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Specialized coding patterns among dorsomedial prefrontal neuronal ensembles predict conditioned reward seeking

Non-overlapping cell populations within dorsomedial prefrontal cortex (dmPFC), defined by gene expression or projection target, control dissociable aspects of reward seeking through unique activity patterns. However, even within these defined cell populations, considerable cell-to-cell variability is found, suggesting that greater resolution is needed to understand information processing in dmPFC. Here, we use two-photon calcium imaging in awake, behaving mice to monitor the activity of dmPFC excitatory neurons throughout Pavlovian reward conditioning. We characterize five unique neuronal ensembles that each encodes specialized information related to a sucrose reward, reward-predictive cues, and behavioral responses to those cues. The ensembles differentially emerge across daily training sessions – and stabilize after learning – in a manner that improves the predictive validity of dmPFC activity dynamics for deciphering variables related to behavioral conditioning. Our results characterize the complex dmPFC neuronal ensemble dynamics that stably predict reward availability and initiation of conditioned reward seeking following cue-reward learning.

27 processing in dmPFC. Here we use two-photon calcium imaging in awake, behaving mice to 28 monitor the activity of dmPFC excitatory neurons throughout Pavlovian sucrose conditioning. We 29 characterize five unique neuronal ensembles that each encode specialized information related to 30 a reward, reward-predictive cues, and behavioral responses to reward-predictive cues. The 31 ensembles differentially emerge across learningand stabilize after learningin a manner that 32 improves the predictive validity of dmPFC activity dynamics for deciphering variables related to 33 behavioral conditioning. Our results characterize the complex dmPFC neuronal ensemble 34 dynamics that relay learning-dependent signals for prediction of reward availability and initiation 35 of conditioned reward seeking.

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The dorsomedial prefrontal cortex (dmPFC) has garnered considerable interest due to its 38 dysregulation in diseases associated with disordered reward processing (Chen et

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dmPFC NAc) or 'generally inhibited' (e.g., dmPFC PVT), such assignment ignores much of 64 the variability that is likely critical for behavioral control. Thus, a more thorough and unbiased 65 means of defining the heterogeneous activity patterns among dmPFC output neurons is needed 66 to understand how these neurons are engaged during reward-related behavioral tasks.

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Here we use in vivo two-photon calcium imaging to measure and longitudinally track the 68 activity dynamics of single dmPFC excitatory output neurons throughout a Pavlovian sucrose 69 conditioning task. We observe five unique neuronal ensembles that encode specialized 70 information related to the sucrose reward, sucrose-predictive cues, and behavioral responses to 71 those cues. These five ensembles differentially emerge across learning in a manner that improves 72 the predictive validity of dmPFC population dynamics for deciphering reward delivery, cue 73 presentation, and behavior. Finally, we find that single neurons within dmPFC neuronal 74 ensembles display day-to-day stability after learning. Overall, we find that heterogeneous 75 excitatory neuronal ensembles in dmPFC evolve specialized coding patterns across cue-reward 76 learning that are maintained after learning. Our results highlight the importance of ensemble-77 specific recording and manipulation strategies for understanding the function of dmPFC activity 78 for reward processing.

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Following recovery from surgery, mice were water restricted (water bottles removed from 107 cages), and 0.6-2.0 mL of water was delivered every day to a dish placed within each home cage.

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Mice were weighed daily and given the appropriate amount of water to maintain ~90% of their 109 initial body weight. No health issues related to dehydration arose at any point during or after 110 implementation of this protocol. Once mice reached ~90% of their free drinking weight, they 111 underwent 3 days of 30-minute habituation sessions, during which they were head-restrained and 112 received droplets of sucrose (12.5% sucrose in water; ~2.0 μl) at random intervals through a 113 gravity-driven, solenoid-controlled lick spout (see Figure 1A). Next, mice underwent head-fixed

