The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Evaluating dynamic bivariate correlations in resting-state fMRI: A comparison study and a new approach
Open accessAbstract
To date, most functional Magnetic Resonance Imaging (fMRI) studies have assumed that the functional connectivity (FC) between time series from distinct brain regions is constant across time. However, recently, there has been increased interest in quantifying possible dynamic changes in FC during fMRI experiments, as it is thought this may provide insight into the fundamental workings of brain networks. In this work we focus on the specific problem of estimating the dynamic behavior of pair-wise correlations between time courses extracted from two different regions of the brain. We critique the commonly used sliding-windows technique, and discuss some alternative methods used to model volatility in the finance literature that could also prove useful in the neuroimaging setting. In particular, we focus on the Dynamic Conditional Correlation (DCC) model, which provides a model-based approach towards estimating dynamic correlations. We investigate the properties of several techniques in a series of simulation studies and find that DCC achieves the best overall balance between sensitivity and specificity in detecting dynamic changes in correlations. We also investigate its scalability beyond the bivariate case to demonstrate its utility for studying dynamic correlations between more than two brain regions. Finally, we illustrate its performance in an application to test-retest resting state fMRI data.
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Cites
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A component based noise correction method (CompCor) for BOLD and perfusion based fMRI
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Tracking whole-brain connectivity dynamics in the resting state
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Brain mediators of cardiovascular responses to social threat
Wager, T. D., Waugh, C. E., Lindquist, M., Noll, D. C., Fredrickson, B. L., & Taylor, S. F. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.05.043 · Cited by 402
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Cited by
Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?
Hindriks, R., Adhikari, M. H., Murayama, Y., Ganzetti, M., Mantini, D., Logothetis, N. K., & Deco, G. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.11.055 · Cited by 632
Questions and controversies in the study of time-varying functional connectivity in resting fMRI
Lurie, D. J., Kessler, D., Bassett, D. S., Betzel, R. F., Breakspear, M., Kheilholz, S., Kucyi, A., Liégeois, R., Lindquist, M. A., McIntosh, A. R., et al. (2020) · Network Neuroscience · doi:10.1162/netn_a_00116 · Cited by 648
Self-citationOn the stability of BOLD fMRI correlations
Laumann, T. O., Snyder, A. Z., Mitra, A., Gordon, E. M., Gratton, C., Adeyemo, B., Gilmore, A. W., Nelson, S. M., Berg, J. J., Greene, D. J., et al. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw265 · Cited by 516
Doss, M. K., Považan, M., Rosenberg, M. D., Sepeda, N. D., Davis, A. K., Finan, P. H., Smith, G. S., Pekar, J. J., Barker, P. B., Griffiths, R. R., et al. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01706-y · Cited by 356
Towards a new approach to reveal dynamical organization of the brain using topological data analysis
Saggar, M., Sporns, O., Gonzalez-Castillo, J., Bandettini, P. A., Carlsson, G., Glover, G., & Reiss, A. L. (2018) · Nature Communications · doi:10.1038/s41467-018-03664-4 · Cited by 253
Betzel, R. F., Fukushima, M., He, Y., Zuo, X.-N., & Sporns, O. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.12.001 · Cited by 249
“For example, while the sliding window approach used here is most common, other methods for estimating dynamic functional connectivity exist, such as component (Calhoun and Adali, 2012) and model-based analyses Lindquist et al. (2014).”
BackgroundMethodInfluentialEstimation of dynamic functional connectivity using multiplication of temporal derivatives
Shine, J. M., Koyejo, O., Bell, P. T., Gorgolewski, K. J., Gilat, M., & Poldrack, R. A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.07.064 · Cited by 166
Temporal irreversibility of large-scale brain dynamics in Alzheimer's disease
Cruzat, J., Herzog, R., Prado, P., Sanz-Perl, Y., Gonzalez-Gomez, R., Moguilner, S., Kringelbach, M. L., Deco, G., Tagliazucchi, E., & Ibañez, A. (2023) · The Journal of Neuroscience · doi:10.1523/jneurosci.1312-22.2022 · Cited by 57
Intrinsic functional connectivity is organized as three interdependent gradients
Zhang, J., Abiose, O., Katsumi, Y., Touroutoglou, A., Dickerson, B. C., & Barrett, L. F. (2019) · Scientific Reports · doi:10.1038/s41598-019-51793-7 · Cited by 47
Yin, D., Liu, W., Zeljic, K., Wang, Z., Lv, Q., Fan, M., Cheng, W., & Wang, Z. (2016) · The Journal of Neuroscience · doi:10.1523/jneurosci.1476-16.2016 · Cited by 43
Resting-state amplitude of low-frequency fluctuation is associated with suicidal ideation
Lan, M. J., Rizk, M. M., Pantazatos, S. P., Rubin‐Falcone, H., Miller, J. M., Sublette, M. E., Oquendo, M. A., Keilp, J. G., & Mann, J. J. (2019) · Depression and Anxiety · doi:10.1002/da.22888 · Cited by 48
Dynamic coupling of complex brain networks and dual-task behavior
Alavash, M., Thiel, C. M., & Gießing, C. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.01.028 · Cited by 25
Metadata
- Authors
- Lindquist, M. A., Xu, Y., Nebel, M. B., & Caffo, B. S.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2014-11
- Volume
- 101
- Pages
- 531-546
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2007894316
- Cited by
- 369
- References
- 5
- FWCI
- 13.62
- Type
- journal article
- Language
- en
- Open access
- green
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