In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Substantial evidence implicates the nucleus accumbens in motivated performance, but very little is known about the neurochemical underpinnings of individual differences in motivation.

Here, we applied 1 H magnetic resonance spectroscopy 1 H-MRS at ultra-high-field in the nucleus accumbens and inquired whether levels of glutamate Glu , glutamine Gln , GABA or their ratios predict interindividual differences in effort-based motivated task performance.

Given the incentive value of social competition, we also examined differences in performance under self-motivated or competition settings.

Our results indicate that higher accumbal Gln-to-Glu ratio predicts better overall performance and reduced effort perception. As performance is the outcome of multiple cognitive, motor and physiological processes, we applied computational modeling to estimate best-fitting individual parameters related to specific processes modeled with utility, effort and performance functions.

This model-based analysis revealed that accumbal Gln-to-Glu ratio specifically relates to stamina; i. It also indicated that competition boosts performance from task onset, particularly for low Gln-to-Glu individuals.

In conclusion, our findings provide novel insights implicating accumbal Gln and Glu balance on the prediction of specific computational components of motivated performance.

This approach and findings can help developing therapeutic strategies based on targeting metabolism to ameliorate deficits in effort engagement. There are substantial individual differences in human achievement in various life domains, from education to work or sports [ 1 , 2 ].

Motivation is key to success and an important factor for goal-directed behavior and well-being [ 3 , 4 ]. Motivational deficits, such as apathy, are prevalent in neurodegeneration and psychiatric disorders [ 5 , 6 , 7 ].

Understanding the neurobiological underpinnings that lead to individual differences in motivated performance can help developing new strategies to ameliorate deficits in reward valuation and effort engagement. In agreement with the rodent literature [ 8 , 9 ], the ventral striatum, including the nucleus accumbens, has emerged in humans as a key component of the motivation brain circuitry regulating motivated behavior [ 10 , 11 , 12 , 13 , 14 ].

In addition to reward processing, the nucleus accumbens has been implicated in cost-based behavioral allocation for both mental and physical effort [ 13 ]. However, knowledge about neurochemical mechanisms linking accumbal function with motivated behavior is scarce.

Recently, the quantification of metabolites related to excitatory [i. Importantly, motivated performance is a complex process that involves multiple behavioral functions [ 1 , 8 ]. To provide evidence for the accumbal specificity of these neurochemical—behavioral associations, we also performed 1 H-MRS in the occipital lobe.

Importantly, in addition to their role in neurotransmission, these three metabolites participate in multiple metabolic pathways, including energy production and the synthesis of the antioxidant glutathione [ 20 ].

To dissect motivation into its component elements, we used a recently developed effort-based monetary incentivized task [ 21 ] that combines aspects from the MID task [ 10 ] and effort-based decision-making paradigms [ 11 , 22 ]. Importantly, this task probes different aspects of motivated performance while capturing a wide range of individual differences [ 21 ].

In addition, in order to account for the modulation of performance by situational factors [ 23 , 24 ], and given the capacity of social competition to improve performance in a variety of settings [ 25 , 26 , 27 , 28 ], we compared performance under competition versus self-motivated performance.

Then, we applied computational modeling designed to dissect performance in this task to specific components such as curvature of the utility function, sprint and endurance stamina, performance baseline and its randomness.

Previous studies [ 29 , 30 , 31 , 32 ] have shown that computational approaches enhance our insight to behavioral mechanisms and reveal otherwise inaccessible neurobiological correlates. Here, applying computational modeling allowed us to reveal critical associations between metabolites, or their ratios, and particular components underlying motivated performance e.

We show that the accumbal ratio of glutamine-to-glutamate specifically predicts effortful performance, by particularly relating to the stamina required to keep up performance throughout the task. For complete information on Materials and Methods, please see Supplementary Methods.

From 43 men, 20—30 years old, originally recruited for the study, we obtained valid MRS data from 27 of them i. Participants were characterized for several personality measurements see section on Personality questionnaires and anthropometric characteristics in Supplementary Methods.

Data analyzed here is part of a larger study from which a report on the link of metabolites with anxiety trait has been previously published [ 33 ].

Informed consent was obtained from all participants in the study. Experiments were performed in accordance with the Declaration of Helsinki and approved by the Cantonal Ethics Committee of Vaud, Switzerland.

See Supplementary Methods for further details. The NAc region of interest voxel VOI was defined by the third ventricle medially, the subcallosal area inferiorly, and the body of the caudate nucleus and the putamen laterally and superiorly, in line with definitions of NAc anatomy identifiable on MRIs [ 34 ] Supplementary Fig.

A representative spectrum of the NAc voxel is shown in Fig. Glu, Gln, and GABA concentrations in the NAc were quantified with CRLB of 2.

Thus, we obtained spectra from the occipital lobe as an experimental control on 17 participants that were successfully recruited for a second scanner. b Accumbal metabolite concentrations for glutamate, glutamine, and GABA.

No differences in metabolite concentrations were expected between the experimental groups i. Mean metabolite concentrations for both groups combined are also shown marked Total in the gray bar. Glu glutamate, Gln glutamine, GABA gamma-aminobutyric acid.

Error bars are shown in standard deviations. c Modified incentive delay task and the performance of the different participants in the isolation and competition context.

Visual stimuli of the modified monetary incentive delay task in the CHF 1 isolation condition. Successive screen images were shown to participants whilst they were performing the hand grip task, and which guided and cued their performance in line with the instructions that were received prior to data acquisition.

We ensured that all participants had seen all visual stimuli and understood the task during the trial experimental practice session.

d Exemplary trial dynamic. e This plot shows the success rate of each participant indicated as a dot , in the isolation and the competition condition. Performance is shown as a function of the three different incentive sizes CHF 0. Each block contained 2 sessions of each 20 trials: 5 rest trials that occurred at an interval of every 3 action trials, with the incentive sizes varying pseudorandomly to ensure that each incentive could be earned 5 times.

The entire MIF task comprised 80 trials. Error bars, SEM. Our modified monetary incentive delay MID task [ 10 ] version [ 21 ] relied on exerting force on a hand grip or dynamometer TSDB-MRI, Biopac Fig.

To investigate the influence of competition on performance, the experiment was run under two experimental conditions, an isolation and a competition condition. To study more intricate aspects of motivated performance, we developed a computational model thereof.

As described above, participants had to reach the same threshold for successful performance with different incentives CHF 0. Hence, at each trial, subjects were primed with different monetary incentive values and they subsequently had to decide whether to invest energy in this trial and to perform the task accordingly.

Subjective utility i. where x α is the reward gain component and τ the effort cost component. For the reward gain component, x is monetary value CHF 0. Values of α under 1 reflected concave utility, which is very common in behavioral economics.

It might seem appropriate to have included another parameter b max instead of 2 a constant to define an upper limit to τ , but as monetary values could also be adjusted arbitrarily on the gain side e. Our model is similar to Le Bouc et al. We modeled loss of energy exponentially as opposed to linearly, as it may be a better approximation.

Because of the nature of our data, we also used discrete points averages of four sessions instead of continuous time. Hence, our stamina parameters are closely linked to fatiguability parameter K f in Le Bouc et al. Twelve performance measures PMs were chosen: success rates in each of the 4 sessions and for each of the 3 incentives.

A Monte Carlo-like stochastic search was used for parameter estimation. Our approach ensured that our search explored the parameter space sufficiently, whilst also leading to considerably accurate and reliable parameter values see Supplementary Methods for more details on parameter generation distributions for the estimation of different model parameters.

The two groups were also equivalent for their levels of physical activity or body mass index, and had similar levels of hand-grip maximal voluntary contraction MVC Supplementary Table 1. We applied 1 H-MRS to measure metabolite concentrations in the NAc Fig.

