The neurotransmitter dopamine is required for the reinforcement of actions by rewarding stimuli1. Neuroscientists have tried to define the functions of dopamine in concise conceptual terms2, but the practical implications of dopamine release depend on its diverse brain-wide consequences. Although molecular and cellular effects of dopaminergic signalling have been extensively studied3, the effects of dopamine on larger-scale neural activity profiles are less well-understood. Here we combine dynamic dopamine-sensitive molecular imaging4 and functional magnetic resonance imaging to determine how striatal dopamine release shapes local and global responses to rewarding stimulation in rat brains. We find that dopamine consistently alters the duration, but not the magnitude, of stimulus responses across much of the striatum, via quantifiable postsynaptic effects that vary across subregions. Striatal dopamine release also potentiates a network of distal responses, which we delineate using neurochemically dependent functional connectivity analyses. Hot spots of dopaminergic drive notably include cortical regions that are associated with both limbic and motor function. Our results reveal distinct neuromodulatory actions of striatal dopamine that extend well beyond its sites of peak release, and that result in enhanced activation of remote neural populations necessary for the performance of motivated actions. Our findings also suggest brain-wide biomarkers of dopaminergic function and could provide a basis for the improved interpretation of neuroimaging results that are relevant to learning and addiction.
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Custom code is available from the corresponding author upon reasonable request.
Wise, R. A. Dopamine, learning and motivation. Nat. Rev. Neurosci. 5, 483–494 (2004).
Berke, J. D. What does dopamine mean? Nat. Neurosci. 21, 787–793 (2018).
Bamford, N. S., Wightman, R. M. & Sulzer, D. Dopamine’s effects on corticostriatal synapses during reward-based behaviors. Neuron 97, 494–510 (2018).
Lee, T., Cai, L. X., Lelyveld, V. S., Hai, A. & Jasanoff, A. Molecular-level functional magnetic resonance imaging of dopaminergic signaling. Science 344, 533–535 (2014).
Olds, J. & Milner, P. Positive reinforcement produced by electrical stimulation of septal area and other regions of rat brain. J. Comp. Physiol. Psychol. 47, 419–427 (1954).
Brustad, E. M. et al. Structure-guided directed evolution of highly selective p450-based magnetic resonance imaging sensors for dopamine and serotonin. J. Mol. Biol. 422, 245–262 (2012).
Kundu, P. et al. Integrated strategy for improving functional connectivity mapping using multiecho fMRI. Proc. Natl Acad. Sci. USA 110, 16187–16192 (2013).
Shapiro, M. G. et al. Directed evolution of a magnetic resonance imaging contrast agent for noninvasive imaging of dopamine. Nat. Biotechnol. 28, 264–270 (2010).
Krautwald, K., Min, H. K., Lee, K. H. & Angenstein, F. Synchronized electrical stimulation of the rat medial forebrain bundle and perforant pathway generates an additive BOLD response in the nucleus accumbens and prefrontal cortex. Neuroimage 77, 14–25 (2013).
Fiallos, A. M. et al. Reward magnitude tracking by neural populations in ventral striatum. Neuroimage 146, 1003–1015 (2017).
O’Doherty, J. P., Dayan, P., Friston, K., Critchley, H. & Dolan, R. J. Temporal difference models and reward-related learning in the human brain. Neuron 38, 329–337 (2003).
Brocka, M. et al. Contributions of dopaminergic and non-dopaminergic neurons to VTA-stimulation induced neurovascular responses in brain reward circuits. Neuroimage 177, 88–97 (2018).
Logothetis, N. K. The neural basis of the blood-oxygen-level-dependent functional magnetic resonance imaging signal. Phil. Trans. R. Soc. Lond. B 357, 1003–1037 (2002).
Mandeville, J. B. et al. A receptor-based model for dopamine-induced fMRI signal. Neuroimage 75, 46–57 (2013).
Boja, J. W. et al. High-affinity binding of [125I]RTI-55 to dopamine and serotonin transporters in rat brain. Synapse 12, 27–36 (1992).
Freed, C. et al. Dopamine transporter immunoreactivity in rat brain. J. Comp. Neurol. 359, 340–349 (1995).
Kapogiannis, D., Campion, P., Grafman, J. & Wassermann, E. M. Reward-related activity in the human motor cortex. Eur. J. Neurosci. 27, 1836–1842 (2008).
