Neuromodulators are chemicals released in the brain that affect the activity of brain cells. Serotonin is a neuromodulator with the most complicated role: it is released in most brain regions and affects behavior in diverse ways. Serotonin is implicated in the regulation of mood, anxiety, impulsivity and learning. Moreover, most medications for depression target serotonin. A lack of motivation is an important symptom of depression, but exactly how serotonin affects motivation still remains unclear.

Meyniel et al. studied how increasing the amount of serotonin in the brain affects motivation in healthy people. The volunteers in the experiments squeezed a handgrip: the longer they squeezed, the more money they got as a reward. Before the experiment, some of the volunteers received an antidepressant drug that increases the amount of serotonin surrounding their brain cells, while others received a placebo.

The experiments revealed that, compared to the people who had the placebo, those who received the drug put in more effort to get a reward. More serotonin could increase motivation by reducing the perceived cost of putting in more effort, or by making people value the reward more. A mathematical model of the results showed that the increased motivation in the antidepressant group was more consistent with serotonin reducing the cost of putting in an effort, rather than increasing how much the reward was valued.

Combined with previous findings, these results suggest that serotonin affects the processing of cost associated with tasks – be that the amount of effort required, delays in getting a reward, or a punishment. Further experiments are now required to understand if the same mechanism operates in people with depression, and if so, whether it can be altered to promote recovery. It will also be important to better understand the interaction between serotonin and other neuromodulators such as dopamine.

Selective serotonin reuptake inhibitors (SSRI) are the most prescribed medications for major depressive episodes (Bauer et al., 2008). The effects of SSRI on improving mood and reducing anxiety have been well documented (Trivedi et al., 2006; Stahl, 2008; Cipriani et al., 2009) and many experimental studies showed that serotonin modulates emotional processing even in healthy volunteers (Harmer et al., 2009; Serretti et al., 2010). Loss of interest or pleasure in daily activities is also a key symptom of depression (American Psychiatric Association, 2013). However, the impact of SSRI on the motivation deficit (apathy) remains rather controversial (Papakostas et al., 2006; Weber et al., 2009).

Indeed, several studies reported that SSRI treatments do not reduce apathy as much as other symptoms (Fava et al., 2014) or can even induce apathy in patients (Barnhart et al., 2004; Sansone and Sansone, 2010; Padala et al., 2012). However, other studies also reported that SSRI treatments increase motivation or at least the sensitivity to reward (Tang et al., 2009; Stoy et al., 2012; Yuen et al., 2014). The related animal literature is also contradictory: some studies on cost/benefit trade-off showed reduced effort expenditure with SSRIs (Yohn et al., 2016) or no effect with serotonin blockers (Denk et al., 2005), while other studies reported increased motor activity induced by SSRI (Weber et al., 2009) or optogenetic stimulation of the dorsal raphe nucleus (Warden et al., 2012).

One issue with clinical studies is that SSRI effects may be confounded by an interaction with the pathological state: an SSRI treatment was reported to decrease apathy in late-life depression (Yuen et al., 2014) and to increase it in Parkinson’s disease (Zahodne et al., 2012). Another issue is that apathy may not be a simple construct. It is typically captured as a loss of behavioral activation (Cléry-Melin et al., 2011; Treadway et al., 2012), which could arise from different causes at the cognitive level: for instance a diminished sensitivity to reward attractiveness, or alternatively an exacerbated sensitivity to action cost. The ambition of the present study therefore was two-fold: we aimed (1) to clarify the effects of an SSRI on the motivation of effortful behavior in healthy volunteers, unconfounded by psychopathology, and (2) to reconcile these effects with a more general role of serotonin in the sensitivity to action costs vs. benefits.

Although experimental findings seem diverse at first sight, they might suggest a general role for serotonin in the behavioral adaptation to action costs. Previous studies suggested that serotonin is implicated in the motor aspects of action production and also the valuation processes that motivate the action. Notably, serotonin was found to condition impulse control and the capacity for behavioral inhibition (Cools et al., 2005; Crockett et al., 2009; Warden et al., 2012; Guitart-Masip et al., 2014) and to promote patience for delayed rewards (Schweighofer et al., 2008; Miyazaki et al., 2014; Worbe et al., 2014). Serotonin also impacts the determinants of actions, for instance, how positive and negative outcomes guide learning in humans (Chamberlain et al., 2006; Crockett et al., 2009; Faulkner and Deakin, 2014), monkeys (Clarke et al., 2004) and rodents (Bari et al., 2010). Several attempts have been made previously to capture serotonin function in a coherent computational theory. Key ideas are that serotonin (1) regulates the impact of action costs such as punishments (Daw et al., 2002; Niv et al., 2007) or delay (Niv et al., 2007; Cools et al., 2011; Miyazaki et al., 2014) and (2) adjusts the propensity to activate versus inhibit the action (Boureau and Dayan, 2011; Cools et al., 2011).

Borrowing from these models, we retain the general working hypothesis that serotonin regulates behavioral activation by modulating the weight of action costs rather than benefits.

We further suggest that the notion of action cost could be more general than initially envisaged. Indeed, in the literature quoted above, actions are mostly implemented as binary responses (e.g., approach vs. avoidance) and their costs or benefits are manipulated through the valence of their outcome (e.g., monetary gain or loss). Such tasks therefore over-simplify the real-life situation, where actions may often require more or less effort, depending on their intensity and duration.