Our approach reliably distinguished the methylene groups of Glu and Gln at 2. Moderate correlations between Glu and Gln, on the one hand, and GABA were also observed Supplementary Table 3. Given that the neurochemical data from 1 H-MRS is not time resolved i. This allows assessing performance depending on incentive level without the confounding of divergent effort exertion across participants that is inherent to effort-based decision-making paradigms [ 1 ].

The ability to maintain responses over time is a fundamental feature of motivational processes [ 22 ], while fatigue from effort exertion tends to impair endurance performance. Accordingly, our task is quite demanding, involving 80 trials distributed across 4 sessions of 20 trials each, enabling to address performance dynamics with time.

Thus, competitive context enhanced success rate, and so did incentive size Fig. Before dissecting general performance into its components with computational modeling, we carried out a first set of correlational analyses on all participants Fig.

b NAc glutamine plotted against performance. d NAc glutamine plotted against effort perception. Glu glutamate, Gln glutamine, GABA gamma-aminobutyric acid, NAc nucleus accumbens. Success rate was quantified in percentage of correct trials.

We applied computational modeling see Supplementary Methods for more information to reveal more intricate components of task performance and, subsequently, to study their relation to the measured NAc metabolite i. Briefly, our model consisted of parameters related to utility curvature α , controlling subjective perception of incentive sizes , effort cost baseline b and stamina parameters indicating increased effort cost due to fatigue and performance function with steepness β of the sigmoidal relationship between outcome utility and success rate.

Effort cost baseline b was indicative of overall initial performance, with lower values denoting better performance. Stamina function controlled the loss of energy due to fatigue: sprint stamina ε spr over adjacent sessions i. The stamina functions in our model is a variation from a generic fatiguability function used in other models e.

We compared it to a more standard single parameter-based formalization, which performed worse. First, we addressed the contribution of each parameter to performance success throughout the four sessions in the task and for the three different incentives CHF 0.

Model simulation results where each parameter is varied separately Supplementary Fig. Because these success rates were correlated among each other, we performed a principal component analysis PCA to determine the effective dimensionality for the twelve success rate performance measures.

Goodness-of-fit error was lower than χ 2 7, 0. Spearman correlation coefficients between the top estimated parameter sets for each parameter ranged between 0. For this reason, applying the best fitting parameter set in statistical analyses seemed appropriate.

We also performed parameter recovery analysis by generating performance measures based on model with known parameters and estimating them using the same procedure. We then compared our model with a simpler formalization of fatiguability, that has a single rate for energy loss e.

Hence, we decided to keep the more flexible model. We then compared performance parameters between the experimental groups. Correlations between parameters Supplementary Table 5 were low to moderate, with only sprint and endurance staminas ε spr and ε end showing high correlation, indicating partially overlapping neurobehavioral mechanisms.

We also tried to fix values of one of these parameters to their average population value and estimated the remaining parameters to see if model fit could be improved considering lower model complexity. Given that anxiety can affect competitive confidence and motivated performance [ 21 , 38 ], we examined whether anxiety trait is related to the model parameters.

We computed associations with measures of state and trait anxiety, and found none of them to be significant Supplementary Table 6. We then performed multiple linear regression with the metabolite concentration ratio and experimental group i.

For model parameters that were not normally distributed, rank regression was used. We also performed multiple linear regression for not normally distributed ε spr using ranks with the metabolite concentration ratio, social context i. We found that overall performance Fig. For effort cost baseline b Fig.

For sprint stamina ε spr Fig. Blue bars: isolation; red bars: competition. The nucleus accumbens is part of the brain circuitry that regulates effort-related motivated functions [ 7 , 8 , 13 ].

Here, we investigated whether variation in NAc metabolite levels relate to individual differences in motivated performance. We found that accumbal Gln-to-Glu ratio at resting state predicts the overall average performance in an effort-based motivated task.

Importantly, as motivated performance is quite multifaceted, with its different components possibly related to Gln-to-Glu ratio in different ways, we applied computational modeling to deconstruct global performance into specific components. Subsequently, we inquired which performance components are better predicted by accumbal metabolites.

