Cognitive function, disease burden and the structural connectome in systemic lupus erythematosus

Wiseman, Stewart; Bastin, Mark E.; Amft, E N; Belch, J. J. F.; Ralston, Stuart H.; Wardlaw, Joanna M.

doi:10.1177/0961203318772666

Cited by 15 publications

(12 citation statements)

References 32 publications

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“…Furthermore, consistency has been found in connectivity profiles across field strength (Zhan et al., 2013). Additionally, 1.5T scanning is still widely used in both clinical and large prospective cohort studies (Wiseman et al., 2018a, 2018b; Alloza et al., 2018; Evans et al., 2018).…”

Section: Discussionmentioning

confidence: 99%

Hierarchical complexity of the adult human structural connectome

et al. 2019

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The structural network of the human brain has a rich topology which many have sought to characterise using standard network science measures and concepts. However, this characterisation remains incomplete and the non-obvious features of this topology have largely confounded attempts towards comprehensive constructive modelling. This calls for new perspectives. Hierarchical complexity is an emerging paradigm of complex network topology based on the observation that complex systems are composed of hierarchies within which the roles of hierarchically equivalent nodes display highly variable connectivity patterns. Here we test the hierarchical complexity of the human structural connectomes of a group of seventy-nine healthy adults. Binary connectomes are found to be more hierarchically complex than three benchmark random network models. This provides a new key description of brain structure, revealing a rich diversity of connectivity patterns within hierarchically equivalent nodes. Dividing the connectomes into four tiers based on degree magnitudes indicates that the most complex nodes are neither those with the highest nor lowest degrees but are instead found in the middle tiers. Spatial mapping of the brain regions in each hierarchical tier reveals consistency with the current anatomical, functional and neuropsychological knowledge of the human brain. The most complex tier (Tier 3) involves regions believed to bridge high-order cognitive (Tier 1) and low-order sensorimotor processing (Tier 2). We then show that such diversity of connectivity patterns aligns with the diversity of functional roles played out across the brain, demonstrating that hierarchical complexity can characterise functional diversity strictly from the network topology.

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Section: Discussionmentioning

confidence: 99%

Hierarchical complexity of the adult human structural connectome

et al. 2019

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“…A previous study on global network connectivity measures and cognition in SLE failed to find a relation between structural network connectivity and disease activity 12 whilst another found a correlation between some regional network properties and disease activity in frontal, occipital and cingulum regions in a sample half the size of the former. 13 Our voxel-based analyses showed locations where all global network measures and disease indicators, including disease activity, were associated with brain lesions in this patient group, and where connectivity patterns differed from those in a control population.…”

Section: Discussionmentioning

confidence: 79%

“…Previous work that used connectomics in SLE found global network measures related to cognitive abilities and clinical systemic damage, but not active disease. 12 Another study found that brain connectivity networks in SLE patients had decreased efficiency and increased characteristic path length compared to healthy controls. 13 Complementary to the previously mentioned study, 12 regional network degree and nodal efficiency in frontal, occipital and cingulum regions negatively correlated with disease activity in this smaller SLE cohort.…”

Section: Introductionmentioning

confidence: 99%

“…12 Another study found that brain connectivity networks in SLE patients had decreased efficiency and increased characteristic path length compared to healthy controls. 13 Complementary to the previously mentioned study, 12 regional network degree and nodal efficiency in frontal, occipital and cingulum regions negatively correlated with disease activity in this smaller SLE cohort. 13 Another study also found abnormal global efficiency and network path length in SLE patients compared to controls despite similar functional hub connectivity measurements.…”

Section: Introductionmentioning

confidence: 99%

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Brain network reorganisation and spatial lesion distribution in systemic lupus erythematosus

Hernández

Smith

Amft

et al. 2020

Lupus

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Objective This work investigates network organisation of brain structural connectivity in systemic lupus erythematosus (SLE) relative to healthy controls and its putative association with lesion distribution and disease indicators. Methods White matter hyperintensity (WMH) segmentation and connectomics were performed in 47 patients with SLE and 47 healthy age-matched controls from structural and diffusion MRI data. Network nodes were divided into hierarchical tiers based on numbers of connections. Results were compared between patients and controls to assess for differences in brain network organisation. Voxel-based analyses of the spatial distribution of WMH in relation to network measures and SLE disease indicators were conducted. Results Despite inter-individual differences in brain network organization observed across the study sample, the connectome networks of SLE patients had larger proportion of connections in the peripheral nodes. SLE patients had statistically larger numbers of links in their networks with generally larger fractional anisotropy weights (i.e. a measure of white matter integrity) and less tendency to aggregate than those of healthy controls. The voxels exhibiting connectomic differences were coincident with WMH clusters, particularly the left hemisphere’s intersection between the anterior limb of the internal and external capsules. Moreover, these voxels also associated more strongly with disease indicators. Conclusion Our results indicate network differences reflective of compensatory reorganization of the neural circuits, reflecting adaptive or extended neuroplasticity in SLE.

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“…Furthermore, consistency has been found in connectivity profiles across field strength 55 . Additionally, 1.5T scanning is still widely used in both clinical and large prospective cohort studies 40,[56][57][58] .…”

Section: Discussionmentioning

confidence: 99%