Hierarchical complexity of the adult human structural connectome

Smith, Keith; Cox, Simon R.; Hernández, María del C. Valdés; Wiseman, Stewart; Escudero, Javier; Sudlow, Cathie

doi:10.1016/j.neuroimage.2019.02.028

Cited by 18 publications

(34 citation statements)

References 58 publications

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“…Comparing each population against their configuration model (first row of figure 4), we can see how in preterm connectomes the structure is not yet strongly distinguishable from random, whereas in term born babies, a more complex hierarchical structure is already present. As expected from previous results, this structure is clearly established in the adults [16].…”

Section: Hierarchical Complexitysupporting

confidence: 92%

“…Tier 2 in adults consistently contained the post central, precentral, rostral middle frontal gyrii, and the insulate, caudate and hippocampus. As in previous findings [16], some of these can be broadly classified as lower order sensory processing regions. As above, the fact that there is poorer differentiation between HC tiers in neonates at this stage reflects the fact that there is a huge amount of development still to come.…”

Section: Tier Analysissupporting

confidence: 60%

“…Once the global connectivity patterns were assessed, we then performed a more refined analysis of HC through different degree strengths in the network. Previous work [16] split each network into four tiers based on maximum degree magnitudes, where each tier comprised a rounded 25% of degrees. The first tier comprised nodes in the top 25% of degrees in the network, the second tier comprised of nodes with the next 25% of largest degrees, and so on.…”

Section: Hierarchical Tiersmentioning

confidence: 99%

“…Group-aggregated degree distributions revealed the neonatal and adult connectomes comprised of four tiers of connectivity, with a heavy-tailed distribution on the right tail, meaning that there are a few nodes (hubs) that are more densely connected compared to others [7]. The original definition of hierarchical tiers was based on quartiles of the maximum degree, which were defined strictly for purposes of a more fine-grained analysis without any particular hypothesis of functionality [16]. These current findings illuminate why that choice turned out to have evident biological meaning.…”

Section: Tier Analysismentioning

confidence: 99%

“…Hierarchical complexity (HC) is a measure of the diversity of connectivity patterns found across hierarchically equivalent (same degree) network nodes, and it reflects the hierarchical and functionally diverse capacities of the human brain. HC provides a tractable signature of brain network architecture in the adult connectome [16,17]: four hierarchical tiers broadly comprise different categories of functional processing -cognitive, sensorimotor, integrative, and memory and emotion [16]. To investigate the possibility that HC is established in the perinatal period, and that alterations in HC patterning might characterize atypical brain development in preterm infants, we have developed an approach to assess HC in the newborn period.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Hierarchical Complexitysupporting

confidence: 92%

Section: Tier Analysissupporting

confidence: 60%

Section: Hierarchical Tiersmentioning

confidence: 99%

Section: Tier Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Hierarchical complexity of the macro-scale neonatal brain

Blesa

Galdi

Cox³

et al. 2020

Preprint

Self Cite

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Structural brain network lateralization across childhood and adolescence

Craig

Geeraert

Kinney‐Lang

et al. 2022

Human Brain Mapping

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Developmental lateralization of brain function is imperative for behavioral specialization, yet few studies have investigated differences between hemispheres in structural connectivity patterns, especially over the course of development. The present study compares the lateralization of structural connectivity patterns, or topology, across children, adolescents, and young adults. We applied a graph theory approach to quantify key topological metrics in each hemisphere including efficiency of information transfer between regions (global efficiency), clustering of connections between regions (clustering coefficient [CC]), presence of hub‐nodes (betweenness centrality [BC]), and connectivity between nodes of high and low complexity (hierarchical complexity [HC]) and investigated changes in these metrics during development. Further, we investigated BC and CC in seven functionally defined networks. Our cross‐sectional study consisted of 211 participants between the ages of 6 and 21 years with 93% being right‐handed and 51% female. Global efficiency, HC, and CC demonstrated a leftward lateralization, compared to a rightward lateralization of BC. The sensorimotor, default mode, salience, and language networks showed a leftward asymmetry of CC. BC was only lateralized in the salience (right lateralized) and dorsal attention (left lateralized) networks. Only a small number of metrics were associated with age, suggesting that topological organization may stay relatively constant throughout school‐age development, despite known underlying changes in white matter properties. Unlike many other imaging biomarkers of brain development, our study suggests topological lateralization is consistent across age, highlighting potential nonlinear mechanisms underlying developmental specialization.

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Normalised degree variance

Smith

Escudero

2020

Appl Netw Sci

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Finding graph indices which are unbiased to network size and density is of high importance both within a given field and across fields for enhancing comparability of modern network science studies. The degree variance is an important metric for characterising network degree heterogeneity. Here, we provide an analytically valid normalisation of degree variance to replace previous normalisations which are either invalid or not applicable to all networks. It is shown that this normalisation provides equal values for graphs and their complements; it is maximal in the star graph (and its complement); and its expected value is constant with respect to density for Erdös-Rényi (ER) random graphs of the same size. We strengthen these results with model observations in ER random graphs, random geometric graphs, scale-free networks, random hierarchy networks and resting-state brain networks, showing that the proposed normalisation is generally less affected by both network size and density than previous normalisation attempts. The closed form expression proposed also benefits from high computational efficiency and straightforward mathematical analysis. Analysis of 184 real-world binary networks across different disciplines shows that normalised degree variance is not correlated with average degree and is robust to node and edge subsampling. Comparisons across subdomains of biological networks reveals greater degree heterogeneity among brain connectomes and food webs than in protein interaction networks.

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Hierarchical complexity of the adult human structural connectome

Cited by 18 publications

References 58 publications

Hierarchical complexity of the macro-scale neonatal brain

Hierarchical complexity of the macro-scale neonatal brain

Structural brain network lateralization across childhood and adolescence

Normalised degree variance

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