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• W2539233845 abstract "•Neural activity was measured during planning and execution of economic saving•Amygdala activity predicted the planned number of saving steps and their value•Amygdala-frontal circuits coded plan components and individual saving differences•The results suggest a new function for the human amygdala in economic planning Economic saving is an elaborate behavior in which the goal of a reward in the future directs planning and decision-making in the present. Here, we measured neural activity while subjects formed simple economic saving strategies to accumulate rewards and then executed their strategies through choice sequences of self-defined lengths. Before the initiation of a choice sequence, prospective activations in the amygdala predicted subjects’ internal saving plans and their value up to two minutes before a saving goal was achieved. The valuation component of this planning activity persisted during execution of the saving strategy and predicted subjects’ economic behavior across different tasks and testing days. Functionally coupled amygdala and prefrontal cortex activities encoded distinct planning components that signaled the transition from saving strategy formation to execution and reflected individual differences in saving behavior. Our findings identify candidate neural mechanisms for economic saving in amygdala and prefrontal cortex and suggest a novel planning function for the human amygdala in directing strategic behavior toward self-determined future rewards. Economic saving is an elaborate behavior in which the goal of a reward in the future directs planning and decision-making in the present. Here, we measured neural activity while subjects formed simple economic saving strategies to accumulate rewards and then executed their strategies through choice sequences of self-defined lengths. Before the initiation of a choice sequence, prospective activations in the amygdala predicted subjects’ internal saving plans and their value up to two minutes before a saving goal was achieved. The valuation component of this planning activity persisted during execution of the saving strategy and predicted subjects’ economic behavior across different tasks and testing days. Functionally coupled amygdala and prefrontal cortex activities encoded distinct planning components that signaled the transition from saving strategy formation to execution and reflected individual differences in saving behavior. Our findings identify candidate neural mechanisms for economic saving in amygdala and prefrontal cortex and suggest a novel planning function for the human amygdala in directing strategic behavior toward self-determined future rewards. Economic saving is an elaborate form of planned behavior characterized by dynamic, sequential choices and a focus on self-defined future reward [1Benhabib J. Bisin A. Modeling internal commitment mechanisms and self-control: A neuroeconomics approach to consumption–saving decisions.Games Econ. Behav. 2005; 52: 460-492Crossref Scopus (133) Google Scholar, 2Prelec D. Loewenstein G. The red and the black: mental accounting of savings and debt.Marketing Sci. 1998; 17: 4-28Crossref Scopus (713) Google Scholar]. Successful saving is a key determinant of the welfare of individuals and societies, which impacts entire economic systems [3Brown A.L. Chua Z.E. Camerer C.F. Learning and visceral temptation in dynamic saving experiments.Q. J. Econ. 2009; 124: 197-231Crossref Scopus (77) Google Scholar]. Theories in psychology, economics, and reinforcement learning have identified basic principles that underlie planned behaviors involving rewards, such as economic saving: a two-stage process that distinguishes the initial formation of a behavioral strategy from its subsequent execution [1Benhabib J. Bisin A. Modeling internal commitment mechanisms and self-control: A neuroeconomics approach to consumption–saving decisions.Games Econ. Behav. 