Naqvi, N. H. & Bechara, A. The hidden island of addiction: the insula. Trends Neurosci. 32, 56–67 (2009).
Arsenault, J. T., Nelissen, K., Jarraya, B. & Vanduffel, W. Dopaminergic reward signals selectively decrease fMRI activity in primate visual cortex. Neuron 77, 1174–1186 (2013).
Stuber, G. D. & Wise, R. A. Lateral hypothalamic circuits for feeding and reward. Nat. Neurosci. 19, 198–205 (2016).
Lee, H. J. et al. Activation of direct and indirect pathway medium spiny neurons drives distinct brain-wide responses. Neuron 91, 412–424 (2016).
Tritsch, N. X. & Sabatini, B. L. Dopaminergic modulation of synaptic transmission in cortex and striatum. Neuron 76, 33–50 (2012).
Ferenczi, E. A. et al. Prefrontal cortical regulation of brainwide circuit dynamics and reward-related behavior. Science 351, aac9698 (2016).
Decot, H. K. et al. Coordination of brain-wide activity dynamics by dopaminergic neurons. Neuropsychopharmacology 42, 615–627 (2017).
Nakano, T., Doi, T., Yoshimoto, J. & Doya, K. A kinetic model of dopamine- and calcium-dependent striatal synaptic plasticity. PLOS Comput. Biol. 6, e1000670 (2010).
Gerfen, C. R. & Surmeier, D. J. Modulation of striatal projection systems by dopamine. Annu. Rev. Neurosci. 34, 441–466 (2011).
Klein-Flügge, M. C., Hunt, L. T., Bach, D. R., Dolan, R. J. & Behrens, T. E. Dissociable reward and timing signals in human midbrain and ventral striatum. Neuron 72, 654–664 (2011).
Koerber, J., Goodman, D., Barnes, J. L. & Grimm, J. W. The dopamine D2 antagonist eticlopride accelerates extinction and delays reacquisition of food self-administration in rats. Behav. Pharmacol. 24, 633–639 (2013).
Verty, A. N., McGregor, I. S. & Mallet, P. E. The dopamine receptor antagonist SCH 23390 attenuates feeding induced by Δ9-tetrahydrocannabinol. Brain Res. 1020, 188–195 (2004).
Calaminus, C. & Hauber, W. Intact discrimination reversal learning but slowed responding to reward-predictive cues after dopamine D1 and D2 receptor blockade in the nucleus accumbens of rats. Psychopharmacology (Berl.) 191, 551–566 (2007).
Lex, A. & Hauber, W. Dopamine D1 and D2 receptors in the nucleus accumbens core and shell mediate Pavlovian-instrumental transfer. Learn. Mem. 15, 483–491 (2008).
Cox, R. W. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29, 162–173 (1996).
Papp, E. A., Leergaard, T. B., Calabrese, E., Johnson, G. A. & Bjaalie, J. G. Waxholm Space atlas of the Sprague Dawley rat brain. Neuroimage 97, 374–386 (2014).
Papp, E. A., Leergaard, T. B., Calabrese, E., Johnson, G. A. & Bjaalie, J. G. Addendum to “Waxholm Space atlas of the Sprague Dawley rat brain” [NeuroImage 97 (2014) 374-386]. Neuroimage 105, 561–562 (2015).
Paxinos, G. & Watson, C. The Rat Brain in Stereotaxic Coordinates Compact 6th Edn (Academic, 2009).
Kundu, P., Inati, S. J., Evans, J. W., Luh, W. M. & Bandettini, P. A. Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI. Neuroimage 60, 1759–1770 (2012).
Peltier, S. J. & Noll, D. C. T. T2* dependence of low frequency functional connectivity. Neuroimage 16, 985–992 (2002).
Kundu, P., Santin, M. D., Bandettini, P. A., Bullmore, E. T. & Petiet, A. Differentiating BOLD and non-BOLD signals in fMRI time series from anesthetized rats using multi-echo EPI at 11.7 T. Neuroimage 102, 861–874 (2014).
Frahm, J., Merboldt, K. D., Hänicke, W., Kleinschmidt, A. & Boecker, H. Brain or vein—oxygenation or flow? On signal physiology in functional MRI of human brain activation. NMR Biomed. 7, 45–53 (1994).
