The DOI matches its Crossref record; 90 of the 90 references it cites were found in a registry or an index.
Multisubject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery
Open accessAbstract
Since the discovery of functional connectivity in fMRI data (i.e., temporal correlations between spatially distinct regions of the brain) there has been a considerable amount of work in this field. One important focus has been on the analysis of brain connectivity using the concept of networks instead of regions. Approximately ten years ago, two important research areas grew out of this concept. First, a network proposed to be "a default mode of brain function" since dubbed the default mode network was proposed by Raichle. Secondly, multisubject or group independent component analysis (ICA) provided a data-driven approach to study properties of brain networks, including the default mode network. In this paper, we provide a focused review of how ICA has contributed to the study of intrinsic networks. We discuss some methodological considerations for group ICA and highlight multiple analytic approaches for studying brain networks. We also show examples of some of the differences observed in the default mode and resting networks in the diseased brain. In summary, we are in exciting times and still just beginning to reap the benefits of the richness of functional brain networks as well as available analytic approaches.
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Citations
Cites
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
A default mode of brain function
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.2.676 · Cited by 12,190
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
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
Independent component analysis, A new concept?
Comon, P. (1994) · Signal Processing · doi:10.1016/0165-1684(94)90029-9 · Cited by 8,205
Independent Component Analysis
(2004) · Independent Component Analysis · doi:10.7551/mitpress/3717.003.0014 · Cited by 7,903
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.018 · Cited by 7,429
Modeling by shortest data description
Rissanen, J. (1978) · Automatica · doi:10.1016/0005-1098(78)90005-5 · Cited by 6,011
Greicius, M. D., Srivastava, G., Reiss, A. L., & Menon, V. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308627101 · Cited by 3,684
Detection of signals by information theoretic criteria
Wax, M., & Kailath, T. (1985) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1985.1164557 · Cited by 3,426
Cited by
Dynamic functional connectivity: Promise, issues, and interpretations
Hutchison, R. M., Womelsdorf, T., Allen, E. A., Bandettini, P. A., Calhoun, V. D., Corbetta, M., Della Penna, S., Duyn, J. H., Glover, G. H., Gonzalez-Castillo, J., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.079 · Cited by 2,962
Self-citationThe Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery
Calhoun, V. D., Miller, R., Pearlson, G., & Adalı, T. (2014) · Neuron · doi:10.1016/j.neuron.2014.10.015 · Cited by 1,378
Self-citationLutz, A., Jha, A. P., Dunne, J. D., & Saron, C. D. (2015) · American Psychologist · doi:10.1037/a0039585 · Cited by 697
Kong, R., Li, J., Orban, C., Sabuncu, M. R., Liu, H., Schaefer, A., Sun, N., Zuo, X.-N., Holmes, A. J., Eickhoff, S. B., et al. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhy123 · Cited by 593
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).”
Method
Metadata
- Authors
- Calhoun, V. D., & Adali, T.
- Venue
- IEEE Reviews in Biomedical Engineering
- Published
- 2012
- Volume
- 5
- Pages
- 60-73
- ISSN
- 1937-3333 (ISSN-L)
- 1941-1189
- OpenAlex
- W1986589478
- Cited by
- 543
- References
- 90
- FWCI
- 14.89
- Type
- journal article
- Language
- en
- Open access
- green
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- Calhoun, V. D., & Adali, T. (2012). Multisubject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery. IEEE Reviews in Biomedical Engineering. https://doi.org/10.1109/rbme.2012.2211076
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