Here, we tested the specific hypothesis that serotonin regulates the weight of effort cost in action production, as opposed to the weight of expected benefit. To assess the impact of changing cerebral serotonin levels and how it unfolds over time, we compared different groups of healthy subjects treated under double blind conditions with escitalopram (an SSRI) or placebo during eight weeks. Participants performed a previously published task (Meyniel et al., 2013), during which they allocated physical effort over time in order to maximize a monetary payoff that increased with effort duration (see Figure 1A). The task thus involves trading a physical effort cost against distinct levels of monetary incentives. Although changes in cost and benefit were independently manipulated in the task, they may result in intricate effects at the behavioral level. To disentangle between potential effects of serotonin on cost or benefit estimates, or both, we used a formal model of how decisions are generated by a hidden level of computations in our task, as previously described (Meyniel et al., 2013, 2014). Based on this computational analysis of the behavior, we could pinpoint the specific effect of serotonin on cost-related parameters, and track the predicted effects of such a computational change onto the experimental measures.

Figure 1

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Figure 1—source data 1

The MATLAB data file contains the payoff earned by each participant at each visit, in the placebo and escitalopram groups.

https://doi.org/10.7554/eLife.17282.004

Download elife-17282-fig1-data1-v1.mat

Our behavioral results in healthy volunteers show that the SSRI escitalopram improves global performance and hence payoff in a task that involves trading effort cost against monetary benefit. Taking advantage of the independent manipulation of cost and benefit, our computational analysis characterized the effect of escitalopram as a specific diminution of effort cost.

Together with previous findings, our results support the hypothesis that cost may be a general functional domain for serotonin (Boureau and Dayan, 2011; Dayan, 2012). For instance, interventions targeting serotonergic transmission during probabilistic and reversal learning paradigms in rodents, monkeys and humans suggested that serotonin impacts sensitivity to negative rather than positive feedback (Clarke et al., 2004; Chamberlain et al., 2006; Crockett et al., 2009; Bari et al., 2010; Cohen et al., 2015). The reduction of serotonergic transmission in humans following acute tryptophan depletion (for a review, see Faulkner and Deakin, 2014) reduced information sampling in a decision-making task, but crucially, only when sampling had a financial cost (Crockett et al., 2012). Serotonin may also impact moral costs, such as unfairness in social decision-making (Crockett et al., 2008). Furthermore, serotonin may control the cost of delay in reward delivery in rodents, such that a higher firing rate of serotonergic neurons correlates with an increased ability to wait for bigger rewards (Miyazaki et al., 2014), without affecting the sensitivity to the reward itself (Fonseca et al., 2015). Conversely, low serotonin levels, e.g. after acute tryptophan depletion, were suggested to exacerbate sensitivity to the cost of waiting, which could result in impulse control disorders (Crockett et al., 2009; Cools et al., 2011; Dayan, 2012). Thus, it is tempting to build a parsimonious computational theory on the idea that serotonin is involved in processing all kinds of action costs. This is compatible with the notion that different types of costs are processed by distinct neural circuits, since neuromodulators such as serotonin can affect many brain regions. However, such a generalization would necessitate assessing the putative role of each mono-amine and their interactions with different types of costs within the same study. Previous attempts in rodents have observed effects that are inconsistent with the present results: in cost/benefit trade-off tasks, serotonin blockers impacted choices only when costs were delays, and not physical efforts (Denk et al., 2005), whereas dopamine blockers affected both types of costs. Such discrepancies may call for caution when translating the results of pharmacological studies in rodents into medication effects in humans.

A general role for serotonin in cost processing also seems compatible with the effects on apathy and impulsivity reported in healthy and pathological conditions (Cools et al., 2005; Papakostas et al., 2006; Schweighofer et al., 2008; Crockett et al., 2009; Weber et al., 2009; Warden et al., 2012; Guitart-Masip et al., 2014; Miyazaki et al., 2014; Worbe et al., 2014). Indeed, according to our interpretation, SSRIs might reduce the perceived cost of performing actions, which would promote behavioral activation, hence the alleviation of apathy. As SSRIs might also reduce the perceived cost of delaying action, they would improve response control and reduce impulsivity (Cools et al., 2005; Cools et al., 2011; Crockett et al., 2009; Dayan, 2012; Bari and Robbins, 2013; Miyazaki et al., 2014; Fonseca et al., 2015).

However, it is important to note that the SSRI effects obtained here relate to effort cost and not to time discounting. Although time is central in our task, it departs from delay and waiting paradigms in several respects. First, the overall task duration was fixed and its pace was independent from how subjects allocated their effort within trials. Second, the reward was gained instantaneously, concomitantly to effort production, without delay. Therefore, the SSRI effect on effort duration (how long subjects sustain an effort) cannot be explained by a change in temporal discounting (how long subjects wait for a reward).

The direction of the effect obtained in the present study suggests that higher serotonin level alleviates the effort cost. This interpretation is based on the assumption that the primary effect of SSRI is to increase serotonin level in the synaptic cleft (Nutt et al., 1999; Stahl, 2008). However, the net effect of SSRI might not be so straightforward to interpret at the molecular level for several reasons. First, a high tonic concentration could have the paradoxical effect of reducing the sensitivity to phasic serotonergic signals (Faulkner and Deakin, 2014). Second, the ubiquitous negative feedback regulation by auto-receptors can initially revert the response expected from high extracellular levels of serotonin (Stahl, 2008; Fischer et al., 2014) and also produce non-linear dose-response functions for cognitive performance (Bari et al., 2010). Third, much evidence suggests that different serotonin projections to different forebrain systems mediate varying acute and chronic adaptive responses to aversive events (Hale et al., 2013; Faulkner and Deakin, 2014). The effect of serotonin also depends on the location in the brain and on the type of receptors, for instance, 5-HT1A and 5-HT2A receptors respectively inhibit and excite motoneurons (Jacobs and Azmitia, 1992). Thus, the superficial interpretation that serotonin helps overcoming effort costs will need to be refined by addressing the complexity of SSRI effects at the molecular level. We nevertheless note that the superficial interpretation is consistent with demonstrations that serotonin also helps overcoming other costs such as delay in humans (Schweighofer et al., 2008).

Refinement is also needed at the computational level. Our model does not specify how exactly serotonin could attenuate the impact of effort cost on action production. An indirect effect through an increase in muscular capacity can be excluded since there was no difference in maximal force between the placebo and SSRI groups at any visit. The computational analysis simply suggests that a slower accumulation of effort cost (lower Se_m) under SSRI prolonged effort duration. It does not distinguish between down-regulation of the cost signal itself, or down-regulation of the weight this cost has in the decision to produce an effort. The former (perceptual) view would be in line with an analgesic effect of escitalopram, which is consistent with the findings that nociception and/or somatosensory perception are both modulated by serotonin at central levels (Jacobs and Azmitia, 1992), that serotonin is targeted by common drugs modulating pain like acetaminophen (Smith, 2009) and that serotonin modulates the inhibitory feedback loop that allows muscular fatigue to down-regulate the motor command (Gandevia, 2001; Cotel et al., 2013). The latter (decisional) view would be more generalizable to the capacity of overcoming other types of costs.

As the serotonin and dopamine systems strongly interact (Dremencov et al., 2009; Cools et al., 2011; Schilström et al., 2011; Fischer et al., 2014), the SSRI effect observed here might also be at least in part mediated by dopamine. Indeed, the beneficial effect of SSRI shown here is reminiscent of previous reports about dopaminergic manipulations in mesolimbic structures, which affected the propensity to choose high reward – high effort over low reward – low effort options in rodents (Cousins et al., 1996; Salamone et al., 2007). However, if SSRIs antagonize dopamine release (Dremencov et al., 2009; Cools et al., 2011), escitalopram should have reduced effort production in our task (but see Schilström et al., 2011). Moreover, the fact that escitalopram did not modulate incentive effects on parameters such as the amplitude or dissipation slope argues against a participation of dopamine. Indeed dopaminergic manipulations in humans have been repeatedly shown to affect reward processing, not only in learning contexts (Frank et al., 2004; Pessiglione et al., 2006; Palminteri et al., 2009) but also in reward/effort trade-off paradigms (Wardle et al., 2011; Treadway et al., 2012; Le Bouc et al., 2016). It might be that serotonin and dopamine have complementary roles in promoting action production (and alleviating apathy): the former by reducing effort cost and the latter by enhancing the incentive value of potential rewards.

Previous studies suggest that we should distinguish between acute and chronic effects of SSRIs (Fischer et al., 2014). We did not find an interaction with time in our results but only a main effect of treatment group. Both parametric and non-parametric statistics showed that such a group difference is very unlikely to arise from chance in sampling the population (under the null hypothesis). General psychological assessment at baseline was also similar in the two groups. However, we acknowledge that our shortest time since treatment onset (3 days) may already depart from the acute regime, that ceiling effects in the SSRI group could in principle mask an interaction with time, and that our study may lack the statistical power to reveal such an effect. This absence of an interaction contrasts with the apparently delayed clinical effect of SSRIs in depressed patients, which usually take weeks to improve mood (Stahl, 2008). However, at the molecular level, serotonin release is boosted by SSRIs from treatment onset (Kobayashi et al., 2008) and SSRI effects on emotional processing also occur with little or no delay in healthy subjects (Harmer et al., 2003). In pathological conditions, time may be needed to adjust to the new, less negative perception of costs, as well as the reduced emotional bias, and convert these implicit changes into a conscious subjective improvement that can be reported to the practitioner (Harmer et al., 2009; Cools et al., 2011). This idea has been formalized in a recent study showing how positive and negative outcomes can shape mood on the long run (Eldar and Niv, 2015). Further studies in depressed patients are needed to assess whether an early detection of effort cost attenuation could be used to predict long-term treatment effects on clinical symptoms.

Finally, our results also indicate that non-trivial behavioral effects can be accounted for by a change in a single computational parameter (Se_m): a specific modulation of cost can result in both longer efforts and an increased sensitivity of effort duration to potential benefits in our results. Intuitively, this effect on reward sensitivity corresponds to the idea that when perceived costs are too high, a change in reward prospect will have little effect. Thus, our analysis shows that the behavioral consequences of cost and benefit estimates are intricate but computationally tractable. Our paradigm and model could provide experimental and conceptual tools to refine the description of motivational disorders. Distinct dysfunctions, such as amplification of effort cost vs. flattening of reward prospects, might call for different treatments: if drugs modulating serotonin only affect cost estimates, then other drugs (possibly dopaminergic) should aim to impact the valuation of potential benefits.

1. Book

   1. American Psychiatric Association

   (2013)

   Diagnostic and Statistical Manual of Mental Disorders (DSM-5)

   American Psychiatric Pub.