Frahm, J., Merboldt, K. D. & Hänicke, W. Functional MRI of human brain activation at high spatial resolution. Magn. Reson. Med. 29, 139–144 (1993).
Kim, S. G., Hendrich, K., Hu, X., Merkle, H. & Uğurbil, K. Potential pitfalls of functional MRI using conventional gradient-recalled echo techniques. NMR Biomed. 7, 69–74 (1994).
Diemling, M., Barth, M. & Moser, E. Quantification of signal changes in gradient recalled echo FMRI. Magn. Reson. Imaging 15, 753–762 (1997).
Wang, X., Zhu, X. H., Zhang, Y. & Chen, W. Large enhancement of perfusion contribution on fMRI signal. J. Cereb. Blood Flow Metab. 32, 907–918 (2012).
This research was funded by National Institutes of Health grants R01 DA038642 and U01 NS103470 to A.J. N.L. was supported by a Stanley Fahn Research Fellowship from the Parkinson’s Disease Foundation. The authors thank T. Lee and L. Cai for initial assistance with the experimental methods; P. Bandettini for advice with multi-echo MRI acquisition; and A. Graybiel, S. Lall and I. Witten for comments on the data and manuscript.
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Behaviourally shaped rats implanted with unilateral cannulae targeting the ventral striatum performed intracranial self-stimulation during continuous injection of 500 μM of the dopamine sensor 9D7 (blue) or saline vehicle (grey), under infusion conditions used for imaging experiments. The number of rewards received per trial is graphed, relative to rewards received before infusion, showing no significant difference between infusion of 9D7 versus saline. Error bars denote s.e.m. of data from n = 5 rats each.
Extended Data Fig. 2 Suppression of haemodynamic signals by contrast agents infused into the ventral striatum.
a, T1-weighted fMRI data from uninjected rats. Mean T1-weighted responses to LH stimulation from five rats that were not injected with MRI contrast agents, measured under conditions identical to those used for the injected rats in Fig. 1b. Negative haemodynamic signals in the ventricles are apparent (dotted box). b, Striatal voxels were scored on the basis of the T1-weighted signal change that they experienced following 50 min of contrast agent infusion (9D7 dopamine sensor or BM3h-WT control protein). Uninjected rats were given a pseudo-score on the basis of the signal change experienced by spatially equivalent voxels in rats injected with 9D7. Box plots show T1-weighted responses evoked by LH stimulation over all voxels as a function of the injection score, in 5% bins, for uninjected rats (left), rats that received infusion of BM3h-WT protein (middle) and rats that received 9D7 (right). Grey shading indicates bins excluded from molecular imaging analysis owing to incomplete suppression of haemodynamic responses. c, Graphs equivalent to those shown in b showing the variation of T2*-weighted signal obtained using multi-echo analysis, as a function of injected contrast agent dose for injected rats or a pseudo-dose for uninfused rats. All box plots indicate median (white line), first and third quartiles (box), and full data ranges (whiskers) over voxels in each bin. Individual voxel intensities are means over five rats in each condition.
a, Echo time dependence of the slow component of the fMRI signal recorded in the presence of the 9D7 dopamine sensor in the ventral striatum. Variation of the slow positive signal with TE provides a basis for extracting the baseline time course using the ME-ICA approach. Error margins are omitted for graphical clarity. b, Quantitative maps of dopamine release formed after baseline correction using the ME-ICA signal. Features correspond closely to the maps in Fig. 2a, which were corrected using a baseline derived from the control BM3h-WT T1-weighted fMRI data, indicating that the choice of baseline correction method makes little difference to the outcome.
a, MRI per cent signal changes (%SC) as a function of dopamine concentration ([DA]) were estimated with respect to [DA] = 0 μM from the in vitro relaxivity of 9D7 and experimental parameters used in the imaging. b, To verify the absence of baseline contributions to dopamine maps obtained with 9D7, mock dopamine imaging was performed using the BM3h-WT control contrast agent. Signal changes induced by LH stimulation were observed in rats injected with the dopamine-insensitive contrast agent BM3h-WT, and mock dopamine maps were computed as described in ‘Quantitative analysis of dopamine concentrations’ for molecular imaging experiments using the 9D7 sensor. Scale bar, 1 mm. c, Number of rats contributing to data from each voxel in b. The results show that minimal dopamine concentrations were observed, indicating effective suppression of background or nonspecific signals to the T1-weighted data. d, Robustness of spatial features in dopamine and BOLD fMRI response maps was verified by examining average data across rats. Dopamine release or BOLD fMRI amplitudes from each individual rat that contributed to Fig. 2a, b were normalized to the mean response level and standard errors were computed to determine error margins, shown in grey shading in the cross-sections shown on the left (for dopamine) and right (for BOLD). These data indicate that the locations of peak dopamine responses in the ventromedial striatum are conserved among rats, whereas the BOLD responses are relatively uniform across the FOV. Scale bars correspond to 3 μM dopamine (left) and 1% BOLD signal modulation (right), before normalization. e, The number of rats contributing to each voxel of the dopamine data averages in d, as well as Fig. 2.
