Abstract
The employment of graph theory to analyze spontaneous fluctuations in resting state BOLD fMRI data has become a dominant theme in brain imaging studies and neuroscience. Analysis of resting state functional brain networks based on graph theory has proven to be a powerful tool to quantitatively characterize functional architecture of the brain and it has provided a new platform to explore the overall structure of local and global functional connectivity in the brain. Due to its increased use and possible expansion to clinical use, it is essential that the reliability of such a technique is very strongly assessed. In this review, we explore the outcome of recent studies in network reliability which apply graph theory to analyze connectome resting state networks. Therefore, we investigate which preprocessing steps may affect reproducibility the most. In order to investigate network reliability, we compared the test-retest (TRT) reliability of functional data of published neuroimaging studies with different preprocessing steps. In particular we tested influence of global signal regression, correlation metric choice, binary versus weighted link definition, frequency band selection and length of time-series. Statistical analysis shows that only frequency band selection and length of time-series seem to affect TRT reliability. Our results highlight the importance of the choice of the preprocessing steps to achieve more reproducible measurements.
| Original language | English |
|---|---|
| Pages (from-to) | 183-192 |
| Number of pages | 10 |
| Journal | Journal of Neuroscience Methods |
| Volume | 253 |
| DOIs | |
| Publication status | Published - Sep 1 2015 |
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Keywords
- Connectivity
- Functional brain networks
- Graph theory
- Meta-summary reliability analysis
- Resting state fMRI
- Review
- Test-retest reliability
ASJC Scopus subject areas
- Neuroscience(all)
Cite this
Test-retest reliability of graph metrics of resting state MRI functional brain networks : A review. / Andellini, Martina; Cannatà, Vittorio; Gazzellini, Simone; Bernardi, Bruno; Napolitano, Antonio.
In: Journal of Neuroscience Methods, Vol. 253, 01.09.2015, p. 183-192.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Test-retest reliability of graph metrics of resting state MRI functional brain networks
T2 - A review
AU - Andellini, Martina
AU - Cannatà, Vittorio
AU - Gazzellini, Simone
AU - Bernardi, Bruno
AU - Napolitano, Antonio
PY - 2015/9/1
Y1 - 2015/9/1
N2 - The employment of graph theory to analyze spontaneous fluctuations in resting state BOLD fMRI data has become a dominant theme in brain imaging studies and neuroscience. Analysis of resting state functional brain networks based on graph theory has proven to be a powerful tool to quantitatively characterize functional architecture of the brain and it has provided a new platform to explore the overall structure of local and global functional connectivity in the brain. Due to its increased use and possible expansion to clinical use, it is essential that the reliability of such a technique is very strongly assessed. In this review, we explore the outcome of recent studies in network reliability which apply graph theory to analyze connectome resting state networks. Therefore, we investigate which preprocessing steps may affect reproducibility the most. In order to investigate network reliability, we compared the test-retest (TRT) reliability of functional data of published neuroimaging studies with different preprocessing steps. In particular we tested influence of global signal regression, correlation metric choice, binary versus weighted link definition, frequency band selection and length of time-series. Statistical analysis shows that only frequency band selection and length of time-series seem to affect TRT reliability. Our results highlight the importance of the choice of the preprocessing steps to achieve more reproducible measurements.
AB - The employment of graph theory to analyze spontaneous fluctuations in resting state BOLD fMRI data has become a dominant theme in brain imaging studies and neuroscience. Analysis of resting state functional brain networks based on graph theory has proven to be a powerful tool to quantitatively characterize functional architecture of the brain and it has provided a new platform to explore the overall structure of local and global functional connectivity in the brain. Due to its increased use and possible expansion to clinical use, it is essential that the reliability of such a technique is very strongly assessed. In this review, we explore the outcome of recent studies in network reliability which apply graph theory to analyze connectome resting state networks. Therefore, we investigate which preprocessing steps may affect reproducibility the most. In order to investigate network reliability, we compared the test-retest (TRT) reliability of functional data of published neuroimaging studies with different preprocessing steps. In particular we tested influence of global signal regression, correlation metric choice, binary versus weighted link definition, frequency band selection and length of time-series. Statistical analysis shows that only frequency band selection and length of time-series seem to affect TRT reliability. Our results highlight the importance of the choice of the preprocessing steps to achieve more reproducible measurements.
KW - Connectivity
KW - Functional brain networks
KW - Graph theory
KW - Meta-summary reliability analysis
KW - Resting state fMRI
KW - Review
KW - Test-retest reliability
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UR - http://www.scopus.com/inward/citedby.url?scp=84937129714&partnerID=8YFLogxK
U2 - 10.1016/j.jneumeth.2015.05.020
DO - 10.1016/j.jneumeth.2015.05.020
M3 - Article
C2 - 26072249
AN - SCOPUS:84937129714
VL - 253
SP - 183
EP - 192
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
SN - 0165-0270
ER -