Motivation facilitates overcoming the cost of effortful actions to attain desired outcomes (Chong et al., 2016) and is key to achievement and well-being (Lepine et al., 2005; Duckworth et al., 2015; Kanfer et al., 2017). Importantly, there are substantial differences in motivated behavior among healthy individuals, manifested as variations in the engagement in effortful activities and in differences in brain activity (Knutson et al., 2005; Berchio et al., 2019). Motivational deficits - such as apathy, anhedonia, or anergia- are prevalent in many brain pathologies (Epstein et al., 2006; Zald and Treadway, 2017; Pessiglione et al., 2018; Salamone et al., 2016). Therefore, unveiling the neurobiological mechanisms underlying individual differences in motivation can help develop novel interventions to boost effortful performance.

A great deal of work in both rodents and humans highlights the nucleus accumbens (NuAc) - a main component of the ventral striatum - as a critical node of the brain’s reward and motivation circuitries (Pessiglione et al., 2018; Salamone et al., 2007; Croxson et al., 2009; Haber, 2011; Schmidt et al., 2012). Indeed, incentives energize effortful behavior (Berchio et al., 2019; Robinson et al., 2014) through the recruitment of the NuAc (Schmidt et al., 2012; Knutson et al., 2001; Pessiglione et al., 2007; Hailwood et al., 2018; Soares-Cunha et al., 2016; Soares-Cunha et al., 2018; Gallo et al., 2018). Moreover, alterations in NuAc function are implicated in psychopathological conditions that involve motivational deficits, such as depression (Epstein et al., 2006; Hanson et al., 2015; Muir et al., 2019; Treadway et al., 2014).

Emerging evidence underscores a key role for accumbal mitochondrial function and metabolism in the regulation of motivated behavior (Hollis et al., 2015; van der Kooij et al., 2018a; Strasser et al., 2020; Gebara et al., 2021) and vulnerability to develop stress-induced depressive-like behaviors (Larrieu et al., 2017; Cherix et al., 2020; Weger et al., 2020). This is in line with the established relevance of cellular and tissue energy metabolism to maintain healthy brain function (Hyder et al., 2002; Smith et al., 2002) and goes beyond to posit a link between variation in the levels of specific accumbal metabolites and motivated behavior. However, knowledge regarding the specific accumbal metabolites involved in effortful performance is still scarce.

Recent work in humans using proton magnetic resonance spectroscopy (¹H-MRS) focusing on the components of the glutamate/GABA-glutamine cycle has shown that glutamine levels, and particularly, an increased glutamine-to-glutamate ratio in the NuAc predict both, a higher performance related to task endurance and a lower effort perception in a physical effort-based motivated task (Strasser et al., 2020). Glutamine is the main precursor for the synthesis of glutamate (Walls et al., 2015), a building block for the production of glutathione (GSH; Forman et al., 2009; Sappington et al., 2016). GSH is a tripeptide (glutamate–cysteine–glycine) essential for several cellular functions and the most prominent antioxidant in the brain (Figure 1a; Rae and Williams, 2017; Zalachoras et al., 2020). It seems, therefore, plausible to hypothesize that GSH levels in the NuAc may be related to the capacity to exert incentivized effort. This hypothesis is supported by convergent evidence indicating that: (i) GSH plays a major role in the reduction of reactive oxygen species (ROS) (Rae and Williams, 2017); (ii) ROS generation is coupled to energy production and cellular activity (Cobley et al., 2018); (iii) effortful behavior engages the activation of the NuAc (Schmidt et al., 2012; Pessiglione et al., 2007; Hailwood et al., 2018; Soares-Cunha et al., 2016; Soares-Cunha et al., 2018; Gallo et al., 2018); and (iv) altered GSH levels have been reported in the brain of patients in disorders that course along with motivational impairments (Lapidus et al., 2014; Wang et al., 2019).

Figure 1 with 2 supplements see all

Download asset Open asset

Figure 1—source data 1

Spectroscopy on human raw data and statistical analysis.

https://cdn.elifesciences.org/articles/77791/elife-77791-fig1-data1-v1.xlsx

Download elife-77791-fig1-data1-v1.xlsx

To assess this hypothesis, we first explored the relationship between effort-based motivated performance in humans and accumbal levels of several metabolites, including GSH, were quantified using ¹H-MRS at 7 Tesla (7T) (Strasser et al., 2020). These experiments identified a relationship between accumbal GSH levels and motivation to exert effort. Then, to go beyond correlational evidence, we investigated the relationship between accumbal GSH levels in rats and reward-incentivized performance. To this end, we used a progressive ratio (PR) schedule of reinforcement in an operant task that requires the maintenance of nose-poking under increasing work demands and modulated GSH levels through specific interventions in the NuAc. Finally, aiming at a translational application, we evaluated the efficiency of nutritional supplementation with N-acetyl-cysteine (NAC) - a cysteine precursor that has been documented to increase GSH levels in the brain (Zalachoras et al., 2020) - to improve rats’ motivated performance and explored potential effects in the excitability of the accumbal circuitry.

Here, we establish a novel role for GSH in specific brain structures in motivation. Specifically, we identify GSH levels in the NuAc as critically related to the individual’s capacity to keep on exerting effortful behavior over time in reward-incentivized tasks. We first underscored GSH levels in the NuAc as predictive of steady execution in an effort-based monetary incentivized task in humans. Participants with higher accumbal GSH levels were able to steadily maintain good performance until the end of the task, while those with lower levels showed a decay in the second half. To investigate causality, we switched to rats in which we confirmed that individual differences in accumbal GSH levels predict performance in both aversively and appetitively motivated tasks. The involvement of GSH in effort-based motivated performance was supported by the observation of impaired performance in the PR test following downregulation of accumbal GSH levels through the microinjection of the GSH synthesis inhibitor BSO. Furthermore, systemic treatment with NAC, a source of cysteine known to increase brain GSH, led to increases in accumbal GSH levels as well as in rats’ breakpoint in the PR task, that was possibly due to a shift in glutamatergic inputs to D1- and D2-MSNs. Our results highlight the ability of accumbal GSH levels to facilitate motivated endurance over time. These findings may open new avenues to consider accumbal GSH as both a biomarker for motivational disturbances and a potential target for therapeutic interventions.

Although we set the study on the specific hypothesis that accumbal GSH may be involved in effort-based motivated performance, our first unbiased analysis considering a range of metabolites measured through ¹H-MRS at 7T in humans yielded GSH as the metabolite specifically predictive of performance out of a total of 10 metabolites analyzed. Given that GSH shows a low physiological concentration and its spectra strongly overlaps with other metabolites, its measurement is challenging, especially at low magnetic fields. Increasing spectral signal-to-noise ratio and resolution at high magnetic field (7T and above) as used here is critical for improving metabolites quantification, especially for J-coupled metabolites such as GSH (Lin et al., 2012; Xin et al., 2013). Using a similar approach in rats (¹H-MRS at 9.4T), we confirmed the capacity of accumbal GSH levels to predict performance in an aversively motivated task, and post-mortem analyses related GSH levels to performance in a reward-incentivized task. Specifically, rats with higher accumbal GSH levels spent less time immobile in the FST (i.e. displaying more active coping responses to adversity) and had higher breakpoints in the PR task than their lower GSH counterparts.

Formerly, we identified a predictive value for glutamine, and particularly the glutamine-to-glutamate ratio in motivated performance in humans, and hypothesized the contribution of these metabolites to fuel mitochondrial function under enhanced NuAc engagement during effortful incentivized behaviors. Indeed, human studies have shown that the NuAc (or the ventral striatum more broadly) is the main brain structure to respond to incentives (Schmidt et al., 2012; Knutson et al., 2001; O’Doherty et al., 2004; Yacubian et al., 2006) being incrementally recruited as the expected reward increases (Knutson et al., 2001; Sescousse et al., 2015). NuAc activity relates to effort-based cost-benefit valuation (Croxson et al., 2009), acting as the motivating force (Pessiglione et al., 2007) that engages both cognitive and motor downstream brain regions (Schmidt et al., 2012). These human studies measured blood oxygen level-dependent signals through functional magnetic resonance imaging, as a proxy of neural activity. Work in rats using in vivo oxygen (O₂) amperometry similarly showed increased O₂ responses to rewards as a function of increased required effort to obtain them. Animals with higher breakpoints showed a greater magnitude of the NuAc O₂ responses, suggesting that individual differences in the capacity to remain on task for longer are related to the magnitude of O₂ responses in the NuAc (Hailwood et al., 2018). Given that the main O₂-consuming cells in the brain are neurons (Hall et al., 2012), increased O₂ in the NuAc mainly represents the energetic cost of neuronal activity. Specifically, oxygen consumption enables to meet increased energy demands in neural circuits by facilitating oxidative phosphorylation in the mitochondria and thus, contributing to the generation of ATP (Schmidt et al., 2012; Hall et al., 2012; Schneider et al., 2019). However, oxygen consumption generates free radicals and other ROS which, when excessive and not sufficiently counteracted by cellular antioxidant systems can produce oxidative stress, toxic to cells and molecules, and to which the brain is particularly susceptible (Cobley et al., 2018). Importantly, higher mitochondrial oxygen respiratory capacity in the NuAc in rodents has been shown to positively relate to the duration of active coping/escaping responses in the FST (Gebara et al., 2021) and to winning a social competition (Hollis et al., 2015; van der Kooij et al., 2018a). Accordingly, our findings here suggest that a higher accumbal GSH production may act in concert with mitochondrial oxidative phosphorylation processes to facilitate motivated effort. Its causal contribution to motivated performance was established in our study by the direct inhibition of GSH levels by BSO intra-NuAc injection.

GSH is the major antioxidant as well as a redox and cell-signaling regulator (Rae and Williams, 2017; Dringen, 2000). Given the high susceptibility of the brain to oxidative stress (Cobley et al., 2018), neurons require high levels of GSH to counteract oxidative stress and sustain cellular integrity and circuit function (Fernandez-Fernandez et al., 2018). Therefore, our results, in both humans and rats, relating individual differences in accumbal GSH content to the capacity to succeed in task performance over time, support the view that higher individual GSH levels provide an advantage to deal with oxidative processes generated by the drastically enhanced energy demand - involving increased O₂ utilization - required in NuAc circuits to maintain performance (see above). However, given our previous findings implicating the glutamine-glutamate ratio in effortful performance (Strasser et al., 2020), and recent evidence indicating that GSH may be a physiological reservoir of glutamate neurotransmitter when the glutamate-glutamine shuttle is inhibited (Sedlak et al., 2019), the alternative non-mutually exclusive possibility that higher GSH levels may be contributing to the glutamate-glutamine cycle [or vice versa; note that glutamine may also contribute directly to GSH synthesis (Sappington et al., 2016)] and, thus, facilitating performance cannot be ruled out.

