Wager, T. D. & Atlas, L. Y. The neuroscience of placebo effects: connecting context, learning and health. Nat. Rev. Neurosci. 16, 403–418 (2015).
Gershman, S. J. & Uchida, N. Believing in dopamine. Nat. Rev. Neurosci. 20, 703–714 (2019).
Ben-Shaanan, T. L. et al. Activation of the reward system boosts innate and adaptive immunity. Nat. Med 22, 940–944 (2016).
Ben-Shaanan, T. L. et al. Modulation of anti-tumor immunity by the brain’s reward system. Nat. Commun. 9, 2723 (2018).
Kayama, T., Ikegaya, Y. & Sasaki, T. Phasic firing of dopaminergic neurons in the ventral tegmental area triggers peripheral immune responses. Sci. Rep. 12, 1447 (2022).
Costi, S. et al. Peripheral immune cell reactivity and neural response to reward in patients with depression and anhedonia. Transl. Psychiatry 11, 565 (2021).
Chat, I. K.-Y. et al. Goal-striving tendencies moderate the relationship between reward-related brain function and peripheral inflammation. Brain Behav. Immun. 94, 60–70 (2021).
Treadway, M. T. et al. Association between interleukin-6 and striatal prediction-error signals following acute stress in healthy female participants. Biol. Psychiatry 82, 570–577 (2017).
Sitaram, R. et al. Closed-loop brain training: the science of neurofeedback. Nat. Rev. Neurosci. 18, 86–100 (2017).
Lubianiker, N. et al. Process-based framework for precise neuromodulation. Nat. Hum. Behav. 3, 436–445 (2019).
Lubianiker, N., Paret, C., Dayan, P. & Hendler, T. Neurofeedback through the lens of reinforcement learning. Trends Neurosci. 45, 579–593 (2022).
Klöbl, M. et al. Reinforcement and punishment shape the learning dynamics in fMRI neurofeedback. Front. Hum. Neurosci. 14, 304 (2020).
Oblak, E. F., Lewis-Peacock, J. A. & Sulzer, J. S. Self-regulation strategy, feedback timing and hemodynamic properties modulate learning in a simulated fMRI neurofeedback environment. PLoS Comput. Biol. 13, e1005681 (2017).
Zoefel, B., Huster, R. J. & Herrmann, C. S. Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance. NeuroImage 54, 1427–1431 (2011).
Autenrieth, M., Kober, S. E., Neuper, C. & Wood, G. How much do strategy reports tell about the outcomes of neurofeedback training? A study on the voluntary up-regulation of the sensorimotor rhythm. Front. Hum. Neurosci. 14, 218 (2020).
Qiu, S. et al. Significant transcriptome and cytokine changes in hepatitis B vaccine non-responders revealed by genome-wide comparative analysis. Hum. Vaccin. Immunother. 14, 1763–1772 (2018).
Kirschner, M. et al. Ventral striatal hypoactivation is associated with apathy but not diminished expression in patients with schizophrenia. J. Psychiatry Neurosci. 41, 152–161 (2016).
Ohmann, H. A., Kuper, N. & Wacker, J. Examining the reliability and validity of two versions of the Effort-Expenditure for Rewards Task (EEfRT). PLOS ONE 17, e0262902 (2022).
Gonen, T. et al. Human mesostriatal response tracks motivational tendencies under naturalistic goal conflict. Soc. Cogn. Affect. Neurosci. 11, 961–972 (2016).
Munshi, S. & Rosenkranz, J. A. Effects of peripheral immune challenge on in vivo firing of basolateral amygdala neurons in adult male rats. Neuroscience 390, 174–186 (2018).
Zhu, X. et al. Somatosensory cortex and central amygdala regulate neuropathic pain-mediated peripheral immune response via vagal projections to the spleen. Nat. Neurosci. 27, 471–483 (2024).
Boukezzi, S. et al. Exaggerated amygdala response to threat and association with immune hyperactivity in depression. Brain, Behav., Immun. 104, 205–212 (2022).
Yin, L. et al. Inflammation and decreased functional connectivity in a widely-distributed network in depression: Centralized effects in the ventral medial prefrontal cortex. Brain Behav. Immun. 80, 657–666 (2019).
Hui, M. & Beier, K. T. Defining the interconnectivity of the medial prefrontal cortex and ventral midbrain. Front. Mol. 15, 971349 (2022).
Jennings, J. H. et al. Distinct extended amygdala circuits for divergent motivational states. Nature 496, 224–228 (2013).
Glimcher, P. W. Understanding dopamine and reinforcement learning: the dopamine reward prediction error hypothesis. Proc. Natl Acad. Sci. 108, 15647–15654 (2011).