2005; 52: 460-492Crossref Scopus (133) Google Scholar, 4Miller G.A. Galanter E. Pribram K.H. Plans and the Structure of Behavior. Holt, Rinehart and Winston, 1960Crossref Google Scholar], and a valuation component that directs behavioral strategies toward future rewards [5Sutton R.S. Barto A.G. Reinforcement Learning. MIT Press, 1998Google Scholar]. Here, we used fMRI to measure neural activity in an economic reward-saving paradigm that modeled these principles by separating the formation of a reward-based strategy from its execution through sequential choices. Based on human lesion [6Shallice T. Burgess P.W. Deficits in strategy application following frontal lobe damage in man.Brain. 1991; 114: 727-741Crossref PubMed Scopus (1599) Google Scholar] and neuroimaging evidence and single-cell recordings in monkeys [7Tanji J. Sequential organization of multiple movements: involvement of cortical motor areas.Annu. Rev. Neurosci. 2001; 24: 631-651Crossref PubMed Scopus (322) Google Scholar], cognitive and action planning are traditionally associated with the frontal lobes. Other prospective functions, such as episodic future thinking and spatial navigation, are associated with medial temporal lobe structures [8Wolbers T. Hegarty M. What determines our navigational abilities?.Trends Cogn. Sci. 2010; 14: 138-146Abstract Full Text Full Text PDF PubMed Scopus (487) Google Scholar, 9Sharot T. Riccardi A.M. Raio C.M. Phelps E.A. Neural mechanisms mediating optimism bias.Nature. 2007; 450: 102-105Crossref PubMed Scopus (484) Google Scholar]. However, much less is known about how the brain mediates the influence of rewards on planning, despite their crucial importance in directing strategy formation and execution [1Benhabib J. Bisin A. Modeling internal commitment mechanisms and self-control: A neuroeconomics approach to consumption–saving decisions.Games Econ. Behav. 2005; 52: 460-492Crossref Scopus (133) Google Scholar, 4Miller G.A. Galanter E. Pribram K.H. Plans and the Structure of Behavior. Holt, Rinehart and Winston, 1960Crossref Google Scholar, 5Sutton R.S. Barto A.G. Reinforcement Learning. MIT Press, 1998Google Scholar]. Studies using intertemporal choice paradigms have uncovered human brain systems for the subjective valuation of delayed rewards [10Kable J.W. Glimcher P.W. The neural correlates of subjective value during intertemporal choice.Nat. Neurosci. 2007; 10: 1625-1633Crossref PubMed Scopus (1288) Google Scholar, 11McClure S.M. Ericson K.M. Laibson D.I. Loewenstein G. Cohen J.D. Time discounting for primary rewards.J. Neurosci. 2007; 27: 5796-5804Crossref PubMed Scopus (641) Google Scholar, 12Peters J. Büchel C. Episodic future thinking reduces reward delay discounting through an enhancement of prefrontal-mediotemporal interactions.Neuron. 2010; 66: 138-148Abstract Full Text Full Text PDF PubMed Scopus (576) Google Scholar]. More recent investigations of complex multistep reinforcement learning showed that frontal-striatal systems evaluate reward outcomes associated with externally defined choice paths [13Doll B.B. Duncan K.D. Simon D.A. Shohamy D. Daw N.D. Model-based choices involve prospective neural activity.Nat. Neurosci. 2015; 18: 767-772Crossref PubMed Scopus (166) Google Scholar, 14Wunderlich K. Dayan P. Dolan R.J. Mapping value based planning and extensively trained choice in the human brain.Nat. Neurosci. 2012; 15: 786-791Crossref PubMed Scopus (214) Google Scholar]. These studies identified critical neural components for prospective reward valuation but did not address the key features of planned economic saving, which involve the internal construction of a reward-directed strategy and its subsequent execution through choice sequences of self-defined length [1Benhabib J. Bisin A. Modeling internal commitment mechanisms and self-control: A neuroeconomics approach to consumption–saving decisions.Games Econ. Behav. 2005; 52: 460-492Crossref Scopus (133) Google Scholar, 3Brown A.L. Chua Z.E. Camerer C.F. Learning and visceral temptation in dynamic saving experiments.Q. J. Econ. 2009; 124: 197-231Crossref Scopus (77) Google Scholar]. Based on recent single-neuron evidence in non-human primates [15Grabenhorst F. Hernádi I. Schultz W. Prediction of economic choice by primate amygdala neurons.Proc. Natl. Acad. Sci. USA. 2012; 109: 18950-18955Crossref PubMed Scopus (60) Google Scholar, 16Hernádi I. Grabenhorst F. Schultz W. Planning activity for internally generated reward goals in monkey amygdala neurons.Nat. Neurosci. 