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Summary

This review was prepared by Deryn O. LeDuke under the supervision of Kay M. Tye as part of a partnership with the HHMI TAP peer review workshop to improve transparency and accountability in peer review.

Machen et al. make the primary claim that dopamine 2 receptor (D2R+) neurons in the paraventricular nucleus of the thalamus encode current, and fascinatingly, expected internal state in mice. Machen et al. followed up on previous work (Beas et al., 2024; PMID: 38458192) demonstrating that D2R+ neurons are tuned to changes in physiological state. This paper specifically focused on in vivo fiber photometry recordings from D2R+ PVT neurons in mice performing a linear maze task. Interoception is a critical ability in animals, encompassing the sensory cues needed to maintain homeostasis (e.g., temperature, thirst, hunger, heart rate, etc.). The potential impact of this research is emblematic in the many psychiatric disorders in which interoception is impaired– including panic disorder, substance abuse, and psychosis. A study demonstrating interoceptive prediction would hold great significance to improve quality of life for these individuals and thus, I find the premise of this paper to be significantly novel. While the arcuate nucleus (ARC) and lateral hypothalamus (LH) are typically involved in “sensing” and “tracking” internal state, Machen et al. make the claim in this paper that PVT(D2R+) neurons may serve as an “integrator” of these signals (current state, environmental cues, and learned experience), anticipating changes in internal state and thus guiding motivated behaviors.

There are several positive elements to appreciate in this paper (i.e., investigating many axes of behavior in one paradigm space, using within-animal comparison to investigate state changes); however, I have major concerns that I believe significantly impact the strength of the claims authors make in the paper. My feedback primarily concerns the experimental design and inclusion of data in the main figures.

Major Points

• Motor Confounds in Data: The authors make the primary claim that PVT(D2R+) neurons correspond to motivation based on changes in internal state; however, the PVT(D2R+) signal is correlated to approach latency in every group: every figure demonstrates the group with the lowest approach latency has the largest signal (examples: Figs 1E/2C/3M), suggesting that the velocity of the mice and their motivational state are confounded in these results. Demonstrating that the velocity of the mice as they move through the linear maze is not correlated to increases in GCaMP signal would greatly improve evidence for their primary claim and resolve this potential confound. The authors could plot the velocity of animals (as opposed to latency in Fig 1K/P) against approach signal or by measuring the signal change when mice are not engaged in the linear maze task and/or are consuming state-relevant rewards (SMS for hunger, H2O for thirst) to parse signal changes between motor output and motivation.

• Training Data: Training data for cohorts are not included in the results, which would be extremely helpful to determine if there are any changes between groups or mice. Training data are included in figures 7-8; however, these seem to be separate groups than those included in Figures 1-6. Explicitly detailing or summarizing training performance in each group would strengthen claims concerning whether animals learned the task. The training performance is a question because it appears the number of premature trials significantly increases over the course of the training period (Fig 8E). The reason why mice may be less proficient at avoiding premature trials is not discussed, however it suggests an order effect for successive training periods that could confound the main conclusions of the paper. Plotting session features that are sensitive to order, like premature trials, from the fiber photometry experiments would strengthen their initial results demonstrating changes following internal state.

• Potential Order Effects: The authors specify sessions in which they altered the internal state of the animal and the reward (i.e., Hungry-strawberry ensure (SMS); Sated-SMS  Hungry-sucralose (Ncal); however, these states/rewards do not appear to be counterbalanced, nor do there appear to be complete groups in some figures (examples: Fig 1C (missing Sated-NCal); Fig 3A (missing Sated-SMS)). The conclusions the authors make concerning internal state are thus confounded with the order effect of the recordings– if the Hungry-SMS condition does not come first in this series, would the neural activity look the same? Further confounding these results are the absence of some necessary controls, namely the sated counterparts for some of the hungry/thirsty conditions (for example, between Hungry-SMS, Hungry-Ncal, and Sated-SMS, there is no comparison to a Sated-Ncal group). If mice were recorded in these conditions, as they appear throughout the rest of the paper, it would be helpful to clarify if these conditions were done in the same mice, which would satisfy this concern. The strength of the authors’ conclusions would be improved if they included a counterbalanced cohort with a complete set of conditions. This point also follows for within-session changes to the reward, specifically the reward size variation task (Fig 6).

• Trial Matching: Depending on the state of the animal, more or less trials are completed in a given session (ex: Fig 1Q). It is unclear if the authors included all trials from all groups or matched the number of trials using random subsampling, which would be a more statistically rigorous approach. If they included all trials, differences in the total population could adjust the variation and thus significantly skew results. The primary claims would be greatly strengthened if authors specified their subsampling method, or otherwise trial-match the data to sufficiently compare across groups.

• Predicted Internal State: The authors’ primary claim not only concerns internal state but predicted internal state. One of the more convincing effects to this claim is in Fig 2Q, in which mice respond to SMS when hungry and H2O when thirsty, but not H2O when hungry, suggesting an effect specific to internal state. The impact of these results is dampened by the lack of reward variation within-session. In the current structure of the task, the given reward does not change, which makes conclusions concerning internal state prediction specifically difficult. This claim could be strengthened if the authors conducted an experiment in which the reward identity varied (for example: a modification in which the mice could differentiate between trials in which they expect a H2O vs. SMS reward). Alternatively, the authors could conduct an experiment in which animals received an unexpected reward (or no reward) when they were expecting one which, if PVT(D2R+) neurons are indeed tracking internal state predictions, would reasonably generate a reward prediction error. These experiments would justify the internal state prediction claim; however, the authors could additionally modify their claims to avoid conclusions on internal state prediction.

Minor Points

• Food and water restriction methods for mice are unclear (l.95-8). The authors comment that food was removed and mice were restricted during training, but there is no language for how long animals were restricted or deprived of food or water before recordings. The clarity of the paper would be improved by explicitly mentioning how long animals were restricted.

• Figure 1(A-B) do not correspond to the linear maze task and instead corresponds to viral approach and histology despite being referenced in the text (l. 283-5). It would be helpful to either include the task paradigm pictured in the first supplementary figure in either the main figure (where there is a diagram of the linear maze task), or otherwise reference Fig. S1 instead.

• The methods describe providing mice with 8 hours of SMS reward for two consecutive days to prevent neophobia (l. 129-30), but this is not done for any of the other rewards (namely, sucralose and sucrose rewards). It would be beneficial to mention if this was done for all rewards in the methods or discuss why they chose not to in the main text.

Competing interests

The authors declare that they have no competing interests.

Use of Artificial Intelligence (AI)

The authors declare that they did not use generative AI to come up with new ideas for their review.