The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Engagement of large-scale networks is related to individual differences in inhibitory control
Open accessAbstract
Understanding which brain regions regulate the execution, and suppression, of goal-directed behavior has implications for a number of areas of research. In particular, understanding which brain regions engaged during tasks requiring the execution and inhibition of a motor response provides insight into the mechanisms underlying individual differences in response inhibition ability. However, neuroimaging studies examing the relation between activation and stopping have been inconsistent regarding the direction of the relationship, and also regarding the anatomical location of regions that correlate with behavior. These limitations likely arise from the relatively low power of vox-elwise correlations with small sample sizes. Here, we pooled data over five separate fMRI studies of the Stop-signal task in order to obtain a sufficiently large sample size to robustly detect brain/behavior correlations. In addition, rather than performing mass univariate correlation analysis across all voxels, we increased statistical power by reducing the dimensionality of the data set using independent components analysis and then examined correlations between behavior and the resulting component scores. We found that components reflecting activity in regions thought to be involved in stopping were associated with better stopping ability, while activity in a default-mode network was associated with poorer stopping ability across individuals. These results clearly show a relationship between individual differences in stopping ability in specific activated networks, including regions known to be critical for the behavior. The results also highlight the usefulness of using dimensionality reduction to increase the power to detect brain/behavior correlations in individual differences research.
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Cites
Advances in functional and structural MR image analysis and implementation as FSL
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.051 · Cited by 13,630
Correspondence of the brain's functional architecture during activation and rest
Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0905267106 · Cited by 5,150
Wandering minds: the default network and stimulus-independent thought
Mason, M. F., Norton, M. I., Van Horn, J. D., Wegner, D. M., Grafton, S. T., & Macrae, C. N. (2007) · Science · doi:10.1126/science.1131295 · Cited by 2,792
On the ability to inhibit thought and action: a theory of an act of control
Logan, G. D., & Cowan, W. B. (1984) · Psychological Review · doi:10.1037/0033-295x.91.3.295 · Cited by 2,590
The relations among inhibition and interference control functions: A latent-variable analysis.
Friedman, N. P., & Miyake, A. (2004) · Journal of Experimental Psychology: General · doi:10.1037/0096-3445.133.1.101 · Cited by 2,309 · cited 1×
“In addition, response inhibition is thought to be involved across a number of other paradigms, including response interference, switching, and reversal learning tasks, and the common factor linking these tasks appears to be the need to maintain a goal in the face of strongly activated, but inappropriate, representatio…”
Stop-signal inhibition disrupted by damage to right inferior frontal gyrus in humans
Aron, A. R., Fletcher, P. C., Bullmore, E. T., Sahakian, B. J., & Robbins, T. W. (2003) · Nature Neuroscience · doi:10.1038/nn1003 · Cited by 1,771
Self-citationResponse inhibition in the stop-signal paradigm
Verbruggen, F., & Logan, G. D. (2008) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2008.07.005 · Cited by 1,373
Immature Frontal Lobe Contributions to Cognitive Control in Children
Bunge, S. A., Dudukovic, N. M., Thomason, M. E., Vaidya, C. J., & Gabrieli, J. D. E. (2002) · Neuron · doi:10.1016/s0896-6273(01)00583-9 · Cited by 1,291 · cited 1×
“Age has been shown to have a significant influence not only on Stop-signal performance, but also on neural activation during response inhibition (Bunge et al., 2002; Rubia et al., 2006).”
Rubia, K., Russell, T., Overmeyer, S., Brammer, M. J., Bullmore, E. T., Sharma, T., Simmons, A., Williams, S. C. R., Giampietro, V., Andrew, C. M., et al. (2001) · NeuroImage · doi:10.1006/nimg.2000.0685 · Cited by 999
Aron, A. R., Behrens, T. E., Smith, S., Frank, M. J., & Poldrack, R. A. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.0519-07.2007 · Cited by 982
Self-citation
Cited by
Inhibition and the right inferior frontal cortex: one decade on
Aron, A. R., Robbins, T. W., & Poldrack, R. A. (2014) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.12.003 · Cited by 2,166
Self-citationSalience network integrity predicts default mode network function after traumatic brain injury
Bonnelle, V., Ham, T. E., Leech, R., Kinnunen, K. M., Mehta, M. A., Greenwood, R. J., & Sharp, D. J. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1113455109 · Cited by 606
Gorgolewski, K. J., Varoquaux, G., Rivera, G., Schwarz, Y., Ghosh, S. S., Maumet, C., Sochat, V. V., Nichols, T. E., Poldrack, R. A., Poline, J.-B., et al. (2015) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2015.00008 · Cited by 572
Self-citationAdolescent impulsivity phenotypes characterized by distinct brain networks
the IMAGEN Consortium, Whelan, R., Conrod, P. J., Poline, J.-B., Lourdusamy, A., Banaschewski, T., Barker, G. J., Bellgrove, M. A., Büchel, C., Byrne, M., et al. (2012) · Nature Neuroscience · doi:10.1038/nn.3092 · Cited by 420
de Wit, S. J., de Vries, F. E., van der Werf, Y. D., Cath, D. C., Heslenfeld, D. J., Veltman, E. M., van Balkom, A. J. L. M., Veltman, D. J., & van den Heuvel, O. A. (2012) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2012.12010073 · Cited by 303
Metadata
- Authors
- Congdon, E., Mumford, J. A., Cohen, J. R., Galvan, A., Aron, A. R., Xue, G., Miller, E., & Poldrack, R. A.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2010-11-01
- Volume
- 53
- Issue
- 2
- Pages
- 653-663
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2106878307
- Cited by
- 167
- References
- 29
- FWCI
- 7.42
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Congdon, E., Mumford, J. A., Cohen, J. R., Galvan, A., Aron, A. R., Xue, G., Miller, E., & Poldrack, R. A. (2010). Engagement of large-scale networks is related to individual differences in inhibitory control. NeuroImage. https://doi.org/10.1016/j.neuroimage.2010.06.062
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
