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Inferring neural signalling directionality from undirected structural connectomes.

ABSTRACT: Neural information flow is inherently directional. To date, investigation of directional communication in the human structural connectome has been precluded by the inability of non-invasive neuroimaging methods to resolve axonal directionality. Here, we demonstrate that decentralized measures of network communication, applied to the undirected topology and geometry of brain networks, can infer putative directions of large-scale neural signalling. We propose the concept of send-receive communication asymmetry to characterize cortical regions as senders, receivers or neutral, based on differences between their incoming and outgoing communication efficiencies. Our results reveal a send-receive cortical hierarchy that recapitulates established organizational gradients differentiating sensory-motor and multimodal areas. We find that send-receive asymmetries are significantly associated with the directionality of effective connectivity derived from spectral dynamic causal modeling. Finally, using fruit fly, mouse and macaque connectomes, we provide further evidence suggesting that directionality of neural signalling is significantly encoded in the undirected architecture of nervous systems.

SUBMITTER: Seguin C

PROVIDER: S-EPMC6753104 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Inferring neural signalling directionality from undirected structural connectomes.

Seguin Caio C Razi Adeel A Zalesky Andrew A

Nature communications 20190919 1

Neural information flow is inherently directional. To date, investigation of directional communication in the human structural connectome has been precluded by the inability of non-invasive neuroimaging methods to resolve axonal directionality. Here, we demonstrate that decentralized measures of network communication, applied to the undirected topology and geometry of brain networks, can infer putative directions of large-scale neural signalling. We propose the concept of send-receive communicat ...[more]

PMID: 31537787

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Project description:BackgroundWhile graph measures are used increasingly to characterize human connectomes, uncertainty remains in how to use these metrics in a quantitative and reproducible manner. Specifically, there is a lack of community consensus regarding the number of streamlines needed to generate connectomes.PurposeThe purpose was to define the relationship between streamline count and graph-measure value, reproducibility, and repeatability.Study typeRetrospective analysis of previously prospective study.PopulationTen healthy subjects, 70% female, aged 25.3 ± 5.9 years.Field strength/sequenceA 3-T, T1-weighted sequences and diffusion-weighted imaging (DWI) with two gradient strengths (b-values = 1200 and 3000 sec/mm2 , echo time [TE] = 68 msec, repetition time [TR] = 5.4 seconds, 120 slices, field of view = 188 mm2 ).AssessmentA total of 13 graph-theory measures were derived for each subject by generating probabilistic whole-brain tractography from DWI and mapping the structural connectivity to connectomes. The streamline count invariance from changes in mean, repeatability, and reproducibility were derived.Statistical testsPaired t-test with P value <0.05 was used to compare graph-measure means with a reference, intraclass correlation coefficient (ICC) to measure repeatability, and concordance correlation coefficient (CCC) to measure reproducibility.ResultsModularity and global efficiency converged to their reference mean with ICC > 0.90 and CCC > 0.99. Edge count, small-worldness, randomness, and average betweenness centrality converged to the reference mean, with ICC > 0.90 and CCC > 0.95. Assortativity and average participation coefficient converged with ICC > 0.75 and CCC > 0.90. Density, average node strength, average node degree, characteristic path length, average local efficiency, and average clustering coefficient did not converge, though had ICC > 0.90 and CCC > 0.99. For these measures, alternate definitions that converge a reference mean are provided.Data conclusionModularity and global efficiency are streamline count invariant for greater than 6 million and 100,000 streamlines, respectively. Density, average node strength, average node degree, characteristic path length, average local efficiency, and average clustering coefficient were strongly dependent on streamline count.Evidence level1.Technical efficacyStage 1.

Dataset Information

Inferring neural signalling directionality from undirected structural connectomes.

Publications

Inferring neural signalling directionality from undirected structural connectomes.

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