Is removal of weak connections necessary for graph-theoretical analysis of dense weighted structural connectomes from diffusion MRI?

Oren Civier, Robert Elton Smith, Chun Hung Yeh, Alan Connelly, Fernando Calamante

Research output: Contribution to journalArticlepeer-review

54 Scopus citations

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.

Original languageEnglish
Pages (from-to)68-81
Number of pages14
JournalNeuroImage
Volume194
DOIs
StatePublished - 1 Jul 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2019 Elsevier Inc.

Funding

We are grateful for the support of the National Health and Medical Research Council of Australia (Australia) , grant numbers APP1091593 and APP1117724 ; the Australian Research Council (Australia) , grant number DP170101815 ; the Victorian Government’s Operational Infrastructure Support (Australia) ; and of Melbourne Bioinformatics at the University of Melbourne (Australia) , grant number UOM0048 . We acknowledge the facilities of the National Imaging Facility, a National Collaborative Research Infrastructure Strategy (NCRIS) capability, at The Florey Institute of Neuroscience and Mental Health (Australia). Data were provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research (United States) ; and by the McDonnell Center for Systems Neuroscience at Washington University (United States) . Lastly, we would like to thank Marion Sourty for assistance with HCP data processing, and Donna Parker, Farnoosh Sadeghian, Valerie Yap, Patrick Carney, Magdalena Kowalczyk and Mira Semmelroch for help with subject recruitment for clinical-grade data acquisition. We are grateful for the support of the National Health and Medical Research Council of Australia (Australia), grant numbers APP1091593 and APP1117724; the Australian Research Council (Australia), grant number DP170101815; the Victorian Government's Operational Infrastructure Support (Australia); and of Melbourne Bioinformatics at the University of Melbourne (Australia), grant number UOM0048. We acknowledge the facilities of the National Imaging Facility, a National Collaborative Research Infrastructure Strategy (NCRIS) capability, at The Florey Institute of Neuroscience and Mental Health (Australia). Data were provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research (United States); and by the McDonnell Center for Systems Neuroscience at Washington University (United States). Lastly, we would like to thank Marion Sourty for assistance with HCP data processing, and Donna Parker, Farnoosh Sadeghian, Valerie Yap, Patrick Carney, Magdalena Kowalczyk and Mira Semmelroch for help with subject recruitment for clinical-grade data acquisition.

FundersFunder number
National Health and Medical Research Council of Australia (Australia)
National Institutes of Health
NIH Blueprint for Neuroscience Research
McDonnell Center for Systems Neuroscience
Australian Research CouncilDP170101815
National Health and Medical Research CouncilAPP1117724, APP1091593
University of MelbourneUOM0048, 1U54MH091657
State Government of Victoria

    Keywords

    • Connectomics
    • Diffusion MRI
    • Fiber tracking
    • Graph-theoretical analysis
    • Tractography
    • Weighted connectome

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