Specificity and robustness of long-distance connections in weighted, interareal connectomes

Abstract

SignificanceInterareal communication occurs along physical pathways. The prevailing hypothesis is that long-distance connections reduce the processing length between brain areas, facilitating efficient communication. We show, in five weighted interareal network datasets, that the correlation of connection weight with distance implies that long-distance connections play only a minor role in reducing path length. Instead, long-distance connections add diversity to brain area inputs and outputs, leading to increasingly complex brain dynamics. These findings help to clarify our understanding of how brain structure contributes to interareal communication.

MAOA‐VNTR genotype affects structural and functional connectivity in distributed brain networks

“…Among the affected node links, we classified more than 80% as edges affecting functional interactions between distant major anatomical subdivisions of the brain ("anisocoupled links"). These observations are consistent with the interpretation that the low expressing MAOA variant preferentially affects long-range connections, which likely serve to control and integrate information across different brain areas with more specialized neural functions enabling a rich set of brain dynamics (Alexander-Bloch et al, 2013;Betzel & Bassett, 2018).…”

“…These observations are consistent with the interpretation that the low expressing MAOA variant preferentially affects long-range connections, which likely serve to control and integrate information across different brain areas with more specialized neural functions enabling a rich set of brain dynamics (Alexander-Bloch et al, 2013;Betzel & Bassett, 2018). Among the affected node links, we classified more than 80% as edges affecting functional interactions between distant major anatomical subdivisions of the brain ("anisocoupled links").…”

“…Specifically, while our DTI networks represent deterministic reconstructions of putative structural fiber tracts, our functional networks represent the stochastic association between the BOLD-fluctuations of brain regions. Moreover, deterministic structural networks are typically sparse, with only 2-10% of the initiated streamlines reaching their target (Betzel & Bassett, 2018;Hagmann et al, 2008), while the functional networks are, in principle, fully connected and show a complex structure of interdependent links, based on the correlative methods by which they are constructed.…”

“…Moreover, deterministic structural networks are typically sparse, with only 2-10% of the initiated streamlines reaching their target(Betzel & Bassett, 2018;Hagmann et al, 2008), while the functional networks are, in principle, fully connected and show a complex structure of interdependent links, based on the correlative methods by which they are constructed. However, the direct comparison of anatomical and functional connections is limited by the inherently different nature of the respective connectivity metrics.…”

A graph representation of functional diversity of brain regions

“…Although the idea of functional specialization or segregation has led to breakthroughs in cognitive neuroscience, it has also been realized that the specific functional attributes of a brain region are related to its connectivity (integration) with other areas of the brain (Cole et al, ; Friston, ; Genon et al, ; Sallet et al, ; Yin et al, ). Specifically, a recent study has argued that long‐range connections are key for the functional diversification of brain regions (Betzel & Bassett, ). In this study, we present a new graph metric (i.e., NDI), via capturing the topological dissimilarity of immediate neighbors, to characterize the functional diversity of brain regions.…”