Importance of self-connections for brain connectivity and spectral connectomics
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ORIGINAL ARTICLE
Importance of self-connections for brain connectivity and spectral connectomics Xiao Gao1,2,3 · P. A. Robinson2,3 Received: 30 June 2020 / Accepted: 2 November 2020 / Published online: 26 November 2020 © The Author(s) 2020
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. Keywords Brain connectivity · Eigenmodes analysis · FMRI · Neural field theory · Self-connections
1 Introduction Communicated by Karl Friston. This work was supported by the Australian Research Council Center of Excellence Grant CE140100007, the Australian Research Council Laureate Fellowship Grant FL1401000225, and a McKenzie Fellowship from The University of Melbourne.
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P. A. Robinson [email protected] Xiao Gao [email protected]
1
Department of Biomedical Engineering, University of Melbourne, Parkville, VIC 3052, Australia
2
School of Physics, The University of Sydney, Sydney, NSW 2006, Australia
3
Center of Excellence for Integrative Brain Function, The University of Sydney, Sydney, NSW 2006, Australia
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