a, Amperometric recording was used to measure dopamine release elicited by LH stimulation. Lateral hypothalamus stimulation at 60-, 120- and 200-Hz frequencies was performed in medetomidine-sedated rats prepared in the same way as for functional imaging experiments. Amperometric recordings of a representative rat were obtained using carbon fibres calibrated after in vivo recording to obtain absolute measurements of dopamine concentration. b, Diagram of a kinetic model that accounts for the introduction of dopamine by synaptic release at the fixed rate Kin during stimulation, interconversion of free and 9D7-bound dopamine with rate constants kon and koff, and removal of free dopamine with the rate constant kout. c, Simulations were performed using parameters chosen to emulate the amperometry data in the absence of 9D7, with Kin = 1.3, 2.3 and 3 μM s−1—corresponding to the three stimulus intensities of 60, 120 and 200 Hz, respectively—and a kout value of 1 s−1. Values of kon (0.013 μM–1 s−1) and koff (0.03 s−1) were derived from stopped flow binding data and empirical estimates of dopamine removal rate reported in a previous publication4. The top trace shows simulated free dopamine concentration in the absence of 9D7 (unperturbed dopamine), and the second trace from the top shows simulated free dopamine in the presence of 40 μM 9D7, revealing a modest buffering effect. The bottom two traces depict the simulated sensor complex concentration in the presence of 40 μM 9D7, as well as the total dopamine concentration under these conditions. These results reveal the expected broadening of total dopamine kinetics in the presence of the 9D7 sensor, but also show that the sensor complex concentration closely tracks total dopamine levels in the system.
a, Scatter plots display the effects of dopamine inhibitors on the correspondence of mean dopamine concentration ([DA]) and BOLD amplitudes (%SC) evoked by LH stimulation. Each dot denotes one voxel in the absence of dopamine receptor blockers (left), or in the presence of SCH 23390 and eticlopride (right). Dashed lines indicate best-fit line of proportionality between the two measures. The addition of D1 and D2 receptor blockers significantly improved the correspondence (F-test P = 0.0019). b, Dopamine inhibition exerts a negligible effect on dopamine release per se. The 9D7 sensor was infused into the ventral striatum as for experiments in Figs. 1, 2, and multigradient imaging was performed to acquire fMRI data in the presence of systemic SCH 23390 and eticlopride treatment. Maps of peak dopamine release computed as in the experiments of Fig. 2a reveal a distribution that corresponds closely to results in the absence of blockers, albeit with somewhat different spatial coverage (cyan outline) due to infusion variability among rats. Coordinates with respect to bregma are noted in the bottom right of each coronal slice. c, Mean time courses of NAc dopamine observed in the absence (cyan) and presence (dark blue) of treatment with D1 and D2 blockers. Shading denotes s.e.m. of five rats (– blockers) or four rats (+ blockers). d, Comparison of mean peak dopamine-release amplitudes in absence (cyan) versus presence (dark blue) of D1 and D2 inhibitors, over three striatal regions for which data were obtained in both conditions. Error bars denote s.e.m. All differences were not significant with t-test P ≥ 0.07.
Relevant ROIs were defined with respect to standard brain atlases and are shown here colour-coded by region: caudate-putamen (CPu), cingulate cortex (CCx), insular cortex (ICx), lateral hypothalamus (LH), lateral septal area (LS), motor cortex (MCx), nucleus accumbens (NAc), olfactory tubercle (Tu), secondary somatosensory cortex (S2) and ventral pallidum (VP). Coordinates of each slice relative to bregma are indicated. Voxel-level definitions of the LS, NAc, Tu and medial CPu are specified in Fig. 2d, and account for experimentally determined anatomical landmarks in the ventral striatum, as reflected in the MRI data.
