Abstract
Bridging the gap between symmetric, direct white matter brain connectivity and neural dynamics that are often asymmetric and polysynaptic may offer insights into brain architecture, but this remains an unresolved challenge in neuroscience. Here, we used the graph Laplacian matrix to simulate symmetric and asymmetric high-order diffusion processes akin to particles spreading through white matter pathways. The simulated indirect structural connectivity outperformed direct as well as absent anatomical information in sculpting effective connectivity, a measure of causal and directed brain dynamics. Crucially, an asymmetric diffusion process determined by the sensitivity of the network nodes to their afferents best predicted effective connectivity. The outcome is consistent with brain regions adapting to maintain their sensitivity to inputs within a dynamic range. Asymmetric network communication models offer a promising perspective for understanding the relationship between structural and functional brain connectomes, both in normalcy and neuropsychiatric conditions.
Highlights
Multimodal neuroimaging analyses are expected to improve our understanding of structurefunction relationships in the brain (Toga et al, 2006; Honey et al, 2010; Sporns, 2014); drawing on measures of structural, functional, and effective brain connectivity (Sporns et al, 2000; Park & Friston, 2013)
White matter (WM) pathways are sufficient for communication between brain regions, but functional brain dynamics can be mediated through polysynaptic connections (Figure 1)
Anatomical Network Diffusion Outperformed Direct Pathways in Sculpting Effective Connectivity We assessed the value of simulated indirect anatomical connectivity afforded by network diffusion under the graph Laplacian (GL) for sculpting effective connectivity, relative to models informed by direct structural connectivity and to dynamic causal models (DCMs) without anatomical information
Summary
Multimodal neuroimaging analyses are expected to improve our understanding of structurefunction relationships in the brain (Toga et al, 2006; Honey et al, 2010; Sporns, 2014); drawing on measures of structural, functional, and effective brain connectivity (Sporns et al, 2000; Park & Friston, 2013). Previous studies suggested the direct structural pathways inferred using dMRI account for only about 55% of measured resting-state functional connectivity patterns (Koch et al, 2002; Honey et al, 2009; Deligianni et al, 2011; Becker et al, 2016). Current measures of anatomical and of resting-state functional connectivity are symmetric in the sense that they do not enable an assessment of whether one orientation of a pathway may be more prominent than the inverse (Friston, 2011). Models of effective connectivity such as dynamic causal models (DCMs) indicate the weights of specific directions of interaction (Friston et al, 2003), and recent data across species suggest that information about directed, asymmetric connectivity may more appropriately reflect brain architecture (Kale et al, 2018; Avena-Koenigsberger et al, 2019; Seguin et al, 2019)
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