estimated computationally by amodularity index. We found that maximum modularity is larger in chronic pain patients compared with a matchedcontrol group, which is consistent with the observed “splitting” of the components of resting state networks. Theobservation that networks subdivide and modularity increases suggest that the characterization of the modules as wellas the identification of provincial and connector hubs, will provide a deeper understanding of how these dynamicalchanges proceed during this chronic diseases.: