Subregional activity in the dentate gyrus is amplified during elevated cognitive demands

  1. Charlotte CM Castillon
  2. Shintaro Otsuka
  3. John N Armstrong
  4. Anis Contractor

  • Curated by eLife

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    eLife Assessment

    This manuscript presents a valuable study of the activity and functional relevance of different circuits in the dentate gyrus of mice performing a pattern separation task. Solid evidence is presented to support the paper's central conclusions. The study is likely to be of interest to those studying the subregional organization and cell type-specific functions of the dentate gyrus.

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Abstract

Neural activity in the dentate gyrus (DG) is required for the detection and discrimination of novelty, context and patterns, amongst other cognitive processes. Previous studies have shown that granule cell activation differs between the supra and infrapyramidal blades of the DG during a range of hippocampal dependent tasks yet how excitatory dynamics within the DG drive and modulate this blade-specific bias under varying cognitive demands remains unclear. Here we used an automated touch screen pattern separation task combined to temporally controlled tagging of active neurons to determine how increasing cognitive demand shapes the spatial patterns of activity in the DG. We observed that as task difficulty increased, DG activation became progressively biased toward the suprapyramidal blade, accompanied by structured distributions of active mature granule cells (mGCs) along the apex-to-blade and hilar-to-molecular layer axes. Selective inhibition of mature granule cells (mGCs) did not affect the patterned distribution of active cells but profoundly impaired performance, as mice were no longer able to discriminate between closely spaced locations. In contrast, chemogenetic inhibition of adult-born granule cells (abDGCs) beyond a critical window of their maturation significantly impaired performance of mice during high-demand conditions, elevated overall mGC activity and disrupted the blade-specific distribution of active mGCs even in animals that successfully completed the task. These findings demonstrate how a high cognitive demand pattern separation task preferentially activates mGCs in subregions of the DG and are consistent with a modulatory role for abDGCs on the dentate circuit which in part governs the spatially organized patterns of activity of mGCs.

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  1. eLife

    eLife Assessment

    This manuscript presents a valuable study of the activity and functional relevance of different circuits in the dentate gyrus of mice performing a pattern separation task. Solid evidence is presented to support the paper's central conclusions. The study is likely to be of interest to those studying the subregional organization and cell type-specific functions of the dentate gyrus.

  2. eLife

    Reviewer #1 (Public review):

    This manuscript investigates how dentate gyrus (DG) granule cell subregions, specifically suprapyramidal (SB) and infrapyramidal (IB) blades, are differentially recruited during a high cognitive demand pattern separation task. The authors combine TRAP2 activity labeling, touchscreen-based TUNL behavior, and chemogenetic inhibition of adult-born dentate granule cells (abDGCs) or mature granule cells (mGCs) to dissect circuit contributions.

    This manuscript presents an interesting and well-designed investigation into DG activity patterns under varying cognitive demands and the role of abDGCs in shaping mGC activity. The integration of TRAP2-based activity labeling, chemogenetic manipulation, and behavioral assays provides valuable insight into DG subregional organization and functional recruitment. However, several methodological and quantitative issues limit the interpretability of the findings. Addressing the concerns below will greatly strengthen the rigor and clarity of the study.

    Major points:

    (1) Quantification methods for TRAP+ cells are not applied consistently across panels in Figure 1, making interpretation difficult. Specifically, Figure 1F reports TRAP+ mGCs as density, whereas Figure 1G reports TRAP+ abDGCs as a percentage, hindering direct comparison. Additionally, Figure 1H presents reactivation analysis only for mGCs; a parallel analysis for abDGCs is needed for comparison across cell types.

    (2) The anatomical distribution of TRAP+ cells is different between low- and high-cognitive demand conditions (Figure 2). Are these sections from dorsal or ventral DG? Is this specific to dorsal DG, as itis preferentially involved in cognitive function? What happens in ventral DG?

    (3) The activity manipulation using chemogenetic inhibition of abDGCs in AsclCreER; hM4 mice was performed; however, because tamoxifen chow was administered for 4 or 7 weeks, the labeled abDGC population was not properly birth-dated. Instead, it consisted of a heterogeneous cohort of cells ranging from 0 to 5-7 weeks old. Thus, caution should be taken when interpreting these results, and the limitations of this approach should be acknowledged.

    (4) There is a major issue related to the quantification of the DREADD experiments in Figure 4, Figure 5, Figure 6, and Figure 7. The hM4 mouse line used in this study should be quantified using HA, rather than mCitrine, to reliably identify cells derived from the Ascl lineage. mCitrine expression in this mouse line is not specific to adult-born neurons (off-targets), and its expression does not accurately reflect hM4 expression.

    (5) Key markers needed to assess the maturation state of abDGCs are missing from the quantification. Incorporating DCX and NeuN into the analysis would provide essential information about the developmental stage of these cells.

    Minor points:

    (1) The labeling (Distance from the hilus) in Figure 2B is misleading. Is that the same location as the subgranular zone (SGZ)? If so, it's better to use the term SGZ to avoid confusion.

    (2) Cell number information is missing from Figures 2B and 2C; please include this data.

    (3) Sample DG images should clearly delineate the borders between the dentate gyrus and the hilus. In several images, this boundary is difficult to discern.

    (4) In Figure 6, it is not clear how tamoxifen was administered to selectively inhibit the more mature 6-7-week-old abDGC population, nor how this paradigm differs from the chow-based approach. Please clarify the tamoxifen administration protocol and the rationale for its specificity.

    Comments on revisions:

    I appreciate the authors' careful and thorough revisions. They have addressed all of my previous concerns satisfactorily, and the manuscript is now significantly strengthened. I have no further concerns.

  3. eLife

    Reviewer #2 (Public review):

    In this study, the authors investigate how increasing cognitive demand shapes activity patterns in the dorsal dentate gyrus (DG). Using a touchscreen-based TUNL task combined with TRAP/c-Fos tagging, birth-dating of adult-born granule cells (abDGCs), and chemogenetic inhibition, they show that higher task demand increases mature granule cell (mGC) recruitment and enhances suprapyramidal (SB) versus infrapyramidal (IB) blade bias. Functionally, mGC inhibition reduces overall activity and impairs performance without disrupting blade bias, whereas inhibition of {less than or equal to}7-week-old abDGCs increases mGC activity, abolishes blade bias, and impairs discrimination under high-demand conditions. These findings suggest that effective pattern separation depends not only on overall DG activity levels but also on the spatial organization of recruited ensembles.

    The integration of touchscreen TUNL with temporally controlled activity tagging and birth-dated cohorts is technically strong. Quantification of SB-IB bias and radial/apical distributions adds anatomical precision beyond bulk activity measures. The comparison between mGC and abDGC inhibition is conceptually compelling and supports dissociable functional roles. Overall, the data convincingly demonstrate that increasing cognitive demand amplifies blade-biased DG recruitment and that mGCs and abDGCs differentially contribute to both behavioral performance and network organization.

    However, how abDGCs are integrated into the mGC network under high cognitive demand remains unresolved. Additional experiments are needed to clarify how abDGCs shape spatial recruitment patterns and whether they directly inhibit or indirectly regulate mGC activity to maintain high performance.

    Furthermore, the authors frame "high cognitive demand" as a multidimensional construct encompassing broad behavioral challenge. It would strengthen the work to delineate how local abDGC-mGC circuit interactions regulate specific task components in real time. This will require higher temporal resolution approaches, as TRAP and c-Fos labeling integrate activity over prolonged windows and primarily reflect sustained engagement rather than moment-to-moment computations.
    The central conclusion that dentate function depends on coordinated spatial recruitment rather than total activity magnitude is supported by the data, although mechanistic interpretations should be tempered given methodological limitations.
    Overall, this work advances models of adult neurogenesis by emphasizing a critical-period modulatory role of abDGCs in organizing DG network activity during high-demand discrimination. The combined behavioral and circuit-level framework is likely to be influential in the field.

  4. eLife

    Reviewer #3 (Public review):

    This study examines the role of dentate gyrus neuronal populations, reflecting neurogenesis and anatomical location (suprapyramidal vs infrapyramidal blade), in a mnemonic discrimination task that taxes the pattern separation functions of the dentate. The authors measure dentate gyrus activity resulting from cognitive training and test whether adult neurogenesis is required for both the anatomical patterns of activity and performance in the cognitive task. The authors find that more cognitively challenging variants of the task evoked more dentate activity, but also distinct patterns of activity (more activity in the suprapyramidal blade, less in the infdrapyramidal blade). Using chemogenetic approaches they silence mature vs immature dentate gyrus neurons and find that only mature neurons (either the general population or specifically mature adult-born neurons), and not immature adult-born neurons, are required for the difficult version of the task. Inhibition of mature adult-born neurons furthermore increased overall activity in the dentate and reduced the biased pattern of activity across the blades, consistent with evidence that adult-born neurons broadly regulate dentate gyrus activity.

    Comments on revisions:

    I appreciate the efforts the authors have taken to revise this manuscript. I have only minor concerns with this revised version of the manuscript:

    Methods state that significance is defined as P<0.05 but some results are interpreted as significant when P=0.05. Either the alpha value needs to change or the interpretation needs to change.

    I believe the statistical results for group and blade effects for the ANOVAs, in Figs 2,3 & 4, appear to be switched (blade should be significant, not group).

    I appreciate that sometimes there is not a perfect overlap between immunohistochemical signals, but I continue to believe that the spatially-non-overlapping TRAP and EDU signals in Fig 3 is caused by these 2 markers being in different cells. A Z-stack or orthogonal projection could verify/disprove this concern.

  5. eLife

    Author Response:

    The following is the authors’ response to the original reviews.

    Public Reviews:

    Reviewer #1 (Public review):

    This manuscript investigates how dentate gyrus (DG) granule cell subregions, specifically suprapyramidal (SB) and infrapyramidal (IB) blades, are differentially recruited during a high cognitive demand pattern separation task. The authors combine TRAP2 activity labeling, touchscreen-based TUNL behavior, and chemogenetic inhibition of adult-born dentate granule cells (abDGCs) or mature granule cells (mGCs) to dissect circuit contributions.

    This manuscript presents an interesting and well-designed investigation into DG activity patterns under varying cognitive demands and the role of abDGCs in shaping mGC activity. The integration of TRAP2-based activity labeling, chemogenetic manipulation, and behavioral assays provides valuable insight into DG subregional organization and functional recruitment. However, several methodological and quantitative issues limit the interpretability of the findings. Addressing the concerns below will greatly strengthen the rigor and clarity of the study.

    Major points:

    (1) Quantification methods for TRAP+ cells are not applied consistently across panels in Figure 1, making interpretation difficult. Specifically, Figure 1F reports TRAP+ mGCs as density, whereas Figure 1G reports TRAP+ abDGCs as a percentage, hindering direct comparison. Additionally, Figure 1H presents reactivation analysis only for mGCs; a parallel analysis for abDGCs is needed for comparison across cell types.

    In Figure 1G and 1H we report TRAP+ abDGCs as a percentage rather than density because we are analyzing colocalization of the two markers, which are very sparse in this population. Given the very low number of double-labeled abDGCs, calculating density would not be practical. In the revised manuscript we have clarified the rationale for using these measures. As noted in the current text, we did not observe abDGCs co-expressing TRAP and c-Fos; we have made this point more explicit to guide interpretation of these data.

    (2) The anatomical distribution of TRAP+ cells is different between low- and high-cognitive demand conditions (Figure 2). Are these sections from dorsal or ventral DG? Is this specific to dorsal DG, as it is preferentially involved in cognitive function? What happens in ventral DG?

    The sections shown in Figure 2 were obtained from the dorsal dentate gyrus (see Methods, “Histology and imaging”: stereotaxic coordinates −1.20 to −2.30 mm relative to bregma, Paxinos atlas). From a feasibility standpoint, it is not possible to analyze the entire longitudinal extent of the hippocampus with these low-throughput histological approaches. We therefore focused on the dorsal DG, for which there is a strong functional rationale. A large body of work indicates that the dorsal hippocampus, and specifically the dorsal DG, is preferentially involved in spatial memory and in the fine contextual discrimination that underlies pattern separation. The dorsal hippocampus is critical for encoding and distinguishing similar spatial representations, a core component of the high-cognitive demand task used here. In contrast, the ventral DG is more strongly associated with emotional regulation and affective memory processing and is less implicated in high-resolution spatial encoding. For these reasons, the present study was designed to assess TRAP+ cell distributions specifically in the dorsal DG.

    (3) The activity manipulation using chemogenetic inhibition of abDGCs in AsclCreER; hM4 mice was performed; however, because tamoxifen chow was administered for 4 or 7 weeks, the labeled abDGC population was not properly birth-dated. Instead, it consisted of a heterogeneous cohort of cells ranging from 0 to 5-7 weeks old. Thus, caution should be taken when interpreting these results, and the limitations of this approach should be acknowledged.

    We agree that prolonged tamoxifen administration results in labeling a heterogeneous population of abDGCs spanning approximately 0 to 5–7 weeks of age, rather than a precisely birth-dated cohort. This is a limitation of this approach and we have included discussion of this in more detail in the revised manuscript.

    (4) There is a major issue related to the quantification of the DREADD experiments in Figure 4, Figure 5, Figure 6, and Figure 7. The hM4 mouse line used in this study should be quantified using HA, rather than mCitrine, to reliably identify cells derived from the Ascl lineage. mCitrine expression in this mouse line is not specific to adult-born neurons (off-targets), and its expression does not accurately reflect hM4 expression.

    We agree that mCitrine is not a marker that allows localization of hM4Di as it is well known that the mCitrine can be independently expressed in a Cre independent manner in this mouse. As suggested, we have removed the figure that showed the mCitrine and have performed immunohistochemical localization of the DREADD with an antibody against the HA tag. This is now shown in Figure 5.

    (5) Key markers needed to assess the maturation state of abDGCs are missing from the quantification. Incorporating DCX and NeuN into the analysis would provide essential information about the developmental stage of these cells.

    The goal of this study was to examine activity patterns of adult-born versus mature granule cells, rather than to assess maturation state. The adult-born neurons analyzed were 25–39 days old, an age at which point most cells have progressed beyond the DCX⁺ stage and are expected to express NeuN based on prior work. We therefore do not think that including DCX or NeuN quantification would provide additional information relevant to the aims or interpretation of this study.

    Minor points:

    (1) The labeling (Distance from the hilus) in Figure 2B is misleading. Is that the same location as the subgranular zone (SGZ)? If so, it's better to use the term SGZ to avoid confusion.

    We have updated Figure 2B, the Methods, and the main text to more explicitly localize this which it the boundary between the subgranular zone (SGZ) and the hilus.

    (2) Cell number information is missing from Figures 2B and 2C; please include this data.

    We have now added the cell number information to the figure legends. In Figures 2B and 2C, each point corresponds to a single cell, with an equal number of mice per group. The total number of TRAP⁺ cells per mouse is shown in Figure 1F, which reports TRAP⁺ cell densities by group.

    (3) Sample DG images should clearly delineate the borders between the dentate gyrus and the hilus. In several images, this boundary is difficult to discern.

    We made the DG-hilus boundaries clearer in the sample images to improve visualization and interpretation.

    (4) In Figure 6, it is not clear how tamoxifen was administered to selectively inhibit the more mature 6-7-week-old abDGC population, nor how this paradigm differs from the chow-based approach. Please clarify the tamoxifen administration protocol and the rationale for its specificity.

    We apologize for the confusion here. The protocol used in Figure 6 is the same tamoxifen chow–based approach as in Figure 5, differing only in the duration of tamoxifen exposure. Mice in Figure 5 received tamoxifen chow for 7 weeks, whereas mice in Figure 6 received it for 4 weeks, restricting labeling to a younger and narrower cohort of adult-born DGCs. Thus, the population targeted in Figure 6 is younger than that in Figure 5 and does not correspond to mature 6–7-week-old neurons. By contrast, the experiment in Figure 4 targets a more mature population, consisting predominantly of ~5-week-old adult-born neurons as well as mature granule cells, which are Dock10-positive and express Cre endogenously, allowing selective manipulation of this later-stage population.

    We have corrected the paragraph accordingly and clarified the age range of the labeled populations in the revised manuscript.

    Reviewer #2 (Public review):

    Summary

    In this manuscript, the authors combine an automated touchscreen-based trial-unique nonmatching-to-location (TUNL) task with activity-dependent labeling (TRAP/c-Fos) and birth-dating of adult-born dentate granule cells (abDGCs) to examine how cognitive demand modulates dentate gyrus (DG) activity patterns. By varying spatial separation between sample and choice locations, the authors operationally increase task difficulty and show that higher demand is associated with increased mature granule cell (mGC) activity and an amplified suprapyramidal (SB) versus infrapyramidal (IB) blade bias. Using chemogenetic inhibition, they further demonstrate dissociable contributions of abDGCs and mGCs to task performance and DG activation patterns.

    The combination of behavioral manipulation, spatially resolved activity tagging, and temporally defined abDGC perturbations is a strength of the study and provides a novel circuit-level perspective on how adult neurogenesis modulates DG function. In particular, the comparison across different abDGC maturation windows is well designed and narrows the functionally relevant population to neurons within the critical period (~4-7 weeks). The finding that overall mGC activity levels, in addition to spatially biased activation patterns, are required for successful performance under high cognitive demand is intriguing.

    Major Comments

    (1) Individual variability and the relationship between performance and DG activation.

    The manuscript reports substantial inter-animal variability in the number of days required to reach the criterion, particularly during large-separation training. Given this variability, it would be informative to examine whether individual differences in performance correlate with TRAP+ or c-Fos+ density and/or spatial bias metrics. While the authors report no correlation between success and TRAP+ density in some analyses, a more systematic correlation across learning rate, final performance, and DG activation patterns (mGC vs abDGC, SB vs IB) could strengthen the interpretation that DG activity reflects task engagement rather than performance only.

    As mentioned, we previously reported no correlation between task success and TRAP+ density. We have now performed additional analyses examining correlations with learning rate, final performance, and DG activation patterns (mGC vs abDGC, SB vs IB), and found no significant relationships. Therefore, as we did not find any positive correlations the original interpretation that DG activity primarily reflects task engagement rather than performance level seems the most parsimonious.

    (2) Operational definition of "cognitive demand".

    The distinction between low (large separation) and high (small separation) cognitive demand is central to the manuscript, yet the definition remains somewhat broad. Reduced spatial separation likely alters multiple behavioral variables beyond cognitive load, including reward expectation, attentional demands, confidence, engagement, and potentially motivation. The authors should more explicitly acknowledge these alternative interpretations and clarify whether "cognitive demand" is intended as a composite construct rather than a strictly defined cognitive operation.

    We agree that reducing spatial separation between stimuli likely engages multiple behavioral and cognitive processes beyond a single, strictly defined operation. We have now clarified this point in the manuscript and explicitly state that our use of the term “cognitive demand” reflects a multidimensional behavioral challenge rather than a singular cognitive process (see Discussion).

    (3) Potential effects of task engagement on neurogenesis.

    Given the extensive behavioral training and known effects of experience on adult neurogenesis, it remains unclear whether the task itself alters the size or maturation state of the abDGC population. Although the focus is on activity and function rather than cell number, it would be useful to clarify whether neurogenesis rates were assessed or controlled for, or to explicitly state this as a limitation.

    While the primary goal of this study was to examine activity and functional recruitment of adult-born granule cells, we also quantified the survival of birth-dated neurons at the end of behavioral training. Density measurements of BrdU⁺ and EdU⁺ cells revealed no differences across experimental groups, indicating that engagement in the pattern separation task, across low to high cognitive demand conditions, did not significantly alter survival of adult-born neurons. In addition, we examined the spatial distribution of BrdU⁺ and EdU⁺ neurons between the suprapyramidal and infrapyramidal blades of the dentate gyrus. The proportion of newborn neurons was consistent across all groups, with approximately 60% located in the suprapyramidal blade and 40% in the infrapyramidal blade. These findings indicate that behavioral training did not alter the baseline distribution of adult-born neurons. We have now clarified these points in the manuscript (See Results).

    (4) Temporal resolution of activity tagging.

    TRAP and c-Fos labeling provide a snapshot of neural activity integrated over a temporal window, making it difficult to determine which task epochs or trial types drive the observed activation patterns. This limitation is partially acknowledged, but the conclusions occasionally imply trial-specific or demand-specific encoding. The authors should more clearly distinguish between sustained task engagement and moment-to-moment trial processing, and temper interpretations accordingly. While beyond the scope of the current study, this also motivates future experiments using in vivo recording approaches.

    We agree and have made changes to the manuscript to discuss these points (see Discussion and Limitations).

    (5) Interpretation of altered spatial patterns following abDGC inhibition.

    In the abDGC inhibition experiments, Cre+ DCZ animals show delayed learning relative to controls. As a result, when animals are sacrificed, they may be at an intermediate learning stage rather than at an equivalent behavioral endpoint. This raises the possibility that altered DG activation patterns reflect the learning stage rather than a direct circuit effect of abDGC inhibition. Additional clarification or analysis controlling for the learning stage would strengthen the causal interpretation.

    We agree that differences in learning stage could in principle confound the interpretation of DG activation patterns. However, although Cre+ DCZ-treated mice exhibited delayed learning, they ultimately reached the same performance criterion as control animals. Thus, adult-born DGC inhibition did not prevent learning but increased the time required to reach criterion, indicating that these neurons are beneficial for learning efficiency rather than strictly necessary for task acquisition. Importantly, all animals were sacrificed only after reaching the predefined success criterion. Therefore, the immunohistochemical analyses were performed at the same behavioral endpoint for Cre+ DCZ and control groups, even though the number of training days differed. Consequently, the observed differences in DG activation reflect circuit recruitment at equivalent task mastery rather than differences in learning stage.

    (6) Relationship between c-Fos density and behavioral performance.

    The study reports that abDGC inhibition increases c-Fos density while impairing performance, whereas mGC inhibition decreases c-Fos density and also impairs performance. This raises an important conceptual question regarding the relationship between overall activity levels and task success. The authors suggest that both sufficient activity and appropriate spatial patterning are required, but the manuscript would benefit from a more explicit discussion of how different perturbations may shift the identity, composition, or coordination of the active neuronal ensemble rather than simply altering total activity levels.

