Greater preference consistency during the Willingness-to-Pay task is related to higher resting state connectivity between the ventromedial prefrontal cortex and the ventral striatum
- 361 Downloads
The significance of why a similar set of brain regions are associated with the default mode network and value-related neural processes remains to be clarified. Here, we examined i) whether brain regions exhibiting willingness-to-pay (WTP) task-related activity are intrinsically connected when the brain is at rest, ii) whether these regions overlap spatially with the default mode network, and iii) whether individual differences in choice behavior during the WTP task are reflected in functional brain connectivity at rest. Blood-oxygen-level dependent (BOLD) signal was measured by functional magnetic resonance imaging while subjects performed the WTP task and at rest with eyes open. Brain regions that tracked the value of bids during the WTP task were used as seed regions in an analysis of functional connectivity in the resting state data. The seed in the ventromedial prefrontal cortex was functionally connected to core regions of the WTP task-related network. Brain regions within the WTP task-related network, namely the ventral precuneus, ventromedial prefrontal and posterior cingulate cortex overlapped spatially with publically available maps of the default mode network. Also, those individuals with higher functional connectivity during rest between the ventromedial prefrontal cortex and the ventral striatum showed greater preference consistency during the WTP task. Thus, WTP task-related regions are an intrinsic network of the brain that corresponds spatially with the default mode network, and individual differences in functional connectivity within the WTP network at rest may reveal a priori biases in choice behavior.
KeywordsfMRI Value Preference Default mode network Ventromedial prefrontal cortex Ventral striatum
This work was funded by a grants received from the National Institute on Drug Abuse: R01 DA027797 & P20 DA027843.
Conflict of interest
The authors declare that they have no conflicts of interest.
- Baldassarre, A., Lewis, C. M., Committeri, G., Snyder, A. Z., Romani, G. L., & Corbetta, M. (2012). Individual variability in functional connectivity predicts performance of a perceptual task. Proceedings of the National Academy of Sciences of the United States of America, 109, 3516–3521.CrossRefPubMedPubMedCentralGoogle Scholar
- Clithero, J. A., & Rangel, A. (2012) Reliable features of the network involved in the neural computation of subjective value. In: Neuroeconomics 10th Annual Meeting Miami, USA.Google Scholar
- Clithero, J. A., & Rangel, A. (2013) Informatic parcellation of the network involved in the computation of subjective value. Soc Cogn Affect Neurosci.Google Scholar
- Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the United States of America, 102, 9673–9678.CrossRefPubMedPubMedCentralGoogle Scholar
- Mackey, S., & Petrides, M. (2014) Architecture and morphology of the human ventromedial prefrontal cortex. European Journal of Neuroscience, 40, 2777–2796.Google Scholar
- Margulies, D. S., Vincent, J. L., Kelly, C., Lohmann, G., Uddin, L. Q., Biswal, B. B., Villringer, A., Castellanos, F. X., Milham, M. P., & Petrides, M. (2009). Precuneus shares intrinsic functional architecture in humans and monkeys. Proceedings of the National Academy of Sciences of the United States of America, 106, 20069–20074.CrossRefPubMedPubMedCentralGoogle Scholar
- Mars, R. B., Jbabdi, S., Sallet, J., O’Reilly, J. X., Croxson, P. L., Olivier, E., Noonan, M. P., Bergmann, C., Mitchell, A. S., Baxter, M. G., Behrens, T. E., Johansen-Berg, H., Tomassini, V., Miller, K. L., & Rushworth, M. F. (2011). Diffusion-weighted imaging tractography-based parcellation of the human parietal cortex and comparison with human and macaque resting-state functional connectivity. Journal of Neuroscience, 31, 4087–4100.CrossRefPubMedPubMedCentralGoogle Scholar
- Ochsner, K., & Gross, J. (2005) The cognitive control of emotion. TRENDS in Cognitive Sciences, 9, 242–249.Google Scholar
- Olafsson, V., Guo, J., Wong, C. W., Kundu, P., Inati, S., Luh, W.-M., Roopchansingh, V., Brenowitz, N., Bandettini, P., Wong, E, Liu, T. (2012). High spatial and temporal resolution fcMRI with BOLD selectivity using multiecho simultaneous multislice EPI. In: Proceedings of the 20th ISMRM, p 2068.Google Scholar
- 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., & Beckmann, C. F. (2009). Correspondence of the brain’s functional architecture during activation and rest. Proceedings of the National Academy of Sciences of the United States of America, 106, 13040–13045.CrossRefPubMedPubMedCentralGoogle Scholar