Lagged and instantaneous dynamical influences related to brain structural connectivity

Carmen Alonso-Montes; Ibai Diez; Lakhdar Remaki; Iñaki Escudero; Beatriz Mateos; Yves Rosseel; Daniele Marinazzo; Sebastiano Stramaglia; Jesus M Cortes

doi:10.3389/fpsyg.2015.01024

Lagged and instantaneous dynamical influences related to brain structural connectivity

Front Psychol. 2015 Jul 21:6:1024. doi: 10.3389/fpsyg.2015.01024. eCollection 2015.

Authors

Carmen Alonso-Montes¹, Ibai Diez², Lakhdar Remaki¹, Iñaki Escudero³, Beatriz Mateos³, Yves Rosseel⁴, Daniele Marinazzo⁴, Sebastiano Stramaglia⁵, Jesus M Cortes⁶

Affiliations

¹ Basque Center for Applied Mathematics Bilbao, Spain.
² Biocruces Health Research Institute, Cruces University Hospital Barakaldo, Spain.
³ Biocruces Health Research Institute, Cruces University Hospital Barakaldo, Spain ; Radiology Service, Cruces University Hospital Barakaldo, Spain.
⁴ Department of Data Analysis, Faculty of Psychological and Pedagogical Sciences, Ghent University Ghent, Belgium.
⁵ Basque Center for Applied Mathematics Bilbao, Spain ; Dipartimento di Fisica, Universitá degli Studi di Bari and INFN Bari, Italy.
⁶ Biocruces Health Research Institute, Cruces University Hospital Barakaldo, Spain ; Ikerbasque, The Basque Foundation for Science Bilbao, Spain ; Department of Cell Biology and Histology, University of the Basque Country Leioa, Spain.

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.

Keywords: functional connectivity; functional magnetic resonance imaging; resting state; structural connectivity; structural equation modeling; tensor diffusion imaging.