    We agree that our findings highlight that successful performance is not determined solely by the overall level of dentate gyrus activity, but rather by the composition and spatial organization of the active neuronal ensemble. In our study, inhibition of abDGCs increased overall mGC activity while disrupting the spatially organized, blade-biased activation pattern and impaired performance. In contrast, direct inhibition of mGCs reduced global excitability but preserved the relative spatial organization of active neurons in animals that continued to perform the task. These findings suggest that different perturbations alter task performance by shifting the identity and coordination of the active neuronal ensemble, rather than simply increasing or decreasing total activity levels. We have now expanded the Discussion to more explicitly address how dentate gyrus computations may depend on the structured recruitment of granule cell ensembles and how distinct manipulations differentially disrupt this organization.

    Reviewer #3 (Public review):

    Summary:

    The authors used genetic models and immunohistochemistry to identify how training in a spatial discrimination working memory task influences activity in the dentate gyrus subregion of the hippocampus. Finding that more cognitively challenging variants of the task evoked more and distinct patterns of activity, they then investigated whether newborn neurons in particular were important for learning this task and regulating the spatial activity patterns.

    Strengths:

    The focus on precise anatomical locations of activity is relatively novel and potentially important, given that little is known about how DG subregions contribute to behavior. The authors also use a task that is known to depend on this memory-related part of the brain.

    Weaknesses:

    Statistical rigor is insufficient. Many statistical results are not stated, inappropriate tests are used, and sample sizes differ across experiments (which appear to potentially underlie null results). The chemogenetic approach to inhibit adult-born neurons also does not appear to be targeting these neurons, as judged by their location in the DG.

    Please refer to the updated statistical analyses in response to the recommendations below.

    Recommendations for the authors:

    Reviewing Editor Comments

    Please note that reviewers agreed that appropriate revisions are needed to increase the strength of evidence for the paper's claims. Concerns were raised about a lack of statistical rigor in the statistical analyses used. Results of statistical tests were not consistently provided (i.e., statistic applied, value of statistic, degrees of freedom, p-value), and seemingly inappropriate statistical tests were used in some instances. Also, some comparisons had lower statistical power than others. When clarifying the statistical approaches used in the manuscript, we also encourage you to consider reading this article that outlines common statistical mistakes (Makin TR, Orban de Xivry JJ. Ten common statistical mistakes to watch out for when writing or reviewing a manuscript. Elife. 2019 Oct 9;8:e48175. doi: 10.7554/eLife.48175.), such as the importance of not basing conclusions on a significant p-value for one pair-wise comparison vs a non-significant p-value for another pairwise comparison (i.e., groups that are being compared should be included in the same statistical analysis, and interaction effects should be reported when appropriate). We hope that you find this information to be helpful should you decide to submit a revised manuscript to eLife.

    Reviewer #1 (Recommendations for the authors):

    (1) Standardize TRAP+ quantification across Figure 1.

    Please report TRAP+ cell numbers using consistent metrics (e.g., density or percentage) to enable comparison across cell types. In addition, extend the TRAP+ reactivation analysis in Figure 1H to include abDGCs so that reactivation dynamics can be compared directly between mGCs and abDGCs.

    Reply in Public Review

    (2) Clarify whether dorsal or ventral DG was analyzed in Figure 2.

    The differing anatomical distributions of TRAP+ cells under low- and high-demand conditions raise important questions about DG axis specificity. Please indicate whether analyses were performed in dorsal DG, ventral DG, or both, and provide data or justification accordingly.

    Reply in Public Review

    (3) Acknowledge limitations of the tamoxifen-chow labeling strategy in AsclCreER; hM4 experiments.

    Since tamoxifen chow administered over 4-7 weeks labels a heterogeneous abDGC population spanning a broad age range, this approach does not generate birth-dated cohorts. This limitation should be clearly addressed in the text and interpretations, particularly related to cell age-dependent effects, should be tempered.

    Reply in Public Review

    (4) Revise DREADD quantification using HA rather than mCitrine.

    The hM4 mouse line requires HA immunostaining to accurately identify Ascl-lineage cells expressing the DREADD receptor. Because mCitrine is not specific to adult-born neurons and does not reliably reflect hM4 expression, quantification based on mCitrine should be revised.

    Reply in Public Review

    (5) Include markers to assess abDGC maturation state.

    Adding quantification of DCX and NeuN would help define the developmental stage of abDGCs in key experiments and improve the interpretation of cell-age-dependent effects.

    Reply in Public Review

    (6) Clarify DG layer boundaries and terminology in Figure 2.

    If the metric labeled "Distance from the hilus" corresponds to the subgranular zone (SGZ), using SGZ terminology would prevent confusion. Additionally, please provide clearer delineation of DG and hilus borders in sample images.

    Reply in Public Review

    (7) Provide missing cell number data for Figures 2B and 2C.

    Reply in Public Review

    (8) Clarify the tamoxifen administration protocol in Figure 6.

    Please describe how the protocol selectively targets 6-7-week-old abDGCs and how it differs from the chow-based approach. This will help readers understand the intended specificity of the manipulation.

    Reply in Public Review

    Reviewer #2 (Recommendations for the authors):

    (1) EdU birth-dating timeline

    The manuscript would benefit from a clearer description of the EdU birth-dating timeline, ideally with a schematic similar to that provided for BrdU in Supplementary Figure 1.

    We appreciate the suggestion. However, we did not include a separate schematic for EdU because its use and birth-dating logic are identical to BrdU (both are thymidine analogs administered systemically and incorporated during S-phase). Therefore, the timeline shown in Supplementary Figure 1 applies equally to both markers. We have clarified this point in the Methods section to avoid confusion.

    (2) Clarity of TUNL task description.

