Disparate Connectivity for Structural and Functional Networks is Revealed When Physical Location of the Connected Nodes is Considered

Abstract

Macroscopic brain networks have been widely described with the manifold of metrics available using graph theory. However, most analyses do not incorporate information about the physical position of network nodes. Here, we provide a multimodal macroscopic network characterization while considering the physical positions of nodes. To do so, we examined anatomical and functional macroscopic brain networks in a sample of twenty healthy subjects. Anatomical networks are obtained with a graph based tractography algorithm from diffusion-weighted magnetic resonance images (DW-MRI). Anatomical connections identified via DW-MRI provided probabilistic constraints for determining the connectedness of 90 different brain areas. Functional networks are derived from temporal linear correlations between blood-oxygenation level-dependent signals derived from the same brain areas. Rentian Scaling analysis, a technique adapted from verylarge-scale integration circuits analyses, shows that functional networks are more random and less optimized than the anatomical networks. We also provide a new metric that allows quantifying the global connectivity arrangements for both structural and functional networks. While the functional networks show a higher contribution of inter-hemispheric connections, the anatomical networks highest connections are identified in a dorsal-ventral arrangement. These results indicate that anatomical and functional networks present different connectivity organizations that can only be identified when the physical locations of the nodes are included in the analysis.

Linking structural and effective brain connectivity: structurally informed Parametric Empirical Bayes (si-PEB)

“…Multimodal integration beyond MRI provides exciting opportunities to consider structural, functional and effective connectivity as distinct yet complementary determinants of functional integration in the brain. For instance, integration of electrophysiology with its exquisite temporal resolution (Bonnefond et al 2017), data on physical network topography (Pineda-Pardo et al 2015) as well as analysis and modelling of more fine-grained features such as connectivity within grey matter or mechanisms of synaptic coupling (Breakspear et al 2003; Leuze et al 2014; Lo et al 2015) may open avenues to better conceptualise brain connectivity. Efforts towards generative models of how function evolves from structural connectivity are underway (Ritter et al 2013; Sanz Leon et al 2013).…”

Spatial Embedding Imposes Constraints on Neuronal Network Architectures

“…To better understand this spatially embedded topology, it is useful to consider methods that can simultaneously (rather than independently) assess topology and geometry. One such method that has proven particularly useful in the study of neural systems from mice to humans is Rentian scaling, which assesses the efficiency of a network's spatial embedding [20,[27][28][29][30]. Originally developed in the context of computer circuits, Rentian scaling describes a power-law scaling relationship between the number of nodes in a volume and the number of connections crossing the boundary of the volume [7,20].…”

Functional brain connectivity is predictable from anatomic network's Laplacian eigen-structure

“…Functional connectivity (FC) is typically defined as the temporal correlation of neurophysiological time series obtained via fMRI or EEG (Chang and Glover, 2010). Euclidean distance between regions is a good predictor of FC (Pineda-Pardo et al, 2015; Vertes et al, 2012). Strong correlation is known between functional and structural connections, where the former appears to be constrained by the latter (Honey et al, 2009; van den Heuvel et al, 2009; Hermundstad et al, 2013; Rubinov et al, 2009; Ghosh et al, 2008; Wang et al, 2014; Park and Friston, 2013).…”