The effect of network thresholding and weighting on structural brain networks in the UK Biobank

Buchanan, Colin R; Bastin, Mark E.; Ritchie, Stuart J.; Liewald, David C.; Madole, James W.; Tucker‐Drob, Elliot M.; Deary, Ian J.; Cox, Simon R.

doi:10.1016/j.neuroimage.2019.116443

Cited by 95 publications

(65 citation statements)

References 64 publications

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“…We thresholded and binarized each individual connectivity matrices to obtain networks with a 30% density. This value is compatible with estimates from animal and human studies and was previously adopted for the study of human brain connectomes ( Roberts et al , 2017 ; Buchanan et al , 2020 ) and for similarly generated networks ( Roberts et al , 2016 ). To ensure that results were not biased to the selected threshold, we ran both the tier modelling and the main HC experiments across a range of thresholds (from 0.2 up to 0.4 in steps of 0.0005), see Supplementary material sections S1 and S2 .…”

Section: Methodssupporting

confidence: 79%

Hierarchical Complexity of the Macro-Scale Neonatal Brain

et al. 2020

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The human adult structural connectome has a rich nodal hierarchy, with highly diverse connectivity patterns aligned to the diverse range of functional specializations in the brain. The emergence of this hierarchical complexity in human development is unknown. Here, we substantiate the hierarchical tiers and hierarchical complexity of brain networks in the newborn period, assess correspondences with hierarchical complexity in adulthood, and investigate the effect of preterm birth, a leading cause of atypical brain development and later neurocognitive impairment, on hierarchical complexity. We report that neonatal and adult structural connectomes are both composed of distinct hierarchical tiers and that hierarchical complexity is greater in term born neonates than in preterms. This is due to diversity of connectivity patterns of regions within the intermediate tiers, which consist of regions that underlie sensorimotor processing and its integration with cognitive information. For neonates and adults, the highest tier (hub regions) is ordered, rather than complex, with more homogeneous connectivity patterns in structural hubs. This suggests that the brain develops first a more rigid structure in hub regions allowing for the development of greater and more diverse functional specialization in lower level regions, while connectivity underpinning this diversity is dysmature in infants born preterm.

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Section: Methodssupporting

confidence: 79%

Hierarchical Complexity of the Macro-Scale Neonatal Brain

et al. 2020

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“…The resulting matrices were then thresholded to a density of 0.3 (consistent with biological evidence that the human brain has a connection density of approximately 30% [46]) and binarized.…”

Section: Tractography and Network Creationmentioning

confidence: 99%

Hierarchical complexity of the macro-scale neonatal brain

Blesa

Galdi

Cox³

et al. 2020

Preprint

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The human adult structural connectome has a rich topology composed of nodal hierarchies containing highly diverse connectivity patterns, aligned to the diverse range of functional specialisations in the brain. The emergence of this hierarchical complexity in human development is unknown. Here, we substantiate the hierarchical tiers and complexity of brain networks in the newborn period, assess correspondences with hierarchical complexity in adulthood, and investigate the effect of preterm birth, a leading cause of neurocognitive impairment and atypical brain development, on hierarchical complexity. We report that the neonatal and adult structural connectomes are both composed of distinct hierarchical tiers. Consistency of ROIs is found at both ends of this hierarchy during early life and in adulthood, but significant differences are evident in intermediate tiers. The neonatal connectome is hierarchically complex in term born neonates, but hierarchically complexity is altered in association with preterm birth. This is mainly due to diversity of connectivity patterns in Tier 3, which is comprised of regions that underlie sensorimotor processing and its integration with cognitive information. For neonates and adults, the highest tier (comprising hub regions) is ordered, rather than complex, with more homogeneous connectivity patterns in structural hubs. This suggests that the brain develops first a more rigid hierarchical structure in hub regions allowing for the development of greater and more diverse functional specialisation in lower level regions, while connectivity underpinning this diversity is dysmature in infants born preterm.

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“…The thresholding of such matrices is an issue of debate nowadays. For example, Buchanan et al (2020) showed that the spurious WM connections reconstructed with probabilistic tractography can be reduced by different thresholding schemes and give better age associations than if the connections were left unthresholded. On the other hand, Civier, Smith, Yeh, Connelly, and Calamante (2019) evaluated the removal of weak connections from structural connectomes and concluded that the removal of those connections is inconsequential for graph theoretical analysis, ultimately advocating against the removal of such connections.…”

Section: Discussionmentioning

confidence: 99%

Predicting MEG resting-state functional connectivity from microstructural information

Messaritaki

Foley

Schiavi

et al. 2021

Network Neuroscience

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Understanding how human brain microstructure influences functional connectivity is an important endeavor. In this work, magnetic resonance imaging data from ninety healthy participants were used to calculate structural connectivity matrices using the streamline count, fractional anisotropy, radial diffusivity and a myelin measure (derived from multi-component relaxometry) to assign connection strength. Unweighted binarized structural connectivity matrices were also constructed. Magnetoencephalography resting-state data from those participants were used to calculate functional connectivity matrices, via correlations of the Hilbert envelopes of beamformer timeseries in the delta, theta, alpha and beta frequency bands. Non-negative matrix factorization was performed to identify the components of the functional connectivity. Shortest-path-length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for each participant. The microstructure-informed algorithms predicted the components of the functional connectivity more accurately than they predicted the total functional connectivity. This provides a methodology to understand functional mechanisms better. The shortest-path-length algorithm exhibited the highest prediction accuracy. Of the weights of the structural connectivity matrices, the streamline count and the myelin measure gave the most accurate predictions, while the fractional anisotropy performed poorly. Overall, different structural metrics paint very different pictures of the structural connectome and its relationship to functional connectivity.

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The effect of network thresholding and weighting on structural brain networks in the UK Biobank

Cited by 95 publications

References 64 publications

Hierarchical Complexity of the Macro-Scale Neonatal Brain

Hierarchical Complexity of the Macro-Scale Neonatal Brain

Hierarchical complexity of the macro-scale neonatal brain

Predicting MEG resting-state functional connectivity from microstructural information

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