   • Google Scholar
2. 1. Balasubramani PP
   2. Chakravarthy VS
   3. Ravindran B
   4. Moustafa AA

   (2015) A network model of basal ganglia for understanding the roles of dopamine and serotonin in reward-punishment-risk based decision making

   Frontiers in Computational Neuroscience 9:76.

   https://doi.org/10.3389/fncom.2015.00076

   • PubMed
   • Google Scholar
3. 1. Bari A
   2. Robbins TW

   (2013) Inhibition and impulsivity: behavioral and neural basis of response control

   Progress in Neurobiology 108:44–79.

   https://doi.org/10.1016/j.pneurobio.2013.06.005

   • PubMed
   • Google Scholar
4. 1. Bari A
   2. Theobald DE
   3. Caprioli D
   4. Mar AC
   5. Aidoo-Micah A
   6. Dalley JW
   7. Robbins TW

   (2010) Serotonin modulates sensitivity to reward and negative feedback in a probabilistic reversal learning task in rats

   Neuropsychopharmacology 35:1290–1301.

   https://doi.org/10.1038/npp.2009.233

   • PubMed
   • Google Scholar
5. 1. Barnhart WJ
   2. Makela EH
   3. Latocha MJ

   (2004) SSRI-induced apathy syndrome: a clinical review

   Journal of Psychiatric Practice 10:196–199.

   https://doi.org/10.1097/00131746-200405000-00010

   • PubMed
   • Google Scholar
6. 1. Bauer M
   2. Monz BU
   3. Montejo AL
   4. Quail D
   5. Dantchev N
   6. Demyttenaere K
   7. Garcia-Cebrian A
   8. Grassi L
   9. Perahia DG
   10. Reed C
   11. Tylee A

   (2008) Prescribing patterns of antidepressants in Europe: results from the Factors Influencing Depression Endpoints Research (FINDER) study

   European Psychiatry 23:66–73.

   https://doi.org/10.1016/j.eurpsy.2007.11.001

   • PubMed
   • Google Scholar
7. 1. Boureau YL
   2. Dayan P

   (2011) Opponency revisited: competition and cooperation between dopamine and serotonin

   Neuropsychopharmacology 36:74–97.

   https://doi.org/10.1038/npp.2010.151

   • PubMed
   • Google Scholar
8. 1. Chamberlain SR
   2. Müller U
   3. Blackwell AD
   4. Clark L
   5. Robbins TW
   6. Sahakian BJ

   (2006) Neurochemical modulation of response inhibition and probabilistic learning in humans

   Science 311:861–863.

   https://doi.org/10.1126/science.1121218

   • PubMed
   • Google Scholar
9. 1. Cipriani A
   2. Furukawa TA
   3. Salanti G
   4. Geddes JR
   5. Higgins JPT
   6. Churchill R
   7. Watanabe N
   8. Nakagawa A
   9. Omori IM
   10. McGuire H
   11. Tansella M
   12. Barbui C

   (2009) Comparative efficacy and acceptability of 12 new-generation antidepressants: a multiple-treatments meta-analysis

   The Lancet 373:746–758.

   https://doi.org/10.1016/S0140-6736(09)60046-5

   • Google Scholar
10. 1. Clarke HF
    2. Dalley JW
    3. Crofts HS
    4. Robbins TW
    5. Roberts AC

    (2004) Cognitive inflexibility after prefrontal serotonin depletion

    Science 304:878–880.

    https://doi.org/10.1126/science.1094987

    • PubMed
    • Google Scholar
11. 1. Cléry-Melin ML
    2. Schmidt L
    3. Lafargue G
    4. Baup N
    5. Fossati P
    6. Pessiglione M

    (2011) Why don't you try harder? An investigation of effort production in major depression

    PLoS One 6:e23178.

    https://doi.org/10.1371/journal.pone.0023178

    • PubMed
    • Google Scholar
12. 1. Cohen JY
    2. Amoroso MW
    3. Uchida N

    (2015) Serotonergic neurons signal reward and punishment on multiple timescales

    eLife 4:e06346.

    https://doi.org/10.7554/eLife.06346

    • Google Scholar
13. 1. Cools R
    2. Blackwell A
    3. Clark L
    4. Menzies L
    5. Cox S
    6. Robbins TW

    (2005) Tryptophan depletion disrupts the motivational guidance of goal-directed behavior as a function of trait impulsivity

    Neuropsychopharmacology 30:1362–1373.

    https://doi.org/10.1038/sj.npp.1300704

    • Google Scholar
14. 1. Cools R
    2. Nakamura K
    3. Daw ND

    (2011) Serotonin and dopamine: unifying affective, activational, and decision functions

    Neuropsychopharmacology 36:98–113.

    https://doi.org/10.1038/npp.2010.121

    • PubMed
    • Google Scholar
15. 1. Cotel F
    2. Exley R
    3. Cragg SJ
    4. Perrier JF

    (2013) Serotonin spillover onto the axon initial segment of motoneurons induces central fatigue by inhibiting action potential initiation

    PNAS 110:4774–4779.

    https://doi.org/10.1073/pnas.1216150110

    • Google Scholar
16. 1. Cousins MS
    2. Atherton A
    3. Turner L
    4. Salamone JD

    (1996) Nucleus accumbens dopamine depletions alter relative response allocation in a T-maze cost/benefit task

    Behavioural Brain Research 74:189–197.

    https://doi.org/10.1016/0166-4328(95)00151-4

    • PubMed
    • Google Scholar
17. 1. Crockett MJ
    2. Clark L
    3. Robbins TW

    (2009) Reconciling the role of serotonin in behavioral inhibition and aversion: acute tryptophan depletion abolishes punishment-induced inhibition in humans

    Journal of Neuroscience 29:11993–11999.

    https://doi.org/10.1523/JNEUROSCI.2513-09.2009

    • PubMed
    • Google Scholar
18. 1. Crockett MJ
    2. Clark L
    3. Smillie LD
    4. Robbins TW

    (2012) The effects of acute tryptophan depletion on costly information sampling: impulsivity or aversive processing?