    The description of the TUNL task, particularly for readers unfamiliar with touchscreen-based paradigms, is difficult to follow without consulting prior literature. A simplified schematic or a clearer step-by-step explanation in the main text or supplementary material would improve accessibility.

    We note that the main steps of the TUNL protocol are illustrated in Figure 1A, Supplementary Figure 2A and 2B. Nevertheless, we agree that the description in the text can be made clearer for readers less familiar with touchscreen-based tasks. Thus , we have now revised the Methods section to provide a clearer step-by-step description of the TUNL.

    (3) Influence of outliers in Figure 1G.

    In Figure 1G, the reported trend that ~1% of 25-39-day-old abDGCs are TRAP+ during LS trials appears to be driven by a small number of outliers. This should be acknowledged, and the wording of the conclusion moderated to reflect the variability in the data.

    We agree with the reviewer that the apparent outliers reflect the inherent sparsity of TRAP labeling in this population. In absolute terms, this corresponds to between 0 and 2 TRAP⁺ 25–39-day-old abDGCs per mouse, such that the presence or absence of a small number of labeled cells can appear as outliers when expressed as a percentage. We have revised the text to acknowledge this (see Results).

    (4) Presentation of learning curves.

    Rather than focusing primarily on "days before criterion" (DBC), it would be helpful to show full learning curves across the entire training period. This would provide a clearer picture of acquisition dynamics and inter-animal variability.

    We agree that learning curves can be informative in many behavioral paradigms. However, in our protocol, mice do not undergo the same number of training days because training stops individually once each animal reaches criterion. As a result, plotting full learning curves would produce trajectories of different lengths, making group comparisons difficult and visually cluttered. For this reason, we aligned animals based on days before criterion (DBC), which allows direct comparison of learning dynamics relative to task acquisition. We also consider the cumulative probability representation to be the most appropriate way to summarize learning progression across animals in this context which are also included in the figures.

    (5) Clarification of Figure 3B labeling

    In Figure 3B, the identity of the orange-labeled group above the LS condition is unclear. Clarification in the figure legend would improve interoperability.

    Figure 3B includes two experimental groups. One group performed both the large- and small-separation conditions; this group is shown in orange and labeled LS. Within this group, the upper orange trace corresponds to performance in the large-separation condition, while the lower orange trace corresponds to performance in the small-separation condition. The second group is a control group that performed only the large-separation configuration, and therefore only a single green trace is shown. We agree that this distinction was not sufficiently clear and have revised the figure legend and text to clarify the identity of each trace.

    Reviewer #3 (Recommendations for the authors):

    (1) Please label figures and, even better, put the legends on the same page.

    (2) Just to confirm, in establishing the task, mice performed above 70% for the small separation trials in one of the sessions on 2 consecutive days, for each criterion? Performance seems to be below 70%.

    Yes. To meet the criterion, each mouse had to reach ≥70% correct performance in at least one of the two daily sessions on two consecutive days. We then averaged the performance across both sessions for each of those days. As a result, if one session was ≥70% but the other was lower, the daily average could fall below 70%. The values shown in the figure correspond to these daily averages, further averaged across mice.

    (3) mGC needs to be explicitly defined. Am I assuming any non-birthdated GC is an mGC according to the authors? (which means it is unknown whether they are in fact mature, though likely most of them are).

    In this study, “mature granule cells” (mGCs) refer operationally to granule cells that are not birth-dated with BrdU or EdU and therefore are not classified as adult-born neurons within the defined labeling window. We agree that this population is not directly age-defined, and that while the majority are expected to be mature based on their birth timing relative to the labeling period, we cannot exclude the possibility that a small fraction may include younger, unlabeled neurons. We have now explicitly defined this usage of mGCs in the Methods and clarified this point in the text to avoid ambiguity.

    (4) Methods state that Kruskal-Wallis tests were used when more than 3 groups were compared, but I don't see these stats presented (e.g., for trap data in Figure 1, blade x task TRAP expt in Figure 3 (should be 2-way RM anova here and elsewhere), etc) or any corrections for multiple comparisons. I appreciate that the mean rates of TRAPed abGCs are higher in the S and LS groups than in the shaping group, but most mice do not have any BrdU+ cells that are also TRAPed, and there are no statistics here to support the claim. I don't think there is enough sampling to accurately quantify activation of abGCs. Also, no stats to support the claim that TRAPing increases at the "tip of the SB after the more demanding LS task".

    We agree with this comment. We have now systematically tested all datasets for normality (by group) and applied parametric tests when the data met normality assumptions, and non-parametric tests otherwise. The statistical analyses have been revised accordingly. We added the appropriate tests (including two-way ANOVA where relevant, such as for blade × group comparisons) and now report full statistics in the figure legends and results sections. For the TRAP analyses in adult-born DGCs, we explicitly acknowledge the very low number of BrdU⁺/TRAP⁺ cells, which limits statistical power and, in some cases, precludes robust statistical testing. These limitations are now clearly stated in the Results and Discussion, and the corresponding interpretations have been tempered. For all Kruskal–Wallis tests, post hoc pairwise comparisons were performed using Dunn’s test, with Bonferroni correction for multiple comparisons, as now specified in the Methods section. We also expanded the Methods to describe the statistical workflow in detail. In addition, we have added the previously missing statistical analysis for Figure 2C. Comparisons were performed between the 0–50% and 50–100% portions of the blade, where 0% corresponds to the apex and 100% corresponds to the distal tip of the blade.

    (5) Figure 3I: I can't figure out which effect is statistically significant here (what does the asterisk signify?). Why no individual data points in this graph?

    We agree that the absence of individual data points reduced interpretability, and we have now updated the figure to include individual data points to better illustrate data distribution and variability.

