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Cited by 198Advanced Neuroimaging Techniques and ApplicationsFunctional Brain Connectivity StudiesComplex Network Analysis Techniques

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Consistency-based thresholding of the human connectome

Roberts, James A., Perry, Alistair, Roberts, Gloria, Mitchell, Philip B., Breakspear, Michael·2017·NeuroImage
DOI 10.1016/j.neuroimage.2016.09.053
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Abstract

Densely seeded probabilistic tractography yields weighted networks that are nearly fully connected, hence containing many spurious fibers. It is thus necessary to prune spurious connections from probabilistically-derived networks to obtain a more reliable overall estimate of the connectivity. A standard method is to threshold by weight, keeping only the strongest edges. Here, by measuring the consistency of edge weights across subjects, we propose a new thresholding method that aims to reduce the rate of false-positives in group-averaged connectivity matrices. Close inspection of the relationship between consistency, weight, and distance suggests that the most consistent edges are in fact those that are strong for their length, rather than simply strong overall. Hence retaining the most consistent edges preserves more long-distance connections than traditional weight-based thresholding, which penalizes long connections for being weak regardless of anatomy. By comparing our thresholded networks to mouse and macaque tracer data, we also show that consistency-based thresholding exhibits the species-invariant exponential decay of connection weights with distance, while weight-based thresholding does not. We also show that consistency-based thresholding can be used to identify highly consistent and highly inconsistent subnetworks across subjects, enabling more nuanced analyses of group-level connectivity than just the mean connectivity.

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Metadata

Authors
Roberts, J. A., Perry, A., Roberts, G., Mitchell, P. B., & Breakspear, M.
Affiliations
  • QIMR Berghofer Medical Research Institute (AU)
  • Black Dog Institute (AU)
Corresponding author
Roberts, James A.
ORCID
Venue
NeuroImage
Publisher
Elsevier BV
Published
2017-01
Volume
145
Issue
Pt A
Pages
118-129
ISSN
1053-8119 (ISSN-L)
DOI
10.1016/j.neuroimage.2016.09.053
OpenAlex
W2523692751
Cited by
189
References
50
Citations by year
2026: 18 · 2025: 19 · 2024: 19 · 2023: 23 · 2022: 25 · 2021: 20 · 2020: 19 · 2019: 17 · 2018: 20 · 2017: 8
FWCI
13.30
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • NeuroImage (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Health and Medical Research Council (1037196)
  • Australian Research Council (CE140100007)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
Keywords
human connectome, probabilistic tractography, long-distance connections, false positives, white matter connectivity
MeSH terms
Adolescent, Adult, Animals, Female, Humans, Image Processing, Computer-Assisted, Male, Nerve Net, Neural Pathways, Young Adult, Diffusion Tensor Imaging, Connectome, White Matter

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