2015; 18: 461-469Crossref PubMed Scopus (34) Google Scholar], we hypothesized that in the current study the human amygdala would show prospective activity related to subjects’ economic saving strategies. Our hypothesis was further motivated by evidence of amygdala functions in basic reward valuation [17Paton J.J. Belova M.A. Morrison S.E. Salzman C.D. The primate amygdala represents the positive and negative value of visual stimuli during learning.Nature. 2006; 439: 865-870Crossref PubMed Scopus (678) Google Scholar, 18Gottfried J.A. O’Doherty J. Dolan R.J. Encoding predictive reward value in human amygdala and orbitofrontal cortex.Science. 2003; 301: 1104-1107Crossref PubMed Scopus (920) Google Scholar, 19Grabenhorst F. Rolls E.T. Parris B.A. d’Souza A.A. How the brain represents the reward value of fat in the mouth.Cereb. Cortex. 2010; 20: 1082-1091Crossref PubMed Scopus (147) Google Scholar, 20Schultz W. Neuronal Reward and Decision Signals: From Theories to Data.Physiol. Rev. 2015; 95: 853-951Crossref PubMed Scopus (553) Google Scholar, 21Murray E.A. Rudebeck P.H. The drive to strive: goal generation based on current needs.Front. Neurosci. 2013; 7: 112Crossref PubMed Scopus (32) Google Scholar, 22Rudebeck P.H. Mitz A.R. Chacko R.V. Murray E.A. Effects of amygdala lesions on reward-value coding in orbital and medial prefrontal cortex.Neuron. 2013; 80: 1519-1531Abstract Full Text Full Text PDF PubMed Scopus (115) Google Scholar], processing of economic choice variables [23De Martino B. Camerer C.F. Adolphs R. Amygdala damage eliminates monetary loss aversion.Proc. Natl. Acad. Sci. USA. 2010; 107: 3788-3792Crossref PubMed Scopus (286) Google Scholar, 24De Martino B. Kumaran D. Seymour B. Dolan R.J. Frames, biases, and rational decision-making in the human brain.Science. 2006; 313: 684-687Crossref PubMed Scopus (986) Google Scholar], and decision-making [25Hampton A.N. Adolphs R. Tyszka M.J. O’Doherty J.P. Contributions of the amygdala to reward expectancy and choice signals in human prefrontal cortex.Neuron. 2007; 55: 545-555Abstract Full Text Full Text PDF PubMed Scopus (158) Google Scholar, 26Grabenhorst F. Schulte F.P. Maderwald S. Brand M. Food labels promote healthy choices by a decision bias in the amygdala.Neuroimage. 2013; 74: 152-163Crossref PubMed Scopus (57) Google Scholar, 27Rutishauser U. Ye S. Koroma M. Tudusciuc O. Ross I.B. Chung J.M. Mamelak A.N. Representation of retrieval confidence by single neurons in the human medial temporal lobe.Nat. Neurosci. 2015; 18: 1041-1050Crossref PubMed Scopus (85) Google Scholar]. We also expected the involvement of prefrontal cortex areas, based on their known valuation, cognitive control, and decision functions [11McClure S.M. Ericson K.M. Laibson D.I. Loewenstein G. Cohen J.D. Time discounting for primary rewards.J. Neurosci. 2007; 27: 5796-5804Crossref PubMed Scopus (641) Google Scholar, 28Bartra O. McGuire J.T. Kable J.W. The valuation system: a coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value.Neuroimage. 2013; 76: 412-427Crossref PubMed Scopus (1077) Google Scholar, 29Grafman J. The structured event complex and the human prefrontal complex.in: Stuss D.T. Knight R.T. Principles of Frontal Lobe Function. Oxford University Press, 2002: 292-310Crossref Scopus (5) Google Scholar, 30Hare T.A. Schultz W. Camerer C.F. O’Doherty J.P. Rangel A. Transformation of stimulus value signals into motor commands during simple choice.Proc. Natl. Acad. Sci. USA. 2011; 108: 18120-18125Crossref PubMed Scopus (234) Google Scholar, 31Kolling N. Behrens T.E.J. Mars R.B. Rushworth M.F.S. Neural mechanisms of foraging.Science. 2012; 336: 95-98Crossref PubMed Scopus (380) Google Scholar, 32Croxson P.L. Walton M.E. O’Reilly J.X. Behrens T.E.J. Rushworth M.F.S. Effort-based cost-benefit valuation and the human brain.J. Neurosci. 2009; 29: 4531-4541Crossref PubMed Scopus (379) Google Scholar]. We designed a sequential economic saving paradigm in which human subjects could form internal strategies to save flavored liquid rewards that accumulated with interest; subjects later executed their strategies through choice sequences of self-defined lengths. Experimental manipulation of reward type and interest rate elicited individual differences in saving strategies. We used primary rewards because they elicit distinct subjective preferences [20Schultz W. Neuronal Reward and Decision Signals: From Theories to Data.Physiol. Rev. 2015; 95: 853-951Crossref PubMed Scopus (553) Google Scholar] and related activations in human reward and decision systems [11McClure S.M. Ericson K.M. Laibson D.I. Loewenstein G. Cohen J.D. Time discounting for primary rewards.J. Neurosci. 