Literature DB >> 30844506

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

Oren Civier1, Robert Elton Smith2, Chun-Hung Yeh3, Alan Connelly2, Fernando Calamante4.   

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.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Connectomics; Diffusion MRI; Fiber tracking; Graph-theoretical analysis; Tractography; Weighted connectome

Mesh:

Year:  2019        PMID: 30844506     DOI: 10.1016/j.neuroimage.2019.02.039

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  15 in total

1.  Connectomic consistency: a systematic stability analysis of structural and functional connectivity.

Authors:  Yusuf Osmanlıoğlu; Jacob A Alappatt; Drew Parker; Ragini Verma
Journal:  J Neural Eng       Date:  2020-07-13       Impact factor: 5.379

2.  The effect of network thresholding and weighting on structural brain networks in the UK Biobank.

Authors:  Colin R Buchanan; Mark E Bastin; Stuart J Ritchie; David C Liewald; James W Madole; Elliot M Tucker-Drob; Ian J Deary; Simon R Cox
Journal:  Neuroimage       Date:  2020-01-10       Impact factor: 6.556

3.  Topological Alterations of the Structural Brain Connectivity Network in Children with Juvenile Neuronal Ceroid Lipofuscinosis.

Authors:  T Roine; U Roine; A Tokola; M H Balk; M Mannerkoski; L Åberg; T Lönnqvist; T Autti
Journal:  AJNR Am J Neuroradiol       Date:  2019-11-14       Impact factor: 3.825

Review 4.  Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review.

Authors:  Fan Zhang; Alessandro Daducci; Yong He; Simona Schiavi; Caio Seguin; Robert E Smith; Chun-Hung Yeh; Tengda Zhao; Lauren J O'Donnell
Journal:  Neuroimage       Date:  2022-01-01       Impact factor: 7.400

5.  Default Mode Network structural alterations in Kocher-Monro trajectory white matter transection: A 3 and 7 tesla simulation modeling approach.

Authors:  Saül Pascual-Diaz; Jose Pineda; Laura Serra; Federico Varriano; Alberto Prats-Galino
Journal:  PLoS One       Date:  2019-11-07       Impact factor: 3.240

6.  Diffusion MRI tractography filtering techniques change the topology of structural connectomes.

Authors:  Matteo Frigo; Samuel Deslauriers-Gauthier; Drew Parker; Abdol Aziz Ould Ismail; Junghoon John Kim; Ragini Verma; Rachid Deriche
Journal:  J Neural Eng       Date:  2020-11-11       Impact factor: 5.379

7.  Developmental neuroplasticity of the white matter connectome in children with perinatal stroke.

Authors:  Brandon T Craig; Alicia Hilderley; Eli Kinney-Lang; Xiangyu Long; Helen L Carlson; Adam Kirton
Journal:  Neurology       Date:  2020-09-04       Impact factor: 9.910

8.  Predicting MEG resting-state functional connectivity from microstructural information.

Authors:  Eirini Messaritaki; Sonya Foley; Simona Schiavi; Lorenzo Magazzini; Bethany Routley; Derek K Jones; Krish D Singh
Journal:  Netw Neurosci       Date:  2021-06-03

Review 9.  The Seven Deadly Sins of Measuring Brain Structural Connectivity Using Diffusion MRI Streamlines Fibre-Tracking.

Authors:  Fernando Calamante
Journal:  Diagnostics (Basel)       Date:  2019-09-06

10.  Network communication models improve the behavioral and functional predictive utility of the human structural connectome.

Authors:  Caio Seguin; Ye Tian; Andrew Zalesky
Journal:  Netw Neurosci       Date:  2020-11-01
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