Network Representation of fMRI Data Using Visibility Graphs: The Impact of Motion and Test-Retest Reliability.

Brain network analysis Resting-state fMRI Timeseries features Visibility graph

Journal

Neuroinformatics
ISSN: 1559-0089
Titre abrégé: Neuroinformatics
Pays: United States
ID NLM: 101142069

Informations de publication

Date de publication:
09 Feb 2024
Historique:
accepted: 02 01 2024
medline: 9 2 2024
pubmed: 9 2 2024
entrez: 9 2 2024
Statut: aheadofprint

Résumé

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.

Identifiants

DOI: 10.1007/s12021-024-09652-y PMID: 38332409
pubmed: 38332409
doi: 10.1007/s12021-024-09652-y
pii: 10.1007/s12021-024-09652-y
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2024. The Author(s).

Références

Ahmadlou, M., & Adeli, H. (2012). Visibility graph similarity: A new measure of generalized synchronization in coupled dynamic systems. Physica D Nonlinear Phenomena, 241(4), 326–332.
doi: 10.1016/j.physd.2011.09.008
Braun, U., Plichta, M. M., Esslinger, C., Sauer, C., Haddad, L., Grimm, O., & Meyer-Lindenberg, A. (2012). Test-retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures. Neuroimage, 59(2), 1404–1412. https://doi.org/10.1016/j.neuroimage.2011.08.044 .
doi: 10.1016/j.neuroimage.2011.08.044 pubmed: 21888983
Donner, R. V., & Donges, J. F. (2012). Visibility graph analysis of geophysical time series: Potentials and possible pitfalls. Acta Geophysica, 60(3), 589–623. https://doi.org/10.2478/s11600-012-0032-x .
doi: 10.2478/s11600-012-0032-x
Gao, Z. K., Feng, Y. H., Ma, C., Ma, K., Cai, Q., & Initia, A. D. N. (2020). Disrupted time-dependent and Functional Connectivity Brain Network in Alzheimer’s Disease: A resting-state fMRI study based on visibility graph. Current Alzheimer Research, 17(1), 69–79.
doi: 10.2174/1567205017666200213100607 pubmed: 32053076
Glasser, M. F., Smith, S. M., Marcus, D. S., Andersson, J. L. R., Auerbach, E. J., Behrens, T. E. J., & Van Essen, D. C. (2016). The human Connectome Project’s neuroimaging approach. Nature Neuroscience, 19(9), 1175–1187. https://doi.org/10.1038/nn.4361 .
doi: 10.1038/nn.4361 pubmed: 27571196 pmcid: 6172654
Gonzalez-Castillo, J., Hoy, K. J. W., C. W., & Bandettini, P. A. (2021). How to interpret resting-state fMRI: Ask your participants. Journal of Neuroscience Methods, 41(6), 1130–1141.
doi: 10.1523/JNEUROSCI.1786-20.2020
Koo, T. K., & Li, M. Y. (2016). A Guideline of selecting and reporting Intraclass correlation coefficients for Reliability Research. J Chiropr Med, 15(2), 155–163. https://doi.org/10.1016/j.jcm.2016.02.012 .
doi: 10.1016/j.jcm.2016.02.012 pubmed: 27330520 pmcid: 4913118
Lacasa, L., Luque, B., Ballesteros, F., Luque, J., & Nuno, J. C. (2008). From time series to complex networks: The visibility graph. Proc Natl Acad Sci U S A, 105(13), 4972–4975. https://doi.org/10.1073/pnas.0709247105 .
doi: 10.1073/pnas.0709247105 pubmed: 18362361 pmcid: 2278201
Luczak, A., McNaughton, B. L., & Kubo, Y. (2022). Neurons learn by predicting future activity. Nat Mach Intell, 4(1), 62–72. https://doi.org/10.1038/s42256-021-00430-y .
doi: 10.1038/s42256-021-00430-y pubmed: 35814496 pmcid: 9262088
Noble, S., Scheinost, D., & Constable, R. T. (2019). A decade of test-retest reliability of functional connectivity: A systematic review and meta-analysis. Neuroimage, 203, 116157. https://doi.org/10.1016/j.neuroimage.2019.116157 .
