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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“Recent studies have corroborated this behavior also in human structural connectivity (Deco & Kringelbach, 2020; Deco et al., 2021; Roberts, Perry, Roberts, Mitchell, & Breakspear, 2017), although the EDR cannot capture all details of white matter connectivity, as this is not isotropic (Betzel &…”
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A single-mode associates global patterns of brain network structure and behavior across the human lifespan
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Influential
Big Data, Small Bias: Harmonizing Diffusion MRI ‐Based Structural Connectomes to Mitigate Site‐Related Bias in Data Integration
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Delayed threshold and spatial diffusion in k-core percolation induced by long-range connectivity
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“In contrast, the average correlation matrix obtained from a larger number of participants can mitigate the potentially noisy connections (Roberts et al. 2017; Betzel et al. 2019).”
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Structural connectome of the human vestibular, pre-motor, and navigation network
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Gradients in excitability generate hippocampal waves and shape their interactions with cortex
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“Connection strengths within each surface decrease according to an exponential-distance rule (EDR), as widely observed in tracer (Horv´at et al., 2016) and Magnetic Resonance Imaging (MRI)-based (Roberts et al., 2017) studies (Fig.”
Self-citation
Structural connectome dimension shapes brain dynamics in health and disease
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Structural network topology and cognitive control in very preterm born young adults
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Developmental deviations of structural connectivity in youths with ADHD predict symptom and treatment outcomesPreprint
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Influential
Overlooked weak structural connections support human cognition under nonlinear connectome scaling
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The global communication architecture of the human brain transcends the subcortical - cortical - cerebellar subdivisionsPreprint
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A novel method for estimating connectivity‐based parcellation of the human brain from diffusion MRI : Application to an aging cohort
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Small-World Propensity Reveals the Frequency Specificity of Resting State Networks
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“In the main analysis, we retained 50% of connection weights with the lowest variability across participants (Roberts et al., 2017).”
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“Consistency-based thresholding avoids the problem by a different route (Roberts et al. 2017); supply matching offers a check applicable to existing pipelines unchanged.”
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Variations of structural–functional coupling in post-traumatic stress disorder are associated with underlying molecular and transcriptional features
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“Second, a group-representative connec-tome was derived by selecting the top 30% of edges with the lowest coefficients of variation across participants [54, 55].”
Structure-function coupling in the human brainstem
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Influential
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“The selection of FN > 3 as the effective connection cri - terion was based on methodological rigor: it aims to filter noise, reduce false positives, and enhance network reliability (Bauer et al., 2017; Buchanan et al., 2020; Roberts et al., 2017; Yeh et al., 2021).”
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“Furthermore, the effect of other thresholding approaches, such as consistency-thresholding (Roberts et al., 2017), can also be investigated.”
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Quantifying biases in reconstructed brain networks
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“…(see Supplementary Algorithm S-1), we reconstruct binary adjacency matrices of structural networks using a consistency-based thresholding approach 33 (see Supplementary Algorithm S-2), while for functional networks we use the percolation threshold approach 34 (see Supplementary Algorithm S-3).”
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“While thresholding was initially believed to be essential for revealing clear small-world topologies, the utility of weak connections has been increasingly recognized as they reflect unique individual differences [15], [18].”
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“Since one of the limitations of tractography is that it overestimates shorter connections, the hard thresholding favours short-range links in detriment of long-range ones 80,81 .”
Constructing representative group networks from tractography: lessons from a dynamical approach
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“To obtain reliable connectivity between brain regions, we adopted a group-consistency thresh-olding method [25] to remove spurious connections in the probabilistic tracking network.”
Quantifying sex differences in brain network topology by aggregating nodal centrality rankings
Chen, W., Zhan, L., Luo, Y., Qiu, J., & Jia, T. (2024) · doi:10.48550/arxiv.2410.05923
“First, we use the Power Atlas and Pearson correlation to construct brain networks, but recent research show that thresholding and weighting schemes [7, 46, 48] and the number of nodes and edges [40] significantly impact network topology.”
Method
Elevated Activity in Left Homologous Music Circuits is Maladaptive for Music Perception but Mediated by Decoupled Structure and FunctionPreprint
Wang, Y., Jin, Z.-S., Huyang, S., Lian, Q., & Wu, D.-X. (2024) · bioRxiv · doi:10.1101/2024.02.04.578219
“Further, a consistency-based method (coefficient of variation of each edge) was applied (Roberts et al., 2017) to reduce spurious connections induced by probabilistic tractography because the criteria contribute to retain consistent both short-and long-range connections across subject.”
Method
Group-common and individual-specific effects of structure-function coupling in human brain networks with graph neural networks
Chen, P., Yang, H., Zheng, X., Jia, H., Hao, J., Xu, X.-Y., Li, C., He, X.-S., Chen, R., Okubo, T. S., et al. (2023) · bioRxiv · doi:10.1101/2023.11.22.568257
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Method
Disconnectome Associated with Progressive Ischemic Periventricular White Matter LesionsPreprint
Li, Z., Dolui, S., Habes, M., Bassett, D. S., Wolk, D. A., & Detre, J. A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2019.12.24.888081
Enhanced prediction of cognitive function using aging-sensitive networks within the human structural connectomePreprint
Madole, J. W., Ritchie, S. J., Cox, S. R., Buchanan, C. R., Hernández, M. V., Maniega, S. M., Wardlaw, J. M., Harris, M. A., Bastin, M. E., Deary, I. J., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2019.12.13.875559
Characterising disease-related and developmental changes in correlation-derived structural and functional brain networks
Váša, F. (2018) · doi:10.17863/cam.25157
“These approaches, initially developed in the context of sparse connectomes obtained from deterministic tractography, have been generalised to dense connectomes from probabilistic tractography by retaining consistently strong (rather than consistently present) edges (Roberts et al., 2017).”
Background
Multifactorial Dynamics of White Matter Connectivity During AdolescencePreprint
Tunç, B., Parker, D., Shinohara, R. T., Elliott, M. A., Ruparel, K., Gur, R. E., Gur, R. C., & Verma, R. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/215152
“In order to demonstrate the robustness of the reported results to such confounding factors, we used consistency based thresholding [27] to prune possibly spurious connections and repeated our experiments with different network density (threshold) levels.”
Method
Structural connectomics: Where we are and where we should be?
Verma, R., & Osmanlıoğlu, Y. (2021) · Advances in magnetic resonance technology and applications · doi:10.1016/b978-0-12-816633-8.00011-9

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: Roberts, James A.
Perry, Alistair
Roberts, Gloria
Mitchell, Philip B.
Breakspear, Michael
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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Consistency-based thresholding of the human connectome

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