This approach allowed us to relate individual differences in accumbal Gln-to-Glu ratio specifically with variation in endurance stamina ε end , the capacity to recover performance following a short rest-break period and—to a lesser extent—sprint stamina ε spr , a measure of fatigability displayed by participants subsequently, during motivated performance.

Glutamine is synthesized from Glu in astrocytes in a reaction catalyzed by Gln synthetase [ 39 ]. Once astrocytes release Gln, it can be taken up by neurons where it is readily converted to Glu [ 39 , 40 ] in GABAergic neurons, Glu is further converted into GABA [ 41 ].

Our computational model-based analysis approach, which has been successful in elucidating internal behavioral variables of reward-based learning such as learning rates [ 42 ], exploration—explotation balance [ 30 ], discounting [ 43 ], episodic memory [ 29 ], effort [ 14 ], mood [ 44 ], social [ 45 ], and economic preferences [ 46 , 47 ] , allowed us here to provide key insights into the specific components of motivated performance that relate to accumbal metabolites.

We found that the Gln-to-Glu ratio is positively linked to stamina necessary to maintain performance over longer periods, whereas the competition context leads to substantially improved initial performance accompanied by a faster loss in stamina, and which was particularly pronounced in individuals with low resting Gln-to-Glu levels.

Importantly, our data suggests that Gln rather than Glu or GABA is the main contributor to the reported association between accumbal Gln-to-Glu ratio and endurance stamina ε end. In addition, the nucleus accumbens receives GABAergic and glutamatergic projections from multiple brain regions [ 48 , 49 ].

Therefore, Glu and GABA concentrations measured in the NAc through 1 H-MRS represent pools of both accumbal and afferent neurons. Due to the more local nature of glial cells, Gln levels in our data primarily represent production by accumbal glial cells including astrocytes and oligodendrocytes [ 50 , 51 ].

Therefore, our findings point towards a key contribution of accumbal glial-derived Gln, and particularly the Gln-to-Glu ratio, on effortful endurance in motivated behavior. It is important to note that, in addition to its role in neurotransmitter production, Gln is also involved in mitochondrial oxidative phosphorylation and, consequently, cellular energy production [ 20 , 52 ].

Accordingly, high Gln concentrations might allow for enhanced GABA and Glu concentrations to be achieved via just-in-time synthesis during task performance [ 55 ], accommodating the behavioral requirements to succeed.

Indeed, our results suggest that performance is not maintained via already present GABA or Glu pools, because neither resting-state GABA nor Glu predicted endurance performance. In addition, high Gln concentrations may contribute to fuel mitochondrial function under enhanced NAc engagement during motivated and effortful behaviors, which are recognized accumbal putative actions [ 10 , 13 , 56 , 57 ].

In support of this hypothesis, work in rodents has shown that the capacity to remain on task for longer and to overcome greater effort costs is related to the magnitude of NAc oxygen responses to delivered rewards [ 58 ].

Oxygen consumption reflects mitochondrial respiration, a critical mitochondrial function and partial proxy for energy production in the form of ATP. Strikingly, higher mitochondrial oxygen respiratory capacity in the NAc in male rodents is positively related to the capacity to win a social competition [ 59 , 60 ].

Therefore, our results relating accumbal Gln-to-Glu ratio to the capacity to exert motivated effort provide novel metabolic insights to this body of data.

One of the mechanisms whereby Gln-to-Glu ratio may contribute to differences in endurance and sprint stamina is the capacity of individuals with a high Gln-to-Glu ratio to overcome fatigue. Fatigue induced by prolonged active task engagement can have a negative impact on endurance performance.

In sports, for example, the psychobiological model of endurance performance states that effort perception plays a crucial role in explaining how fatigue reduces the willingness to perform [ 62 ] and negatively affects performance [ 61 ].

Recent accounts of human motivated performance emphasize the perception of effort as a crucial factor underlying motor endurance performance [ 63 ].