We also carried out a translational approach and examined the potential of a prolonged treatment with the GSH booster NAC to improve motivation (see Figure 1a). Our administration regime effectively increased accumbal GSH content, supporting the efficiency of systemic NAC treatment in targeting antioxidant levels in the ventral striatum. Importantly, NAC treatment was also effective in increasing rats’ breakpoint in the PR task, supporting its ability to ameliorate motivational deficiencies. Selective activation of specific glutamatergic inputs to the NuAc has been shown to promote reward-based motivation (Stuber et al., 2008; Day et al., 2007; Flagel et al., 2011). Hence, we provide novel evidence that prolonged systemic treatment with NAC induces cell-subtype-specific changes in synaptic transmission in the NuAc core: quantal glutamatergic currents were larger in D1-MSNs, while the excitatory drive onto D2-MSNs was overall diminished. This suggests that, in NAC-treated rats, excitatory inputs promote activation of D1-MSNs over D2-MSNs as a result of the increased postsynaptic responses in D1-MSNs as well as the concomitant reduction in collateral inhibition provided by D2-MSNs (Ducrocq et al., 2020). The shift toward D1-MSN activation is expected to support motivated behavior, according to the canonical roles of D1- and D2-MSNs in positive reinforcement and aversion, respectively (Kravitz et al., 2012; Lobo et al., 2010; Floresco, 2015); but note that several studies have suggested that D2-MSNs may also contribute to reward-based motivation (Ducrocq et al., 2020; Trifilieff et al., 2013; Filla et al., 2018) and that MSN subtype-specific actions may depend on different patterns of activation (Soares-Cunha et al., 2020). The mechanisms whereby NAC treatment can affect glutamatergic function in the NuAc are multiple. In addition to promoting GSH synthesis, cystine derived from NAC also binds the cystine-glutamate exchanger in glial cells and shifts the extracellular levels of glutamate (Tardiolo et al., 2018). Moreover, alterations in the metabolic pathways leading to the synthesis of GSH can affect glutamate levels and accumulation (Koga et al., 2011), and glutamate activity can also be shaped by GSH (Sedlak et al., 2019). Future studies are needed to tackle the molecular mechanisms mediating these cell-type-specific rearrangements. Notably, opposite effects on glutamatergic transmission have been shown to occur in D1- and D2-MSNs upon drug exposure (Zinsmaier et al., 2022), confirming that synaptic adaptations in the NuAc can take place in a cell-type-specific manner. NAC interventions have been applied in animal models of addiction and have proven successful in rescuing drug-seeking behavior by normalizing extracellular glutamate levels, thereby modulating accumbal excitatory transmission via metabotropic receptors (Olive et al., 2012). However, in contrast to our results, such cellular mechanisms have been addressed in response to acute NAC administration and do not exhibit cell-type specificity. Thus, our findings are likely the result of the potent antioxidant capacity of GSH facilitating the engagement of accumbal circuitry to produce motivated behavior, as well as GSH-independent mechanisms involving more complex and long-term modifications, including a reshaping of synaptic glutamatergic activity.

Variability in accumbal GSH levels can be due to both genetic and non-genetic life and situational factors. Importantly, as shown in some cell types, intra-individual variation in GSH content seems to be rather stable over time (van ‘t Erve et al., 2013). Individual capacity to produce GSH is determined by genetic variability in enzymes involved in its production and/or regeneration, including several common single nucleotide polymorphisms that regulate GSH levels and associated processes (Dasari et al., 2018). In addition, several life conditions can also affect brain GSH levels, particularly under situations leading to increased ROS expression and oxidative stress such as during ageing or psychogenic stress (Zalachoras et al., 2020; Sohal et al., 1994). Accordingly, several psychopathological alterations - such as depression or schizophrenia - in which low GSH levels have been reported are associated with motivational deficits or fatigue (Lapidus et al., 2014; Wang et al., 2019; Xin et al., 2016). Remarkably, we report that inter-individual differences in accumbal GSH levels in healthy, human and rodent populations (i.e. within the normal physiological range) are related to differences in effortful behavior. Therefore, our findings here go well beyond previously reported correlations of brain GSH levels with psychiatric conditions to causally implicate accumbal GSH levels in motivated behavior in healthy individuals. Moreover, we provide a proof of principle that nutritional treatments leading to 15–20% change in accumbal GSH content are able to modulate motivated performance in healthy individuals.

In conclusion, we establish GSH levels in the NuAc both as a predictive marker of differences in reward-based effortful performance and as a potential target for nutritional or other type of interventions. We also provide strong evidence for a promising potential of chronic NAC supplementation to boost accumbal GSH levels and to regulate motivation to exert reward-incentivized effort. However, it is important to note that our sample sizes are relatively small, and thus the absence of significant effects should be interpreted with caution. As our studies were performed in male humans and rodents, it is important to note as a limitation to our findings. We are currently expanding our studies to female populations, where we hope to find similar relationships between GSH and motivated behavior.

All data generated or analysed during this study are included in the manuscript and supporting files; Source Data files have been provided for all the figures.

1. 1. Berchio C
   2. Rodrigues J
   3. Strasser A
   4. Michel CM
   5. Sandi C

   (2019) Trait anxiety on effort allocation to monetary incentives: a behavioral and high-density EEG study

   Translational Psychiatry 9:174.

   https://doi.org/10.1038/s41398-019-0508-4

   • PubMed
   • Google Scholar
2. 1. Berridge KC
   2. Kringelbach ML

   (2008) Affective neuroscience of Pleasure: reward in humans and animals

   Psychopharmacology 199:457–480.

   https://doi.org/10.1007/s00213-008-1099-6

   • PubMed
   • Google Scholar
3. 1. Bottino F
   2. Lucignani M
   3. Napolitano A
   4. Dellepiane F
   5. Visconti E
   6. Rossi Espagnet MC
   7. Pasquini L

   (2021) In vivo brain GSH: MRS methods and clinical applications

   Antioxidants 10:1407.

   https://doi.org/10.3390/antiox10091407

   • PubMed
   • Google Scholar
4. 1. Cherix A
   2. Larrieu T
   3. Grosse J
   4. Rodrigues J
   5. McEwen B
   6. Nasca C
   7. Gruetter R
   8. Sandi C

   (2020) Metabolic signature in nucleus accumbens for anti-depressant-like effects of acetyl-L-carnitine

   eLife 9:e50631.

   https://doi.org/10.7554/eLife.50631

   • PubMed
   • Google Scholar
5. Book

   1. Chong TTJ
   2. Bonnelle V
   3. Husain M

   (2016)

   Progress in Brain Research

   Elsevier.

   • Google Scholar
6. 1. Cobley JN
   2. Fiorello ML
   3. Bailey DM

   (2018) 13 reasons why the brain is susceptible to oxidative stress

   Redox Biology 15:490–503.

   https://doi.org/10.1016/j.redox.2018.01.008

   • PubMed
   • Google Scholar
7. 1. Croxson PL
   2. Walton ME
   3. O’Reilly JX
   4. Behrens TEJ
   5. Rushworth MFS

   (2009) Effort-based cost-benefit valuation and the human brain

   The Journal of Neuroscience 29:4531–4541.

   https://doi.org/10.1523/JNEUROSCI.4515-08.2009

   • PubMed
   • Google Scholar
8. 1. Dasari S
   2. Gonuguntla S
   3. Ganjayi MS
   4. Bukke S
   5. Sreenivasulu B
   6. Meriga B

   (2018) Genetic polymorphism of glutathione S-transferases: relevance to neurological disorders

   Pathophysiology 25:285–292.

   https://doi.org/10.1016/j.pathophys.2018.06.001

   • PubMed
   • Google Scholar
9. 1. Day JJ
   2. Roitman MF
   3. Wightman RM
   4. Carelli RM

   (2007) Associative learning mediates dynamic shifts in dopamine signaling in the nucleus accumbens

   Nature Neuroscience 10:1020–1028.

   https://doi.org/10.1038/nn1923

   • PubMed
   • Google Scholar
10. 1. Dringen R

    (2000) Metabolism and functions of glutathione in brain

    Progress in Neurobiology 62:649–671.

    https://doi.org/10.1016/s0301-0082(99)00060-x

    • PubMed
    • Google Scholar
11. 1. Duckworth AL
    2. Eichstaedt JC
    3. Ungar LH

    (2015) The mechanics of human achievement

    Social and Personality Psychology Compass 9:359–369.

    https://doi.org/10.1111/spc3.12178

    • PubMed
    • Google Scholar
12. 1. Ducrocq F
    2. Walle R
    3. Contini A
    4. Oummadi A
    5. Caraballo B
    6. van der Veldt S
    7. Boyer M-L
    8. Aby F
    9. Tolentino-Cortez T
    10. Helbling J-C
    11. Martine L
    12. Grégoire S
    13. Cabaret S
    14. Vancassel S
    15. Layé S
    16. Kang JX
    17. Fioramonti X
    18. Berdeaux O
    19. Barreda-Gómez G
    20. Masson E
    21. Ferreira G
    22. Ma DWL
    23. Bosch-Bouju C
    24. De Smedt-Peyrusse V
    25. Trifilieff P

    (2020) Causal link between n-3 polyunsaturated fatty acid deficiency and motivation deficits

    Cell Metabolism 31:755–772.

    https://doi.org/10.1016/j.cmet.2020.02.012

    • PubMed
    • Google Scholar
13. 1. Epstein J
    2. Pan H
    3. Kocsis JH
    4. Yang Y
    5. Butler T
    6. Chusid J
    7. Hochberg H
    8. Murrough J
    9. Strohmayer E
    10. Stern E
    11. Silbersweig DA

    (2006) Lack of ventral striatal response to positive stimuli in depressed versus normal subjects

    The American Journal of Psychiatry 163:1784–1790.

    https://doi.org/10.1176/ajp.2006.163.10.1784

    • PubMed
    • Google Scholar
14. 1. Fernandez-Fernandez S
    2. Bobo-Jimenez V
    3. Requejo-Aguilar R
    4. Gonzalez-Fernandez S
    5. Resch M
    6. Carabias-Carrasco M
    7. Ros J
    8. Almeida A
    9. Bolaños JP

    (2018) Hippocampal neurons require a large pool of glutathione to sustain dendrite integrity and cognitive function

    Redox Biology 19:52–61.

    https://doi.org/10.1016/j.redox.2018.08.003

    • PubMed
    • Google Scholar
15. 1. Filla I
    2. Bailey MR
    3. Schipani E
    4. Winiger V
    5. Mezias C
    6. Balsam PD
    7. Simpson EH

    (2018) Striatal dopamine D2 receptors regulate effort but not value-based decision making and alter the dopaminergic encoding of cost

    Neuropsychopharmacology 43:2180–2189.

    https://doi.org/10.1038/s41386-018-0159-9

    • PubMed
    • Google Scholar
16. 1. Flagel SB
    2. Clark JJ
    3. Robinson TE
    4. Mayo L
    5. Czuj A
    6. Willuhn I
    7. Akers CA
    8. Clinton SM
    9. Phillips PEM
    10. Akil H

    (2011) A selective role for dopamine in stimulus-reward learning

    Nature 469:53–57.

    https://doi.org/10.1038/nature09588

    • PubMed
    • Google Scholar
17. 1. Floresco SB

    (2015) The nucleus accumbens: an interface between cognition, emotion, and action

    Annual Review of Psychology 66:25–52.

    https://doi.org/10.1146/annurev-psych-010213-115159

    • PubMed
    • Google Scholar
18. 1. Forman HJ
    2. Zhang H
    3. Rinna A

    (2009) Glutathione: overview of its protective roles, measurement, and biosynthesis

    Molecular Aspects of Medicine 30:1–12.

    https://doi.org/10.1016/j.mam.2008.08.006

    • PubMed
    • Google Scholar
19. 1. Gallo EF
    2. Meszaros J
    3. Sherman JD
    4. Chohan MO
    5. Teboul E
    6. Choi CS
    7. Moore H
    8. Javitch JA
    9. Kellendonk C

    (2018) Accumbens dopamine D2 receptors increase motivation by decreasing inhibitory transmission to the ventral pallidum

    Nature Communications 9:1086.

    https://doi.org/10.1038/s41467-018-03272-2

    • PubMed
    • Google Scholar
20. 1. Gebara E
    2. Zanoletti O
    3. Ghosal S
    4. Grosse J
    5. Schneider BL
    6. Knott G
    7. Astori S
    8. Sandi C

    (2021) Mitofusin-2 in the nucleus accumbens regulates anxiety and depression-like behaviors through mitochondrial and neuronal actions

    Biological Psychiatry 89:1033–1044.

    https://doi.org/10.1016/j.biopsych.2020.12.003

    • PubMed
    • Google Scholar
21. 1. Guitart X
    2. Méndez R
    3. Ovalle S
    4. Andreu F
    5. Carceller A
    6. Farré AJ
    7. Zamanillo D

    (2000) Regulation of ionotropic glutamate receptor subunits in different rat brain areas by a preferential sigma (1) receptor ligand and potential atypical antipsychotic

    Neuropsychopharmacology 23:539–546.

    https://doi.org/10.1016/S0893-133X(00)00142-1

    • PubMed
    • Google Scholar
22. Book

    1. Haber SN

    (2011)

    Neuroanatomy of reward: A view from the ventral striatum

    In: Gottfried JA, editors. Neurobiology of Sensation and Reward. CRC Press. pp. 49–458.