2007; 27: 5796-5804Crossref PubMed Scopus (641) Google Scholar, 19Grabenhorst F. Rolls E.T. Parris B.A. d’Souza A.A. How the brain represents the reward value of fat in the mouth.Cereb. Cortex. 2010; 20: 1082-1091Crossref PubMed Scopus (147) Google Scholar, 33Small D.M. Veldhuizen M.G. Felsted J. Mak Y.E. McGlone F. Separable substrates for anticipatory and consummatory food chemosensation.Neuron. 2008; 57: 786-797Abstract Full Text Full Text PDF PubMed Scopus (141) Google Scholar], and because they induce “visceral temptations” that promote variation in saving behavior, as shown in previous experimental studies of real-life saving decisions [3Brown A.L. Chua Z.E. Camerer C.F. Learning and visceral temptation in dynamic saving experiments.Q. J. Econ. 2009; 124: 197-231Crossref Scopus (77) Google Scholar]. We observed prospective amygdala activations that predicted subjects’ internal saving strategies up to two minutes before their behavioral completion. This prospective activity encoded two crucial planning components: the number of forthcoming choice steps implied by the current saving strategy, and their subjective evaluation. Amygdala planning activity was functionally coupled to specific prefrontal areas that encoded distinct planning components and reflected individual differences in strategy formation and saving performance. These findings suggest a previously unrecognized planning function for the human amygdala and identify neural components for simple economic saving strategies in functionally coupled amygdala-prefrontal reward circuits. Healthy volunteers (n = 24) performed choice sequences of self-defined lengths to save (accumulate) primary rewards (flavored dairy drinks) before choosing to spend (consume) the accumulated rewards (Figures 1A–1C). A sequence began with the planning phase (Figures 1A and 1B), in which pre-trained cues signaled current interest rate and reward type (Figure 1C), allowing subjects to form an internal saving strategy toward a specific reward goal. Subjects then entered the choice phase, in which they progressed toward their goal by making sequential, trial-by-trial save versus spend choices. Following a spend choice, computer-controlled pumps delivered the saved reward. Throughout each sequence, current trial position and saved reward amount were not cued, requiring subjects to track progress internally. Importantly, as learned in a training session, subjects could not influence the occurrence of reward type and interest rate conditions over consecutive sequences. This task design allowed subjects to autonomously plan their behavior within a saving sequence up to 2 min in advance (up to 10 consecutive save choices with ∼13 s cycle time, following the ∼13 s planning phase). Saving behavior, measured by observed choice sequence lengths, depended on current reward type, current interest rate, and their interaction (Figures 1D and 1E; all p < 0.005, multiple regression). Subjects generally saved longer with higher interest rates and with the high-fat reward type (Figure 1E). Crucially, changes in reward type and interest rate produced substantial variation in saving behavior, both between subjects (Figure 1E, gray dots) and within subjects (Figure 1D; Figure S1), which confirmed the importance of subjective preferences in the present task. As economic choices critically depend on the subjective values individuals derive from the choice options, we estimated the value of each saving sequence (“sequence value”) from observed choice frequencies (see Supplemental Experimental Procedures). These subjective values depended on final reward amounts and current reward type but also on expenditure related to sequence length. As higher reward amounts required longer sequences (determined by current interest rate), the value of the sequence was compromised by temporal delay and physical effort. To capture these influences on value in a direct manner, we followed the general notion of standard economic choice theory and estimated