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Behavioral sessions were controlled through a custom MATLAB graphical user interface 148 connected to Arduino and associated electronics. Transistor-transistor logic (TTL) pulses 149 between the Arduino and the microscope were used to start and stop imaging and behavioral 150 programs, and to allow frame timestamp collection for post-hoc synchronization of the behavioral 151 and imaging data. Behavioral data were recorded and extracted using MATLAB, analyzed and 152 graphed using Python, and figures were produced using Adobe Illustrator. Behavioral data were 153 presented as normalized auROC "cue discrimination" scores (2 x (auROC-0.5)), comparing licking 154 rates during the one second period between each CS+ and reward (1s trace interval) or CS-and 155 reward omission (1s). A cue discrimination score of -1.0 would therefore suggest more licking 156 during all CS-trials versus CS+ trials. In contrast, a score of +1 would suggest more licking on all CS+ trials versus CS-trials. Following data collection, independent t-tests were used to compare 158 behavioral data during all imaging/behavioral sessions across days (e.g., early in learning vs. late 159 in learning). Additionally, a two-way ANOVA was used to compare baseline-subtracted lick rates 160 (Δ lick rate; calculated as: 1 second trace interval licking frequency -3 second baseline licking 161 frequency), followed by Bonferroni multiple comparisons tests if appropriate (Otis et al., 2017).

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Fluorescence signals from each cell were extracted from the motion-corrected data using

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such that the contribution of each ensemble to variable decoding could be evaluated. To determine whether decoding performance was significantly better than chance, we compared the 209 decoding accuracy to that of randomized "shuffled" data using two-way ANOVAs (population 210 decoding) or one-way ANOVAs (ensemble contribution), followed by program-recommended 211 post-hoc analysis (Dunnett's for one-way ANOVA, Tukey's for two-way ANOVA). Because 212 shuffled data were not significantly different between ensembles, these data were combined to 213 improve data visualization and simplify analysis (dotted lines in Figure 3C).

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Unique excitatory neuronal ensembles in dmPFC during sucrose seeking.

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We employed a Pavlovian sucrose conditioning task wherein head-restrained mice were 225 trained to associate one tone conditioned stimulus (CS+), but not another (CS-), with the delivery 226 of a liquid sucrose reward (Figure 1A-B). Mice readily acquired this task, showing conditioned 227 licking behavior between the CS+ offset and reward delivery (trace interval), but not the CS-offset 228 and reward omission (Figure 1C-D). An independent t-test showed that cue discrimination scores 229 (auROCs) from late in learning behavioral sessions were significantly higher than scores from

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We monitored the activity dynamics of putative dmPFC excitatory output neurons 239 throughout the Pavlovian sucrose conditioning task using two-photon calcium imaging (via AAVdj-

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To better classify these post-learning activity patterns, we used a spectral clustering algorithm 244 (Namboodiri et al., 2019) to isolate unique responses between recorded neurons. The analysis 245 revealed the existence of 5 clusters or "neuronal ensembles" that comprise the majority of 246 response variability (Figure 1H, 1J). Each neuronal ensemble displayed a unique activity pattern 247 during the CS+ and/or CS-trials, with qualitative analyses revealing the following dynamics:

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Overall, we find that proportion of neurons within Cluster 5 (which showed inhibitory responses 265 during CS+ trials) predicts cue discrimination licking scores (Figure 2A). Pearson-R correlation 266 values, found in the inset of each subpanel (Figure 2A

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We also investigated whether the number of neurons within a particular neuronal 276 ensemble predicted the probability that mice would increase licking during the CS-after learning 277 (auROC, CS-vs. baseline lick rate), which could be considered the initiation of a licking "error".

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Overall, we find that the relative proportion of neurons within Cluster 3 (which showed equivalent 279 excitatory CS+ and CS-responses) predicts an increase in licking during CS-trials ( Figure 2B).

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We find that dmPFC excitatory neuronal ensembles display unique activity patterns after   Figure 5D) and CS-trials ( Figure 5E).
Overall, these data suggest that activity patterns among dmPFC excitatory neuronal ensembles 363 are stable between days after learning.

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Here we characterize unique excitatory neuronal ensembles in dmPFC that differentially 367 predict behavioral task performance and encode specialized information related to Pavlovian 368 sucrose conditioning. The responses of each ensemble differentially emerge across learning in a 369 manner that improves the predictive validity of dmPFC population dynamics for deciphering 370 reward delivery, reward-predictive cue presentation, and task-related behavioral output.

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Considering the stability of dmPFC activity dynamics after learning, our results suggest that each

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Here we identify several distinct dmPFC excitatory neuronal ensembles during a 434 Pavlovian sucrose-conditioning task. Despite the apparent simplicity of this task, our findings 435 reveal complex and specialized coding patterns among these heterogeneous neuronal 436 ensembles, which are unlikely to be specific to one particular projection pathway or gene 437 expression profile. Finally, our data suggest that unique aspects of reward seeking may be