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Reproducibility of graph measures at the subject level using resting‐state fMRI

INTRODUCTION: Graph metrics have been proposed as potential biomarkers for diagnosis in clinical work. However, before it can be applied in a clinical setting, their reproducibility should be evaluated. METHODS: This study systematically investigated the effect of two denoising pipelines and differe...

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Autores principales: Ran, Qian, Jamoulle, Tarik, Schaeverbeke, Jolien, Meersmans, Karen, Vandenberghe, Rik, Dupont, Patrick
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7428495/
https://www.ncbi.nlm.nih.gov/pubmed/32614515
http://dx.doi.org/10.1002/brb3.1705
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author Ran, Qian
Jamoulle, Tarik
Schaeverbeke, Jolien
Meersmans, Karen
Vandenberghe, Rik
Dupont, Patrick
author_facet Ran, Qian
Jamoulle, Tarik
Schaeverbeke, Jolien
Meersmans, Karen
Vandenberghe, Rik
Dupont, Patrick
author_sort Ran, Qian
collection PubMed
description INTRODUCTION: Graph metrics have been proposed as potential biomarkers for diagnosis in clinical work. However, before it can be applied in a clinical setting, their reproducibility should be evaluated. METHODS: This study systematically investigated the effect of two denoising pipelines and different whole‐brain network constructions on reproducibility of subject‐specific graph measures. We used the multi‐session fMRI dataset from the Brain Genomics Superstruct Project consisting of 69 healthy young adults. RESULTS: In binary networks, the test–retest variability for global measures was large at low density irrespective of the denoising strategy or the type of correlation. Weighted networks showed very low test–retest values (and thus a good reproducibility) for global graph measures irrespective of the strategy used. Comparing the test–retest values for different strategies, there were significant main effects of the type of correlation (Pearson correlation vs. partial correlation), the (partial) correlation value (absolute vs. positive vs. negative), and weight calculation (based on the raw (partial) correlation values vs. based on transformed Z‐values). There was also a significant interaction effect between type of correlation and weight calculation. Similarly as for the binary networks, there was no main effect of the denoising pipeline. CONCLUSION: Our results demonstrated that normalized global graph measures based on a weighted network using the absolute (partial) correlation as weight were reproducible. The denoising pipeline and the granularity of the whole‐brain parcellation used to define the nodes were not critical for the reproducibility of normalized graph measures.
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spelling pubmed-74284952020-08-17 Reproducibility of graph measures at the subject level using resting‐state fMRI Ran, Qian Jamoulle, Tarik Schaeverbeke, Jolien Meersmans, Karen Vandenberghe, Rik Dupont, Patrick Brain Behav Original Research INTRODUCTION: Graph metrics have been proposed as potential biomarkers for diagnosis in clinical work. However, before it can be applied in a clinical setting, their reproducibility should be evaluated. METHODS: This study systematically investigated the effect of two denoising pipelines and different whole‐brain network constructions on reproducibility of subject‐specific graph measures. We used the multi‐session fMRI dataset from the Brain Genomics Superstruct Project consisting of 69 healthy young adults. RESULTS: In binary networks, the test–retest variability for global measures was large at low density irrespective of the denoising strategy or the type of correlation. Weighted networks showed very low test–retest values (and thus a good reproducibility) for global graph measures irrespective of the strategy used. Comparing the test–retest values for different strategies, there were significant main effects of the type of correlation (Pearson correlation vs. partial correlation), the (partial) correlation value (absolute vs. positive vs. negative), and weight calculation (based on the raw (partial) correlation values vs. based on transformed Z‐values). There was also a significant interaction effect between type of correlation and weight calculation. Similarly as for the binary networks, there was no main effect of the denoising pipeline. CONCLUSION: Our results demonstrated that normalized global graph measures based on a weighted network using the absolute (partial) correlation as weight were reproducible. The denoising pipeline and the granularity of the whole‐brain parcellation used to define the nodes were not critical for the reproducibility of normalized graph measures. John Wiley and Sons Inc. 2020-07-02 /pmc/articles/PMC7428495/ /pubmed/32614515 http://dx.doi.org/10.1002/brb3.1705 Text en © 2020 The Authors. Brain and Behavior published by Wiley Periodicals LLC. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Research
Ran, Qian
Jamoulle, Tarik
Schaeverbeke, Jolien
Meersmans, Karen
Vandenberghe, Rik
Dupont, Patrick
Reproducibility of graph measures at the subject level using resting‐state fMRI
title Reproducibility of graph measures at the subject level using resting‐state fMRI
title_full Reproducibility of graph measures at the subject level using resting‐state fMRI
title_fullStr Reproducibility of graph measures at the subject level using resting‐state fMRI
title_full_unstemmed Reproducibility of graph measures at the subject level using resting‐state fMRI
title_short Reproducibility of graph measures at the subject level using resting‐state fMRI
title_sort reproducibility of graph measures at the subject level using resting‐state fmri
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7428495/
https://www.ncbi.nlm.nih.gov/pubmed/32614515
http://dx.doi.org/10.1002/brb3.1705
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