    • Google Scholar
23. 1. Hailwood JM
    2. Gilmour G
    3. Robbins TW
    4. Saksida LM
    5. Bussey TJ
    6. Marston HM
    7. Gastambide F

    (2018) Oxygen responses within the nucleus accumbens are associated with individual differences in effort exertion in rats

    The European Journal of Neuroscience 48:2971–2987.

    https://doi.org/10.1111/ejn.14150

    • PubMed
    • Google Scholar
24. 1. Hall CN
    2. Klein-Flügge MC
    3. Howarth C
    4. Attwell DOP

    (2012) Oxidative phosphorylation, not glycolysis, powers presynaptic and postsynaptic mechanisms underlying brain information processing

    The Journal of Neuroscience 32:8940–8951.

    https://doi.org/10.1523/JNEUROSCI.0026-12.2012

    • PubMed
    • Google Scholar
25. 1. Hanson JL
    2. Hariri AR
    3. Williamson DE

    (2015) Blunted ventral striatum development in adolescence reflects emotional neglect and predicts depressive symptoms

    Biological Psychiatry 78:598–605.

    https://doi.org/10.1016/j.biopsych.2015.05.010

    • PubMed
    • Google Scholar
26. 1. Herrero AI
    2. Sandi C
    3. Venero C

    (2006) Individual differences in anxiety trait are related to spatial learning abilities and hippocampal expression of mineralocorticoid receptors

    Neurobiology of Learning and Memory 86:150–159.

    https://doi.org/10.1016/j.nlm.2006.02.001

    • PubMed
    • Google Scholar
27. 1. Hodos W

    (1961) Progressive ratio as a measure of reward strength

    Science 134:943–944.

    https://doi.org/10.1126/science.134.3483.943

    • PubMed
    • Google Scholar
28. 1. Hollis F
    2. van der Kooij MA
    3. Zanoletti O
    4. Lozano L
    5. Cantó C
    6. Sandi C

    (2015) Mitochondrial function in the brain links anxiety with social subordination

    PNAS 112:15486–15491.

    https://doi.org/10.1073/pnas.1512653112

    • PubMed
    • Google Scholar
29. 1. Hyder F
    2. Rothman DL
    3. Shulman RG

    (2002) Total neuroenergetics support localized brain activity: implications for the interpretation of fmri

    PNAS 99:10771–10776.

    https://doi.org/10.1073/pnas.132272299

    • PubMed
    • Google Scholar
30. 1. Kanfer R
    2. Frese M
    3. Johnson RE

    (2017) Motivation related to work: a century of progress

    The Journal of Applied Psychology 102:338–355.

    https://doi.org/10.1037/apl0000133

    • PubMed
    • Google Scholar
31. 1. Knutson B
    2. Adams CM
    3. Fong GW
    4. Hommer D

    (2001) Anticipation of increasing monetary reward selectively recruits nucleus accumbens

    The Journal of Neuroscience 21:RC159.

    https://doi.org/10.1523/JNEUROSCI.21-16-j0002.2001

    • PubMed
    • Google Scholar
32. 1. Knutson B
    2. Taylor J
    3. Kaufman M
    4. Peterson R
    5. Glover G

    (2005) Distributed neural representation of expected value

    The Journal of Neuroscience 25:4806–4812.

    https://doi.org/10.1523/JNEUROSCI.0642-05.2005

    • PubMed
    • Google Scholar
33. 1. Koga M
    2. Serritella AV
    3. Messmer MM
    4. Hayashi-Takagi A
    5. Hester LD
    6. Snyder SH
    7. Sawa A
    8. Sedlak TW

    (2011) Glutathione is a physiologic reservoir of neuronal glutamate

    Biochemical and Biophysical Research Communications 409:596–602.

    https://doi.org/10.1016/j.bbrc.2011.04.087

    • PubMed
    • Google Scholar
34. 1. Kravitz AV
    2. Tye LD
    3. Kreitzer AC

    (2012) Distinct roles for direct and indirect pathway striatal neurons in reinforcement

    Nature Neuroscience 15:816–818.

    https://doi.org/10.1038/nn.3100

    • PubMed
    • Google Scholar
35. 1. Lapidus KAB
    2. Gabbay V
    3. Mao X
    4. Johnson A
    5. Murrough JW
    6. Mathew SJ
    7. Shungu DC

    (2014) In vivo (1) H MRS study of potential associations between glutathione, oxidative stress and anhedonia in major depressive disorder

    Neuroscience Letters 569:74–79.

    https://doi.org/10.1016/j.neulet.2014.03.056

    • PubMed
    • Google Scholar
36. 1. Larrieu T
    2. Cherix A
    3. Duque A
    4. Rodrigues J
    5. Lei H
    6. Gruetter R
    7. Sandi C

    (2017) Hierarchical status predicts behavioral vulnerability and nucleus accumbens metabolic profile following chronic social defeat stress

    Current Biology 27:2202–2210.

    https://doi.org/10.1016/j.cub.2017.06.027

    • PubMed
    • Google Scholar
37. 1. Lepine JA
    2. Podsakoff NP
    3. Lepine MA

    (2005) A meta-analytic test of the challenge stressor–hindrance stressor framework: an explanation for inconsistent relationships among stressors and performance

    Academy of Management Journal 48:764–775.

    https://doi.org/10.5465/amj.2005.18803921

    • Google Scholar
38. 1. Lin Y
    2. Stephenson MC
    3. Xin L
    4. Napolitano A
    5. Morris PG

    (2012) Investigating the metabolic changes due to visual stimulation using functional proton magnetic resonance spectroscopy at 7 T

    Journal of Cerebral Blood Flow and Metabolism 32:1484–1495.

    https://doi.org/10.1038/jcbfm.2012.33

    • PubMed
    • Google Scholar
39. 1. Lobo MK
    2. Covington HE
    3. Chaudhury D
    4. Friedman AK
    5. Sun H
    6. Damez-Werno D
    7. Dietz DM
    8. Zaman S
    9. Koo JW
    10. Kennedy PJ
    11. Mouzon E
    12. Mogri M
    13. Neve RL
    14. Deisseroth K
    15. Han MH
    16. Nestler EJ

    (2010) Cell type-specific loss of BDNF signaling mimics optogenetic control of cocaine reward

    Science 330:385–390.

    https://doi.org/10.1126/science.1188472

    • PubMed
    • Google Scholar
40. 1. Muir J
    2. Lopez J
    3. Bagot RC

    (2019) Wiring the depressed brain: optogenetic and chemogenetic circuit interrogation in animal models of depression

    Neuropsychopharmacology 44:1013–1026.

    https://doi.org/10.1038/s41386-018-0291-6

    • PubMed
    • Google Scholar
41. 1. O’Doherty J
    2. Dayan P
    3. Schultz J
    4. Deichmann R
    5. Friston K
    6. Dolan RJ

    (2004) Dissociable roles of ventral and dorsal striatum in instrumental conditioning

    Science 304:452–454.

    https://doi.org/10.1126/science.1094285

    • PubMed
    • Google Scholar
42. 1. Olive MF
    2. Cleva RM
    3. Kalivas PW
    4. Malcolm RJ

    (2012) Glutamatergic medications for the treatment of drug and behavioral addictions

    Pharmacology, Biochemistry, and Behavior 100:801–810.

    https://doi.org/10.1016/j.pbb.2011.04.015

    • PubMed
    • Google Scholar
43. 1. Pessiglione M
    2. Schmidt L
    3. Draganski B
    4. Kalisch R
    5. Lau H
    6. Dolan RJ
    7. Frith CD

    (2007) How the brain translates money into force: a neuroimaging study of subliminal motivation

    Science 316:904–906.

    https://doi.org/10.1126/science.1140459

    • PubMed
    • Google Scholar
44. 1. Pessiglione M
    2. Vinckier F
    3. Bouret S
    4. Daunizeau J
    5. Le Bouc R

    (2018) Why not try harder? computational approach to motivation deficits in Neuro-psychiatric diseases

    Brain 141:629–650.

    https://doi.org/10.1093/brain/awx278

    • PubMed
    • Google Scholar
45. 1. Proulx CD
    2. Aronson S
    3. Milivojevic D
    4. Molina C
    5. Loi A
    6. Monk B
    7. Shabel SJ
    8. Malinow R

    (2018) A neural pathway controlling motivation to exert effort

    PNAS 115:5792–5797.

    https://doi.org/10.1073/pnas.1801837115

    • PubMed
    • Google Scholar
46. 1. Rae CD
    2. Williams SR

    (2017) Glutathione in the human brain: review of its roles and measurement by magnetic resonance spectroscopy

    Analytical Biochemistry 529:127–143.

    https://doi.org/10.1016/j.ab.2016.12.022

    • PubMed
    • Google Scholar
47. 1. Richardson NR
    2. Roberts DCS

    (1996) Progressive ratio schedules in drug self-administration studies in rats: a method to evaluate reinforcing efficacy

    Journal of Neuroscience Methods 66:1–11.

    https://doi.org/10.1016/0165-0270(95)00153-0

    • PubMed
    • Google Scholar
48. 1. Robinson TE
    2. Yager LM
    3. Cogan ES
    4. Saunders BT

    (2014) On the motivational properties of reward cues: individual differences

    Neuropharmacology 76 Pt B:450–459.

    https://doi.org/10.1016/j.neuropharm.2013.05.040

    • PubMed
    • Google Scholar
49. 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
50. 1. Salamone JD
    2. Yohn SE
    3. López-Cruz L
    4. San Miguel N
    5. Correa M

    (2016) Activational and effort-related aspects of motivation: neural mechanisms and implications for psychopathology

    Brain 139:1325–1347.

    https://doi.org/10.1093/brain/aww050

    • PubMed
    • Google Scholar
51. 1. Salamone JD
    2. Ecevitoglu A
    3. Carratala-Ros C
    4. Presby RE
    5. Edelstein GA
    6. Fleeher R
    7. Rotolo RA
    8. Meka N
    9. Srinath S
    10. Masthay JC
    11. Correa M

    (2022) Complexities and paradoxes in understanding the role of dopamine in incentive motivation and instrumental action: exertion of effort vs. anhedonia

    Brain Research Bulletin 182:57–66.

    https://doi.org/10.1016/j.brainresbull.2022.01.019

    • PubMed
    • Google Scholar
52. 1. Sappington DR
    2. Siegel ER
    3. Hiatt G
    4. Desai A
    5. Penney RB
    6. Jamshidi-Parsian A
    7. Griffin RJ
    8. Boysen G

    (2016) Glutamine drives glutathione synthesis and contributes to radiation sensitivity of A549 and H460 lung cancer cell lines

    Biochimica et Biophysica Acta 1860:836–843.

    https://doi.org/10.1016/j.bbagen.2016.01.021

    • PubMed
    • Google Scholar
53. 1. Schmidt L
    2. Lebreton M
    3. Cléry-Melin ML
    4. Daunizeau J
    5. Pessiglione M

    (2012) Neural mechanisms underlying motivation of mental versus physical effort

    PLOS Biology 10:e1001266.

    https://doi.org/10.1371/journal.pbio.1001266

    • PubMed
    • Google Scholar
54. 1. Schneider J
    2. Berndt N
    3. Papageorgiou IE
    4. Maurer J
    5. Bulik S
    6. Both M
    7. Draguhn A
    8. Holzhütter H-G
    9. Kann O

    (2019) Local oxygen homeostasis during various neuronal network activity states in the mouse hippocampus

    Journal of Cerebral Blood Flow and Metabolism 39:859–873.

    https://doi.org/10.1177/0271678X17740091

    • PubMed
    • Google Scholar
55. 1. Sedlak TW
    2. Paul BD
    3. Parker GM
    4. Hester LD
    5. Snowman AM
    6. Taniguchi Y
    7. Kamiya A
    8. Snyder SH
    9. Sawa A