    Psychopharmacology 219:587–597.

    https://doi.org/10.1007/s00213-011-2577-9

    • PubMed
    • Google Scholar
19. 1. Crockett MJ
    2. Clark L
    3. Tabibnia G
    4. Lieberman MD
    5. Robbins TW

    (2008) Serotonin modulates behavioral reactions to unfairness

    Science 320:1739.

    https://doi.org/10.1126/science.1155577

    • PubMed
    • Google Scholar
20. 1. Daunizeau J
    2. Adam V
    3. Rigoux L

    (2014) VBA: a probabilistic treatment of nonlinear models for neurobiological and behavioural data

    PLoS Computational Biology 10:e1003441.

    https://doi.org/10.1371/journal.pcbi.1003441

    • PubMed
    • Google Scholar
21. 1. Daw ND
    2. Kakade S
    3. Dayan P

    (2002) Opponent interactions between serotonin and dopamine

    Neural Networks 15:603–616.

    https://doi.org/10.1016/S0893-6080(02)00052-7

    • PubMed
    • Google Scholar
22. 1. Dayan P

    (2012) Twenty-five lessons from computational neuromodulation

    Neuron 76:240–256.

    https://doi.org/10.1016/j.neuron.2012.09.027

    • PubMed
    • Google Scholar
23. 1. Denk F
    2. Walton ME
    3. Jennings KA
    4. Sharp T
    5. Rushworth MF
    6. Bannerman DM

    (2005) Differential involvement of serotonin and dopamine systems in cost-benefit decisions about delay or effort

    Psychopharmacology 179:587–596.

    https://doi.org/10.1007/s00213-004-2059-4

    • PubMed
    • Google Scholar
24. 1. Doya K

    (2002) Metalearning and neuromodulation

    Neural Networks 15:495–506.

    https://doi.org/10.1016/S0893-6080(02)00044-8

    • PubMed
    • Google Scholar
25. 1. Dremencov E
    2. El Mansari M
    3. Blier P

    (2009)

    Effects of sustained serotonin reuptake inhibition on the firing of dopamine neurons in the rat ventral tegmental area

    Journal of Psychiatry & Neuroscience 34:223–229.

    • PubMed
    • Google Scholar
26. 1. Eldar E
    2. Niv Y

    (2015) Interaction between emotional state and learning underlies mood instability

    Nature Communications 6:6149.

    https://doi.org/10.1038/ncomms7149

    • PubMed
    • Google Scholar
27. 1. Faulkner P
    2. Deakin JF

    (2014) The role of serotonin in reward, punishment and behavioural inhibition in humans: insights from studies with acute tryptophan depletion

    Neuroscience & Biobehavioral Reviews 46:365–378.

    https://doi.org/10.1016/j.neubiorev.2014.07.024

    • PubMed
    • Google Scholar
28. 1. Fava M
    2. Ball S
    3. Nelson JC
    4. Sparks J
    5. Konechnik T
    6. Classi P
    7. Dube S
    8. Thase ME

    (2014) Clinical relevance of fatigue as a residual symptom in major depressive disorder

    Depression and Anxiety 31:250–257.

    https://doi.org/10.1002/da.22199

    • PubMed
    • Google Scholar
29. 1. Fischer AG
    2. Jocham G
    3. Ullsperger M

    (2015) Dual serotonergic signals: a key to understanding paradoxical effects?

    Trends in Cognitive Sciences 19:21–26.

    https://doi.org/10.1016/j.tics.2014.11.004

    • Google Scholar
30. 1. Fonseca MS
    2. Murakami M
    3. Mainen ZF

    (2015) Activation of dorsal raphe serotonergic neurons promotes waiting but is not reinforcing

    Current Biology 25:306–315.

    https://doi.org/10.1016/j.cub.2014.12.002

    • PubMed
    • Google Scholar
31. 1. Frank MJ
    2. Seeberger LC
    3. O'reilly RC

    (2004) By carrot or by stick: cognitive reinforcement learning in parkinsonism

    Science 306:1940–1943.

    https://doi.org/10.1126/science.1102941

    • PubMed
    • Google Scholar
32. 1. Gandevia SC

    (2001)

    Spinal and supraspinal factors in human muscle fatigue

    Physiological Reviews 81:1725–1789.

    • PubMed
    • Google Scholar
33. 1. Guitart-Masip M
    2. Economides M
    3. Huys QJ
    4. Frank MJ
    5. Chowdhury R
    6. Duzel E
    7. Dayan P
    8. Dolan RJ

    (2014) Differential, but not opponent, effects of L -DOPA and citalopram on action learning with reward and punishment

    Psychopharmacology 231:955–966.

    https://doi.org/10.1007/s00213-013-3313-4

    • PubMed
    • Google Scholar
34. 1. Hale MW
    2. Raison CL
    3. Lowry CA

    (2013) Integrative physiology of depression and antidepressant drug action: implications for serotonergic mechanisms of action and novel therapeutic strategies for treatment of depression

    Pharmacology & Therapeutics 137:108–118.

    https://doi.org/10.1016/j.pharmthera.2012.09.005

    • PubMed
    • Google Scholar
35. 1. Harmer CJ
    2. Bhagwagar Z
    3. Perrett DI
    4. Völlm BA
    5. Cowen PJ
    6. Goodwin GM

    (2003) Acute SSRI administration affects the processing of social cues in healthy volunteers

    Neuropsychopharmacology 28:148–152.

    https://doi.org/10.1038/sj.npp.1300004

    • PubMed
    • Google Scholar
36. 1. Harmer CJ
    2. Goodwin GM
    3. Cowen PJ

    (2009) Why do antidepressants take so long to work? A cognitive neuropsychological model of antidepressant drug action

    The British Journal of Psychiatry 195:102–108.

    https://doi.org/10.1192/bjp.bp.108.051193

    • PubMed
    • Google Scholar
37. 1. Harmer CJ
    2. Shelley NC
    3. Cowen PJ
    4. Goodwin GM

    (2004) Increased positive versus negative affective perception and memory in healthy volunteers following selective serotonin and norepinephrine reuptake inhibition

    American Journal of Psychiatry 161:1256–1263.

    https://doi.org/10.1176/appi.ajp.161.7.1256

    • PubMed
    • Google Scholar
38. 1. Jacobs BL
    2. Azmitia EC

    (1992)

    Structure and function of the brain serotonin system

    Physiological Reviews 72:165–229.