doi: 10.1016/j.neuroimage.2019.116157 pubmed: 31494250
Poskanzer, C., Fang, M., Aglinskas, A., & Anzellotti, S. (2022). Controlling for Spurious Nonlinear dependence in connectivity analyses. Neuroinformatics, 20(3), 599–611. https://doi.org/10.1007/s12021-021-09540-9 .
doi: 10.1007/s12021-021-09540-9 pubmed: 34519963
Poudel, G. R., Hawes, S., Innes, C. R. H., Parsons, N., Drummond, S. P. A., Caeyensberghs, K., & Jones, R. D. (2021). RoWDI: Rolling window detection of sleep intrusions in the awake brain using fMRI. Journal of Neural Engineering, 18(5). https://doi.org/10.1088/1741-2552/ac2bb9 .
Power, J. D., Schlaggar, B. L., & Petersen, S. E. (2015). Recent progress and outstanding issues in motion correction in resting state fMRI. Neuroimage, 105, 536–551.
doi: 10.1016/j.neuroimage.2014.10.044 pubmed: 25462692
Raval, V., Nguyen, K. P., Pinho, M., Dewey, R. B. Jr., Trivedi, M., & Montillo, A. A. (2022). Pitfalls and recommended strategies and Metrics for suppressing motion artifacts in functional MRI. Neuroinformatics, 20(4), 879–896. https://doi.org/10.1007/s12021-022-09565-8 .
doi: 10.1007/s12021-022-09565-8 pubmed: 35291020
Salimi-Khorshidi, G., Douaud, G., Beckmann, C. F., Glasser, M. F., Griffanti, L., & Smith, S. M. (2014). Automatic denoising of functional MM data: Combining independent component analysis and hierarchical fusion of classifiers. Neuroimage, 90, 449–468.
doi: 10.1016/j.neuroimage.2013.11.046 pubmed: 24389422
Sannino, S., Stramaglia, S., & Lacasa, L.,D., M (2017). Visibility graphs for fMRI data: Multiplex temporal graphs and their modulations across resting-state networks. Network Neuroscience, 1(3), 208–221.
doi: 10.1162/NETN_a_00012 pubmed: 29911672 pmcid: 5988401
Silva, V. F., Silva, M. E., Ribeiro, P., & Silva, F. (2022). Novel features for time series analysis: A complex networks approach. Data Mining and Knowledge Discovery, 36(3), 1062–1101.
doi: 10.1007/s10618-022-00826-3
Smith, S. M., Beckmann, C. F., Andersson, J., Auerbach, E. J., Bijsterbosch, J., Douaud, G., & Consortium, W. M. H. (2013). Resting-state fMRI in the human Connectome Project. Neuroimage, 80, 144–168.
doi: 10.1016/j.neuroimage.2013.05.039 pubmed: 23702415
Stephen, M., Gu, C., & Yang, H. (2015). Visibility graph based Time Series Analysis. PLoS One, 10(11), e0143015. https://doi.org/10.1371/journal.pone.0143015 .
doi: 10.1371/journal.pone.0143015 pubmed: 26571115 pmcid: 4646626
Supriya, S., Siuly, S., Wang, H., Cao, J. L., & Zhang, Y. C. (2016). Weighted visibility graph with Complex Network Features in the detection of Epilepsy. Ieee Access, 4, 6554–6566.
doi: 10.1109/ACCESS.2016.2612242
Supriya, S., Siuly, S., Wang, H., & Zhang, Y. C. (2021). EEG Sleep Stages Analysis and classification based on weighed Complex Network features. Ieee Transactions on Emerging Topics in Computational Intelligence, 5(2), 236–246.
doi: 10.1109/TETCI.2018.2876529
Tang, X. Y., Xia, L., Liao, Y. Z., Liu, W. F., Peng, Y. H., Gao, T. X., & Zeng, Y. J. (2013). New Approach to epileptic diagnosis using visibility graph of high-frequency Signal. Clinical Eeg and Neuroscience, 44(2), 150–156.
doi: 10.1177/1550059412464449 pubmed: 23508995
