Medial prefrontal cortex encodes but is not required to generate goal-directed actions under threat

Reviewer #1 (Public review):

Summary:

This study investigates the role of the medial prefrontal cortex (mPFC) in generating goal-directed actions under threat, using a progressive behavioral paradigm, neural recordings, and optogenetic inhibition in mice. The authors demonstrate that while mPFC GABAergic neurons strongly encode cues, actions, and errors, particularly under high cognitive demand, this neural activity is not causally required for executing avoidance behaviors. By rigorously controlling for movement and arousal, the researchers found that much of the observed mPFC signaling actually reflects baseline behavioral states rather than the generation of the actions themselves. This dissociation between encoding and causality challenges traditional views of mPFC as an executive controller of action and provides a nuanced understanding of its role in evaluative and contextual processing.

Strengths:

The behavioral paradigm employed in this study is one of its greatest strengths, offering a rigorous, progressive, and well-controlled framework to dissect the neural mechanisms underlying avoidance under threat. This three-phase task design is particularly well-suited to tease apart the contributions of learning, discrimination, and cognitive load to both behavior and neural activity.

By tracking movement (speed, rotations) and including it as a covariate in statistical models, the authors also underscore the need to control for movement and baseline activity when interpreting cortical signals, which is relevant for all studies of brain-behavior relationships, ensuring that behavioral changes are not due to general arousal or motor activity.

Finally, the study combines multiple advanced techniques-fiber photometry, single-cell calcium imaging (miniscopes), and two distinct optogenetic inhibition methods-to provide a comprehensive look at both neural encoding and causal necessity.

Weaknesses:

The authors conclude that mPFC is not required for avoidance, based on the minimal behavioral effects of optogenetic inhibition. While this interpretation is supported by the data, the choice of viral constructs could lead to an underestimation of the mPFC's role for other reasons. First, the choice of viral constructs could lead to an underestimation of the mPFC's role for several reasons. Specifically, the efficacy of eArch3.0 inhibition was not verified beyond histology, and its non-cell-type-specific nature could lead to disinhibition or compensatory activity in downstream regions. Although the authors' use of visual cortex (VI) inhibition as a control suggests that broad cortical inhibition does not impair avoidance, subcortical compensation cannot be ruled out. Additionally, Vgat-ChR2 targets only GABAergic neurons, potentially missing glutamatergic contributions. Addressing these limitations in the Discussion section would strengthen the manuscript.

Reviewer #2 (Public review):

Summary:

The manuscript by Sajid et al. describes a comprehensive behavioral, imaging, and optogenetic dataset investigating the role of the mPFC in avoidance and escape behaviors. Although many movement- and task-related variables are encoded by mPFC GABAergic neurons, the main conclusion is that they are unlikely to control behavioral output.

Strengths:

The manuscript is generally well executed and plausible in its conclusions. It provides an alternative viewpoint to many articles describing the involvement of mPFC in behavior, based on a complex multi-stage behavioral paradigm acquired and analyzed in an unbiased way.

Weaknesses:

This reviewer sees three main weaknesses.

(1) There are few details on the linear mixed models in the methods. This section could be improved by including a mathematical description. More importantly, the reader never learns how accurately the models capture the data. Given that most conclusions rely on the models, it seems central to address this point carefully. For example, what is the explained variance, marginal, and conditional? Were the nested models compared to non-nested ones (e.g., AIC), what are the specific outputs of the likelihood ratio tests briefly mentioned in the methods?

(2) For several figures, there is a disconnect with the main text, in the sense that it is difficult to understand how statements in the main text connect with specific figure panels or bars in their graphs. This is particularly the case for the most complex figures, e.g., Figures 3, 4, and their supplements. It would be beneficial to introduce subfigure labels (A1, etc) and state explicitly in the main text what figure panel is described (in parentheses). Alternatively breakdown the figures into multiple ones, decreasing ambiguity. This is important because it will help the reader better assess the strength of the results.

(3) It does not appear that the code and data used to produce the figures are made available. That would be very beneficial, given the complexity of the analysis and dataset collection procedures. It would also help readers better understand the results and probe their validity.

Reviewer #3 (Public review):

I first want to state that I am not an expert in the field, making it hard for me to provide informed comments on the value of the scientific results. But from where I stand, the study seems very carefully designed, very well controlled, and the statistical methodology used across the manuscript is strong and sound.

Summary:

The authors investigated the role of PFC interneurons in cue-guided behaviour under threat. They designed a behavioural task with increasing levels of difficulty that allows them not only to correlate the activation of cortical interneurons with different parameters of the tasks, but also to assess if this correlation changes with increasing cognitive load. They carefully take into account confounding factors such as movement and show that indeed neuronal activity is strongly driven by movement. Using generalised linear models throughout their manuscript, the authors could include movement as a confounding factor in their statistical analysis, thus allowing them to next correlate interneuron activity with task-specific parameters. Using first fibre photometry to image bulk activity of the interneurons and by comparing the responses in the PFC and in the visual cortex, they identify that PFC neurons show stronger activation related to punishment compared to the sensory cortex. Interestingly, under high cognitive demand, PFC interneurons show cue-specific activation, which could reflect the involvement of the PFC in cue-selective action selection.

In a second set of experiments, they use Miniscope to image individual interneurons. They classified interneurons, not based on their expression of specific markers as usually done, but based on their correlation with movement. Using this classification, they identify clusters of neurons that show activity modulation related to various behavioural parameters.

Lastly, they performed optogenetic manipulations to silence the PFC during cue-guided behaviour and showed little behavioural effect of the manipulation, which they suggest means the PFC is not involved in taking action in this task.

Strengths:

The design of the study is backed by convincing arguments from the authors. The confounding factors are carefully taken into account and integrated into state-of-the-art statistics. The results thus appear robust and reliable. The authors do not overinterpret their results; quite the contrary, they are prone to toning down the interpretation of statistically significant results and they warn the readers about potential misinterpretation or confounding factors. The discussion makes for a very interesting and informative reading.

Weaknesses:

The main weakness, in my view, lies in the Results section. In the figures, the authors do not present any raw data, and the plots are shown as mean {plus minus} SEM without displaying the distribution of individual data points. It is both a strength and a weakness that the authors do not attempt to guide the reader through the Results section and instead present the findings with very little emphasis on the key outcomes of the GLM. While this approach is arguably the most transparent way to report results, it also makes the section quite difficult to follow and may discourage readers.

I would recommend rewriting the Results section to make it more accessible to a broader audience. A similar issue applies to the figures: presenting all plots reflects a commendable commitment to transparency, but it would greatly benefit from a clearer narrative. As it stands, it is difficult to grasp the message of each figure by simply browsing through them.

Author response:

The authors will revise the manuscript to address all the comments and recommendations raised by the reviewers, and they will share all the data on Dryad as usual.