    (6) The gradient of activity (shap < S < LS) could be due to how long they've been trained on a given stage (e.g. less activity during shaping because they have habituated, and neurons encoding that task phase have already been selected)

    We agree that task duration and habituation could, in principle, influence activity levels. Under this interpretation, higher activity would primarily reflect task novelty rather than cognitive demand. However, our data do not support this explanation. Specifically, we found no correlation between the number of training days required to reach criterion and c-Fos–positive or TRAP-positive cell density within a given stage. Thus, animals that reached criterion rapidly did not show higher activity levels than animals that required more days of training and were presumably more habituated to the task demands. This suggests that the observed activity gradient (shaping < S < LS) is not driven by exposure duration or habituation, but rather reflects differences in cognitive demand across task stages.

    (7) The TRAP+ EDU+ cell in Figure 3 looks odd because the BrdU signal is (a lot) larger than the TRAP signal, but BrdU is in the nucleus and should be smaller.

    We agree that the example in Figure 3 is not optimal. In dividing cells, BrdU/EdU signals can sometimes appear broader or closely apposed, which may affect their apparent size.

    (8) For the Ascl-HM4Di experiment, HM4Di appears to be expressed in all of the areas of the granule cell layer where abGCs are NOT located (i.e. no expression in the deep cell layer, near the sgz). This is problematic because it suggests perhaps abGCs are not inhibited as expected.

    As noted in our response to Reviewer #1, we did not use the mCitrine to localize the DREADD receptor as it has been demonstrated that mCitrine expression is expressed in a Cre-independent manner and not correlated with hM4Di expression. In the revised manuscript we include a representative image were we performed immunostaining using an HA antibody to directly visualize hM4Di and confirm its expression in adult-born granule cells (Figure 5).

    (9) Line 267: "6-7 week old neurons by themselves do not influence either the performance of mice in the task". I don't think this is fair because this experiment wasn't designed with as much power to detect an effect. The group trends are in the same direction, but there are many fewer mice in this experiment (n=6/group) than in the =<7w experiment (n=11/group), where the effect just reached statistical significance.

    We are sorry for this confusion which came from an incorrect version. The experiment shown in Figure 6 does not target 6–7-week-old neurons specifically. It uses the same tamoxifen chow–based protocol as Figure 5, but with a shorter exposure (4 weeks vs. 7 weeks), thereby labeling a younger and more restricted cohort of adult-born DGCs. By contrast, Figure 4 targets a more mature population, consisting predominantly of ~5-week-old adult-born neurons as well as mature granule cells (Dock10+).

    We have corrected the paragraph accordingly and clarified the age range of the labeled populations in the revised manuscript.

  6. Version published to 10.7554/elife.109611.2 on eLife
  7. Version published to 10.7554/elife.109611 on eLife
  8. eLife

    eLife Assessment

    This manuscript presents a valuable study of the activity and functional relevance of different circuits in the dentate gyrus of mice performing a pattern separation task. The study is likely to be of interest to those studying the subregional organization and cell type-specific functions of the dentate gyrus. However, the strength of evidence for the study's conclusions is currently incomplete.

  9. eLife

    Reviewer #1 (Public review):

    This manuscript investigates how dentate gyrus (DG) granule cell subregions, specifically suprapyramidal (SB) and infrapyramidal (IB) blades, are differentially recruited during a high cognitive demand pattern separation task. The authors combine TRAP2 activity labeling, touchscreen-based TUNL behavior, and chemogenetic inhibition of adult-born dentate granule cells (abDGCs) or mature granule cells (mGCs) to dissect circuit contributions.

    This manuscript presents an interesting and well-designed investigation into DG activity patterns under varying cognitive demands and the role of abDGCs in shaping mGC activity. The integration of TRAP2-based activity labeling, chemogenetic manipulation, and behavioral assays provides valuable insight into DG subregional organization and functional recruitment. However, several methodological and quantitative issues limit the interpretability of the findings. Addressing the concerns below will greatly strengthen the rigor and clarity of the study.

    Major points:

    (1) Quantification methods for TRAP+ cells are not applied consistently across panels in Figure 1, making interpretation difficult. Specifically, Figure 1F reports TRAP+ mGCs as density, whereas Figure 1G reports TRAP+ abDGCs as a percentage, hindering direct comparison. Additionally, Figure 1H presents reactivation analysis only for mGCs; a parallel analysis for abDGCs is needed for comparison across cell types.

    (2) The anatomical distribution of TRAP+ cells is different between low- and high-cognitive demand conditions (Figure 2). Are these sections from dorsal or ventral DG? Is this specific to dorsal DG, as itis preferentially involved in cognitive function? What happens in ventral DG?

    (3) The activity manipulation using chemogenetic inhibition of abDGCs in AsclCreER; hM4 mice was performed; however, because tamoxifen chow was administered for 4 or 7 weeks, the labeled abDGC population was not properly birth-dated. Instead, it consisted of a heterogeneous cohort of cells ranging from 0 to 5-7 weeks old. Thus, caution should be taken when interpreting these results, and the limitations of this approach should be acknowledged.

    (4) There is a major issue related to the quantification of the DREADD experiments in Figure 4, Figure 5, Figure 6, and Figure 7. The hM4 mouse line used in this study should be quantified using HA, rather than mCitrine, to reliably identify cells derived from the Ascl lineage. mCitrine expression in this mouse line is not specific to adult-born neurons (off-targets), and its expression does not accurately reflect hM4 expression.

    (5) Key markers needed to assess the maturation state of abDGCs are missing from the quantification. Incorporating DCX and NeuN into the analysis would provide essential information about the developmental stage of these cells.

