Abstract
Contemporary neuroimaging methods can shed light on the basis of human neural and cognitive specializations, with important implications for neuroscience and medicine. Indeed, different MRI acquisitions provide different brain networks at the macroscale; whilst diffusion-weighted MRI (dMRI) provides a structural connectivity (SC) coincident with the bundles of parallel fibers between brain areas, functional MRI (fMRI) accounts for the variations in the blood-oxygenation-level-dependent T2* signal, providing functional connectivity (FC). Understanding the precise relation between FC and SC, that is, between brain dynamics and structure, is still a challenge for neuroscience. To investigate this problem, we acquired data at rest and built the corresponding SC (with matrix elements corresponding to the fiber number between brain areas) to be compared with FC connectivity matrices obtained by three different methods: directed dependencies by an exploratory version of structural equation modeling (eSEM), linear correlations (C) and partial correlations (PC). We also considered the possibility of using lagged correlations in time series; in particular, we compared a lagged version of eSEM and Granger causality (GC). Our results were two-fold: firstly, eSEM performance in correlating with SC was comparable to those obtained from C and PC, but eSEM (not C, nor PC) provides information about directionality of the functional interactions. Second, interactions on a time scale much smaller than the sampling time, captured by instantaneous connectivity methods, are much more related to SC than slow directed influences captured by the lagged analysis. Indeed the performance in correlating with SC was much worse for GC and for the lagged version of eSEM. We expect these results to supply further insights to the interplay between SC and functional patterns, an important issue in the study of brain physiology and function.
Highlights
Three connectivity matrices were calculated for each subject from the rs-functional magnetic resonance imaging (MRI) (fMRI) time series: exploratory version of structural equation modeling (eSEM), C and partial correlations (PC)
Here, we calculated structural connectivity (SC), functional connectivity (FC), and effective connectivity (EC) for a very specific brain parcellation, with Region of Interest (ROI) covering the entirety of three well-know resting networks, the executive control, the default mode and the sensory motor network
We obtained 15 different ROIs and by performing to the same subject two classes of MRI acquisitions we made a careful comparison between SC, FC and EC
Summary
Three different main classes of brain networks are currently investigated (Friston, 1994; Sporns et al, 2004, 2005; Bonifazi et al, 2009; Friston, 2011): networks defined by theirstructural connectivity (SC) refer to anatomical connections between brain regions; networks defined by their functional connectivity (FC) account for statistical similarities in the dynamics between distinct neuronal populations; and effective connectivity (EC) networks identify interactions or information flow between regions. Current magnetic resonance imaging (MRI) techniques have allowed SC, FC, and EC brain networks to be measured at the macroscale. SC networks have been obtained from diffusion tensor images (DTI) and high-resolution tractography (Craddock et al, 2013) while FC networks have been obtained from correlations between blood oxygen-level dependent (BOLD) time-series (Biswal et al, 1995)
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