Is removal of weak connections necessary for graph-theoretical analysis of dense weighted structural connectomes from diffusion MRI?
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Abstract
Recent advances in diffusion MRI tractography permit the generation of dense weighted structural connectomes that offer greater insight into brain organization. However, these efforts are hampered by the lack of consensus on how to extract topological measures from the resulting graphs. Here we evaluate the common practice of removing the graphs' weak connections, which is primarily intended to eliminate spurious connections and emphasize strong connections. Because this processing step requires arbitrary or heuristic-based choices (e.g., setting a threshold level below which connections are removed), and such choices might complicate statistical analysis and inter-study comparisons, in this work we test whether removing weak connections is indeed necessary. To this end, we systematically evaluated the effect of removing weak connections on a range of popular graph-theoretical metrics. Specifically, we investigated if (and at what extent) removal of weak connections introduces a statistically significant difference between two otherwise equal groups of healthy subjects when only applied to one of the groups. Using data from the Human Connectome Project, we found that removal of weak connections had no statistical effect even when removing the weakest ∼70-90% connections. Removing yet a larger extent of weak connections, thus reducing connectivity density even further, did produce a predictably significant effect. However, metric values became sensitive to the exact connectivity density, which has ramifications regarding the stability of the statistical analysis. This pattern persisted whether connections were removed by connection strength threshold or connectivity density, and for connectomes generated using parcellations at different resolutions. Finally, we showed that the same pattern also applies for data from a clinical-grade MRI scanner. In conclusion, our analysis revealed that removing weak connections is not necessary for graph-theoretical analysis of dense weighted connectomes. Because removal of weak connections provides no practical utility to offset the undesirable requirement for arbitrary or heuristic-based choices, we recommend that this step is avoided in future studies.
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Selection of tractography filtering strategies is important and has been shown to affect the analysis of the topology of brain networks and affect sensitivity and specificity when analyzing connectomes.
“Selection of tractography filtering strategies is important and has been shown to affect the analysis of the topology of brain networks (Civier et al., 2019; Frigo et al., 2020; Yeh et al., 2016a) and affect sensitivity and specificity when analyzing the connectomes (Zalesky et al., 2016a).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Civier et al., 2019
The Introduction says methods used to construct graphs from tractograms can strongly influence connectome characteristics, and §3.2 reports that heavy pruning changes graph-theoretical metrics. But the paper tests removal of connectome connections, not a comparison of tractography filtering strategies; §4.8 says it cannot rule out effects of its fixed pipeline on topology. The sensitivity-and-specificity clause is attributed to Zalesky et al., not Civier et al. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThere is ongoing disagreement about whether filtering methods are beneficial or detrimental to the structural connectome.
“Research is still underway to determine the best method(s) for filtering, with some disagreement on the beneficial versus detrimental effect of these methods on the structural connectome (Buchanan et al., 2020; Civier et al., 2019; Drakesmith et al., 2015; Frigo et al., 2020; Schiavi et al., 2020a; Yeh et al., 2016a).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Civier et al., 2019
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Self-citationMapping Structural Connectivity Using Diffusion <scp>MRI</scp> : Challenges and Opportunities
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- Authors
- Civier, O., Smith, R. E., Yeh, C.-H., Connelly, A., & Calamante, F.
- Affiliations
- The University of Sydney (AU)
- The University of Melbourne (AU)
- Florey Institute of Neuroscience and Mental Health (AU)
- Corresponding author
- Civier, Oren
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2019-07
- Volume
- 194
- Pages
- 68-81
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2919731394
- Cited by
- 95
- References
- 7
- Citations by year
- 2026: 8 · 2025: 19 · 2024: 14 · 2023: 10 · 2022: 16 · 2021: 8 · 2020: 15 · 2019: 5
- FWCI
- 6.41
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Swinburne figshare (Swinburne University of Technology) (submittedVersion)
- Funders
- National Health and Medical Research Council of Australia (Australia) (APP1091593, APP1117724)
- Australian Research Council (Australia) (DP170101815)
- Victorian Government’s Operational Infrastructure Support (Australia)
- Melbourne Bioinformatics at the University of Melbourne (Australia) (UOM0048)
- National Collaborative Research Infrastructure Strategy (NCRIS)
- NIH Blueprint for Neuroscience Research (United States)
- McDonnell Center for Systems Neuroscience at Washington University (United States)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- structural connectome, diffusion MRI tractography, connectivity density, Human Connectome Project, statistical stability, brain network topology
- MeSH terms
- Brain, Humans, Image Processing, Computer-Assisted, Models, Neurological, Diffusion Tensor Imaging, Connectome
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