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Network representation of fMRI data using visibility graphs : The impact of motion and test-retest reliability
Poudel, Govinda R. Sharma, Prabin Lorenzetti, Valentina Parsons, Nicholas Cerin, Ester
Poudel, Govinda R.
Sharma, Prabin
Lorenzetti, Valentina
Parsons, Nicholas
Cerin, Ester
Abstract
Visibility graphs provide a novel approach for analysing time-series data. Graph theoretical analysis of visibility graphs can provide new features for data mining applications in fMRI. However, visibility graphs features have not been used widely in the field of neuroscience. This is likely due to a lack of understanding of their robustness in the presence of noise (e.g., motion) and their test-retest reliability. In this study, we investigated visibility graph properties of fMRI data in the human connectome project (N = 1010) and tested their sensitivity to motion and test-retest reliability. We also characterised the strength of connectivity obtained using degree synchrony of visibility graphs. We found that strong correlation (r > 0.5) between visibility graph properties, such as the number of communities and average degrees, and motion in the fMRI data. The test-retest reliability (Intraclass correlation coefficient (ICC)) of graph theoretical features was high for the average degrees (0.74, 95% CI = [0.73, 0.75]), and moderate for clustering coefficient (0.43, 95% CI = [0.41, 0.44]) and average path length (0.41, 95% CI = [0.38, 0.44]). Functional connectivity between brain regions was measured by correlating the visibility graph degrees. However, the strength of correlation was found to be moderate to low (r < 0.35). These findings suggest that even small movement in fMRI data can strongly influence robustness and reliability of visibility graph features, thus, requiring robust motion correction strategies prior to data analysis. Further studies are necessary for better understanding of the potential application of visibility graph features in fMRI.
Keywords
visibility graph, resting-state fmri, brain network analysis, timeseries features
Date
2024
Type
Journal article
Journal
NeuroInformatics
Book
Volume
22
Issue
Page Range
107-118
Article Number
ACU Department
Mary MacKillop Institute for Health Research
Faculty of Health Sciences
School of Behavioural and Health Sciences
Faculty of Health Sciences
School of Behavioural and Health Sciences
Relation URI
Source URL
Event URL
Open Access Status
Published as ‘gold’ (paid) open access
License
CC BY 4.0
File Access
Open
Notes
© The Author(s) 2024.
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