The inverse relationship that we found between Gln-to-Glu levels and effort perception may provide support for this account, especially in light of our decomposition of motivated performance into an early component that is boosted in low Gln-to-Glu individuals under competition as a sign of excessive effort and the stamina component, which is, subsequently, reduced in the same individuals.

Although, to the best of our knowledge, no previous study assessed NAc metabolism in the context of fatigue, substantial influence relates fatigue with reduced Gln in blood [ 64 ] and potentially also with alterations in Gln metabolism in the brain [ 65 ].

Oral glutamine supplementation has been reported to reduce subjective fatigue and ratings of perceived exertion during demanding tasks [ 66 , 67 ] and to increase striatal Gln levels [ 68 ]. At the neurobiological level, several mechanisms may account for the observed differences in Gln and Gln-to-Glu ratio, including differences in Gln production, metabolite catabolism and the availability of cellular transporters for Gln and Glu.

The nucleus accumbens has been implicated in anhedonia, reduced motivation, and decreased energy typically found in individuals with depression [ 69 ] and substantial data indicate alterations in these metabolic pathways and depression.

For example, reduced cortical density of glutamine synthetase-expressing astrocytes has been found in post mortem brains of major depression patients [ 50 ] and genetic variation in the gene coding for this enzyme associated with depression [ 70 ]. In addition, reduced levels in NAc Gln were reported in a rat model of stress-induced depression [ 44 ].

We also found that performance in our task was enhanced by social competition, in alignment with a reported facilitative role of competition in task performance [ 25 , 26 , 27 , 28 ]. Our computational model revealed that the facilitating effect of competition on the overall performance was mostly due to improvement in initial performance expressed through lower values of effort cost baseline b , which persisted despite lower values of sprint stamina ε spr , which manifested itself in poorer performance in later task sessions.

Furthermore, we found a significant interaction between competition context and Gln-to-Glu ratios in determining initial performance: the initial performance boost triggered by social competition was only observed for participants with a low Gln-to-Glu ratio.

Participants characterized by a high Gln-to-Glu ratio seemed to have superior task engagement from the onset of the task, which was not significantly boosted by competition.

This observation suggests a link between high accumbal Gln-to-Glu and self-motivated performance in effortful incentivized challenges. Finally, the associations between metabolites and performance were not observed in the occipital lobe, in agreement with reports indicating a lack of correspondence in Gln or Gln-to-Glu levels across different brain regions [ 71 ].

However, a note of caution should be added, as due to technical limitations, metabolites in the occipital lobe were acquired on a different day in a subsample of the participants. This implies that the power to detect a significant behavior—metabolite association in the occipital lobe was lower, and it would be important in future studies to verify the specificity of the reported associations for the NAc.

In this study, only males were included as our prediction for a link between accumbal metabolism and motivated performance was inspired in background studies involving male rodents [ 59 , 72 , 73 ].

Therefore, future studies are warranted to test whether our findings are generalized to the general population. Finally, although our approach allowed us to reveal important components of motivated performance, in the future it will be important to disentangle further aspects such as effort-related decision-making and vigor of response.

In conclusion, our results provide the first solid evidence for the role of accumbal metabolites—particularly the Gln-to-Glu ratio—in different components of effortful performance. We envision that this approach and findings can help developing metabolism-targeting strategies to ameliorate deficits in motivated effort engagement.

This work was supported by grants from the Swiss National Science Foundation CR20I; NCCR Synapsy grant number 51NF and intramural funding from the EPFL. The authors declare no competing interests.

Open Access funding provided by EPFL Lausanne. The data that support the findings of this study are available from the corresponding authors upon request.

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Jay Tischfield for their support with real-time PCR. This work is supported by grants from the BrightFocus Foundation A, from NIH 1R01NS and from the Hong Kong Research Grants Council HKSAR GRF JC and KH designed the study.

YC, JC, LL carried out the blood glutamine, glucose, body weight and life span studies and involved in the data analysis. JC, YC, HC and YZ did the neuronal culture experiments RT-PCR, western blot and immunostaining and participated in data analysis.