Termenon, M., Jaillard, A., Delon-Martin, C., & Achard, S. (2016). Reliability of graph analysis of resting state fMRI using test-retest dataset from the human Connectome Project. Neuroimage, 142, 172–187. https://doi.org/10.1016/j.neuroimage.2016.05.062 .
doi: 10.1016/j.neuroimage.2016.05.062 pubmed: 27282475
Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., Ugurbil, K., & Consortium, W. M. H. (2013). The WU-Minn Human Connectome Project: An overview. Neuroimage, 80, 62–79.
doi: 10.1016/j.neuroimage.2013.05.041 pubmed: 23684880
Varley, T. F., & Sporns, O. (2022). Network Analysis of Time Series: Novel approaches to Network Neuroscience. Frontiers in Neuroscience, 15.
Wang, C. H., Ong, J. L., Patanaik, A., Zhou, J., & Chee, M. W. L. (2016). Spontaneous eyelid closures link vigilance fluctuation with fMRI dynamic connectivity states. Proceedings of the National Academy of Sciences of the United States of America, 113(34), 9653–9658. https://doi.org/10.1073/pnas.1523980113 .
doi: 10.1073/pnas.1523980113 pubmed: 27512040 pmcid: 5003283
Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., & Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125–1165. https://doi.org/10.1152/jn.00338.2011 .
doi: 10.1152/jn.00338.2011 pubmed: 21653723
Zhang, X. H., Landsness, E. C., Chen, W., Miao, H. Y., Tang, M. C. L., Brier, L. M., & Anastasio, M. A. (2022). Automated sleep state classification of wide-field calcium imaging data via multiplex visibility graphs and deep learning. Journal of Neuroscience Methods, 366.
Zheng, M., Domanskyi, S., Piermarocchi, C., & Mias, G. I. (2021). Visibility graph based temporal community detection with applications in biological time series. Scientific Reports, 11(1), 5623. https://doi.org/10.1038/s41598-021-84838-x .
doi: 10.1038/s41598-021-84838-x pubmed: 33707481 pmcid: 7952737
Zhu, G. H., Li, Y., & Wen, P. (2014a). Analysis and classification of Sleep stages based on difference visibility Graphs from a single-Channel EEG Signal. Ieee Journal of Biomedical and Health Informatics, 18(6), 1813–1821.
doi: 10.1109/JBHI.2014.2303991 pubmed: 25375678
Zhu, G. H., Li, Y., & Wen, P. (2014b). Epileptic seizure detection in EEGs signals using a fast weighted horizontal visibility algorithm. Computer Methods and Programs in Biomedicine, 115(2), 64–75.
doi: 10.1016/j.cmpb.2014.04.001 pubmed: 24768081

Auteurs

Govinda R Poudel (GR)

Mary Mackillop Institute for Health Research, Australian Catholic University, 215 Spring Street, Melbourne, 3000, Australia. Govinda.Poudel@acu.edu.au.
Braincast Neurotechnologies, Melbourne, Australia. Govinda.Poudel@acu.edu.au.
ORCID: 0000-0002-0043-7531

Prabin Sharma (P)

Department of Computer Science, University of Massachusetts, Boston, MA, USA. Prabin.Sharma001@umb.edu.

Valentina Lorenzetti (V)

Neuroscience of Addiction and Mental Health Program, The Healthy Brain and Mind Research Centre, School of Behavioural and Health Sciences, Faculty of Health Sciences, Australian Catholic University, Melbourne, Australia. Valentina.Lorenzetti@acu.edu.au.

Nicholas Parsons (N)

School of Psychological Sciences, Monash University, Melbourne, Australia. nicholas.parsons@monash.edu.
Braincast Neurotechnologies, Melbourne, Australia. nicholas.parsons@monash.edu.

Ester Cerin (E)

Mary Mackillop Institute for Health Research, Australian Catholic University, 215 Spring Street, Melbourne, 3000, Australia. ester.cerin@acu.edu.au.

Classifications MeSH