subjective values from observed behavioral choices. We assumed that a saving sequence had a higher subjective value if the subject chose it more frequently. Values derived in this manner provided a suitable description of the observed saving choices, as confirmed by logistic regression (Figure 1F; Figure S2A; across-subjects pseudo-R2 = 0.62 ± 0.02), out-of-sample validation (Figure S2A, inset), correlation with stated saving intentions (R = 0.33, p < 0.001), and correlation with subjects’ bids for the same reward in a separate, auction-like mechanism (Becker-DeGroot-Marschak [BDM] [34Becker G.M. DeGroot M.H. Marschak J. Measuring utility by a single-response sequential method.Behav. Sci. 1964; 9: 226-232Crossref PubMed Scopus (1417) Google Scholar]; R = 0.39, p < 0.001). Notably, subjective values provided a better description of subjects’ choices than the objective factors reward type and interest rate, or their interaction (Figure S2). Response times were related to subjective values, differed significantly between save and spend choice trials, and depended on the forthcoming sequence length (Figure S2), consistent with internally planned saving. Furthermore, while subjects approximated objectively optimal decisions in the low-fat/low-interest condition (maximizing rate of reward return, i.e., liquid per trial), they deviated from optimality in other conditions, with substantial inter-subject variation (Figure S3). This further suggested that behavior was guided by subjective valuations of factors reward type and interest rate. Behavior in the current sequence did not depend on the length of the previous sequence (p > 0.05, multiple regression), which confirmed that subjects treated sequences as independent. Taken together, the combination of reward and interest rate that defined each choice sequence elicited subjective valuations of that sequence, which guided saving behavior. Classically, the amygdala is associated with affective responses to immediate sensory events [35Phelps E.A. LeDoux J.E. Contributions of the amygdala to emotion processing: from animal models to human behavior.Neuron. 2005; 48: 175-187Abstract Full Text Full Text PDF PubMed Scopus (2257) Google Scholar, 36Rolls E.T. Limbic systems for emotion and for memory, but no single limbic system.Cortex. 2015; 62: 119-157Crossref PubMed Scopus (211) Google Scholar] rather than internally driven behavioral strategies. Such cue reactivity is also a dominant theme in current views of human amygdala function [37Janak P.H. Tye K.M. From circuits to behaviour in the amygdala.Nature. 2015; 517: 284-292Crossref PubMed Scopus (1040) Google Scholar, 38Phelps E.A. Lempert K.M. Sokol-Hessner P. Emotion and decision making: multiple modulatory neural circuits.Annu. Rev. Neurosci. 2014; 37: 263-287Crossref PubMed Scopus (215) Google Scholar, 39Rutishauser U. Mamelak A.N. Adolphs R. The primate amygdala in social perception - insights from electrophysiological recordings and stimulation.Trends Neurosci. 2015; 38: 295-306Abstract Full Text Full Text PDF PubMed Scopus (83) Google Scholar, 40Seymour B. Dolan R. Emotion, decision making, and the amygdala.Neuron. 2008; 58: 662-671Abstract Full Text Full Text PDF PubMed Scopus (200) Google Scholar]. By contrast, recent neurophysiological investigations implicate the amygdala in more complex, sequential decision-making [15Grabenhorst F. Hernádi I. Schultz W. Prediction of economic choice by primate amygdala neurons.Proc. Natl. Acad. Sci. USA. 2012; 109: 18950-18955Crossref PubMed Scopus (60) Google Scholar, 16Hernádi I. Grabenhorst F. Schultz W. Planning activity for internally generated reward goals in monkey amygdala neurons.Nat. Neurosci. 