The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Measuring interregional functional connectivity using coherence and partial coherence analyses of fMRI data
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Citations
Cites
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812
Detection of functional connectivity using temporal correlations in MR images
Hampson, M., Peterson, B. S., Skudlarski, P., Gatenby, J. C., & Gore, J. C. (2002) · Human Brain Mapping · doi:10.1002/hbm.10022 · Cited by 699
The Predictive Value of Changes in Effective Connectivity for Human Learning
Büchel, C., Coull, J. T., & Friston, K. J. (1999) · Science · doi:10.1126/science.283.5407.1538 · Cited by 447 · cited 2×
“Though most current fMRI studies perform univariate analyses, there has been a recent trend towards the development and integration of multivariate analyses to study functional interactivity (Buchel et al., 1999; Horwitz, 1991; McIntosh and Gonzalez-Lima, 1994; McIntosh et al., 1996; Moeller and Strother, 1991).”
BackgroundArfanakis, K., Cordes, D., Haughton, V. M., Moritz, C. H., Quigley, M. A., & Meyerand, M. E. (2000) · Magnetic Resonance Imaging · doi:10.1016/s0730-725x(00)00190-9 · Cited by 191
Cited by
A component based noise correction method (CompCor) for BOLD and perfusion based fMRI
Behzadi, Y., Restom, K., Liau, J., & Liu, T. T. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.04.042 · Cited by 4,502
Achard, S., Salvador, R., Whitcher, B., Suckling, J., & Bullmore, E. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.3874-05.2006 · Cited by 2,393
Large-scale brain networks in cognition: emerging methods and principles
Bressler, S. L., & Menon, V. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.04.004 · Cited by 2,348
Dynamic reconfiguration of human brain networks during learning
Bassett, D. S., Wymbs, N. F., Porter, M. A., Mucha, P. J., Carlson, J. M., & Grafton, S. T. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1018985108 · Cited by 1,705
Neural reuse: A fundamental organizational principle of the brain
Anderson, M. L. (2010) · Behavioral and Brain Sciences · doi:10.1017/s0140525x10000853 · Cited by 1,212
Metadata
- Authors
- Sun, F. T., Miller, L. M., & D'Esposito, M.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2004-02
- Volume
- 21
- Issue
- 2
- Pages
- 647-658
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2003120749
- Cited by
- 406
- References
- 4
- FWCI
- 8.48
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
