Frequency-based approach to the study of semantic brain networks connectivity.

The interactions among cerebral regions involved in semantic word generations are explored through connectivity analysis based on fMRI data through multivariate autoregressive model (MVAR). Connections among the pars triangularis of the left inferior frontal gyrus (L45), the lateral fusiform girus (LFG) and the left medial fusiform girus (MFG) were investigated. Ten healthy subjects were asked to covertly generate nouns belonging to two semantic categories (Animals and Tools). Time series for each voxel were derived from fMRI images, averaged within each area and concatenated over all subjects. The MVAR model allowed estimating spectral power, coherence and partial coherence between pairs of time series, and causality relations assessed through direct directed transfer function (dDTF). Spectral power is mostly concentrated in the frequency range of the imposed stimulus and the activation in the specific areas is modulated by conditions as well as coherence and partial coherence. dDTF values revealed stronger connections between L45 and LFG in "Tools" conditions, while a stronger causality was found between L45 and MFG in "Animals" conditions. In addition, comparing the same connections in the two conditions, a mirror reversal of the two weights was observed, with stronger causality L45-LFG in "Tools" vs "Animals" and stronger causality L45-MFG in "Animals" vs "Tools". The present study confirms and extends previous results obtained by structural equation modeling analysis, suggesting the suitability of a data-driven Granger causality approach in identifying condition-dependent effective connectivity from BOLD signals. The proposed methodology completes and integrates other analysis procedures providing new tools to explore brain functions.