Inferring neural signalling directionality from undirected structural connectomes

Abstract

Neural information flow is inherently directional. To date, investigation of directional communication in the human structural connectome has been precluded by the inability of non-invasive neuroimaging methods to resolve axonal directionality. Here, we demonstrate that decentralized measures of network communication, applied to the undirected topology and geometry of brain networks, can predict putative directions of large-scale neural signalling. We propose the concept of send-receive communication asymmetry to characterize cortical regions as senders, receivers or neutral, based on differences between their incoming and outgoing communication efficiencies. Our results reveal a send-receive cortical hierarchy that recapitulates established organizational gradients differentiating sensory-motor and multimodal areas. We find that send-receive asymmetries are significantly associated with the directionality of effective connectivity derived from spectral dynamic causal modeling. Finally, using fruit fly, mouse and macaque connectomes, we provide further evidence suggesting that directionality of neural signalling is significantly encoded in the undirected architecture of nervous systems.

The structural connectome constrains fast brain dynamics

“…Finally, our results explain how the large-scale activity unfolding in time might lead to the previous observation that average rsfMRI connectivity has topological features that mirror those of the structural connectome 12 . The neural avalanche framework opens up new opportunities to investigate polysynaptic models of network communication, which aim to describe patterns of signalling between anatomically unconnected regions 13,14 . Therefore, our work provides a foundational step towards elucidating the mechanisms governing communication in the human connectome.…”

Synchronization lag in post stroke: relation to motor function and structural connectivity

“…Linking structural and functional properties of the human connectome is a major goal in neuroscience (Amico & Goñi, 2018; Diez et al, 2017; Honey et al, 2009; Mišić et al, 2016; Seguin, Razi, & Zalesky, 2019). In the present study, we focused on understanding the putative link between synchronization lag (functional property) and the efficiency of signal propagation in the connectome (structural property).…”

Toward an information theoretical description of communication in brain networks

“…Inspired by a recent work (Seguin et al, 2019), we further explored this regional specificity by evaluating the asymmetry of broadcasting, for each communication regime, on the brain regions with highest nodal broadcasting strength. To do so, we distinguished those brain regions when being a target (receiver) or a source (sender).…”

“…Hybrid models exploring a spectrum of communication dynamics, including search information (Goñi et al, 2013(Goñi et al, , 2014, navigation (Seguin et al, 2018), or k-shortest path ensembles (Avena-Koenigsberger et al, 2017), have also been investigated. Recent studies have also looked into alternative network communication measures such as Markovian queuing networks (Mišić et al, 2014b), linear transmission models of spreading dynamics (Mišić et al, 2015;Worrell et al, 2017), cooperative learning (Tipnis et al, 2018), and diffusion processes based on memory-biased random walks (Masuda et al, 2017), as well as studying asymmetries of communication in large-scale brain networks (Seguin et al, 2019).…”