2015; 18: 461-469Crossref PubMed Scopus (34) Google Scholar]. We therefore investigated whether activity in the human amygdala reflected the key strategy components that guided subjects’ saving decisions. Broadly contrasting neural activity in planning and choice phases identified brain areas previously implicated in cognitive control, decision-making, and motivation (Figure 2A; Table S1, GLM1). However, our most striking finding was future-oriented activity in the amygdala that occurred during the planning phase, even before subjects initiated a saving sequence. This “planning activity” predicted the length of the forthcoming choice sequence, up to 2 min before its completion (Figure 2B, GLM1). It was not explained by simple cue responses or reported saving intentions (Figure S4). Importantly, sequence lengths were self-defined by the subjects, rather than instructed, and only existed as an internal, mental representation during the planning phase. In this sense, the observed correlation between amygdala activity and sequence length suggested that amygdala planning activity “predicted” subsequent behavior. Thus, prospective amygdala activity reflected the length of the internally planned choice sequence, which defined the subjects’ behavioral saving strategy. We observed a second form of prospective amygdala activity that reflected subjects’ valuations of saving strategies, which is crucial for directing planned behavior toward preferred reward goals [1Benhabib J. Bisin A. Modeling internal commitment mechanisms and self-control: A neuroeconomics approach to consumption–saving decisions.Games Econ. Behav. 2005; 52: 460-492Crossref Scopus (133) Google Scholar, 5Sutton R.S. Barto A.G. Reinforcement Learning. MIT Press, 1998Google Scholar]. Regressing activity on the subjective value of the forthcoming saving sequence (sequence value derived from observed choices) revealed a selective effect in the amygdala (Figure 2C, GLM2), distinct from encoding of planned sequence length (Figure 2D). Importantly, by varying the experimental factors reward type and interest rate, we partly decorrelated chosen sequence lengths from associated values (Figures 1D and 1E; Figure S1), which allowed detection of separate neural effects. The prospective valuation activity encoded specifically the value of the currently planned, forthcoming saving sequence, rather than simply reflecting the average value of the condition cue (regressor for mean sequence value of each condition; p = 0.28, t(23) = 1.1). Thus, in addition to encoding planned sequence length, prospective amygdala activity reflected the subjective value of the current saving strategy. We tested whether these amygdala planning signals predicted behavior also in a different value elicitation mechanism. On separate days, subjects placed bids in an auction-like mechanism (BDM) to indicate their willingness to pay for the same rewards and choice sequences as in the saving task (Figure 2E). Using a multiple-regression approach, we dissected the amygdala’s planning activity, measured in the saving task, by modeling its two distinct planning signals that correlated with the behavioral saving plan (sequence length) and its value (sequence value), respectively. Only the activity component captured by the sequence value regressor also predicted subjects’ BDM bids in the separate task (Figure 2F). Thus, prospective amygdala value signals predicted behavior in a different economic task, suggesting a flexible economic valuation mechanism. Further analysis investigated relationships between amygdala activity and saving behavior across individual participants. A psychometric-neurometric comparison identified matching sensitivities between individuals’ neural and behavioral measures associated with strategy choice: across individuals, the behavioral influence of factors reward type and interest rate, which determined the choice of saving strategy, matched the neural influence of these factors on amygdala activity (Figures 3A–3C). In other words, individual differences in saving behavior were expressed in the integration of different strategic factors, and amygdala planning activity reflected this integration. Consistently, a model of amygdala planning activity that incorporated these subjective integrations also predicted willingness-to-pay bids elicited in a separate task (Figure 3D). Thus, amygdala planning activity correlated well with individual differences in saving behavior. Taken together, these data suggest that prospective amygdala activity in the planning phase encoded two crucial components of economic saving strategies [1Benhabib J. Bisin A. Modeling internal commitment mechanisms and self-control: A neuroeconomics approach to consumption–saving decisions.Games Econ. Behav. 