ArticleNetwork neuroscience (Cambridge, Mass.)2019
Distance-dependent consensus thresholds for generating group-representative structural brain networks.
Article in Network neuroscience (Cambridge, Mass.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 103 papers.
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103 citing papers in PubMed.
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- Global network and local vulnerabilities underlie brain atrophy across Parkinson's disease stages.Brain : a journal of neurology · 2026Article
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- Multiscale structural connectome eigenmodes constrain human brain functional dynamics.Communications biology · 2026Article
- Neuromorphic hierarchical modular reservoirs.Nature communications · 2026Article
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- Structure-function dynamics in healthy cognitive aging: a graph signal processing approach.Brain structure & function · 2026Article
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- White matter pathways mediating dorsolateral prefrontal TMS therapy for depression.Nature neuroscience · 2026Article
- Dynamic brain reorganization and the role of motivation during the first year of foreign language learning: A longitudinal study.Psychonomic bulletin & review · 2026Article
- Structural signatures of synergy and redundancy in human brain function.bioRxiv : the preprint server for biology · 2026Article
- Structure-function coupling in the human brainstem.bioRxiv : the preprint server for biology · 2026Article
- The role of white matter myelin in structural-functional network coupling.Communications biology · 2026Article
- Controlling the human connectome with spatially diffuse input signals.Communications biology · 2026Article
- Sulcal Widening in Schizophrenia Maps onto Sulcal Hubs and Energy-Synaptic Genes.bioRxiv : the preprint server for biology · 2026Article
- Variation in high-amplitude events across the human lifespan.Network neuroscience (Cambridge, Mass.) · 2026Article
43 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
Large-scale structural brain networks encode white matter connectivity patterns among distributed brain areas. These connection patterns are believed to support cognitive processes and, when compromised, can lead to neurocognitive deficits and maladaptive behavior. A powerful approach for studying the organizing principles of brain networks is to construct group-representative networks from multisubject cohorts. Doing so amplifies signal to noise ratios and provides a clearer picture of brain network organization. Here, we show that current approaches for generating sparse group-representative networks overestimate the proportion of short-range connections present in a network and, as a result, fail to match subject-level networks along a wide range of network statistics. We present an alternative approach that preserves the connection-length distribution of individual subjects. We have used this method in previous papers to generate group-representative networks, though to date its performance has not been appropriately benchmarked and compared against other methods. As a result of this simple modification, the networks generated using this approach successfully recapitulate subject-level properties, outperforming similar approaches by better preserving features that promote integrative brain function rather than segregative. The method developed here holds promise for future studies investigating basic organizational principles and features of large-scale structural brain networks.
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