GV and MP carried out LTP recording experiments and data analysis. JL and RH performed the expression studies and data analysis. JC drafted the manuscript. JC, KH, HC, RH and MP participated in the revision of the manuscript.

All authors read and approved the manuscript. All animal procedures were approved by the Rutgers University Institutional Animal Care and Use Committee protocol 06— Department of Cell Biology and Neuroscience, Rutgers University, Allison Road, Piscataway, NJ, , USA.

Jianmin Chen, Yanping Chen, Graham Vail, Lauren Louie, Jiali Li, Ronald P. Hart, Mark R. Division of Life Science, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong. Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China.

You can also search for this author in PubMed Google Scholar. Correspondence to Jianmin Chen. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.

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Hart 1 , Mark R. Background Glutamine Gln or Q is the most abundant free amino acid in the human blood stream. Methods A-T mouse models For our A-T model, we chose the Atm tm1Awb mutant allele [ 13 ] and the Atm tm1Bal mutant allele [ 14 ] from The Jackson Laboratories.

Blood glucose and glutamine measurements About 0. Western blots and immunocytochemistry For western blot analysis, cultured neurons were lysed in RIPA buffer Thermo Scientific containing proteinase and phosphatase inhibitors Roche Diagnostics.

RT PCR and real-time PCR Total RNA was purified using TRIzol reagent Life Technologies following the standard protocol. Primer sets used for real-time PCR include: For 36B4 control Forward: atcgtctttaaaccccgcgt Reverse: acgttgtctgctcccacaat; For mBDNF common region Forward: gaaggctgcaggggcatagacaaa Reverse: tacacaggaagtgtctatccttatg.

Primer sets used for RT-PCR were: BDNF exon2 Forward: ggaagtggaagaaaccgtctagagca Reverse: gaagtgtacaagtccgcgtcctta BDNF exon3 Forward: gctttctatcatccctccccgagagt Reverse: gaagtgtacaagtccgcgtcctta BDNF exon4 Forward: ctctgcctagatcaaatggagcttc Reverse: gaagtgtacaagtccgcgtcctta 36B4 Forward: atcgtctttaaaccccgcgt Reverse: acgttgtctgctcccacaat Field potential recording Extracellular recordings of field EPSPs fEPSPs were made with ACSF-filled glass electrodes 5—10 μm tip diameter according to protocols previously established in our lab [ 16 ].

Statistics For lifespan study, the significance of the difference between the glutamine and control curves was determined by the Mantel-Cox log-rank test.

Results Glutamine modulates the metabolomics of ATM deficiency Our previous research has shown that glutamine has a broad neuroprotective effect [ 1 ], so we became interested in whether glutamine deficiency contributed to the A-T phenotype.

Full size image. Discussion Cultured neurons exposed to exogenous stressors such as DNA damage, heavy metals, oxidation or the Aβ peptide have heightened vulnerability to glutamine deprivation [ 1 ].

Conclusions We have shown previously that glutamine is neuroprotective in vitro and in mouse models of AD. References Chen J, Herrup K. Article CAS PubMed PubMed Central Google Scholar Gatti RA, et al.

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Article CAS PubMed Google Scholar Rubio I, et al. Acknowledgements The authors thank Dr. Funding This work is supported by grants from the BrightFocus Foundation A, from NIH 1R01NS and from the Hong Kong Research Grants Council HKSAR GRF Availability of supporting data and materials All data generated or analyzed during this study are included in this published article.

Competing interests The authors declare that they have no competing interest. Consent for publication Not applicable. Ethical approval and consent to participate All animal procedures were approved by the Rutgers University Institutional Animal Care and Use Committee protocol 06— Author information Authors and Affiliations Department of Cell Biology and Neuroscience, Rutgers University, Allison Road, Piscataway, NJ, , USA Jianmin Chen, Yanping Chen, Graham Vail, Lauren Louie, Jiali Li, Ronald P.

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