Distinct representational properties of cues and contexts shape fear learning and extinction

Reviewer #1 (Public review):

Summary:

The authors conducted a human neuroimaging study investigating the role of context in the representation of fear associations when the contingencies between a conditioned stimulus and shock unconditioned stimulus switch between contexts. The novelty of the analysis centered on neural pattern similarity to derive a measure of context and cue stability and generalization across different regions of the brain. Given the complexity and nuance of the results, it is kind of difficult to provide a concise summary. But during fear and reversal, there was cue generalization (between current CS+ cues) in the canonical fear network, and "item stability" for cues that changed their association with the shock in the IFG and precuneus. Reinstatement was quantified as pattern similarity for items or sets of cues from the earlier phases to the test phases, and they found different patterns in the IFG and dmPFC. A similar analytical strategy was applied to contexts.

Strengths:

Overall, I found this to be a novel use of MVPA to study the role of context in the reversal/extinction of human fear conditioning that yielded interesting results. The paper was overall well-written, with a strong introduction and fairly detailed methods and results. The lack of any univariate contrast results from the test phases was used as motivation for the neural pattern similarity approach, which I appreciated as a reader.

Weaknesses:

This is quite a complicated protocol and analysis plan. The authors did a decent job explaining it, given the complexity of the approach and the dense results. But it did take reading it a couple of times to start to understand it. I'm not sure if there is a simpler way to describe the approach though. Just an observation. But perhaps there is a better way to explain the density of the different comparisons between the multiple cues and contexts. It can be difficult to totally avoid jargon in a complex scientific article, but the paper is very jargon-y.

Here are a few more comments and stray observations, in no particular order of importance.

(1) I had a difficult time unpacking lines 419-420: "item stability represents the similarity of the neural representation of an item to other representations of this same item."

(2) The authors use the phrase "representational geometry" several times in the paper without clearly defining what they mean by this.

(3) The abstract is quite dense and will likely be challenging to decipher for those without a specialized knowledge of both the topic (fear conditioning) and the analytical approach. For instance, the goal of the study is clearly articulated in the first few sentences, but then suddenly jumps to a sentence stating "our data show that contingency changes during reversal induce memory traces with distinct representational geometries characterized by stable activity patterns across repetitions..." this would be challenging for a reader to grok without having a clear understanding of the complex analytical approach used in the paper.

(4) Minor: I believe it is STM200 not the STM2000.

(5) Line 146: "...could be particularly fruitful as a means to study the influence of fear reversal or extinction on context representations, which have never been analyzed in previous fear and extinction learning studies." I direct the authors to Hennings et al., 2020, Contextual reinstatement promotes extinction generalization in healthy adults but not PTSD, as an example of using MVPA to decipher reinstatement of the extinction context during test.

(6) This is a methodological/conceptual point, but it appears from Figure 1 that the shock occurs 2.5 seconds after the CS (and context) goes off the screen. This would seem to be more like a trace conditioning procedure than a standard delay fear conditioning procedure. This could be a trivial point, but there have been numerous studies over the last several decades comparing differences between these two forms of fear acquisition, both behaviorally and neurally, including differences in how trace vs delay conditioning is extinguished.

(7) In Figure 4, it would help to see the individual data points derived from the model used to test significance between the different conditions (reinstatement between Acq, reversal, and test-new).

Reviewer #2 (Public review):

Summary:

This is a timely and original study on the geometry of macroscopic (2.5 mm) brain representations of multiple cues and contexts in Pavlovian fear conditioning. The authors report that these representations differ between initial learning, and reversal learning, and remain stable during extinction.

Strengths:

The authors address an important question and use a rigorous experimental methodology.

Weaknesses:

The findings are limited (a) by the chosen spatial resolution (2.5 mm) which is far away from what modern fMRI can achieve, and (b) by the statistical analysis method. While transparently reported, their voxel-wise correction for multiple comparisons rests on a false discovery rate (i.e. 5% of the reported findings should be considered false positives) and there is no correction for the number of hypothesis tests (with an exception in some post hoc tests). Furthermore, there are some minor presentation issues that the authors could address to improve clarity.

Author response:

We would like to sincerely thank the editors and reviewers for their thoughtful comments, which provide valuable insights, and will help us enhance the overall quality of our manuscript. We will address all comments comprehensively in our revised submission.

It appears to us that two major concerns were raised by the reviewers and highlighted by the editor, regarding statistical methodology and manuscript readability.

As a provisional response, we would like to summarize our approach for addressing them in our revised manuscript:

(1) Statistical Methodology

Two specific concerns were raised regarding the statistical methods:

First, regarding FDR versus FWE correction in our voxelwise (searchlight) analyses. We recognize that our methods section might have created some confusion on this point. While we stated that "all analyses are FDR-corrected unless noted otherwise", this was meant to refer only to ROI-based analyses. For all voxel-wise analyses, including searchlight RSA analyses, we actually employed FWE correction. This was briefly mentioned in the section on univariate analyses. However, we did not emphasize this information in the searchlight section of the methods, and it is to our understanding that this might have created some confusion.

To clarify: we used (1) FWE correction for all voxel-based analyses and (2) FDR correction for ROI-based analyses (which could thus be considered exploratory). However, to fully address the concerns raised by the reviewers, and avoid potential confusion for the future readers, we will use exclusively FWE correction methods in the revised version of the manuscript. If some category of ROI-based analysis only yields not-significant results when corrected with FWE, we plan to report the uncorrected p-values, and pinpoint the exploratory nature of these results.

Second, regarding the alpha threshold adjustment for searchlight analyses involving multiple comparisons within the same experimental phase: We acknowledge this concern and will address it thoroughly in our revision.

(2) Manuscript Readability

We agree that readability should be improved despite the paradigm's inherent complexity. In our revision, we will:

- Replace non-essential technical terminology with clearer descriptions

- Improve writing quality in particularly dense or conceptually complex sections

- Enhance the overall structure to better guide readers through our methods and findings