2005; 52: 460-492Crossref Scopus (133) Google Scholar, 2Prelec D. Loewenstein G. The red and the black: mental accounting of savings and debt.Marketing Sci. 1998; 17: 4-28Crossref Scopus (713) Google Scholar]: the number of forthcoming choice steps that define the subject’s behavioral saving strategy, and the subjective value that reflects the strategy’s focus on reward. The observed involvement of human amygdala in economic planning required comparisons to prefrontal cortex regions with well-established roles in cognitive control and decision-making [11McClure S.M. Ericson K.M. Laibson D.I. Loewenstein G. Cohen J.D. Time discounting for primary rewards.J. Neurosci. 2007; 27: 5796-5804Crossref PubMed Scopus (641) Google Scholar, 28Bartra O. McGuire J.T. Kable J.W. The valuation system: a coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value.Neuroimage. 2013; 76: 412-427Crossref PubMed Scopus (1077) Google Scholar, 29Grafman J. The structured event complex and the human prefrontal complex.in: Stuss D.T. Knight R.T. Principles of Frontal Lobe Function. Oxford University Press, 2002: 292-310Crossref Scopus (5) Google Scholar, 30Hare T.A. Schultz W. Camerer C.F. O’Doherty J.P. Rangel A. Transformation of stimulus value signals into motor commands during simple choice.Proc. Natl. Acad. Sci. USA. 2011; 108: 18120-18125Crossref PubMed Scopus (234) Google Scholar, 31Kolling N. Behrens T.E.J. Mars R.B. Rushworth M.F.S. Neural mechanisms of foraging.Science. 2012; 336: 95-98Crossref PubMed Scopus (380) Google Scholar, 32Croxson P.L. Walton M.E. O’Reilly J.X. Behrens T.E.J. Rushworth M.F.S. Effort-based cost-benefit valuation and the human brain.J. Neurosci. 2009; 29: 4531-4541Crossref PubMed Scopus (379) Google Scholar]. Similar to amygdala, the dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC) were more active during the planning phase than during the choice phase (Figure 4A, GLM1), and their activity predicted the forthcoming number of choice steps (Figure 4B, GLM1). However, neither area reflected the value of the planned saving strategy (nor individual reward preferences; Figure S5). Thus, these frontal areas partly resembled the amygdala by encoding subjects’ behavioral saving strategies (sequence length), but they did not encode initial strategy valuations (sequence value). Because DLPFC activity is involved in behavioral intentions and information maintenance [41Sakai K. Rowe J.B. Passingham R.E. Active maintenance in prefrontal area 46 creates distractor-resistant memory.Nat. Neurosci. 2002; 5: 479-484Crossref PubMed Scopus (305) Google Scholar], we tested whether it encoded subjects’ saving intentions in addition to behaviorally executed plans. In the planning phase, DLPFC activity also correlated with subjects’ initially stated willingness to save (WTS; Figure 4C), which suggested joint encoding of intended and executed saving strategies. Reported and executed strategies often corresponded, but subjects also frequently deviated from their stated intentions, which allowed detection of separate neural effects (Figure S2D). These deviations were not random but were partly explained by a combination of objective task factors, subjective valuations, and planning activity in DLPFC (but not ACC or amygdala; Figure S2F). Consistent with these results, discrepant DLPFC coding strengths for stated and executed strategies were related to subjects’ behavioral deviations from stated strategies (Figure 4D). Frontal cortex planning activities not only resembled amygdala planning activity, they were also functionally coupled to it (Figure 4E; Table S5; Supplemental Experimental Procedures GLM PPI 1-3). Psychophysiological interaction (PPI) analysis in the planning phase with amygdala as seed region identified functional connectivity with ACC. This connection depended on reward type in the current sequence, with enhanced amygdala-ACC connectivity for the typically preferred high-fat rewards compared to low-fat rewards. We found similar connectivity between ACC and another region with known decision functions, the medial prefrontal cortex (MPFC) [10Kable J.W. Glimcher P.W. The neural correlates of s" @default.
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• W2539233845 title "Neural Basis for Economic Saving Strategies in Human Amygdala-Prefrontal Reward Circuits" @default.
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