    (2019) The glutathione cycle shapes synaptic glutamate activity

    PNAS 116:2701–2706.

    https://doi.org/10.1073/pnas.1817885116

    • PubMed
    • Google Scholar
56. 1. Sescousse G
    2. Li Y
    3. Dreher JC

    (2015) A common currency for the computation of motivational values in the human striatum

    Social Cognitive and Affective Neuroscience 10:467–473.

    https://doi.org/10.1093/scan/nsu074

    • PubMed
    • Google Scholar
57. 1. Smith AJ
    2. Blumenfeld H
    3. Behar KL
    4. Rothman DL
    5. Shulman RG
    6. Hyder F

    (2002) Cerebral energetics and spiking frequency: the neurophysiological basis of fmri

    PNAS 99:10765–10770.

    https://doi.org/10.1073/pnas.132272199

    • PubMed
    • Google Scholar
58. 1. Soares-Cunha C
    2. Coimbra B
    3. David-Pereira A
    4. Borges S
    5. Pinto L
    6. Costa P
    7. Sousa N
    8. Rodrigues AJ

    (2016) Activation of D2 dopamine receptor-expressing neurons in the nucleus accumbens increases motivation

    Nature Communications 7:11829.

    https://doi.org/10.1038/ncomms11829

    • PubMed
    • Google Scholar
59. 1. Soares-Cunha C
    2. Coimbra B
    3. Domingues AV
    4. Vasconcelos N
    5. Sousa N
    6. Rodrigues AJ

    (2018) Nucleus accumbens microcircuit underlying D2-MSN-driven increase in motivation

    ENeuro 5:ENEURO.0386-18.2018.

    https://doi.org/10.1523/ENEURO.0386-18.2018

    • PubMed
    • Google Scholar
60. 1. Soares-Cunha C
    2. de Vasconcelos NAP
    3. Coimbra B
    4. Domingues AV
    5. Silva JM
    6. Loureiro-Campos E
    7. Gaspar R
    8. Sotiropoulos I
    9. Sousa N
    10. Rodrigues AJ

    (2020) Nucleus accumbens medium spiny neurons subtypes signal both reward and aversion

    Molecular Psychiatry 25:3241–3255.

    https://doi.org/10.1038/s41380-019-0484-3

    • PubMed
    • Google Scholar
61. 1. Sohal RS
    2. Ku HH
    3. Agarwal S
    4. Forster MJ
    5. Lal H

    (1994) Oxidative damage, mitochondrial oxidant generation and antioxidant defenses during aging and in response to food restriction in the mouse

    Mechanisms of Ageing and Development 74:121–133.

    https://doi.org/10.1016/0047-6374(94)90104-x

    • PubMed
    • Google Scholar
62. 1. Stewart WJ

    (1975) Progressive reinforcement schedules: a review and evaluation

    Australian Journal of Psychology 27:9–22.

    https://doi.org/10.1080/00049537508255235

    • Google Scholar
63. 1. Strasser A
    2. Luksys G
    3. Xin L
    4. Pessiglione M
    5. Gruetter R
    6. Sandi C

    (2020) Glutamine-to-glutamate ratio in the nucleus accumbens predicts effort-based motivated performance in humans

    Neuropsychopharmacology 45:2048–2057.

    https://doi.org/10.1038/s41386-020-0760-6

    • PubMed
    • Google Scholar
64. 1. Stuber GD
    2. Klanker M
    3. de Ridder B
    4. Bowers MS
    5. Joosten RN
    6. Feenstra MG
    7. Bonci A

    (2008) Reward-predictive cues enhance excitatory synaptic strength onto midbrain dopamine neurons

    Science 321:1690–1692.

    https://doi.org/10.1126/science.1160873

    • PubMed
    • Google Scholar
65. 1. Tardiolo G
    2. Bramanti P
    3. Mazzon E

    (2018) Overview on the effects of N-acetylcysteine in neurodegenerative diseases

    Molecules 23:E3305.

    https://doi.org/10.3390/molecules23123305

    • PubMed
    • Google Scholar
66. 1. Treadway MT
    2. Buckholtz JW
    3. Martin JW
    4. Jan K
    5. Asplund CL
    6. Ginther MR
    7. Jones OD
    8. Marois R

    (2014) Corticolimbic gating of emotion-driven punishment

    Nature Neuroscience 17:1270–1275.

    https://doi.org/10.1038/nn.3781

    • PubMed
    • Google Scholar
67. 1. Trifilieff P
    2. Feng B
    3. Urizar E
    4. Winiger V
    5. Ward RD
    6. Taylor KM
    7. Martinez D
    8. Moore H
    9. Balsam PD
    10. Simpson EH
    11. Javitch JA

    (2013) Increasing dopamine D2 receptor expression in the adult nucleus accumbens enhances motivation

    Molecular Psychiatry 18:1025–1033.

    https://doi.org/10.1038/mp.2013.57

    • PubMed
    • Google Scholar
68. 1. van der Kooij MA
    2. Hollis F
    3. Lozano L
    4. Zalachoras I
    5. Abad S
    6. Zanoletti O
    7. Grosse J
    8. Guillot de Suduiraut I
    9. Canto C
    10. Sandi C

    (2018a) Diazepam actions in the VTA enhance social dominance and mitochondrial function in the nucleus accumbens by activation of dopamine D1 receptors

    Molecular Psychiatry 23:569–578.

    https://doi.org/10.1038/mp.2017.135

    • PubMed
    • Google Scholar
69. 1. van der Kooij MA
    2. Zalachoras I
    3. Sandi C

    (2018b) GABA_a receptors in the ventral tegmental area control the outcome of a social competition in rats

    Neuropharmacology 138:275–281.

    https://doi.org/10.1016/j.neuropharm.2018.06.023

    • PubMed
    • Google Scholar
70. 1. van ‘t Erve TJ
    2. Wagner BA
    3. Ryckman KK
    4. Raife TJ
    5. Buettner GR

    (2013) The concentration of glutathione in human erythrocytes is a heritable trait

    Free Radical Biology and Medicine 65:742–749.

    https://doi.org/10.1016/j.freeradbiomed.2013.08.002

    • PubMed
    • Google Scholar
71. 1. Walls AB
    2. Waagepetersen HS
    3. Bak LK
    4. Schousboe A
    5. Sonnewald U

    (2015) The glutamine-glutamate/GABA cycle: function, regional differences in glutamate and GABA production and effects of interference with GABA metabolism

    Neurochemical Research 40:402–409.

    https://doi.org/10.1007/s11064-014-1473-1

    • PubMed
    • Google Scholar
72. 1. Wanat MJ
    2. Bonci A
    3. Phillips PEM

    (2013) CRF acts in the midbrain to attenuate accumbens dopamine release to rewards but not their predictors

    Nature Neuroscience 16:383–385.

    https://doi.org/10.1038/nn.3335

    • PubMed
    • Google Scholar
73. 1. Wang AM
    2. Pradhan S
    3. Coughlin JM
    4. Trivedi A
    5. DuBois SL
    6. Crawford JL
    7. Sedlak TW
    8. Nucifora FC Jr
    9. Nestadt G
    10. Nucifora LG
    11. Schretlen DJ
    12. Sawa A
    13. Barker PB

    (2019) Assessing brain metabolism with 7-T proton magnetic resonance spectroscopy in patients with first-episode psychosis

    JAMA Psychiatry 76:314–323.

    https://doi.org/10.1001/jamapsychiatry.2018.3637

    • PubMed
    • Google Scholar
74. 1. Weger M
    2. Alpern D
    3. Cherix A
    4. Ghosal S
    5. Grosse J
    6. Russeil J
    7. Gruetter R
    8. de Kloet ER
    9. Deplancke B
    10. Sandi C

    (2020) Mitochondrial gene signature in the prefrontal cortex for differential susceptibility to chronic stress

    Scientific Reports 10:18308.

    https://doi.org/10.1038/s41598-020-75326-9

    • PubMed
    • Google Scholar
75. 1. Xin L
    2. Schaller B
    3. Mlynarik V
    4. Lu H
    5. Gruetter R

    (2013) Proton T1 relaxation times of metabolites in human occipital white and gray matter at 7 T

    Magnetic Resonance in Medicine 69:931–936.

    https://doi.org/10.1002/mrm.24352

    • PubMed
    • Google Scholar
76. 1. Xin L
    2. Mekle R
    3. Fournier M
    4. Baumann PS
    5. Ferrari C
    6. Alameda L
    7. Jenni R
    8. Lu H
    9. Schaller B
    10. Cuenod M
    11. Conus P
    12. Gruetter R
    13. Do KQ

    (2016) Genetic polymorphism associated prefrontal glutathione and its coupling with brain glutamate and peripheral redox status in early psychosis

    Schizophrenia Bulletin 42:1185–1196.

    https://doi.org/10.1093/schbul/sbw038

    • PubMed
    • Google Scholar
77. 1. Yacubian J
    2. Gläscher J
    3. Schroeder K
    4. Sommer T
    5. Braus DF
    6. Büchel C

    (2006) Dissociable systems for gain- and loss-related value predictions and errors of prediction in the human brain

    The Journal of Neuroscience 26:9530–9537.

    https://doi.org/10.1523/JNEUROSCI.2915-06.2006

    • PubMed
    • Google Scholar
78. 1. Zalachoras I
    2. Hollis F
    3. Ramos-Fernández E
    4. Trovo L
    5. Sonnay S
    6. Geiser E
    7. Preitner N
    8. Steiner P
    9. Sandi C
    10. Morató L

    (2020) Therapeutic potential of glutathione-enhancers in stress-related psychopathologies

    Neuroscience and Biobehavioral Reviews 114:134–155.

    https://doi.org/10.1016/j.neubiorev.2020.03.015

    • PubMed
    • Google Scholar
79. 1. Zald DH
    2. Treadway MT

    (2017) Reward processing, neuroeconomics, and psychopathology

    Annual Review of Clinical Psychology 13:471–495.

    https://doi.org/10.1146/annurev-clinpsy-032816-044957

    • PubMed
    • Google Scholar
80. 1. Zinsmaier AK
    2. Dong Y
    3. Huang YH

    (2022) Cocaine-induced projection-specific and cell type-specific adaptations in the nucleus accumbens

    Molecular Psychiatry 27:669–686.

    https://doi.org/10.1038/s41380-021-01112-2

    • PubMed
    • Google Scholar

Decision letter after peer review:

Thank you for submitting your article "Glutathione in the nucleus accumbens regulates motivation to exert reward- incentivized effort" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, including Naoshige Uchida as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Kate Wassum as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Hanneke EM den Ouden (Reviewer #2).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

This study use both humans and rats to demonstrate that the level of glutathione in the nucleus accumbens correlates with effortful behaviors. The authors provide causal evidence for glutathione in rats by manipulating enzymes involved in the synthesis of glutathione. Although how exactly glutathione regulates effort-related behavior remains to be clarified, this study provides intriguing observations and causal evidence. As summarized below all the reviewers found the manuscript to be potentially very interesting. However, they identified some issues that need to be addressed before publication in eLife.

1) Even when multiple statistical tests were performed in a given component of the study, the authors have not corrected the significance level for multiple comparisons. This should be corrected. Furthermore, at various points, analyses were performed on subgroups / subsets of the data, and differences in terms of statistical significance were interpreted as a significant difference between the groups. Instead, these differences between groups/subsets should be tested directly. Only if significant, post-hoc simple tests are justified. Please refer to the reviewer 2's comments for further suggestions and specific points.

2) The authors conclude that the GSH level in the nucleus accumbens is critical for effortful behaviors but the authors do not examine other brain regions both in humans and in rats. This makes it difficult to distinguish whether the correlation observed in humans is specific to this brain region or a brain-wide phenomenon. Although the causal experiments in rats partially address this issue, it would be very helpful to know whether the GSH level co-fluctuates widely in the brain or is altered specifically in the nucleus accumbens. If 1H-MRS data has already been acquired, it would be very useful to include in the manuscript. If the specificity of nucleus accumbens is difficult to establish, conclusions would need to be adjusted.