    • PubMed
    • Google Scholar
39. 1. Kobayashi T
    2. Hayashi E
    3. Shimamura M
    4. Kinoshita M
    5. Murphy NP

    (2008) Neurochemical responses to antidepressants in the prefrontal cortex of mice and their efficacy in preclinical models of anxiety-like and depression-like behavior: a comparative and correlational study

    Psychopharmacology 197:567–580.

    https://doi.org/10.1007/s00213-008-1070-6

    • PubMed
    • Google Scholar
40. 1. Le Bouc R
    2. Rigoux L
    3. Schmidt L
    4. Degos B
    5. Welter ML
    6. Vidailhet M
    7. Daunizeau J
    8. Pessiglione M

    (2016) Computational dissection of dopamine motor and motivational functions in humans

    Journal of Neuroscience 36:In press.

    https://doi.org/10.1523/JNEUROSCI.3078-15.2016

    • PubMed
    • Google Scholar
41. 1. Meyniel F
    2. Safra L
    3. Pessiglione M

    (2014) How the brain decides when to work and when to rest: dissociation of implicit-reactive from explicit-predictive computational processes

    PLoS Computational Biology 10:e1003584.

    https://doi.org/10.1371/journal.pcbi.1003584

    • PubMed
    • Google Scholar
42. 1. Meyniel F
    2. Sergent C
    3. Rigoux L
    4. Daunizeau J
    5. Pessiglione M

    (2013) Neurocomputational account of how the human brain decides when to have a break

    PNAS 110:2641–2646.

    https://doi.org/10.1073/pnas.1211925110

    • Google Scholar
43. 1. Miyazaki KW
    2. Miyazaki K
    3. Tanaka KF
    4. Yamanaka A
    5. Takahashi A
    6. Tabuchi S
    7. Doya K

    (2014) Optogenetic activation of dorsal raphe serotonin neurons enhances patience for future rewards

    Current Biology 24:2033–2040.

    https://doi.org/10.1016/j.cub.2014.07.041

    • PubMed
    • Google Scholar
44. 1. Niv Y
    2. Daw ND
    3. Joel D
    4. Dayan P

    (2007) Tonic dopamine: opportunity costs and the control of response vigor

    Psychopharmacology 191:507–520.

    https://doi.org/10.1007/s00213-006-0502-4

    • PubMed
    • Google Scholar
45. 1. Nutt DJ
    2. Forshall S
    3. Bell C
    4. Rich A
    5. Sandford J
    6. Nash J
    7. Argyropoulos S

    (1999) Mechanisms of action of selective serotonin reuptake inhibitors in the treatment of psychiatric disorders

    European Neuropsychopharmacology 9:S81–S86.

    https://doi.org/10.1016/S0924-977X(99)00030-9

    • PubMed
    • Google Scholar
46. 1. Padala PR
    2. Padala KP
    3. Monga V
    4. Ramirez DA
    5. Sullivan DH

    (2012) Reversal of SSRI-associated apathy syndrome by discontinuation of therapy

    Annals of Pharmacotherapy 46:e8.

    https://doi.org/10.1345/aph.1Q656

    • PubMed
    • Google Scholar
47. 1. Palminteri S
    2. Lebreton M
    3. Worbe Y
    4. Grabli D
    5. Hartmann A
    6. Pessiglione M

    (2009) Pharmacological modulation of subliminal learning in Parkinson's and Tourette's syndromes

    PNAS 106:19179–19184.

    https://doi.org/10.1073/pnas.0904035106

    • Google Scholar
48. 1. Papakostas GI
    2. Nutt DJ
    3. Hallett LA
    4. Tucker VL
    5. Krishen A
    6. Fava M

    (2006) Resolution of sleepiness and fatigue in major depressive disorder: A comparison of bupropion and the selective serotonin reuptake inhibitors

    Biological Psychiatry 60:1350–1355.

    https://doi.org/10.1016/j.biopsych.2006.06.015

    • PubMed
    • Google Scholar
49. 1. Pessiglione M
    2. Seymour B
    3. Flandin G
    4. Dolan RJ
    5. Frith CD

    (2006) Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans

    Nature 442:1042–1045.

    https://doi.org/10.1038/nature05051

    • PubMed
    • Google Scholar
50. 1. Salamone JD
    2. Correa M
    3. Farrar A
    4. Mingote SM

    (2007) Effort-related functions of nucleus accumbens dopamine and associated forebrain circuits

    Psychopharmacology 191:461–482.

    https://doi.org/10.1007/s00213-006-0668-9

    • PubMed
    • Google Scholar
51. 1. Sansone RA
    2. Sansone LA

    (2010)

    SSRI-Induced Indifference

    Psychiatry (Edgmont) 7:14–18.

