Brain Research Bulletin 72 (2007) 284–292 Brain network for passive word listening as evaluated with ICA and Granger causality A. Londei a,b,∗ , A. D’Ausilio a,b , D. Basso c,d , C. Sestieri e,f , C. Del Gratta e,f , G.L. Romani e,f , M. Olivetti Belardinelli a,b a Department of Psychology, University of Rome “La Sapienza”, Rome, Italy b ECONA, Interuniversity Centre for the Research in Cognitive Processing of Natural and Artificial Systems, Rome, Italy c Laboratory of Clinical Biochemistry and Molecular Biology, University of Pisa, Pisa, Italy d Department of Psychology, University of Pavia, Pavia, Italy e Department of Clinical Sciences and Bio-Imaging, “G. D’Annunzio” University, Chieti, Italy f ITAB, Institute for Advanced Biomedical Technologies, “G. D’Annunzio” University, Chieti, Italy Received 6 July 2006; received in revised form 5 January 2007; accepted 10 January 2007 Available online 30 January 2007 Abstract Brain network modeling is probably the biggest challenge in fMRI data analysis. Higher cognitive processes in fact, rely on complex dynamics of temporally and spatially segregated brain activities. A number of different techniques, mostly derived from paradigmatic hypothesis-driven methods, have been successfully applied for such purpose. This paper instead, presents a new data-driven analysis approach that applies both independent components analysis (ICA) and the Granger causality (GC). The method includes two steps: (1) ICA is used to extract the independent functional activities; (2) the GC is applied to the independent component (IC) most correlated with the stimuli, to indicate its functional relation with other ICs. This new method is applied to the analysis of fMRI study of listening to high-frequency trisyllabic words, non-words and reversed words. As expected, activity was found in the primary and secondary auditory cortices. Additionally, a parieto-frontal network of activations, supported by temporal and causality relationships, was found. This network is modulated by experimental conditions in agreement with the most recent models presented for word perception. The results have confirmed the validity of the proposed method, and seem promising for the detection of cognitive causal relationships in neuroimaging data. © 2007 Elsevier Inc. All rights reserved. Keywords: Independent component analysis; Granger causality; Brain circuits; Auditory 1. Introduction Current theories agree that human higher cognitive func- tions emerge from a network of areas with precise interaction dynamics [28]. A series of methods based on different theo- retical frameworks have been proposed to extract the spatial and temporal properties of those networks in PET and fMRI data [11,20,29,30]. Recently, effective connectivity between brain areas has been extracted from neuroimaging data applying methods such as the Covariance Structural Equation Model- ∗ Corresponding author at: University of Rome “La Sapienza”, Department of Psychology, Via dei Marsi, 78, 00185 Roma, Italy. Tel.: +39 06 44917609; fax: +39 06 4462449. E-mail address: alessandro.londei@uniroma1.it (A. Londei). URL: http://w3.uniroma1.it/labcog/index.asp (A. Londei). ing (CSEM [29]), and the Dynamical Causal Modelling (DCM [12]). A disadvantage of these methods is the amount of a priori knowledge necessary for modeling. The anatomical connections as well as the expected activations need to be specified at some point. In the present work we combined two data-driven algo- rithms, independent component analysis (ICA) and the Granger causality (GC) [14,16,22], to extract functional brain networks without any specified anatomo-functional model. The methodol- ogy has already been partially tested on a simulated dataset [25], and here we probe this approach on real fMRI data. Our test was conducted on a well-known brain functional network such as the one involved in word perception. The Wernicke–Geschwind model assume that Wernicke’s territory (BA21/22, superior tem- poral gyrus), Geschwind’s territory (BA39/40, inferior parietal lobule) and Broca’s territory (BA44/45, inferior frontal gyrus) 0361-9230/$ – see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.brainresbull.2007.01.008