    Minor points:

    (1) The labeling (Distance from the hilus) in Figure 2B is misleading. Is that the same location as the subgranular zone (SGZ)? If so, it's better to use the term SGZ to avoid confusion.

    (2) Cell number information is missing from Figures 2B and 2C; please include this data.

    (3) Sample DG images should clearly delineate the borders between the dentate gyrus and the hilus. In several images, this boundary is difficult to discern.

    (4) In Figure 6, it is not clear how tamoxifen was administered to selectively inhibit the more mature 6-7-week-old abDGC population, nor how this paradigm differs from the chow-based approach. Please clarify the tamoxifen administration protocol and the rationale for its specificity.

  10. eLife

    Reviewer #2 (Public review):

    Summary

    In this manuscript, the authors combine an automated touchscreen-based trial-unique nonmatching-to-location (TUNL) task with activity-dependent labeling (TRAP/c-Fos) and birth-dating of adult-born dentate granule cells (abDGCs) to examine how cognitive demand modulates dentate gyrus (DG) activity patterns. By varying spatial separation between sample and choice locations, the authors operationally increase task difficulty and show that higher demand is associated with increased mature granule cell (mGC) activity and an amplified suprapyramidal (SB) versus infrapyramidal (IB) blade bias. Using chemogenetic inhibition, they further demonstrate dissociable contributions of abDGCs and mGCs to task performance and DG activation patterns.

    The combination of behavioral manipulation, spatially resolved activity tagging, and temporally defined abDGC perturbations is a strength of the study and provides a novel circuit-level perspective on how adult neurogenesis modulates DG function. In particular, the comparison across different abDGC maturation windows is well designed and narrows the functionally relevant population to neurons within the critical period (~4-7 weeks). The finding that overall mGC activity levels, in addition to spatially biased activation patterns, are required for successful performance under high cognitive demand is intriguing.

    Major Comments

    (1) Individual variability and the relationship between performance and DG activation.

    The manuscript reports substantial inter-animal variability in the number of days required to reach the criterion, particularly during large-separation training. Given this variability, it would be informative to examine whether individual differences in performance correlate with TRAP+ or c-Fos+ density and/or spatial bias metrics. While the authors report no correlation between success and TRAP+ density in some analyses, a more systematic correlation across learning rate, final performance, and DG activation patterns (mGC vs abDGC, SB vs IB) could strengthen the interpretation that DG activity reflects task engagement rather than performance only.

    (2) Operational definition of "cognitive demand".

    The distinction between low (large separation) and high (small separation) cognitive demand is central to the manuscript, yet the definition remains somewhat broad. Reduced spatial separation likely alters multiple behavioral variables beyond cognitive load, including reward expectation, attentional demands, confidence, engagement, and potentially motivation. The authors should more explicitly acknowledge these alternative interpretations and clarify whether "cognitive demand" is intended as a composite construct rather than a strictly defined cognitive operation.

    (3) Potential effects of task engagement on neurogenesis.

    Given the extensive behavioral training and known effects of experience on adult neurogenesis, it remains unclear whether the task itself alters the size or maturation state of the abDGC population. Although the focus is on activity and function rather than cell number, it would be useful to clarify whether neurogenesis rates were assessed or controlled for, or to explicitly state this as a limitation.

    (4) Temporal resolution of activity tagging.

    TRAP and c-Fos labeling provide a snapshot of neural activity integrated over a temporal window, making it difficult to determine which task epochs or trial types drive the observed activation patterns. This limitation is partially acknowledged, but the conclusions occasionally imply trial-specific or demand-specific encoding. The authors should more clearly distinguish between sustained task engagement and moment-to-moment trial processing, and temper interpretations accordingly. While beyond the scope of the current study, this also motivates future experiments using in vivo recording approaches.

    (5) Interpretation of altered spatial patterns following abDGC inhibition.

    In the abDGC inhibition experiments, Cre+ DCZ animals show delayed learning relative to controls. As a result, when animals are sacrificed, they may be at an intermediate learning stage rather than at an equivalent behavioral endpoint. This raises the possibility that altered DG activation patterns reflect the learning stage rather than a direct circuit effect of abDGC inhibition. Additional clarification or analysis controlling for the learning stage would strengthen the causal interpretation.

    (6) Relationship between c-Fos density and behavioral performance.

    The study reports that abDGC inhibition increases c-Fos density while impairing performance, whereas mGC inhibition decreases c-Fos density and also impairs performance. This raises an important conceptual question regarding the relationship between overall activity levels and task success. The authors suggest that both sufficient activity and appropriate spatial patterning are required, but the manuscript would benefit from a more explicit discussion of how different perturbations may shift the identity, composition, or coordination of the active neuronal ensemble rather than simply altering total activity levels.

  11. eLife

    Reviewer #3 (Public review):

    Summary:

    The authors used genetic models and immunohistochemistry to identify how training in a spatial discrimination working memory task influences activity in the dentate gyrus subregion of the hippocampus. Finding that more cognitively challenging variants of the task evoked more and distinct patterns of activity, they then investigated whether newborn neurons in particular were important for learning this task and regulating the spatial activity patterns.

    Strengths:

    The focus on precise anatomical locations of activity is relatively novel and potentially important, given that little is known about how DG subregions contribute to behavior. The authors also use a task that is known to depend on this memory-related part of the brain.

    Weaknesses:

    Statistical rigor is insufficient. Many statistical results are not stated, inappropriate tests are used, and sample sizes differ across experiments (which appear to potentially underlie null results). The chemogenetic approach to inhibit adult-born neurons also does not appear to be targeting these neurons, as judged by their location in the DG.

  12. Version published to 10.7554/elife.109611.1 on eLife
  13. Version published to 10.1101/2024.10.31.621367 on bioRxiv