Functional and effective networks inferred from time series are at the core of network neuroscience. Since it is common practice to compare network properties between patients and controls, it is crucial for inferred network models to reflect key underlying structural properties. However , even a few spurious links severely distort the shortest-path length and derived measuresincluding the small-world coefficient, providing misleading insights into the macroscopic topology of the network. This poses a challenge for functional connectomesbased on correlation, whose transitivity necessarily induce numerous false positives . We study how false positives bias the properties of the networks inferred by bivariate and multivariate algorithms . Mutual information , bivariate transfer entropy, and multivariate transfer entropy are respectively used for inferring functional, directed-functional, and effective networks . The validation is performed on synthetic ground truth networks with 100--200 nodes and various topologies (regular lattice, small-world, random, scale-free, modular) , as well as on a real macaque connectome. The time series of node activity are simulated via autoregressive dynamics .  We find that multivariate transfer entropy captures key properties of all network structures for longer time series. Bivariate methods can achieve higher recall (sensitivity) for shorter time series ; however, as available data increases, they are unable to control false positives (lower specificity)  . This leads to overestimated clustering, small-world, and rich-club coefficients, underestimated shortest path lengths and hub centrality, and fattened degree distribution tails. Caution should therefore be used when interpreting network properties of functional connectomes obtained via correlation or pairwise statistical dependence measures, rather than more holistic (yet data-hungry) multivariate models.
Functional and effective networks inferred from time series are at the core of network neuroscience. Interpreting their properties requires inferred network models to reflect key underlying structural features; however , even a few spurious links can distort network measures, challenging functional connectomes . We study the extent to which micro- and macroscopic properties of underlying networks can be inferred by algorithms based on mutual information and bivariate/ multivariate transfer entropy . The validation is performed on two macaque connectomes and on synthetic networks with various topologies (regular lattice, small-world, random, scale-free, modular) . Simulations are based on a neural mass model and on autoregressive dynamics (employing Gaussian estimators for direct comparison to functional connectivity and Granger causality). We find that multivariate transfer entropy captures key properties of all networks for longer time series. Bivariate methods can achieve higher recall (sensitivity) for shorter time series but are unable to control false positives (lower specificity) as available data increases . This leads to overestimated clustering, small-world, and rich-club coefficients, underestimated shortest path lengths and hub centrality, and fattened degree distribution tails. Caution should therefore be used when interpreting network properties of functional connectomes obtained via correlation or pairwise statistical dependence measures, rather than more holistic (yet data-hungry) multivariate models.
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Since it is common practice to compare network properties between patients and controls, it is crucial for
Interpreting their properties requires
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properties. However
features; however
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severely distort the shortest-path length and derived measuresincluding the small-world coefficient, providing misleading insights into the macroscopic topology of the network. This poses a challenge for functional connectomesbased on correlation, whose transitivity necessarily induce numerous false positives
can distort network measures, challenging functional connectomes
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how false positives bias the properties of the networks inferred by bivariate and multivariate algorithms . Mutual information , bivariate transfer entropy, and
the extent to which micro- and macroscopic properties of underlying networks can be inferred by algorithms based on mutual information and bivariate/
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are respectively used for inferring functional, directed-functional, and effective networks
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synthetic ground truth networks with 100--200 nodes and
two macaque connectomes and on synthetic networks with
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, as well as on a real macaque connectome. The time series of node activity are simulated via autoregressive dynamics
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Simulations are based on a neural mass model and on autoregressive dynamics (employing Gaussian estimators for direct comparison to functional connectivity and Granger causality).
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network structures
networks
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; however, as available data increases, they
but
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as available data increases