Anterior cingulate cortex in complex associative learning: monitoring action state and action content
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eLife Assessment
Huang and colleagues examined neural responses in mouse anterior cingulate cortex (ACC) during a discrimination-avoidance task. The authors present useful findings that ACC neurons encode primarily post-action variables over extended periods rather than the outcomes or values of those actions. Though the methodological approach was sound, the evidence ruling out alternative explanations is incomplete and requires substantial control analyses.
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Abstract
Environmental changes necessitate adaptive responses, and thus the ability to monitor one’s actions and their connection to specific cues and outcomes is crucial for survival. The anterior cingulate cortex (ACC) is implicated in these processes, yet its precise role in action monitoring and outcome evaluation remains unclear. To investigate this, we developed a novel discrimination–avoidance task for mice, designed with clear temporal separation between actions and outcomes. Our findings show that ACC neurons primarily encode post-action variables over extended periods, reflecting the animal’s preceding actions rather than the outcomes or values of those actions. Specifically, we identified two distinct subpopulations of ACC neurons: one encoding the action state (whether an action was taken) and the other encoding the action content (which action was taken). Importantly, increased post-action ACC activity was associated with better performance in subsequent trials. These findings suggest that the ACC supports complex associative learning through extended signaling of rich action-relevant information, thereby bridging cue, action, and outcome associations.
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eLife Assessment
Huang and colleagues examined neural responses in mouse anterior cingulate cortex (ACC) during a discrimination-avoidance task. The authors present useful findings that ACC neurons encode primarily post-action variables over extended periods rather than the outcomes or values of those actions. Though the methodological approach was sound, the evidence ruling out alternative explanations is incomplete and requires substantial control analyses.
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Reviewer #1 (Public review):
Summary:
In the current study, Huang et al. examined ACC response during a novel discrimination-avoid task. The authors concluded that ACC neurons primarily encode post-action variables over extended periods, reflecting the animal's preceding actions rather than the outcomes or values of those actions. Specifically, they identified two subgroups of ACC neurons that responded to different aspects of the actions. This work represents admirable efforts to investigate the role of ACC in task-performing mice. However, in my opinion, alternative explanations of the data were not sufficiently explored, and some key findings were not well supported.
Strengths:
The development of the new discrimination-avoid task is applauded. Single-unit electrophysiology in task-performing animals represents admirable efforts and …
Reviewer #1 (Public review):
Summary:
In the current study, Huang et al. examined ACC response during a novel discrimination-avoid task. The authors concluded that ACC neurons primarily encode post-action variables over extended periods, reflecting the animal's preceding actions rather than the outcomes or values of those actions. Specifically, they identified two subgroups of ACC neurons that responded to different aspects of the actions. This work represents admirable efforts to investigate the role of ACC in task-performing mice. However, in my opinion, alternative explanations of the data were not sufficiently explored, and some key findings were not well supported.
Strengths:
The development of the new discrimination-avoid task is applauded. Single-unit electrophysiology in task-performing animals represents admirable efforts and the datasets are valuable. The identification of different groups of encoding neurons in ACC can be potentially important.
Weaknesses:
One major conclusion is that ACC primarily encodes the so-called post-action variables (specifically shuttle crossing). However, only a single example session was included in Figure 2, while in Supplementary Figure 2 a considerable fraction of ACC neurons appears to respond to either the onset of movement or ramp up their activity prior to movement onset. How did the authors reach the conclusion that ACC preferentially respond to shuttle crossing?
In Figure 4, it was concluded that ACC neurons respond to action independent of outcome. Since these neurons are active on both correct and incorrect shuttle but not stay trials, they seem to primarily respond to overt movement. If so, the rationale for linking ACC activity and adaptive behavior/associative learning is not very clear to me. Further analyses are needed to test whether their firing rates correlated with locomotion speed or acceleration/deceleration. On a similar note, to what extent are the action state neurons actually responding to locomotion-related signals? And can ACC activity actually differentiate correct vs. incorrect stays?
Given that a considerable amount of ACC neurons encode 'action content', it is not surprising that by including all neurons the model is able to make accurate predictions in Figure 6. How would the model performance change by removing the content neurons?
Moving on to Figure 7. Since Figure 4 showed that ACC neurons respond to movement regardless of outcome, it is somewhat puzzling how ACC activity can be linked to future performance.
Two mice contributed about 50% of all the recorded cells. How robust are the results when analyzing mouse by mouse?
Lastly, the development of the new discrimination-avoid task is applauded. However, a major missing piece here is to show the importance of ACC in this task and what aspects of this behavior require ACC.