3) Conversely, whether various manipulations in the rat nucleus accumbens specifically affected effortful behaviors or compromised the function of the nucleus accumbens generally is unclear. Some results based on other behaviors would be very helpful to distinguish these possibilities. Furthermore, it would be helpful to discuss (further) how the level of GSH levels can affect effort specifically.

4) It is important to distinguish whether the results in the causal experiments in rats are due to a specific effect of various manipulations on the GSH levels or some general loss of function (e.g. toxicity) in cells in the nucleus accumbens. To address this point, the authors examined whether the BSO effects were reversible but the results in Figure 4 are not convincing. The difference between the groups appears to be mainly due to a reduced statistical power in the BSO group. A better dataset or analyses are required to make this result convincing.

5) Please address the limitation of the exclusion of females from this study.

We hope that the authors can address the above essential points by additional data (and potentially additional experiments) and analyses. Below the reviewers have also provided more detailed comments and additional points to which we would like to see your response. We hope that you can address these issues within 2-3 months.

Reviewer #1 (Recommendations for the authors):

1. It is unclear how the level of glutathione regulates effortful behaviors. Glutathione is synthesized with glutamate, glycine and cysteine. Is it possible that some alterations in the glutathione synthesis pathway, either naturally occurring or experimentally, causes some effects on neurotransmitters such as glutamate or glycine. It would be useful to discuss possibilities of such indirect effects.

2. Many results are presented after binary classification of high versus low GSH levels. It would be more useful to use correlations as the level of GSH is continuous. At least, it would be good to increase scatter plots as in Figure 1c.

3. Figure 2c needs to be a better quality.

4. The authors use the acronym, NuAc, for the nucleus accumbens, but in some places "NAc" is used (e.g. page 9, line 8). Please be consistent.

5. The metabolic pathways as well as enzymes are difficult to understand to those who are not familiar with them. Also, there appear to be different names or acronyms depending on literature. It would be helpful for people outside the field if the authors provide a schematic of metabolic pathways and enzymes as a supplementary figure.

Reviewer #2 (Recommendations for the authors):

Construct validity

In the monetary incentivized effort task, the required effort is held constant, while the incentives are varied. However, unless effort required and reward that can be obtained are varied independently (see e.g. the task used in Draper et al. 2018 Neuropsychopharmacology) one cannot attribute the effects to one or the other. In relation to this, why do the authors use the number of successfully completed trials rather than the actual effort exerted? This should be a more sensitive measure, as the definition of a successful trial is effectively a thresholded approximation of effort executed (i.e. more or less than the required 50%).

Generalisability / specificity of the results

This study is performed only in males. Why? This needs to be justified in the paper (I am not convinced there is a good justification). In addition, the MRS results are based on a voxel only in the nucleus accumbens. Therefore, one cannot claim specificity of these findings to the accumbens, and may reflect the metabolic state of the entire brain. This should be acknowledged, and conclusions worded so that specificity is not claimed. This also holds for the animal work, where all manipulations and measures are only done in accumbens.

Statistical analyses

Overall, within all different components of the study, multiple statistical tests are performed. These should be corrected for multiple comparisons. Furthermore, at various points analyses are performed on subgroups / subsets of the data, and differences in terms of statistical significance are interpreted as a significant difference between the groups. Instead, these differences between groups/subsets should be tested directly. Only if significant, post-hoc simple tests are justified. Below I will highlight a few examples of this.

The authors perform 2 x 10 pairwise correlation analysis to assess (one set for all motivation levels, once set for the high reward only). First, statistical inference should be corrected for multiple comparisons (so α level = 0.005). However, as the various MRS measures are likely to be correlated, so in order to attribute effects that are selective for GSH, all MRS measures should be entered as covariates in a single regression, to assess the unique contribution of GSH to performance.

When assessing whether effects are different for different conditions, e.g. for early versus late blocks, or dependent on the incentive magnitude, these should be included as factors in the analysis (e.g. a repeated measures ANOVA with the conditions block x incentive value, and the MRS measures as covariates, or using a mixed model approach). Then only when significant interactions are detected (e.g. incentive magnitude x GSH), can you test post hoc the simple effects of GSH in each condition.

If the effects for the human data remain significant using the correct analyses (particularly including all MRS measures in a single multiple regression model), then these analyses are certainly interesting to include in the current study. However, given the effect size I am not sure that they will, and then I am not sure if they are of much added value to the much more intricate animal work.

Reviewer #3 (Recommendations for the authors):

In general the experiments are well designed and follow a linear logic. I have several points related to the experimental design that limit interpretation of the results in the manuscripts current form.

1. The rationale for why these experiments are only performed in males in the introduction is not well justified.

2. The reversibility experiments outlined in Figure 4 are not convincing. The authors find that following a washout period of the BSO compound in a subset of male rats that the rats no longer show statistically significant changes in motivated behavior. Based on these observations, the authors conclude: "These set of data supports the view that lowering accumbal GSH levels leads to a transient impairment in effort-based incentivized performance without inducing toxicity". This is not an accurate assessment because the magnitude of the effect following 'washout' in the subset of rats in identical in strength and direction to the results obtained prior to the washout. The only difference is that the statistical power is reduced because only a subset of rats in investigated and this reduced power prevents the results from achieving statistical significance. To make this claim, the authors have to compare the performance of the same rats before and after the washout period.

3. The miniature excitatory postsynaptic potential analysis of MSNs in Figure 6 is a nice addition, but is incomplete. The effects of N-acetyl-cysteine are investigated in context of changes in motivated behavior, but the authors do not assess how motivated behavior correlates with these changes within subjects. The authors performed elevated plus maze an open field prior to their recordings, but did not perform the motivated behavioral analysis which would have strengthened their findings. This lack of within subjects analysis also make it difficult to assess which of the observed changes are likely to contribute to the observed behavior effects. Finally, the rationale for performing these experiments in the core region is not clear. The review cited by the authors that is used for justification more strongly implicates the shell region in sustained efforts associated with increased motivation.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Glutathione in the nucleus accumbens regulates the motivation to exert reward-incentivized effort" for further consideration by eLife. Your revised article has been evaluated by Kate Wassum (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

As you will see below, Reviewer 2 requests that the strengths of concluding an absence of effect should be examined using a Bayesian statistical test. Although such a test may not yet be common in some fields, it is rapidly becoming common in human cognitive neuroscience, and of particular importance when interpreting null results. For your information, the reviewer pointed out that an open-source statistics program, JASP (https://jasp-stats.org/), could be useful for this purpose. We would like to see your response to this as well as other issues (making the figure of metabolic pathways into a main figure) before making a final decision.

Reviewer #1 (Recommendations for the authors):

The authors have addressed my previous concerns adequately. This study provides a novel mechanism that regulates motivated behaviors and thus will be of interest to those who study neural mechanisms underlying motivated behaviors.

Reviewer #2 (Recommendations for the authors):

Overall, the authors did a thorough job in this revision.

I only have a few more suggestions:

Most importantly, for any control analyses where the absence of an effect is concluded, conduct Bayesian statistical tests and provide evidence in favour of the null hypothesis. This is particularly important in the relatively small samples used here. When evidence in favour of the null hypothesis is not convincing, please rephrase the associated conclusions, that you cannot conclude that there was no difference. This is particularly for the null results of the BSO and occipital voxel analyses but holds for all control analyses where the absence of an effect is important.

Great that you also have an occipital voxel in a subset of the participants. To make this transparent (also the fact that this is a very small group), please add the panels C-F in figure 2 also for the occipital voxels (so exactly the same figure). This will highlight the degree of specificity of the finding.

What false discovery rate did you use for the BH procedure? Normally this is done a priori (see e.g. here: https://www.statology.org/benjamini-hochberg-procedure/). How did you compute the corrected p-values?

I really like the added figure of the metabolic pathways. I think it would be helpful for readers to add this to the main manuscript, perhaps in figure 1.

Reviewer #3 (Recommendations for the authors):

The authors have adequately addressed my previous concerns and the additional data significantly strengthens the conclusions of the study.

Essential revisions:

This study use both humans and rats to demonstrate that the level of glutathione in the nucleus accumbens correlates with effortful behaviors. The authors provide causal evidence for glutathione in rats by manipulating enzymes involved in the synthesis of glutathione. Although how exactly glutathione regulates effort-related behavior remains to be clarified, this study provides intriguing observations and causal evidence. As summarized below all the reviewers found the manuscript to be potentially very interesting. However, they identified some issues that need to be addressed before publication in eLife.

1) Even when multiple statistical tests were performed in a given component of the study, the authors have not corrected the significance level for multiple comparisons. This should be corrected. Furthermore, at various points, analyses were performed on subgroups / subsets of the data, and differences in terms of statistical significance were interpreted as a significant difference between the groups. Instead, these differences between groups/subsets should be tested directly. Only if significant, post-hoc simple tests are justified. Please refer to the reviewer 2's comments for further suggestions and specific points.

Thank you for highlighting these issues and suggesting corresponding changes to the study. We have now addressed them thoroughly by correcting multiple comparisons due to the various correlations between metabolites and performance for false discovery rate. We have also reduced the number of performed correlations by merging two pairs of metabolites that share common pathways and by excluding data from one metabolite that had excessive missing values. We have also assessed triple interactions in full factorial models and performed post-hoc tests (with significance levels corrected for multiple comparisons) only after significant interactions.

For more information on the steps undertaken, we would like to refer to our detailed responses to Reviewer 2’s comments below.

2) The authors conclude that the GSH level in the nucleus accumbens is critical for effortful behaviors but the authors do not examine other brain regions both in humans and in rats. This makes it difficult to distinguish whether the correlation observed in humans is specific to this brain region or a brain-wide phenomenon. Although the causal experiments in rats partially address this issue, it would be very helpful to know whether the GSH level co-fluctuates widely in the brain or is altered specifically in the nucleus accumbens. If 1H-MRS data has already been acquired, it would be very useful to include in the manuscript. If the specificity of nucleus accumbens is difficult to establish, conclusions would need to be adjusted.

This is an important point that we could address by analyzing data on GSH levels in other regions that were acquired as well during the 1^H-MRS scans (mPFC in rats and OL in humans) and by measuring GSH by HPLC (in mPFC of rats). GSH levels were not different in the OL of humans and mPFC of rats categorized by median NuAc GSH levels, neither in the cohort used for the 1^H-MRS experiments, nor for the cohort used for operant experiments and measured by HPLC. We have included these data in Figure1—figure supplement 1d, for the human data, in Figure 2—figure supplement 1a-c (measured by 1^H-MRS) and Figure 3-supplement figure 1a (measured by HPLC), for the rats. In the Figure 2—figure supplement 1a-c, the left panel shows the plot with the GSH levels in L/H-GSH levels (median-split by GSH levels in the NuAc); the right panel shows the ROI corresponding to the mPFC in the coronal and sagittal sections during the scanning process. In Figure 3- supplement figure 1a we added the GSH levels in mPFC expressed by % of change vs L-GSH group.

3) Conversely, whether various manipulations in the rat nucleus accumbens specifically affected effortful behaviors or compromised the function of the nucleus accumbens generally is unclear. Some results based on other behaviors would be very helpful to distinguish these possibilities. Furthermore, it would be helpful to discuss (further) how the level of GSH levels can affect effort specifically.

We have addressed this point in several ways.

First, given the NuAc’s involvement in reward processing and anhedonia (Berridge and Kringelbach, 2008), we have included data on saccharine preference in H- and L-GSH rats. As shown in the new panel n in Figure 3—figure supplement1, we found no significant differences in sucrose preference between these two groups. This is in addition to the lack of differences between groups that we had already reported in anxiety-like, exploratory, and appetitive behavior (Panels b, c, d, e, and m in Figure 3 Supplement1). Therefore, a NuAc non-effortful behavior, such as the hedonic behavior inherent to saccharine preference is not related to current findings regarding GSH-related differences in NuAc.

