The DOI matches its Crossref record; 292 of the 292 references it cites were found in a registry or an index.
Using fMRI to study reward processing in humans: past, present, and future
Open accessAbstract
Functional magnetic resonance imaging (fMRI) is a noninvasive tool used to probe cognitive and affective processes. Although fMRI provides indirect measures of neural activity, the advent of fMRI has allowed for1) the corroboration of significant animal findings in the human brain, and2) the expansion of models to include more common human attributes that inform behavior. In this review, we briefly consider the neural basis of the blood oxygenation level dependent signal to set up a discussion of how fMRI studies have applied it in examining cognitive models in humans and the promise of using fMRI to advance such models. Specifically, we illustrate the contribution that fMRI has made to the study of reward processing, focusing on the role of the striatum in encoding reward-related learning signals that drive anticipatory and consummatory behaviors. For instance, we discuss how fMRI can be used to link neural signals (e.g., striatal responses to rewards) to individual differences in behavior and traits. While this functional segregation approach has been constructive to our understanding of reward-related functions, many fMRI studies have also benefitted from a functional integration approach that takes into account how interconnected regions (e.g., corticostriatal circuits) contribute to reward processing. We contend that future work using fMRI will profit from using a multimodal approach, such as combining fMRI with noninvasive brain stimulation tools (e.g., transcranial electrical stimulation), that can identify causal mechanisms underlying reward processing. Consequently, advancements in implementing fMRI will promise new translational opportunities to inform our understanding of psychopathologies.
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“In line with reward circuitry, studies utilizing fMRI to measure reward processing revealed increased brain activation signals in response to the anticipation and receipt of reward (Wang et al., 2016).”
From The effects of early life stress on reward processing (2018) · cites this paper as Wang et al., 2016
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“Both tasks have two phases, an anticipation phase, in which the subject is given information about the potential for reward, and a delivery phase, in which the reward is received. By measuring brain activity during the anticipation and delivery phases, a motivational component and a reward responsiveness component can be inferred, respectively (Knutson et al., 2000; Lutz and Widmer, 2014; Wang et”
From The effects of early life stress on reward processing (2018) · cites this paper as Wang et al., 2016
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Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
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
Statistical parametric maps in functional imaging: A general linear approach
Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J. ‐., Frith, C. D., & Frackowiak, R. S. J. (1994) · Human Brain Mapping · doi:10.1002/hbm.460020402 · Cited by 9,688
A neural substrate of prediction and reward
Schultz, W., Dayan, P., & Montague, P. R. (1997) · Science · doi:10.1126/science.275.5306.1593 · Cited by 9,388
Voxel-Based Morphometry—The Methods
Ashburner, J., & Friston, K. J. (2000) · NeuroImage · doi:10.1006/nimg.2000.0582 · Cited by 8,676
Power failure: why small sample size undermines the reliability of neuroscience
Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3475 · Cited by 7,641
The organization of the human cerebellum estimated by intrinsic functional connectivity
Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C., & Yeo, B. T. T. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00339.2011 · Cited by 7,304
Neurophysiological investigation of the basis of the fMRI signal
Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., & Oeltermann, A. (2001) · Nature · doi:10.1038/35084005 · Cited by 6,551
Flagged first, then by citations
Research works printed but not linked: 5
- Berens P, 2010, Nat Prec, 5216.1
- Hanson JL, 2015, Soc Cogn Affect Neurosci
- 10.1097/WNR.0b013e3282f85c01 · DOI
- Rescorla RA, 1972, Classical Conditioning: Current Research and Theory, 64
- Smith DV, 2009, Front Integr Neurosci, 3, 26
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Du, J., Yang, Z., & Chen, Y. (2026) · Frontiers in Nutrition · doi:10.3389/fnut.2026.1870484 · Cited by 1
Zhao, Z., Guerrero, I. E., Ali, S. I., Spinner, H. K., Fernandez, A. M., Vides‐Varini, H. F., Abber, S. R., Hill, N. G., & Keel, P. K. (2026) · European Eating Disorders Review · doi:10.1002/erv.70170
Sadeghi‐Bazargani, H., Ghojazadeh, M., Bonabi, M. A., Narimani, M., Abdolrahmanzadeh, R., Shally, A. A., & Taghizadeh, S. (2026) · Topics in Magnetic Resonance Imaging · doi:10.1097/rmr.0000000000000325
Metadata
- Authors
- Wang, K. S., Smith, D. V., & Delgado, M. R.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- Corresponding author
- Delgado, Mauricio R.
- Venue
- Journal of Neurophysiology
- Publisher
- American Physiological Society
- Published
- 2016-03-01
- Volume
- 115
- Issue
- 3
- Pages
- 1664-1678
- ISSN
- 0022-3077 (ISSN-L)
- 1522-1598
- PMC
- PMC4808130
- OpenAlex
- W2228455888
- Cited by
- 105
- References
- 292
- Citations by year
- 2026: 9 · 2025: 12 · 2024: 7 · 2023: 6 · 2022: 13 · 2021: 10 · 2020: 13 · 2019: 16 · 2018: 12 · 2017: 4 · 2016: 3
- FWCI
- 1.23
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Neurophysiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- HHS | NIH | National Institute on Drug Abuse (NIDA) (R01-DA027764)
- HHS | NIH | National Institute of Mental Health (NIMH) (F32-MH107175)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- reward processing, striatum, BOLD signal, individual differences, functional integration, corticostriatal circuits, transcranial electrical stimulation, causal mechanisms, psychopathology, translational neuroscience
- MeSH terms
- Brain, Brain Mapping, Humans, Magnetic Resonance Imaging, Reward
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