Macroscale coupling between structural and effective connectivity in the mouse brain

Abstract

How the emergent functional connectivity (FC) relates to the underlying anatomy (structural connectivity, SC) is one of the biggest questions of modern neuroscience. At the macro-scale level, no one-to-one correspondence between structural and functional links seems to exist. And we posit that to better understand their coupling, two key aspects should be taken into account: the directionality of the structural connectome and the limitations of describing network functions in terms of FC. Here, we employed an accurate directed SC of the mouse brain obtained by means of viral tracers, and related it with single-subject effective connectivity (EC) matrices computed by applying a recently developed DCM to whole-brain resting-state fMRI data. We analyzed how SC deviates from EC and quantified their couplings by conditioning both on the strongest SC links and EC links. We found that when conditioning on the strongest EC links, the obtained coupling follows the unimodal-transmodal functional hierarchy. Whereas the reverse is not true, as there are strong SC links within high-order cortical areas with no corresponding strong EC links. This mismatch is even more clear across networks. Only the connections within sensory motor networks align both in terms of effective and structural strength.

Structurally informed resting-state effective connectivity recapitulates cortical hierarchy

“…Importantly, this study provides, to our knowledge, the first demonstration of such a network-dependent modulation of the relationship between structural and effective connectivity in humans (see ref. 16 for a related study in mice), demonstrating the predictive validity of the hierarchical empirical Bayes framework, but also hinting at a deeper organizational principle. Namely, that the principal, unimodal-transmodal gradient of functional connectivity may fundamentally be explained in terms of the constraints that structural connectivity exerts on effective connectivity.…”

“…First, a Bayesian approach that involves constraining the inversion of generative models with structural connectivity-based (i.e., structure-based) priors. [13][14][15][16][17][18][19][20][21][22] Second, a mechanistic approach, via which structural connectivity is incorporated directly into a generative model's equations (rather than being incorporated into priors over the equations' parameters). [23][24][25] Finally, a data-driven machine learning (ML) approach, which leverages various ML techniques to infer a map of directed interactions from both structural and functional connectivity taken together.…”

Controlling target brain regions by optimal selection of input nodes

“…We proposed an approach to brain controllability based on effective connectivity (EC) inferred from fMRI, instead of structural connectivity (SC) as in the standard approach. To what extent EC depends on the underlying SC is an open question [ 63 ]. The EC model is in principle better suited to represent activity propagation, but we are not aware of previous studies presenting a thorough analysis of controllability properties of whole-brain EC networks.…”

Controlling target brain regions by optimal selection of input nodes

“…We proposed an approach to brain controllability based on effective connectivity (EC) inferred from fMRI, instead of structural connectivity (SC) as in the standard approach. To what extent EC depends on the underlying SC is an open question [60]. The EC model is in principle better suited to represent activity propagation, but we are not aware previous studies presenting a June thorough analysis of controllability properties of whole-brain EC networks.…”