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Reviewer #2 (Public review):
Summary:
The current dataset utilized a 2x2 factorial shuttle-escape task in combination with extracellular single-unit recording in the anterior cingulate cortex (ACC) of mice to determine ACC action coding. The contributions of neocortical signaling to action-outcome learning as assessed by behavioral tasks outside of the prototypical reward versus non-reward or punished vs non-punished is an important and relevant research topic, given that ACC plays a clear role in several human neurological and psychiatric conditions. The authors present useful findings regarding the role of ACC in action monitoring and learning. The core methods themselves - electrophysiology and behavior - are adequate; however, the analyses are incomplete since ruling out alternative explanations for neural activity, such as movement …
Reviewer #2 (Public review):
Summary:
The current dataset utilized a 2x2 factorial shuttle-escape task in combination with extracellular single-unit recording in the anterior cingulate cortex (ACC) of mice to determine ACC action coding. The contributions of neocortical signaling to action-outcome learning as assessed by behavioral tasks outside of the prototypical reward versus non-reward or punished vs non-punished is an important and relevant research topic, given that ACC plays a clear role in several human neurological and psychiatric conditions. The authors present useful findings regarding the role of ACC in action monitoring and learning. The core methods themselves - electrophysiology and behavior - are adequate; however, the analyses are incomplete since ruling out alternative explanations for neural activity, such as movement itself, requires substantial control analyses, and details on statistical methods are not clear.
Strengths:
(1) The factorial design nicely controls for sensory coding and value coding, since the same stimulus can signal different actions and values.
(2) The figures are mostly well-presented, labeled, and easy to read.
(3) Additional analyses, such as the 2.5/7.5s windows and place-field analysis, are nice to see and indicate that the authors were careful in their neural analyses.
(4) The n-trial + 1 analysis where ACC activity was higher on trials that preceded correct responses is a nice addition, since it shows that ACC activity predicts future behavior, well before it happens.
(5) The authors identified ACC neurons that fire to shuttle crossings in one direction or to crossings in both directions. This is very clear in the spike rasters and population-scaled color images. While other factors such as place fields, sensory input, and their integration can account for this activity, the authors discuss this and provide additional supplemental analyses.
Weaknesses:
(1) The behavioral data could use slightly more characterization, such as separating stay versus shuttle trials.
(2) Some of the neural analyses could use the necessary and sufficient comparisons to strengthen the authors' claims.
(3) Many of the neural analyses seem to utilize long time windows, not leveraging the very real strength of recording spike times. Specifics on the exact neural activity binning/averaging, tests, classifier validation, and methods for quantification are difficult to find.
(4) The neural analyses seem to suggest that ACC neurons encode one variable or the other, but are there any that multiplex? Given the overwhelming evidence of multiplexing in the ACC a bit more discussion of its presence or absence is warranted.
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Reviewer #3 (Public review):
Summary:
The authors record from the ACC during a task in which animals must switch contexts to avoid shock as instructed by a cue. As expected, they find neurons that encode context, with some encoding of actions prior to the context, and encoding of neurons post-action. The primary novelty of the task seems to be dynamically encoding action-outcome in a discrimination-avoidance domain, while this is traditionally done using operant methods. While I'm not sure that this task is all that novel, I can't recall this being applied to the frontal cortex before, and this extends the well-known action/context/post-context encoding of ACC to the discrimination-avoidance domain.
While the analysis is well done, there are several points that I believe should be elaborated upon. First, I had questions about several …
Reviewer #3 (Public review):
Summary:
The authors record from the ACC during a task in which animals must switch contexts to avoid shock as instructed by a cue. As expected, they find neurons that encode context, with some encoding of actions prior to the context, and encoding of neurons post-action. The primary novelty of the task seems to be dynamically encoding action-outcome in a discrimination-avoidance domain, while this is traditionally done using operant methods. While I'm not sure that this task is all that novel, I can't recall this being applied to the frontal cortex before, and this extends the well-known action/context/post-context encoding of ACC to the discrimination-avoidance domain.
While the analysis is well done, there are several points that I believe should be elaborated upon. First, I had questions about several details (see point 3 below). Second, I wonder why the authors downplayed the clear action coding of ACC ensembles. Third, I wonder if the purported 'novelty' of the task (which I'm not sure of) and pseudo-debate on ACC's role undermines the real novelty - action/context/outcome encoding of ACC in discrimination-avoidance and early learning.
Strengths:
Recording frontal cortical ensembles during this task is particularly novel, and the analyses are sophisticated. The task has the potential to generate elegant comparisons of action and outcome, and the analyses are sophisticated.
Weaknesses:
I had some questions that might help me understand this work better.
(1) I wonder if the field would agree that there is a true 'debate' and 'controversy' about the ACC and conflict monitoring, or if this is a pseudodebate (Line 34). They cite 2 very old papers to support this point. I might reframe this in terms of the frontal cortex studying action-outcome associations in discrimination-avoidance, as the bulk of evidence in rodents comes from overtrained operant behavior, and in humans comes from high-level tasks, and humans are unlikely to get aversive stimuli such as shocks.
(2) Does the purported novelty of the task undermine the argument? While I don't have an exhaustive knowledge of this behavior, the novelty involves applying this ACC. There are many paradigms where a shock triggers some action that could be antecedents to this task.
