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


Journal

NeuroImage
ISSN: 1095-9572
Titre abrégé: Neuroimage
Pays: United States
ID NLM: 9215515

Informations de publication

Date de publication:
01 07 2019
Historique:
received: 04 11 2018
revised: 10 02 2019
accepted: 14 02 2019
pubmed: 8 3 2019
medline: 21 12 2019
entrez: 8 3 2019
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 30844506
pii: S1053-8119(19)30135-1
doi: 10.1016/j.neuroimage.2019.02.039
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

68-81

Informations de copyright

Copyright © 2019 Elsevier Inc. All rights reserved.

Auteurs

Oren Civier (O)

The University of Sydney, School of Aerospace, Mechanical and Mechatronic Engineering, J07 University of Sydney, Camperdown, NSW, 2006, Australia. Electronic address: orenciv@gmail.com.

Robert Elton Smith (RE)

Florey Institute of Neuroscience and Mental Health, Melbourne, 245 Burgundy Street, Heidelberg, VIC, 3084, Australia; Florey Department of Neuroscience and Mental Health, University of Melbourne, 245 Burgundy Street, Heidelberg, VIC, 3084, Australia.

Chun-Hung Yeh (CH)

Florey Institute of Neuroscience and Mental Health, Melbourne, 245 Burgundy Street, Heidelberg, VIC, 3084, Australia.

Alan Connelly (A)

Florey Institute of Neuroscience and Mental Health, Melbourne, 245 Burgundy Street, Heidelberg, VIC, 3084, Australia; Florey Department of Neuroscience and Mental Health, University of Melbourne, 245 Burgundy Street, Heidelberg, VIC, 3084, Australia.

Fernando Calamante (F)

The University of Sydney, School of Aerospace, Mechanical and Mechatronic Engineering, J07 University of Sydney, Camperdown, NSW, 2006, Australia; Florey Department of Neuroscience and Mental Health, University of Melbourne, 245 Burgundy Street, Heidelberg, VIC, 3084, Australia; The University of Sydney, Sydney Imaging, 94 Mallett Street, Camperdown, NSW, 2050, Australia.

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