Analytical Operations Relate Structural and Functional Connectivity in the Brain

PLoS One. 2016 Aug 18;11(8):e0157292. doi: 10.1371/journal.pone.0157292. eCollection 2016.

Abstract

Resting-state large-scale brain models vary in the amount of biological elements they incorporate and in the way they are being tested. One might expect that the more realistic the model is, the closer it should reproduce real functional data. It has been shown, instead, that when linear correlation across long BOLD fMRI time-series is used as a measure for functional connectivity (FC) to compare simulated and real data, a simple model performs just as well, or even better, than more sophisticated ones. The model in question is a simple linear model, which considers the physiological noise that is pervasively present in our brain while it diffuses across the white-matter connections, that is structural connectivity (SC). We deeply investigate this linear model, providing an analytical solution to straightforwardly compute FC from SC without the need of computationally costly simulations of time-series. We provide a few examples how this analytical solution could be used to perform a fast and detailed parameter exploration or to investigate resting-state non-stationarities. Most importantly, by inverting the analytical solution, we propose a method to retrieve information on the anatomical structure directly from functional data. This simple method can be used to complement or guide DTI/DSI and tractography results, especially for a better assessment of inter-hemispheric connections, or to provide an estimate of SC when only functional data are available.

MeSH terms

  • Brain / anatomy & histology*
  • Brain / diagnostic imaging
  • Brain / physiology
  • Functional Neuroimaging
  • Humans
  • Magnetic Resonance Imaging
  • Models, Neurological
  • Neural Pathways / anatomy & histology*
  • Neural Pathways / physiology

Grant support

VJ acknowledges the support of the Brain Network Recovery Group through the James S. McDonnell Foundation and funding from the European Union Seventh Framework Programme (FP7-ICT BrainScales and Human Brain Project [grant no. 60402]). PR acknowledges the support of the James S. McDonnell Foundation (Brain Network Recovery Group JSMF22002082), the German Ministry of Education and Research (Bernstein Focus State Dependencies of Learning 01GQ0971) and the Max-Planck Society (Minerva Program). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.