Spectral mapping of brain functional connectivity from diffusion imaging

Abstract

Understanding the relationship between the dynamics of neural processes and the anatomical substrate of the brain is a central question in neuroscience. On the one hand, modern neuroimaging technologies, such as diffusion tensor imaging, can be used to construct structural graphs representing the architecture of white matter streamlines linking cortical and subcortical structures. On the other hand, temporal patterns of neural activity can be used to construct functional graphs representing temporal correlations between brain regions. Although some studies provide evidence that whole-brain functional connectivity is shaped by the underlying anatomy, the observed relationship between function and structure is weak, and the rules by which anatomy constrains brain dynamics remain elusive. In this article, we introduce a methodology to map the functional connectivity of a subject at rest from his or her structural graph. Using our methodology, we are able to systematically account for the role of structural walks in the formation of functional correlations. Furthermore, in our empirical evaluations, we observe that the eigenmodes of the mapped functional connectivity are associated with activity patterns associated with different cognitive systems.

Structural optimality and neurogenetic expression mediate functional dynamics in the human brain

“…Next, we probed the capacity of the optimal connections to predict whole‐brain functional connectivity (Figure ‐Appendix S1). We employed a recently developed algorithm that predicts resting‐state functional connectivity using the eigen‐structure of a structural connectivity matrix (Becker et al, ). Specifically, the predicted functional connectivity matrix was obtained by a polynomial transformation of the structural connectivity matrix utilizing the latter's path information thus allowing us to assess how navigability on paths consisting of optimal (and nonoptimal connections) related to the emerging functional connectivity (Figure ‐Appendix S1 and Methods).…”

“…The higher the order of the polynomial transformation, the longer the paths that are considered for prediction. Based on previous work (Becker et al, ), we chose k = 5 as our order of interest. Specifically, for this k the algorithm produces maximal correspondence between predicted and real functional connectivity matrices while the prediction accuracy plateaus when considering higher‐order (k > 5) polynomial transformations.…”

“…We next used structural‐to‐functional matrix association in order to identify the extent to which optimal connections can predict functional connectivity. The prediction algorithm utilized path information embedded into the structural connectivity matrix by expressing functional connectivity as a weighted combination of the powers of the matrix, each one associated with paths of different lengths (Becker et al, ). Remarkably, we found that optimal connections were predictive of whole‐brain resting‐state functional connectivity.…”

“…In turn, we attempted to verify that information communicated upon optimal structural connections could explain functional connectivity. We employed an algorithm that predicted resting‐state functional connectivity by utilizing paths of a structural network (Becker et al, ). Using this method we showed that optimal structural connections were essential for the accurate prediction of resting‐state functional connectivity compared to using the features of the entire structural network, On the other hand, we hypothesized that task‐based connectivity (in this case during a working memory experiment) would be better predicted by nonoptimal connections.…”

A Framework to Control Functional Connectivity in the Human Brain

“…Today, common measures of functional connectivity rely on resting-state functional magnetic resonance imaging (rs-fMRI) timeseries to quantify the level of correlated activity between brain regions. The relationships between structural and functional connectivity have recently received considerable attention [3], [4], and the tantalizing idea of controlling functional states by leveraging or modifying brain structure has given birth to a new, thrilling, field of research [5]- [7].…”