(3) The lack of details was confusing to me:
a) How many total mice? Are the same mice in all analyses? Are the same neurons? Which training day? Is it 4 mice in Figure 3? Five mice in line 382? An accounting of mice should be in the methods. All data points and figures should have the number of neurons and mice clearly indicated, along with a table. Without these details, it is challenging to interpret the findings.
b) How many neurons are from which stage of training? In some figures, I see 325, in some ~350, and in S5/S2B, 370. The number of neurons should be clearly indicated in each figure, and perhaps a table.
c) Were the tetrodes driven deeper each day? The depth should be used as a regressor in all analyses?
d) Was is really ACC (Figure 2A)? Some shanks are in M2? All electrodes from all mice need to be plotted as a main figure with the drive length indicated.
e) It's not clear which sessions and how many go into which analysis
f) How many correct and incorrect trials (<7?) are there per session?
g) Why 'up to 10 shocks' on line 358? What amplitudes were tried? What does scrambled mean?
(4) Why do the authors downplay pre-action encoding? It is clearly evident in the PETHs, and the classifiers are above chance. It's not surprising that post-shuttle classification is so high because the behavior has occurred. This is most evident in Figure S2B, which likely should be a main figure.
(5) The statistics seem inappropriate. A linear mixed effects model accounting for between-mouse variance seems most appropriate. Statistical power or effect size is needed to interpret these results. This is important in analyses like Figure 7C or 6B.
(6) Better behavioral details might help readers understand the task. These can be pulled from Figures S2 and S5. This is particularly important in a 'novel' task.
(7) Can the authors put post-action encoding on the same classification accuracy axes as Figure 6B? It'd be useful to compare.
(8) What limitations are there? I can think of several - number of animals, lack of causal manipulations, ACC in rodents and humans.
Minor:
(1) Each PCA analysis needs a scree plot to understand the variance explained.
(2) Figure 4C - y and x-axes have the same label?
(3) What bin size do the authors use for machine learning (Not clear from line 416)?
(4) Why not just use PCA instead of 'dimension reduction' (of which there are many?)
(5) Would a video enhance understanding of the behavior?
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Author response:
We thank the reviewers for their insightful feedback. Incorporating their recommendations will greatly enhance our manuscript for resubmission. Based on the review, it seems a major challenge to the interpretation of our study surrounds whether locomotion, itself, is responsible for increased ACC activity during our task. This was a shared concern for us during our analysis. We included data in our initial submission hoping to address these concerns. Specifically, we show that post-action activity outlasts movement termination, in most cases, on the order seconds after termination (Supplementary Fig 2). Likewise, post-action activity is not tied to shuttle initiations as ACC activity onset can vary greatly before and after initiation (Supplementary Fig 2). Lastly, the unique nature of action content neurons further …
Author response:
We thank the reviewers for their insightful feedback. Incorporating their recommendations will greatly enhance our manuscript for resubmission. Based on the review, it seems a major challenge to the interpretation of our study surrounds whether locomotion, itself, is responsible for increased ACC activity during our task. This was a shared concern for us during our analysis. We included data in our initial submission hoping to address these concerns. Specifically, we show that post-action activity outlasts movement termination, in most cases, on the order seconds after termination (Supplementary Fig 2). Likewise, post-action activity is not tied to shuttle initiations as ACC activity onset can vary greatly before and after initiation (Supplementary Fig 2). Lastly, the unique nature of action content neurons further supports a distinction from locomotor activity. They selectively fire for specific directions and, as a result, do not fire during movement in opposite directions. Despite these findings, we agree with reviews that inclusion of additional analyses, such as examining firing rates in respect to locomotion speed and acceleration/deceleration, will greatly strengthen our claim of ACC’s role in post-action activity. In our resubmission, we will seek to perform such an analysis, among others, to elucidate completely the role of locomotion in ACC post-action activity.
Reviewers also pointed out an overall lack of details surrounding our task, analysis, statistical methods and experimental approaches. We will consider all the recommendations from the reviewers and integrate them into our resubmission to provide more detailed information. Notably, we will adjust our approach in describing our task. Reviewers discussed some criticism regarding the perceived novelty of the task as it shares many similarities with previous discrimination-avoidance tasks. The distinction with our task is regarding the nuance of how the meaning (safety vs shock) of the context and sensory stimuli dynamically changes based on the current environment (context x sound). This requires not only the discrimination of contextual and sensory stimuli but also the inter-modal integration of stimuli, which varies throughout the task. Sound A/B leads to different outcomes depending on the context, and similarly, the meaning of the context shifts in a sound-dependent manner.
Lastly, in our follow-up submission we will work to include more robust analyses to utilize our temporal sensitivity of our recordings. We also will provide greater clarity on how each individual animal contributes to our overall findings. To conclude, we would like to once again thank our reviewers for their feedback and evaluation of our manuscript. We look forward to making the necessary adjustments for our future submission.
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