Second, we performed additional experiments to exclude that the observed BSO effects were due to non-specific actions that could have compromised NuAc integrity or function, and have conducted several subsequent analyses. At the behavioral level, we tested animals in tasks that engage the NuAc (Berridge and Kringelbach, 2008) but do not require effortful behaviors, such as consumption of palatable rewards and operant learning at a low fixed ratio (FR1). When given free access to 20 sucrose pellets (accounting for the maximum they get access in a progressive ratio session) 24h following either Sal or BSO intra-NuAc infusions (i.e., a time point that corresponds with the timing when animals were tested in the progressive ratio following these treatments), animals from both groups did not differ as they all ingested all the pellets within 15 minutes (Figure 4—figure supplements 1j). Then, to assess performance in a FR1 task, we divided animals previously trained and tested in the standard progressive ratio task and formerly infused with Sal into two groups, one of them receiving intra-NuAc Sal and the other BSO. In this case, FR1 training was modified as follows: training was performed in different operant boxes, involving levers instead of nosepokes and a tone as discriminative stimulus (instead of house light). We also changed the environment, including a new floor. No differences were observed between groups (Figure4—figure supplement 1k), indicating that BSO in the NAc does not interfere with incentivized operant learning that requires minimum effortful behavior (i.e., FR instead of PR).

Furthermore, in order to rule out a possible toxic effect on the NuAc of the BSO dose used, we performed an activated caspase 3 (a marker of the execution-phase of cellular apoptosis) and neuN (a marker of mature neurons) staining assays on accumbal sections from a subset of animals tested in the PR test (Figure 4—figure supplement1 m and o). No staining differences were observed between Sal and BSO groups, indicating a lack of BSO toxicity at the dose used in this experiment.

These new experiments and text have now included in the main text and corresponding supplementary figures.

4) It is important to distinguish whether the results in the causal experiments in rats are due to a specific effect of various manipulations on the GSH levels or some general loss of function (e.g. toxicity) in cells in the nucleus accumbens. To address this point, the authors examined whether the BSO effects were reversible but the results in Figure 4 are not convincing. The difference between the groups appears to be mainly due to a reduced statistical power in the BSO group. A better dataset or analyses are required to make this result convincing.

To address this point, we have included a new cohort of animals for the BSO washout experiment, increasing the number of animals per group (from 8 to 17 in both Sal and BSO groups). As can be observed in the new Figure 4g-k, the two groups do not differ in their breakpoint, rewards obtained, or correct nosepoke number and percentage, indicating that the BSO effect in the operant performance is not observed after a washout period (here measured 4 weeks after treatment).

In this revision, we have also increased the number of samples processed for potential toxicity (from 4 reported in our first submission to 10 here, see Figure 4—figure supplement1 m and o) and reanalyzed the images with a more accurate method (QuPath software included in material and methods section) that detects automatically in DAPI positive cells the presence of fluorescence in the green channel (corresponding to activated Casp3 or NeuN). The same results were obtained: lack of toxicity of the BSO treatment used in our study.

5) Please address the limitation of the exclusion of females from this study.

We fully agree and are deeply convinced on the imperative need of including both sexes in any biomedical work. This is currently the practice in our laboratory, but we have been ‘victims’ of the general trend in life sciences for many years. In this case, the study started by evaluating data from a human MRS study that started already 8-10 years ago, and that was originally motivated by former data in the lab on social hierarchies in rodents (and given the sex-specificity of the nature of social competitions, it involved only males). Given that our analyses of the metabolites identified the potential importance of GSH in the NuAc in the context of effortful motivation, we set up experiments in rodents to address the causal importance of this antioxidant. In order to progress mechanistically in this question, we back translated the studies in men to male rats. Our first findings were encouraging and we set up a collaboration to investigate the observed phenomenon more deeply.

But then, the pandemic hit and we had to sacrifice animals, stop experiments and limit our original ambitious greatly. We remain committed to perform studies in the two sexes, both in humans and rodents, and currently have launched one study (that will be followed by a second related one in humans) in which we will have a larger sample of both men and women and study metabolites in different brain regions in the context of motivated behavior. We will perform follow up studies in rodents, in a similar way as in the current study, to address causality.

However, we strongly feel now that the set of data we have managed to gather and complete so far, as reported in this manuscript, is critically new and important and deserves publication as soon as possible. Completing parallel analyses in females would certainly take an additional period of 2-3 years. We hope very much that editors and reviewers would convene with us on the importance of publishing the study as currently stands.

We have addressed this important point in the manuscript (conclusion) by stating “As our studies were performed in male humans and rodents, it is important to note as a limitation to our findings. We are currently expanding our studies to female populations, where we hope to find similar relationships between GSH and motivated behavior.”

Reviewer #1 (Recommendations for the authors):

1. It is unclear how the level of glutathione regulates effortful behaviors. Glutathione is synthesized with glutamate, glycine and cysteine. Is it possible that some alterations in the glutathione synthesis pathway, either naturally occurring or experimentally, causes some effects on neurotransmitters such as glutamate or glycine. It would be useful to discuss possibilities of such indirect effects.

As mentioned in the main text, GSH may act as a glutamate reservoir. In addition, NAC can induce activation of the cystine (CySS)/glutamate (see Appendix) antiporter by providing supplementary CySS. This transporter controls extracellular glutamate levels and regulates glutamate release. For example, in models of schizophrenia, NAC shows a reversed increase in extracellular glutamate accompanied by beneficial effects at the behavioral level (Baker D.A., Madayag A., Kristiansen L.V., Meador-Woodruff J.H., Haroutunian V., Raju I. Contribution of cystine-glutamate antiporters to the psychotomimetic effects of phencyclidine. Neuropsychopharmacology). Thus, NAC can restore glutamate levels when needed. In parallel, the antioxidant effect of GSH can regulate NMDAR activity. Since high levels of oxidizing agents reduce NMDAR activity through binding to redox-sensitive extracellular sites, the reduction of oxidants by GSH may recapitulate such condition (Synaptic plasticity impairment and hypofunction of NMDA receptors induced by glutathione deficit: Relevance to schizophrenia. Neuroscience. 2006). Our study also demonstrates that intrinsic GSH levels in NuAc can promote reward-based motivation in humans and rats, and this can be mediated by GSH as a source of glutamate and cysteine/cystine, acting through this antiporter and regulating glutamate extracellular levels in NuAc or as antioxidant improving NMDAR activity.

2. Many results are presented after binary classification of high versus low GSH levels. It would be more useful to use correlations as the level of GSH is continuous. At least, it would be good to increase scatter plots as in Figure 1c.

We thank the reviewer for this recommendation. As suggested, we performed a correlation between GSH levels and operant condition performance. Interestingly, we observed a significant correlation between the number of rewards obtained and the levels of GSH in the NuAc. This correlation was lost in rats infused with BSO. These results confirm the effect of GSH levels on operant condition performance, since when we consider GSH levels as continuous and not just a mean threshold, the effect of binary classification is maintained. Indeed, when GSH levels are decreased through BSO, the correlation is lost indicating that this effect is specific to GSH levels in NuAc (included in the Figure 3—figure supplement1l and Figure 4—figure supplement1i ).

3. Figure 2c needs to be a better quality.

Following the reviewer’s advice we have added an image with better quality. We have also included better quality images of the Coronal and Saggital sections of the NuAc (see new Figure 2).

4. The authors use the acronym, NuAc, for the nucleus accumbens, but in some places "NAc" is used (e.g. page 9, line 8). Please be consistent.

We apologize for the inconsistency, that has now been corrected throughout the manuscript.

5. The metabolic pathways as well as enzymes are difficult to understand to those who are not familiar with them. Also, there appear to be different names or acronyms depending on literature. It would be helpful for people outside the field if the authors provide a schematic of metabolic pathways and enzymes as a supplementary figure.

We agree with the reviewer that a schematic figure may improve the understanding of the treatments used in the manuscript and the molecular pathways enhanced/disrupted by them. To aid the reader’s comprehension, we have designed a schematic of the main enzymes and players in the GSH synthesis cycle, which is now added in the Appendix.

GSH metabolic pathways. GSH or γ-L-glutamyl-L-cysteinylglycine, is a tripeptide, that can exist in a reduced (GSH) or in an oxidized (GSSG) state. It is synthesized in the cytosol by an ATP-dependent two-step process catalyzed by γ-glutamyl cysteine synthase/ligase (γ-GCS) and GSH synthetase (GSS), being the γ-GCS the rate-limiting enzyme. GSH is freely distributed in the cytosol (up to 10mM concentration) and compartmentalized in the nucleus, endoplasmic reticulum and mitochondria (up to 15%). GSH participates in the neutralization of H2O2 in the cytosol in collaboration with the glutathione peroxidase (GPx) which takes hydrogens from two GSH molecules, converting one molecule of H2O2 into two of H₂O and one of GSSG. Glutathione reductase (GSR) reduces GSSG back to GSH using NADPH as an electron donor. The main enzyme that initiates the process of GSH degradation is named glutamyl transpeptidase (GGT). This enzyme is localized in the plasma membrane, facing outwards and acting on the extracellular pool of GSH released by the cells. The degradation of GSH by GGTs generates γ-glutamyl-aminoacids and cysteine-glycine dipeptides, which are subjected to the action of membrane-bound peptidase to yield cysteine (Cys) and glycine (Gly). Therefore, the major function of GGT is to recover GSH precursors from extracellular pools which can be lately taken up by cells and used for the new synthesis of GSH. The N-acetyl-cysteine (NAC) is a donor of Cys (the rate-limiting substrate) and the Buthionine sulfoximine (BSO) is a specific inhibitor of γ-GCS. In this work the systemic treatment of NAC in the drinking water was used to increase GSH levels in NuAc and the BSO intra-nucleus accumbens infusion to reduce GSH in NuAc.

Reviewer #2 (Recommendations for the authors):

Construct validity

In the monetary incentivized effort task, the required effort is held constant, while the incentives are varied. However, unless effort required and reward that can be obtained are varied independently (see e.g. the task used in Draper et al. 2018 Neuropsychopharmacology) one cannot attribute the effects to one or the other. In relation to this, why do the authors use the number of successfully completed trials rather than the actual effort exerted? This should be a more sensitive measure, as the definition of a successful trial is effectively a thresholded approximation of effort executed (i.e. more or less than the required 50%).

Thank you for this insightful suggestion. We agree with the reviewer that the experimental design in Draper et al. (2018) allows for the parametrization of effort and reward sensitivity and to disentangle both effects. In our paradigm, (relative) effort is constant throughout so it is less clear if drops in successful 1 CHF trials in low GSH participants is due to reward sensitivity or effort sensitivity changes alone. Following a later recommendation from the current reviewer (further expanded the corresponding reply), we modeled our successful trials with a 2x2x2 mixed design ANOVA with block (first, last) as the within subjects’ factor, reward (low, high) and GSH level (low, high). The triple interaction block*reward*GSH level effect was significant, but not the interaction effects of block*GSH level or reward*GSH level. If sensitivity to effort would decrease with time in different GSH levels, one would expect to find the bock*GSH level interaction significant as well. The same reasoning applies to the reward*GSH interaction.

Regarding the use of the number of exerted efforts, it indeed should be more sensitive to energy expenditure but we also expect this measure to be more heterogeneous, since it can be associated with different behavioral decisions (failure to achieve initial threshold, failure to remain above the maintenance threshold and not attempting the trial) that can change throughout the experiment as participants decide which strategy is best. Since the experiment was not designed for this purpose, we left out this analysis. However, there are similar effects to those found for the number of successful trials (i.e., triple interaction in the aforementioned mixed design ANOVA and a significant interaction block*GSH level for the 1 CHF reward). These data were added to the Figure1—figure supplement 1 and the analysis’ results were added to the Figure1-Source Data 1.

Generalisability / specificity of the results

This study is performed only in males. Why? This needs to be justified in the paper (I am not convinced there is a good justification).