    • PubMed
    • Google Scholar
52. 1. Schilström B
    2. Konradsson-Geuken A
    3. Ivanov V
    4. Gertow J
    5. Feltmann K
    6. Marcus MM
    7. Jardemark K
    8. Svensson TH

    (2011) Effects of S-citalopram, citalopram, and R-citalopram on the firing patterns of dopamine neurons in the ventral tegmental area, N-methyl-D-aspartate receptor-mediated transmission in the medial prefrontal cortex and cognitive function in the rat

    Synapse 65:357–367.

    https://doi.org/10.1002/syn.20853

    • PubMed
    • Google Scholar
53. 1. Schweighofer N
    2. Bertin M
    3. Shishida K
    4. Okamoto Y
    5. Tanaka SC
    6. Yamawaki S
    7. Doya K

    (2008) Low-serotonin levels increase delayed reward discounting in humans

    Journal of Neuroscience 28:4528–4532.

    https://doi.org/10.1523/JNEUROSCI.4982-07.2008

    • PubMed
    • Google Scholar
54. 1. Serretti A
    2. Calati R
    3. Goracci A
    4. Di Simplicio M
    5. Castrogiovanni P
    6. De Ronchi D

    (2010) Antidepressants in healthy subjects: what are the psychotropic/psychological effects?

    European Neuropsychopharmacology 20:433–453.

    https://doi.org/10.1016/j.euroneuro.2009.11.009

    • PubMed
    • Google Scholar
55. 1. Smith HS

    (2009)

    Potential analgesic mechanisms of acetaminophen

    Pain Physician 12:269–280.

    • PubMed
    • Google Scholar
56. 1. Stahl SM

    (2008)

    Stahl’s Essential Psychopharmacology. Neuroscientific Basis and Practical Applications

    Chapter 12 - Antidepressants, Stahl’s Essential Psychopharmacology. Neuroscientific Basis and Practical Applications, (2nd ed), Cambridge University Press.

    • Google Scholar
57. 1. Stephan KE
    2. Penny WD
    3. Daunizeau J
    4. Moran RJ
    5. Friston KJ

    (2009) Bayesian model selection for group studies

    NeuroImage 46:1004–1017.

    https://doi.org/10.1016/j.neuroimage.2009.03.025

    • PubMed
    • Google Scholar
58. 1. Stoy M
    2. Schlagenhauf F
    3. Sterzer P
    4. Bermpohl F
    5. Hägele C
    6. Suchotzki K
    7. Schmack K
    8. Wrase J
    9. Ricken R
    10. Knutson B
    11. Adli M
    12. Bauer M
    13. Heinz A
    14. Ströhle A

    (2012) Hyporeactivity of ventral striatum towards incentive stimuli in unmedicated depressed patients normalizes after treatment with escitalopram

    Journal of Psychopharmacology 26:677–688.

    https://doi.org/10.1177/0269881111416686

    • PubMed
    • Google Scholar
59. 1. Tang TZ
    2. DeRubeis RJ
    3. Hollon SD
    4. Amsterdam J
    5. Shelton R
    6. Schalet B

    (2009) A placebo-controlled test of the effects of paroxetine and cognitive therapy on personality risk factors in depression

    Arch Gen Psychiatry 66:1322–1330.

    https://doi.org/10.1001/archgenpsychiatry.2009.166

    • Google Scholar
60. 1. Treadway MT
    2. Bossaller NA
    3. Shelton RC
    4. Zald DH

    (2012) Effort-based decision-making in major depressive disorder: a translational model of motivational anhedonia

    Journal of Abnormal Psychology 121:553–558.

    https://doi.org/10.1037/a0028813

    • PubMed
    • Google Scholar
61. 1. Trivedi MH
    2. Rush AJ
    3. Wisniewski SR
    4. Nierenberg AA
    5. Warden D
    6. Ritz L
    7. Norquist G
    8. Howland RH
    9. Lebowitz B
    10. McGrath PJ
    11. Shores-Wilson K
    12. Biggs MM
    13. Balasubramani GK
    14. Fava M
    15. STAR*D Study Team

    (2006) Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice

    American Journal of Psychiatry 163:28–40.

    https://doi.org/10.1176/appi.ajp.163.1.28

    • PubMed
    • Google Scholar
62. 1. Warden MR
    2. Selimbeyoglu A
    3. Mirzabekov JJ
    4. Lo M
    5. Thompson KR
    6. Kim SY
    7. Adhikari A
    8. Tye KM
    9. Frank LM
    10. Deisseroth K

    (2012) A prefrontal cortex-brainstem neuronal projection that controls response to behavioural challenge

    Nature 492:428–432.

    https://doi.org/10.1038/nature11617

    • PubMed
    • Google Scholar
63. 1. Wardle MC
    2. Treadway MT
    3. Mayo LM
    4. Zald DH
    5. de Wit H

    (2011) Amping up effort: effects of d-amphetamine on human effort-based decision-making

    Journal of Neuroscience 31:16597–16602.

    https://doi.org/10.1523/JNEUROSCI.4387-11.2011

    • Google Scholar
64. 1. Weber M
    2. Talmon S
    3. Schulze I
    4. Boeddinghaus C
    5. Gross G
    6. Schoemaker H
    7. Wicke KM

    (2009) Running wheel activity is sensitive to acute treatment with selective inhibitors for either serotonin or norepinephrine reuptake

    Psychopharmacology 203:753–762.

    https://doi.org/10.1007/s00213-008-1420-4

    • PubMed
    • Google Scholar
65. 1. Worbe Y
    2. Savulich G
    3. Voon V
    4. Fernandez-Egea E
    5. Robbins TW

    (2014) Serotonin depletion induces 'waiting impulsivity' on the human four-choice serial reaction time task: cross-species translational significance

    Neuropsychopharmacology 39:1519–1526.

    https://doi.org/10.1038/npp.2013.351

    • PubMed
    • Google Scholar
66. 1. Yohn SE
    2. Collins SL
    3. Contreras-Mora HM
    4. Errante EL
    5. Rowland MA
    6. Correa M
    7. Salamone JD

    (2016) Not all antidepressants are created equal: differential effects of monoamine uptake inhibitors on effort-related choice behavior