Extended Data Fig. 8 Effect of intracerebrospinal fluid administration of D1 and D2 inhibitors on reward-induced brain activation.
Three rats were implanted with a cannula targeting the cerebrospinal fluid (CSF) at the cisterna magna and imaged during rewarding stimulation of LH. Maps show per cent signal change (%SC) before (top) and after (middle) infusion of a cocktail containing SCH 23390 and eticlopride, both for voxels with significant activation in the pre-blocker condition (P ≤ 10−5). The bottom row shows the corresponding difference signal map. Labels in the top panel denote coordinates with respect to bregma. Filled arrowheads denote areas of reduced activation in the ICx and S2 region (−1.5 mm) and in MCx (+1.5 and +2.5 mm) observed upon dopamine receptor blockade and similar to effects observed with systemic inhibition treatment in Fig. 4a. Open arrowheads denote differences from the systemic treatment results along the midline (+3.5 mm) and in ventral areas (−0.5 and +0.5 mm) that probably received much higher doses of the inhibition cocktail owing to their proximity to the CSF infusion route.
Extended Data Fig. 9 Functional connectivity between striatal dopamine and distal BOLD signals before and after dopamine receptor blockade.
Regression analysis was used to determine the amplitude of dopamine-tracking signals (βDA, F-test P ≤ 0.05) observed throughout the brain in regions distal to 9D7 infusion sites in the ventral striatum using the same methods as for experiments shown in Fig. 4c, d. The analysis was performed on two groups of rats, one untreated with SCH 23390 and eticlopride (top) (n = 5) and one pre-treated with the cocktail of systemic D1 and D2 inhibitors (n = 4). In each case, dopamine and BOLD data were obtained from the same rats, and βDA values reflect the shared variance of simultaneously acquired, temporally varying dopamine and BOLD signals across multiple individuals. Labels in the top panel denote coordinates with respect to bregma. Arrowheads highlight areas in which blockade of the D1 and D2 receptors substantially reduces tracking behaviour in the MCx (+2.5 mm) and in ICx and S2 (−1.5 mm).
Extended Data Fig. 10 Effect of ventral-striatal receptor blockade on reward-induced brain activation.
a, D1 and D2 inhibitors were intracranially infused into five rats implanted with cannulae targeting the ventral striatum, and imaged during rewarding LH stimulation. Maps show per cent signal change (%SC) before (top) and after (middle) administration of a cocktail containing SCH 23390 and eticlopride, both for voxels with significant activation in the pre-blocker condition (P ≤ 10−5). The corresponding difference map is presented in Fig. 4e. Labels in the top panel denote coordinates with respect to bregma. Filled arrowheads denote areas of highly reduced activation in the ICx (−1.5 mm) and MCx (+1.5 and +2.5 mm) observed upon local blockade of the D1 and D2 receptors. Reduced activation in the Tu and ventral pallidum (open arrowheads) probably reflects direct effects of the locally infused dopamine blockers. b, A combination of dopamine inhibitors and a noradrenaline inhibitor was intracranially infused into four rats implanted with cannulae targeting the ventral striatum (vStr), and imaged during rewarding stimulation of LH. Maps show per cent signal change (%SC) before (top) and after (middle) infusion of a cocktail containing SCH 23390, eticlopride and the α2 receptor antagonist yohimbine, both for voxels with significant activation in the pre-blocker condition (P ≤ 10−5). The bottom row shows the corresponding difference signal map. Filled arrowheads at bregma −1.5 and +1.5 denote areas at the intersection of ICx and S2, and also in MCx, in which the reduction of the BOLD signal parallels effects observed with blockade of the D1 and D2 receptors alone (a, Fig. 4e). Open arrowhead at bregma −1.5 mm indicates an amygdalar region that may be sensitive to the addition of yohimbine in the treatment mixture.
About this article
Cite this article
Li, N., Jasanoff, A. Local and global consequences of reward-evoked striatal dopamine release. Nature 580, 239–244 (2020). https://doi.org/10.1038/s41586-020-2158-3
Applied Microscopy (2021)
A randomized placebo-controlled PET study of ketamine´s effect on serotonin1B receptor binding in patients with SSRI-resistant depression
Translational Psychiatry (2020)