Community structure in large-scale cortical networks during motor acts Fabrizio De Vico Fallani a,b,⇑ , Alessandro Chessa f,g,h , Miguel Valencia d , Mario Chavez e , Laura Astolfi a,c , Febo Cincotti a , Donatella Mattia a , Fabio Babiloni b,a a Neuroelectrical Imaging and BCI Laboratory, IRCCS ‘‘Fondazione Santa Lucia’’, Rome, Italy b Department of Physiology and Pharmacology, University of Rome ‘‘Sapienza’’, Rome, Italy c Department of Computer and System Science, University of Rome ‘‘Sapienza’’, Rome, Italy d Neurophysiology Laboratory, Division of Neurosciences, CIMA, University of Navarra, Pamplona, Spain e Institut du Cerveau et de la Moelle Epinière, Hôpital de La Pitié-Salpêtrière, Paris, France f Department of Physics, University of Cagliari, Complesso Universitario di Monserrato, Cagliari, Italy g Linkalab, Complex Systems Computational Laboratory, Cagliari, Italy h Institute for Complex Systems ðISC Þ, CNR UOS Department of Physics, University of Rome ‘‘Sapienza’’, Rome, Italy article info Article history: Available online 14 March 2012 abstract The purpose of the present work is to evaluate the community structure of the cortical net- work subserving the neurophysiologic processes in simple motor acts. To this end, we studied the topological properties of the functional brain connectivity in the frequency domain. The functional networks were estimated by means of the imaginary coherence from a dataset of high-resolution EEG recordings (4094 cortical sources) in a group of healthy subjects (n = 10) during a finger extension task. The analysis of the com- munity structure was addressed through a particular detection algorithm that optimizes the modularity, a function related to the level of internal clustering inside the communities in the network. The principal results indicate that the cortical network changes its struc- tural organization during the motor execution with respect to a baseline condition. Notably in the Beta band (12.5–30 Hz), the level of intra-module connectivity decreases, while inter-module connectivity increases reflecting the need for a neural integration of distant regions. Notably, this distributed interaction involves anatomical regions belonging to both the hemispheres including pre-motor and primary motor areas in the frontal and central part of the cortex as well as parietal associative regions, which are related to the planning, selection and execution of actions. Ó 2012 Elsevier Ltd. All rights reserved. 1. Introduction Over the last decade, there has been a growing interest in the detection of functional connectivity in the brain from different electromagnetic and hemodynamic signals recorded by neuroimaging devices such as the functional magnetic resonance (fMRI) scanner, electroencephalogra- phy (EEG), and magnetoencephalography (MEG) apparatus [1–3]. In particular, the need for a clear comprehension of the structure of functional brain networks is assuming an essential role in the neuroscience. Like other connectivity systems in nature, the functional networks estimated from the actual brain-imaging technologies (MEG, fMRI, and EEG) can be analyzed by means of graph theory [4–6], where graphs are mathematical representations of net- works, which are constituted essentially by nodes and con- nections between nodes. The usefulness of the graph theoretical approach in neuroscience was firstly demon- strated on a set of anatomical brain networks [7] and it has now become a well established framework [8]. Recently, thanks to advanced signal processing methods [9,10], it is possible to estimate from standard scalp EEG sig- nals the original activity generated by whole cortical 0960-0779/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.chaos.2012.02.006 ⇑ Corresponding author at: IRCCS ‘‘Fondazione Santa Lucia’’, Via Ardeatina, 306 – 00179 Rome, Italy. Tel.: +39 06 5150 1510; fax: +39 06 5150 1465. E-mail address: fabrizio.devicofallani@uniroma1.it (F. De Vico Fallani). Chaos, Solitons & Fractals 45 (2012) 603–610 Contents lists available at SciVerse ScienceDirect Chaos, Solitons & Fractals Nonlinear Science, and Nonequilibrium and Complex Phenomena journal homepage: www.elsevier.com/locate/chaos