    Neuropsychopharmacology 41:686–694.

    https://doi.org/10.1038/npp.2015.188

    • PubMed
    • Google Scholar
67. 1. Yuen GS
    2. Gunning FM
    3. Woods E
    4. Klimstra SA
    5. Hoptman MJ
    6. Alexopoulos GS

    (2014) Neuroanatomical correlates of apathy in late-life depression and antidepressant treatment response

    Journal of Affective Disorders 166:179–186.

    https://doi.org/10.1016/j.jad.2014.05.008

    • PubMed
    • Google Scholar
68. 1. Zahodne LB
    2. Bernal-Pacheco O
    3. Bowers D
    4. Ward H
    5. Oyama G
    6. Limotai N
    7. Velez-Lago F
    8. Rodriguez RL
    9. Malaty I
    10. McFarland NR
    11. Okun MS

    (2012) Are selective serotonin reuptake inhibitors associated with greater apathy in Parkinson’s disease?

    Journal of Neuropsychiatry and Clinical Neurosciences 24:326–330.

    https://doi.org/10.1176/appi.neuropsych.11090210

    • Google Scholar

Author details

1. Florent Meyniel

   1. Motivation, Brain and Behavior team, Institut du Cerveau et de la Moelle épinière, Groupe Hospitalier Pitié-Salpêtrière, Paris, France
   2. INSERM UMRS 1127, CNRS UMR 7225, Université Pierre et Marie Curie (UPMC-P6), Paris, France
   3. Cognitive Neuroimaging Unit, NeuroSpin Center, INSERM U992, Institut d'Imagerie Biomédicale, Direction de la recherche fondamentale, Commissariat à l'énergie atomique et aux énergies alternatives, Gif-sur-Yvette, France

   Contribution

   FM, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   florent.meyniel@gmail.com

   Competing interests

   FM: Supported by The French Ministère de la Recherche and the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 604102 (Human Brain Project)

   "This ORCID iD identifies the author of this article:" 0000-0002-6992-678X

2. Guy M Goodwin

   1. Department of Psychiatry, University of Oxford, Oxford, United Kingdom
   2. Warneford Hospital, University of Oxford, Oxford, United Kingdom

   Contribution

   GMG, Conception and design, Drafting or revising the article

   Competing interests

   GMG: Holds shares in P1vital Ltd and has served as consultant, advisor or CME speaker in the last 12 months for AstraZeneca, Cephalon/Teva, Convergence, Eli Lilly, GSK, Lundbeck, Medscape, Otsuka, Servier, Sunovion, Takeda

3. JF William Deakin

   Neuroscience and Psychiatry Unit, The University of Manchester, Manchester, United Kingdom

   Contribution

   JFWD, Conception and design, Drafting or revising the article

   Competing interests

   JFWD: Currently advises or carries out research funded by Autifony, Sunovion, Lundbeck, AstraZeneca and Servier; all payment is to the University of Manchester; he has share options in P1vital Ltd

   "This ORCID iD identifies the author of this article:" 0000-0002-2750-962X

4. Corinna Klinge

   1. Department of Psychiatry, University of Oxford, Oxford, United Kingdom
   2. Warneford Hospital, University of Oxford, Oxford, United Kingdom

   Contribution

   CK, Collected all data together with CMF from the Oxford center, Acquisition of data

   Competing interests

   No competing interests declared.

5. Christine MacFadyen

   1. Department of Psychiatry, University of Oxford, Oxford, United Kingdom
   2. Warneford Hospital, University of Oxford, Oxford, United Kingdom

   Contribution

   CM, Collected all data together with CK from the Oxford center, Acquisition of data

   Competing interests

   No competing interests declared.

6. Holly Milligan

   Neuroscience and Psychiatry Unit, The University of Manchester, Manchester, United Kingdom

   Contribution

   HM, Collected all data together with EM from the Manchester center, Acquisition of data

   Competing interests

   No competing interests declared.

7. Emma Mullings

   Neuroscience and Psychiatry Unit, The University of Manchester, Manchester, United Kingdom

   Contribution

   EM, Collected all data together with EM from the Manchester center, Acquisition of data

   Competing interests

   No competing interests declared.

8. Mathias Pessiglione

   1. Motivation, Brain and Behavior team, Institut du Cerveau et de la Moelle épinière, Groupe Hospitalier Pitié-Salpêtrière, Paris, France
   2. INSERM UMRS 1127, CNRS UMR 7225, Université Pierre et Marie Curie (UPMC-P6), Paris, France

   Contribution

   MP, Interpretation of the result, Conception and design, Drafting or revising the article

   Contributed equally with

   Raphaël Gaillard

   Competing interests

   No competing interests declared.

9. Raphaël Gaillard

   1. Centre Hospitalier Sainte-Anne, Service de Psychiatrie, Paris, France
   2. Faculté de Médecine Paris Descartes, Université Paris Descartes, Sorbonne Paris Cité, Paris, France
   3. Laboratoire de Physiopathologie des maladies Psychiatriques, Centre de Psychiatrie et Neurosciences, INSERM U894, Université Paris Descartes, Sorbonne Paris Cité, Paris, France
   4. Human Histopathology and Animal Models, Department of Infection and Epidemiology, Institut Pasteur, Paris, France

   Contribution

   RG, Interpretation of the results, Conception and design, Drafting or revising the article

   Contributed equally with

   Mathias Pessiglione

   Competing interests

   RG: Received compensation as a member of the scientific advisory board of Janssen, Lundbeck, Roche, Takeda; he has served as consultant and/or speaker for Astra Zeneca, Pierre Fabre, Lilly, Otsuka, SANOFI, Servier and received compensation, and he has received research support from Servier

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