The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Characterizing variation in the functional connectome: promise and pitfalls
Open accessAbstract
The functional MRI (fMRI) community has zealously embraced resting state or intrinsic functional connectivity approaches to mapping brain organization. Having demonstrated their utility for charting the large-scale functional architecture of the brain, the field is now leveraging task-independent methods for the investigation of phenotypic variation and the identification of biomarkers for clinical conditions. Enthusiasm aside, questions regarding the significance and validity of intrinsic brain phenomena remain. Here, we discuss these challenges and outline current developments that, in moving the field toward discovery science, permit a shift from cartography toward a mechanistic understanding of the neural bases of variation in cognition, emotion and behavior.
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Citations
Cites
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
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Self-citationThe organization of the human cerebellum estimated by intrinsic functional connectivity
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Network modelling methods for FMRI
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The brain's default network and its adaptive role in internal mentation
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Investigating the electrophysiological basis of resting state networks using magnetoencephalography
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“Already, non-invasive investigations in humans using EEG [78] and MEG [79,80] have demonstrated relationships between spatial and temporal indices of oscillatory electrophysiological activity and iFC measures.”
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Cited by
Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity
Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., Papademetris, X., & Constable, R. T. (2015) · Nature Neuroscience · doi:10.1038/nn.4135 · Cited by 2,747
Di Martino, A., Yan, C.-G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., Anderson, J. S., Assaf, M., Bookheimer, S. Y., Dapretto, M., et al. (2014) · Molecular Psychiatry · doi:10.1038/mp.2013.78 · Cited by 2,780
Self-citationYan, C.-G., Cheung, B., Kelly, C., Colcombe, S., Craddock, R. C., Di Martino, A., Li, Q., Zuo, X.-N., Castellanos, F. X., & Milham, M. P. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.03.004 · Cited by 1,461
Self-citationOn the relationship between the “default mode network” and the “social brain
Mars, R. B., Neubert, F.-X., Noonan, M. P., Sallet, J., Toni, I., & Rushworth, M. F. S. (2012) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2012.00189 · Cited by 766
Enhancing studies of the connectome in autism using the autism brain imaging data exchange II
Di Martino, A., O’Connor, D., Chen, B., Alaerts, K., Anderson, J. S., Assaf, M., Balsters, J. H., Baxter, L., Beggiato, A., Bernaerts, S., et al. (2017) · Scientific Data · doi:10.1038/sdata.2017.10 · Cited by 710
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Metadata
- Authors
- Kelly, C., Biswal, B. B., Craddock, R. C., Castellanos, F. X., & Milham, M. P.
- Venue
- Trends in Cognitive Sciences
- Publisher
- Elsevier BV
- Published
- 2012-03
- Volume
- 16
- Issue
- 3
- Pages
- 181-188
- ISSN
- 1364-6613 (ISSN-L)
- OpenAlex
- W2044186073
- Cited by
- 257
- References
- 22
- FWCI
- 1.88
- Type
- journal article
- Language
- en
- Open access
- green
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