Lerner, T. N., Holloway, A. L. & Seiler, J. L. Dopamine, updated: reward prediction error and beyond. Curr. Opin. Neurobiol. 67, 123–130 (2021).
Niv, Y. Cost, benefit, tonic, phasic. Ann. N. Y. Acad. Sci. 1104, 357–376 (2007).
Beeler, J. A. Tonic dopamine modulates exploitation of reward learning. Front. Behav. Neurosci. 4, 170 (2010).
Niv, Y., Daw, N. D., Joel, D. & Dayan, P. Tonic dopamine: opportunity costs and the control of response vigor. Psychopharmacology 191, 507–520 (2007).
Hahn, A. et al. Functional dynamics of dopamine synthesis during monetary reward and punishment processing. J. Cereb. Blood Flow. Metab. 41, 2973–2985 (2021).
Schmidt, *Clemens et al. Multimodal assessment of dopamine synthesis and bold- signaling in reward and punishment processing - a hybrid fpet/fmri study. Int. J. Neuropsychopharmacol. 28, i39–i40 (2025).
Fields, H. L. & Margolis, E. B. Understanding opioid reward. Trends Neurosci. 38, 217–225 (2015).
Laurent, V., Morse, A. K. & Balleine, B. W. The role of opioid processes in reward and decision-making. Br. J. Pharmacol. 172, 449–459 (2015).
Bossong, M. G., Wilson, R., Appiah-Kusi, E., McGuire, P. & Bhattacharyya, S. Human striatal response to reward anticipation linked to hippocampal glutamate levels. Int. J. Neuropsychopharmacol. 21, 623–630 (2018).
Benedetti, F. & Amanzio, M. Mechanisms of the placebo response. Pulm. Pharmacol. Therapeutics 26, 520–523 (2013).
Zubieta, J.-K. & Stohler, C. S. Neurobiological mechanisms of placebo responses. Ann. N. Y. Acad. Sci. 1156, 198–210 (2009).
MacInnes, J. J., Dickerson, K. C., Chen, N. & Adcock, R. A. Cognitive neurostimulation: learning to volitionally sustain ventral tegmental area activation. Neuron 89, 1331–1342 (2016).
Totah, N. K. B., Kim, Y. & Moghaddam, B. Distinct prestimulus and poststimulus activation of VTA neurons correlates with stimulus detection. J. Neurophysiol. 110, 75–85 (2013).
Howe, M. W., Tierney, P. L., Sandberg, S. G., Phillips, P. E. M. & Graybiel, A. M. Prolonged dopamine signalling in striatum signals proximity and value of distant rewards. Nature 500, 575–579 (2013).
Kahn, I. & Shohamy, D. Intrinsic connectivity between the hippocampus, nucleus accumbens, and ventral tegmental area in humans. Hippocampus 23, 187–192 (2013).
Cauda, F. et al. Functional connectivity and coactivation of the nucleus accumbens: a combined functional connectivity and structure-based meta-analysis. J. Cogn. Neurosci. 23, 2864–2877 (2011).
Hammes, J. et al. Dopamine metabolism of the nucleus accumbens and fronto-striatal connectivity modulate impulse control. Brain 142, 733–743 (2019).
Zunhammer, M., Gerardi, M. & Bingel, U. The effect of dopamine on conditioned placebo analgesia in healthy individuals: a double-blind randomized trial. Psychopharmacology 235, 2587–2595 (2018).
Kunkel, A. et al. Dopamine has no direct causal role in the formation of treatment expectations and placebo analgesia in humans. PLOS Biol. 22, e3002772 (2024).
Lange, T., Dimitrov, S., Bollinger, T., Diekelmann, S. & Born, J. Sleep after vaccination boosts immunological memory. J. Immunol. 187, 283–290 (2011).
Yu, X. et al. GABA and glutamate neurons in the VTA regulate sleep and wakefulness. Nat. Neurosci. 22, 106–119 (2019).
Schiller, M., Ben-Shaanan, T. L. & Rolls, A. Neuronal regulation of immunity: why, how and where? Nat. Rev. Immunol. 21, 20–36 (2021).
Castro, D. C. & Bruchas, M. R. A motivational and neuropeptidergic hub: anatomical and functional diversity within the nucleus accumbens shell. Neuron 102, 529–552 (2019).
van Dongen, Y. C. et al. Anatomical evidence for direct connections between the shell and core subregions of the rat nucleus accumbens. Neuroscience 136, 1049–1071 (2005).
Cloninger, C. R., Przybeck, T. R. & Svrakic, D. M. The tridimensional personality questionnaire: U.S. normative data. Psychol. Rep. 69, 1047–1057 (1991).
Costa, P. T. & McCrae, R. R. Normal personality assessment in clinical practice: the NEO Personality Inventory. Psychol. Assess. 4, 5 (1992).