We fully agree and are deeply convinced on the imperative need of including both sexes in any biomedical work. This is currently the practice in our laboratory, but we have been ‘victims’ of the general trend in life sciences for many years. In this case, the study started by evaluating data from a human MRS study that started already 8-10 years ago, and that was originally motivated by former data in the lab on social hierarchies in rodents (and given the sex-specificity of the nature of social competitions, it involved only males). Given that our analyses of the metabolites identified the potential importance of GSH in the NuAc in the context of effortful motivation, we set up experiments in rodents to address the causal importance of this antioxidant. In order to progress mechanistically in this question, we back translated the studies in men to male rats. Our first findings were encouraging and we set up a collaboration to investigate the observed phenomenon more deeply.

But then, the pandemic hit and we had to sacrifice animals, stop experiments and limit our original ambitions greatly. We remain committed to performing studies in the two sexes, both in humans and rodents, and currently have launched one study (that will be followed by a second related one in humans) in which we will have a larger sample of both men and women and study metabolites in different brain regions in the context of motivated behavior. We will perform follow up studies in rodents, in a similar way as in the current study, to address causality.

However, we strongly feel now that the set of data we have managed to gather and complete so far, as reported in this manuscript, is critically new and important and deserves publication as soon as possible. Completing parallel analyses in females would certainly take an additional period of 2-3 years. We hope very much that editors and reviewers would convene with us on the importance of publishing the study as currently stands.

We have addressed this important point in the manuscript (conclusion) by stating “Future studies are warranted to expand the current analyses to females and to investigate the mechanisms whereby GSH levels in the NuAc affect motivated performance.”

In addition, the MRS results are based on a voxel only in the nucleus accumbens. Therefore, one cannot claim specificity of these findings to the accumbens, and may reflect the metabolic state of the entire brain. This should be acknowledged, and conclusions worded so that specificity is not claimed. This also holds for the animal work, where all manipulations and measures are only done in accumbens.

We have addressed this in Figure 1-supplement 1d for the human experiment (1^H-MRS results), and Figure 2- supplement 1a-c (region specificity measured by 1^H-MRS) and Figure 3-supplement figure 1a (specificity of NuAc measured by HPLC) for the animal work. For the human data: Occipital cortex 1^H-MRS was also performed for a subset of participants (GSH low N=6, GSH high N=7) was tested with a 2x2x2 mixed design ANOVA with block (first, last) as the within subjects factor, reward (low, high) and GSH level (low, high) (Figure1-Source Data 1). The triple interaction block*reward*GSH level effect was not significant (p=0.215). This result was added to the manuscript. For the animal work: we have included the ¹H-MRS and HPLC data from the mPFC, discussed above in our answers to the Essential revisions-question 2.

Statistical analyses

Overall, within all different components of the study, multiple statistical tests are performed. These should be corrected for multiple comparisons. Furthermore, at various points analyses are performed on subgroups / subsets of the data, and differences in terms of statistical significance are interpreted as a significant difference between the groups. Instead, these differences between groups/subsets should be tested directly. Only if significant, post-hoc simple tests are justified. Below I will highlight a few examples of this.

The authors perform 2 x 10 pairwise correlation analysis to assess (one set for all motivation levels, once set for the high reward only). First, statistical inference should be corrected for multiple comparisons (so α level = 0.005).

Thank you for this comment. In order to reduce the number of comparisons -and given that several metabolites are known to be metabolically related- we have now merged Cr and PCr metabolites into CrPCr and NAA with NAAG into NAANAAG. We also removed the metabolite GPC since it was only identified in 15 participants (and therefore, many subjects had missing values for this metabolite due to low detection). This led to 7 comparisons per reward level (all rewards, 1 CHF, 0.5 CHF and 0.2 CHF), each of these corrected for false discovery rate with the Benjamini–Hochberg (BH) procedure. Both GSH and PE levels correlated significantly with the total number of 1 CHF rewards (respectively: p_corrected = 0.049; p_corrected = 0.049). Figures 1b and Figure1-supplement figure1 are now updated with these new comparisons and significance levels.

However, as the various MRS measures are likely to be correlated, so in order to attribute effects that are selective for GSH, all MRS measures should be entered as covariates in a single regression, to assess the unique contribution of GSH to performance.

Thank you for this interesting comment, but we respectfully have a different view on this issue. In our view, the correlation between these metabolites does not necessarily suggests that these should be regressed out of each pairwise association between metabolites and performance. We expect metabolites to covary due to common pathways and controlling for all other metabolites could hinder our ability to observe any effect, due to removing too much variation due to individual metabolites. Furthermore, the proposed approach would result in highly colinear predictors which is also undesirable in regression models: modeling the data as suggested results in variance inflation factors (VIFs) ranging from 1.27 to 4.65, with 4 of the 7 predictors with VIFs > 2, which can be concerning. Therefore, to still control for the influence of other metabolites and avoid the collinearity problem, we transformed the 7 variables space into a 7 orthogonal principal component space using PCA, followed by varimax rotation to maximize the uniqueness of each principal component. We then used the components that GSH didn’t load into as covariates. This provided 5 of the 7 principal components, explaining a total of 67.9% of the total variance in the dataset, to be used as covariates. A regression model predicting performance 1 CHF with GSH and these 5 principal components as covariates still has GSH as a significant predictor (standardized coefficient = 0.45, p = 0.031) and no colinear predictors (all VIFs <1.1). Ins and CrPCr are also loading to the principal components that were left out as covariates, meaning some of their influence is not being accounted but if ran a partial Spearman correlation between GSH and 1 CHF performance and control for CrPCr and Ins, the correlation is still present (Spearman’s Ro = 0.41, one-tailed-p=0.036); on the other hand, the pairwise correlations between both CrPCr and Ins, and 1 CHF performance remain not significant after controlling for GSH (one-tailed ps>0.425). Following the same approach for PE, since it is also associated with the response variable, we used 6 of the 7 components that didn’t load PE, explaining a total of 84.79% of the total dataset variance. The component left out loads PE exclusively, meaning the influence from all other metabolites can be accounted for. The regression model predicting performance 1 CHF with PE and these 6 principal components as covariates also still has PE as a significant predictor (standardized coefficient = 0.40, p = 0.035) and no colinear predictors (all VIFs <1.1). Corrected both p-values for FDR using the BH procedure results in p=0.035 and p=0.035 for GSH and PE respectively. Changes were added to the Results section and PCA and VIF information to the Figure1-Source Data 1.

When assessing whether effects are different for different conditions, e.g. for early versus late blocks, or dependent on the incentive magnitude, these should be included as factors in the analysis (e.g. a repeated measures ANOVA with the conditions block x incentive value, and the MRS measures as covariates, or using a mixed model approach). Then only when significant interactions are detected (e.g. incentive magnitude x GSH), can you test post hoc the simple effects of GSH in each condition.

If the effects for the human data remain significant using the correct analyses (particularly including all MRS measures in a single multiple regression model), then these analyses are certainly interesting to include in the current study. However, given the effect size I am not sure that they will, and then I am not sure if they are of much added value to the much more intricate animal work.

Thank you for this suggestion. We ran these models for both GSH and PE, dichotomized into low and high with median split, since their scores were both correlating with 1 CHF performance. Hence, we modeled successful trials with a 2x2x2 mixed design ANCOVA with block (first, last) as the within subjects’ factor, reward (low, high) and metabolite level (low, high) and the principal components that don’t load each metabolite as in the previous analysis. The triple interaction block*reward*GSH level was significant (p=0.031, partial eta square = 0.275) while the triple interaction block*reward*PE level was not (P = 0.123, partial eta square = 0.162). Hence, we proceeded with post-hoc tests for GSH. We tested the double interaction block*GSH level in two similar mixed designed ANCOVAs for low and high reward. This factor was significant for high but not low reward (respectively: p_corrected = 0.046, partial eta square = 0.301; p = 0.341, partial eta square = 0.061). Finally, high and low GSH groups were significantly different in the last block (p_corrected = 0.040, Cohen’s D = 1.08) but not in the first block (p_corrected = 0.238) due to a decrease in performance in the low GSH group and a maintenance of performance in the high GSH group (respectively Wilcoxon W = 21.0, p = 0.017, corrected p = 0.034, rank biserial correlation = 1.00; p_corrected = 0.553, Cohen’s D = 0.185).

Reviewer #3 (Recommendations for the authors):

In general the experiments are well designed and follow a linear logic. I have several points related to the experimental design that limit interpretation of the results in the manuscripts current form.

1. The rationale for why these experiments are only performed in males in the introduction is not well justified.

We fully agree and are deeply convinced on the imperative need of including both sexes in any biomedical work. This is currently the practice in our laboratory, but we have been ‘victims’ of the general trend in life sciences for many years. In this case, the study started by evaluating data from a human MRS study that started already 8-10 years ago, and that was originally motivated by former data in the lab on social hierarchies in rodents (and given the sex-specificity of the nature of social competitions, it involved only males). Given that our analyses of the metabolites identified the potential importance of GSH in the NuAc in the context of effortful motivation, we set up experiments in rodents to address the causal importance of this antioxidant. In order to progress mechanistically in this question, we back translated the studies in men to male rats. Our first findings were encouraging and we set up a collaboration to investigate the observed phenomenon more deeply.

But then, the pandemic hit and we had to sacrifice animals, stop experiments and limit our original ambitious greatly. We remain committed to perform studies in the two sexes, both in humans and rodents, and currently have launched one study (that will be followed by a second related one in humans) in which we will have a larger sample of both men and women and study metabolites in different brain regions in the context of motivated behavior. We will perform follow up studies in rodents, in a similar way as in the current study, to address causality.

However, we strongly feel now that the set of data we have managed to gather and complete so far, as reported in this manuscript, is critically new and important and deserves publication as soon as possible. Completing parallel analyses in females would certainly take an additional period of 2-3 years. We hope very much that editors and reviewers would convene with us on the importance of publishing the study as currently stands.

We have addressed this important point in the manuscript (conclusion) by stating “Future studies are warranted to expand the current analyses to females and to investigate the mechanisms whereby GSH levels in the NuAc affect motivated performance.”

2. The reversibility experiments outlined in Figure 4 are not convincing. The authors find that following a washout period of the BSO compound in a subset of male rats that the rats no longer show statistically significant changes in motivated behavior. Based on these observations, the authors conclude: "These set of data supports the view that lowering accumbal GSH levels leads to a transient impairment in effort-based incentivized performance without inducing toxicity". This is not an accurate assessment because the magnitude of the effect following 'washout' in the subset of rats in identical in strength and direction to the results obtained prior to the washout. The only difference is that the statistical power is reduced because only a subset of rats in investigated and this reduced power prevents the results from achieving statistical significance. To make this claim, the authors have to compare the performance of the same rats before and after the washout period.

In order to address this concern, we have performed a new washout experiment, increasing the number of animals per group (to 17 instead of 8) to increase the statistical power of the results and clarify the time effect of the BSO and its toxicity effect. Again, in this new cohort of animals (new Figure 4g-k), no differences were observed in the breakpoint, rewards or correct nosepoke number and percentage. These results demonstrate that the BSO effect can disappear following a washout of 4 weeks. We have also increased the number of samples for the toxicity assay (from 4 to 10, new Figure4—figure supplement1 m and o) and, thus, strengthen our findings that no toxicity is observed after BSO infusions.

3. The miniature excitatory postsynaptic potential analysis of MSNs in Figure 6 is a nice addition, but is incomplete. The effects of N-acetyl-cysteine are investigated in context of changes in motivated behavior, but the authors do not assess how motivated behavior correlates with these changes within subjects. The authors performed elevated plus maze an open field prior to their recordings, but did not perform the motivated behavioral analysis which would have strengthened their findings. This lack of within subjects analysis also make it difficult to assess which of the observed changes are likely to contribute to the observed behavior effects. Finally, the rationale for performing these experiments in the core region is not clear. The review cited by the authors that is used for justification more strongly implicates the shell region in sustained efforts associated with increased motivation.

