Abstract

Spectral analysis and neural field theory are used to investigate the role of local connections in brain connectivity matrices (CMs) that quantify connectivity between pairs of discretized brain regions. This work investigates how the common procedure of omitting such self-connections (i.e., the diagonal elements of CMs) in published studies of brain connectivity affects the properties of functional CMs (fCMs) and the mutually consistent effective CMs (eCMs) that correspond to them. It is shown that retention of self-connections in the fCM calculated from two-point activity covariances is essential for the fCM to be a true covariance matrix, to enable correct inference of the direct total eCMs from the fCM, and to ensure their compatibility with it; the deCM and teCM represent the strengths of direct connections and all connections between points, respectively. When self-connections are retained, inferred eCMs are found to have net inhibitory self-connections that represent the local inhibition needed to balance excitation via white matter fibers at longer ranges. This inference of spatially unresolved connectivity exemplifies the power of spectral connectivity methods, which also enable transformation of CMs to compact diagonal forms that allow accurate approximation of the fCM and total eCM in terms of just a few modes, rather than the full N^2 CM entries for connections between N brain regions. It is found that omission of fCM self-connections affects both local and long-range connections in eCMs, so they cannot be omitted even when studying the large-scale. Moreover, retention of local connections enables inference of subgrid short-range inhibitory connectivity. The results are verified and illustrated using the NKI-Rockland dataset from the University of Southern California Multimodal Connectivity Database. Deletion of self-connections is common in the field; this does not affect case-control studies but the present results imply that such fCMs must have self-connections restored before eCMs can be inferred from them.

Highlights

  • A typical method to analyze brain connectivity is through connectivity matrices (CMs), which contain the strengths of connections between pairs of discretized regions of interest (RoIs), which are usually chosen to be functionally homogeneous and spatially contiguous; for example, RoIs can be chosen to be voxels in functional magnetic resonance imaging, subdivisions of specific Brodman areas, or based on the subject’s own anatomy (Friston et al 2006; Poldrack 2007)

  • These negative values cannot be used to validly calculate Effective connectivity matrices (eCMs) (Robinson et al 2014). It is mathematically essential for self-connections to be retained. (iii) Because the functional CMs (fCMs), direct effective CMs (deCMs), and total effective CMs (teCMs) are symmetric and commute, they share the same eigenvectors and can be represented in closely related diagonalized forms

  • Deletion of fCM self-connections implies widespread long-range differences in the corresponding effective connectivities and removes the ability to infer short-range net inhibitory connections at subgrid scales. (Note that these include the sub-mm Mexicanhat structure of very short range excitatory connections with inhibitory surround, plus longer excitatory connections out to ∼ 2 cm but within the same region of interest.) (iv) We have decomposed the fCM and teCM via eigenmode analysis, retaining self-connections and confirming that the first few eigenmodes suffice to reproduce the main features of C (Robinson et al 2014, 2016) and T

Read more

Summary

Introduction

A typical method to analyze brain connectivity is through connectivity matrices (CMs), which contain the strengths of connections between pairs of discretized regions of interest (RoIs), which are usually chosen to be functionally homogeneous and spatially contiguous; for example, RoIs can be chosen to be voxels in functional magnetic resonance imaging (fMRI), subdivisions of specific Brodman areas, or based on the subject’s own anatomy (Friston et al 2006; Poldrack 2007). The CMs that embody the strengths of direct connections between points in a given brain state are termed direct effective CMs (deCMs), whereas total effective CMs (teCMs) describe the total connectivity between points via both direct and indirect paths (Robinson 2012)

Objectives
Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call