Torrubia, R., Avila, C., Moltó, J. & Caseras, X. The sensitivity to punishment and sensitivity to reward questionnaire (SPSRQ) as a measure of Gray’s anxiety and impulsivity dimensions. Personal. Individ. differences 31, 837–862 (2001).
Treadway, M. T., Buckholtz, J. W., Schwartzman, A. N., Lambert, W. E. & Zald, D. H. Worth the ‘EEfRT’? The effort expenditure for rewards task as an objective measure of motivation and anhedonia. PloS one 4, e6598 (2009).
Knutson, B., Westdorp, A., Kaiser, E. & Hommer, D. FMRI visualization of brain activity during a monetary incentive delay task. Neuroimage 12, 20–27 (2000).
Kirschner, M. et al. Deficits in context-dependent adaptive coding of reward in schizophrenia. npj Schizophr. 2, 16020 (2016).
Murty, V. P. et al. Resting state networks distinguish human ventral tegmental area from substantia nigra. Neuroimage 100, 580–589 (2014).
Koush, Y. et al. OpenNFT: an open-source Python/Matlab framework for real-time fMRI neurofeedback training based on activity, connectivity and multivariate pattern analysis. NeuroImage 156, 489–503 (2017).
Lindquist, M. A. The statistical analysis of fMRI data. Statist. Sci. 23, (2008).
Hinds, O. et al. Computing moment-to-moment BOLD activation for real-time neurofeedback. NeuroImage 54, 361–368 (2011).
Koush, Y., Zvyagintsev, M., Dyck, M., Mathiak, K. A. & Mathiak, K. Signal quality and Bayesian signal processing in neurofeedback based on real-time fMRI. NeuroImage 59, 478–489 (2012).
Bagarinao, E., Matsuo, K., Nakai, T. & Sato, S. Estimation of general linear model coefficients for real-time application. NeuroImage 19, 422–429 (2003).
McCrae, R. R. & Costa, P. T. Empirical and theoretical status of the five-factor model of personality traits. SAGE Handb. Personal. Theory Assess. 1, 273–294 (2008).
Esteban, O. et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat. Methods 16, 111–116 (2019).
Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W. & Smith, S. M. Fsl. Neuroimage 62, 782–790 (2012).
Oldham, S. et al. The anticipation and outcome phases of reward and loss processing: a neuroimaging meta-analysis of the monetary incentive delay task. Hum. Brain Mapp. 39, 3398–3418 (2018).
Woolrich, M. Robust group analysis using outlier inference. NeuroImage 41, 286–301 (2008).
Beckmann, C. F., Jenkinson, M. & Smith, S. M. General multilevel linear modeling for group analysis in fMRI. NeuroImage 20, 1052–1063 (2003).
Woolrich, M. W., Behrens, T. E. J., Beckmann, C. F., Jenkinson, M. & Smith, S. M. Multilevel linear modelling for FMRI group analysis using Bayesian inference. NeuroImage 21, 1732–1747 (2004).
R Core Team, R. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).
Chikhi, S., Matton, N., Sanna, M. & Blanchet, S. Mental strategies and resting state EEG: effect on high alpha amplitude modulation by neurofeedback in healthy young adults. Biol. Psychol. 178, 108521 (2023).
Kober, S., Witte, M., Ninaus, M., Neuper, C. & Wood, G. Learning to modulate one’s own brain activity: the effect of spontaneous mental strategies. Front. Hum. Neurosci. 7, 695 (2013).
McLaren, D. G., Ries, M. L., Xu, G. & Johnson, S. C. A generalized form of context-dependent psychophysiological interactions (gPPI): a comparison to standard approaches. NeuroImage 61, 1277–1286 (2012).
Sescousse, G., Caldú, X., Segura, B. & Dreher, J.-C. Processing of primary and secondary rewards: a quantitative meta-analysis and review of human functional neuroimaging studies. Neurosci. Biobehav. Rev. 37, 681–696 (2013).
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).
Kühn, S. & Gallinat, J. Segregating cognitive functions within hippocampal formation: a quantitative meta-analysis on spatial navigation and episodic memory. Hum. Brain Mapp. 35, 1129–1142 (2014).
Arsalidou, M. & Taylor, M. J. Is 2 + 2 = 4? Meta-analyses of brain areas needed for numbers and calculations. NeuroImage 54, 2382–2393 (2011).
Hétu, S. et al. The neural network of motor imagery: an ALE meta-analysis. Neurosci. Biobehav. Rev. 37, 930–949 (2013).
McNorgan, C. A meta-analytic review of multisensory imagery identifies the neural correlates of modality-specific and modality-general imagery. Front. Hum. Neurosci. 6, 285 (2012).