The primary aim of the ex vivo electrophysiological experiments was to assess the cellular effects of the N-acetyl-cysteine treatment in MSNs. We have performed these experiments in naïve animals, as this approach has allowed us to isolate the impact of this treatment from the plastic changes expected to be induced in MSNs by the operant behavior paradigm. Plasticity of excitatory synapses within the NuAc contributes to cue-reward association (Andrzejewski et al., 2103; Floresco et al., 2001; Di Ciano et al., 2001; Hernandez et al., 2005; Kelley et al., 1997), and direct link between NMDAR-dependent synaptic plasticity within the NuAc core and the development of cue-evoked neural activity during learning has been confirmed in a recent report (Vega-Villar et al., 2019). Further synaptic changes are likely to be induced by the exposure to the progressive ratio task, although, to our knowledge, the specific NuAc division and cell-type involved have not yet been ascertained. Thus, although we agree with the Reviewer that a correlation between electrophysiological findings and behavioral performance would underscore the relevance of the cellular observations, such a correlational approach would require an extensive investigation with a large number of subjects, in order to 1.: obtain sufficient variability in the behavioral performance distribution, such that the ‘behavioral score’ can be correlated with the adequate statistical power with a ‘cellular excitability score’ extracted from the electrophysiological recordings after behavior; 2.: include the investigation of additional conditions to disentangle the direct effect of N-acetyl-cysteine from the learning-induced modifications in the accumbal circuits (e.g., checking the interaction treatment x PR exposure).

To our understanding, there is no demonstration of an exclusive role of the NuAc shell in sustaining the effort during the progressive ratio paradigm. The review by Floresco (2015) emphasizes that the core and shell division of the NuAc can differentially govern the outcome of motivated behavior. Inactivating the core reduces cue-based responding, whereas inactivating the shell can increase the performance towards an irrelevant, non-rewarded, or less preferable outcome. Thus, whereas the core initiates goal-directed behavior towards the reward, the shell inhibits goal-irrelevant behavior. As we now indicate in the results, in our experiments ex vivo, we prioritized the core because on this initiator role, although we agree that a complete description of the cellular effects of NAC should have included both accumbal divisions. However, due to the procedures required for post hoc identification of the recorded cells (biocytin filling, fixation, and successful identification after RNAscope processing), we were forced to limit the number of recordings performed in each slice (typically 2-3 cells) and decided to focus on a single accumbal region in order to maximize the chance of collecting sufficient recordings from both D1- and D2-MSNs.

References:

Andrzejewski, M. E., McKee, B. L., Baldwin, A. E., Burns, L., & Hernandez, P.. The clinical relevance of neuroplasticity in corticostriatal networks during operant learning. Neuroscience and biobehavioral reviews, 37(9 Pt A), 2071–2080 (2013).

Floresco SB. The nucleus accumbens: an interface between cognition, emotion, and action. Annu Rev Psychol. Jan 3;66:25-52 (2015).

Floresco, S. B., Blaha, C. D., Yang, C. R. & Phillips, A. G. Dopamine D1 and NMDA receptors mediate potentiation of basolateral amygdala-evoked firing of nucleus accumbens neurons. J. Neurosci. 21, 6370–6376 (2001).

Di Ciano, P., Cardinal, R. N., Cowell, R. A., Little, S. J. & Everitt, B. J. Differential involvement of NMDA, AMPA/kainate, and dopamine receptors in the nucleus accumbens core in the acquisition and performance of pavlovian approach behavior. J. Neurosci. 21, 9471–9477 (2001).

Hernandez, P. J., Andrzejewski, M. E., Sadeghian, K., Panksepp, J. B. & Kelley, A. E. AMPA/kainate, NMDA, and dopamine D1 receptor function in the nucleus accumbens core: a context-limited role in the encoding and consolidation of instrumental memory. Learn. Mem. 12, 285–295 (2005).

Kelley, A. E., Smith-Roe, S. L. & Holahan, M. R. Response-reinforcement learning is dependent on N-methyl-D-aspartate receptor activation in the nucleus accumbens core. Proc. Natl Acad. Sci. USA 94, 12174–12179 (1997).

Vega-Villar, M., Horvitz, J.C., Nicola, S.M.. NMDA receptor-dependent plasticity in the nucleus accumbens connects reward-predictive cues to approach responses. Nat Commun. Sep 27;10(1):4429 (2019).

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

As you will see below, Reviewer 2 requests that the strengths of concluding an absence of effect should be examined using a Bayesian statistical test. Although such a test may not yet be common in some fields, it is rapidly becoming common in human cognitive neuroscience, and of particular importance when interpreting null results. For your information, the reviewer pointed out that an open-source statistics program, JASP (https://jasp-stats.org/), could be useful for this purpose. We would like to see your response to this as well as other issues (making the figure of metabolic pathways into a main figure) before making a final decision.

We would like to thank the editor and reviewers for their consideration and appreciation of our efforts to improve the quality of the manuscript. We have now addressed the different requests from Reviewer 2, as requested. First, we have examined the absence of effect using Bayesian statistical test with the suggested JASP programme. We have also added an additional Figure 1—figure supplement 2 to include the occipital voxels and a representative spectra of the same region. Furthermore, we have included the metabolic pathway diagrams in Figure 1, as suggested. None of the additional analyses modify the results and conclusions of our data, but the opposite, they further reinforce them, strengthening the manuscript and study. See our point-by point response below.

Reviewer #2 (Recommendations for the authors):

Overall, the authors did a thorough job in this revision.

I only have a few more suggestions:

Most importantly, for any control analyses where the absence of an effect is concluded, conduct Bayesian statistical tests and provide evidence in favour of the null hypothesis. This is particularly important in the relatively small samples used here. When evidence in favour of the null hypothesis is not convincing, please rephrase the associated conclusions, that you cannot conclude that there was no difference. This is particularly for the null results of the BSO and occipital voxel analyses but holds for all control analyses where the absence of an effect is important.

Great that you also have an occipital voxel in a subset of the participants. To make this transparent (also the fact that this is a very small group), please add the panels C-F in figure 2 also for the occipital voxels (so exactly the same figure). This will highlight the degree of specificity of the finding.

What false discovery rate did you use for the BH procedure? Normally this is done a priori (see e.g. here: https://www.statology.org/benjamini-hochberg-procedure/). How did you compute the corrected p-values?

I really like the added figure of the metabolic pathways. I think it would be helpful for readers to add this to the main manuscript, perhaps in figure 1.

We thank the reviewer for these additional suggestions. Following their input, we have now conducted a Bayesian statistical test for the absence of effect, especially in cases when small samples were used (i.e., not just for the null results of the BSO and the occipital lobe voxel analysis, but more generally). According to standards in the field, when BF01 was greater than 3, it was considered as moderate evidence in favour of the null hypothesis and stated that there was no difference. When BF01 was lower than 3, we rephrased our statements in the manuscript as “we observe no statistically significant differences”. The reviewer can also find the result of the Bayesian tests in the corresponding figure legends in the article file and in the Stats GSH manuscript file. None of these analyses modify the results and conclusions of our data.

We have also included a new Figure1—figure supplement 2 in order to show the occipital voxels used for spectroscopy and a representative spectra of the same region. This figure further supports the specificity of our measurements.

Regarding the false discovery rate, we used the p.adjust default Benjamini–Hochberg procedure, which follows the method described in Benjamini and Hochberg (1995). Instead of computing a critical value, this function returns adjusted P values, so they can be compared with false discovery rate (q*) directly. It sorts the P values in ascending order, multiplies them by the number of comparisons and divides them by their sort rank after. For example:

p.adjust(c(0.03, 0.04, 0.01, 0.02), method = ‘BH’) sorts the (N=4) P values: 0.01, 0.02, 0.03, 0.04 (rank 1, 2, 3, 4).Then adjusts them:0.01*4/1, 0.02*4/2, 0.03*4/3, 0.04*4/4 Resulting in: 0.04, 0.04, 0.04, 0.04. The null hypothesis rejected if they are lower than a chosen false discovery rate. We used a false discovery rate of 0.05 to be consistent with the α value of 0.05 and the examples in Benjamini and Hochberg (1995). We made the used false discovery rate clearer in the Statistical Analysis subsection. None of these analyses modify the results and conclusions of our data.

Ref: https://www.jstor.org/stable/2346101

Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing Yoav Benjamini and Yosef Hochberg Journal of the Royal Statistical Society. Series B (Methodological) Vol. 57, No. 1 (1995), pp. 289-300.

As requested, we have modified Figure 1 by adding the figure of the metabolic pathways (previously supplementary) in panel a.

Author details

1. Ioannis Zalachoras

   Laboratory of Behavioral Genetics (LGC), Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

   Contribution

   Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, I.Z. performed the behavioral and molecular experiments in rats and analyzed the data

   Contributed equally with

   Eva Ramos-Fernández

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8113-3512

2. Eva Ramos-Fernández

   Laboratory of Behavioral Genetics (LGC), Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

   Contribution

   Data curation, Formal analysis, Writing – review and editing, E.R. performed the behavioral and molecular experiments in rats and analyzed the data

   Contributed equally with

   Ioannis Zalachoras

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3771-2189

3. Fiona Hollis

   1. Laboratory of Behavioral Genetics (LGC), Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
   2. Department of Pharmacology, Physiology, and Neuroscience, University of South Carolina School of Medicine, Columbia, United States

   Present address

   University of South Carolina School of Medicine, Columbia, United States

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, F.H. performed the behavioral and molecular experiments in rats and analyzed the data

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6559-5736

4. Laura Trovo

   Nestlé Institute of Health Sciences, Nestlé Research, Société des Produits Nestlé SA, Vers-chez-les-Blanc, Lausanne, Switzerland

   Contribution

   Conceptualization, Supervision, Project administration, Writing – review and editing

   Competing interests

   LT is employee from Nestle S.A

5. João Rodrigues

   Laboratory of Behavioral Genetics (LGC), Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

   Contribution

   Formal analysis, Visualization, Writing – review and editing, J.R. analyzed the data from the behavioral and MRS experiments in humans

   Competing interests

   No competing interests declared

6. Alina Strasser

   Laboratory of Behavioral Genetics (LGC), Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

   Contribution

   Data curation, Formal analysis, A.S. performed the behavioral and MRS experiments in humans

   Competing interests

   No competing interests declared

7. Olivia Zanoletti

   Laboratory of Behavioral Genetics (LGC), Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

   Contribution

   Data curation, Formal analysis, Methodology

   Competing interests

   No competing interests declared

8. Pascal Steiner

   Nestlé Institute of Health Sciences, Nestlé Research, Société des Produits Nestlé SA, Vers-chez-les-Blanc, Lausanne, Switzerland

   Contribution

   Conceptualization, Supervision, Funding acquisition, Project administration, Writing – review and editing

   Competing interests

   PS is employee from Nestle S.A

9. Nicolas Preitner

   Nestlé Institute of Health Sciences, Nestlé Research, Société des Produits Nestlé SA, Vers-chez-les-Blanc, Lausanne, Switzerland

   Contribution

   Supervision, Project administration, Writing – review and editing

   Competing interests

   NP is employee from Nestle S.A

10. Lijing Xin

    Animal Imaging and Technology Core (AIT), Center for Biomedical Imaging (CIBM), EPFL, Lausanne, Switzerland

    Contribution

    Data curation, Supervision, Methodology, L.X. performed the behavioral and MRS experiments in humans

    Competing interests

    No competing interests declared

11. Simone Astori

    Laboratory of Behavioral Genetics (LGC), Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Contribution

    Data curation, Formal analysis, Investigation, Visualization, Writing – review and editing, S.A. performed and analyzed the ex vivo electrophysiological recordings

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-7698-8332

12. Carmen Sandi

    Laboratory of Behavioral Genetics (LGC), Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Contribution

    Conceptualization, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    carmen.sandi@epfl.ch

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-7713-8321

• 2,068

  Page views

• 197

  Downloads

• 1

  Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.