Locus coeruleus modulation of single-cell representation and population dynamics in the mouse prefrontal cortex during attentional switching

  1. Marco Nigro
  2. Lucas Silva Tortorelli
  3. Machhindra Garad
  4. Natalie E Zlebnik
  5. Hongdian Yang

  • Curated by eLife

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    eLife Assessment

    This study presents a valuable finding on how the locus coeruleus modulates the involvement of medial prefrontal cortex in set shifting using calcium imaging. The evidence supporting the claims was viewed as incomplete in comparisons of extra- (EDS) and intradimensional shifts (IDS). The work is of broad interest to those studying flexible cognition.

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Abstract

Behavioral flexibility, the ability to adjust behavioral strategies in response to changing environmental contingencies and internal demands, is fundamental to cognitive functions. Despite a large body of pharmacology and lesion studies, the precise neurophysiological mechanisms that underlie behavioral flexibility are still under active investigations. This work is aimed to determine the role of a brainstem-to-prefrontal cortex circuit in flexible rule switching. We trained mice to perform a set-shifting task, in which they learned to switch attention to distinguish complex sensory cues. Using chemogenetic inhibition, we selectively targeted genetically-defined locus coeruleus (LC) neurons or their input to the medial prefrontal cortex (mPFC). We revealed that suppressing either the LC or its mPFC projections severely impaired switching behavior, establishing the critical role of the LC-mPFC circuit in supporting attentional switching. To uncover the neurophysiological substrates of the behavioral deficits, we paired endoscopic calcium imaging of the mPFC with chemogenetic inhibition of the LC in task-performing mice. We found that mPFC prominently responded to attentional switching and that LC inhibition not only enhanced the engagement of mPFC neurons but also broadened single-neuron tuning in the task. At the population level, LC inhibition disrupted mPFC dynamic changes and impaired the encoding capacity for switching. Our results highlight the profound impact of the ascending LC input on modulating prefrontal dynamics and provide new insights into the cellular and circuit-level mechanisms that support behavioral flexibility.

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  1. eLife

    eLife Assessment

    This study presents a valuable finding on how the locus coeruleus modulates the involvement of medial prefrontal cortex in set shifting using calcium imaging. The evidence supporting the claims was viewed as incomplete in comparisons of extra- (EDS) and intradimensional shifts (IDS). The work is of broad interest to those studying flexible cognition.

  2. eLife

    Reviewer #1 (Public review):

    Summary:

    The authors note that there is a large corpus of research establishing the importance of LC-NE projections to medial prefrontal cortex (mPFC) of rats and mice in attentional set or 'rule' shifting behaviours. However, this is complex behavior and the authors were attempting to gain an understanding of how locus coeruleus modulation of the mPFC contributes to set shifting.

    The authors replicated the ED-shift impairment following NE denervation of mPFC by chemogenetic inhibition of the LC. They further showed that LC inhibition changed the way neurons in mPFC responded to the cues, with a greater proportion of individual neurons responsive to 'switching', but the individual neurons also had broader tuning, responding to other aspects of the task (i.e., response choice and response history). The population dynamics was also changed by LC inhibition, with reduced separation of population vectors between early-post-switch trials, when responding was at chance, and later trials when responding was correct. This was what they set out to demonstrate and so one can conclude they achieved their aims.

    The authors concluded that LC inhibition disrupted mPFC "encoding capacity for switching" and suggest that this "underlie[s] the behavioral deficits."

    Strengths:

    The principal strength is combining inactivation of LC with calcium imaging in mPFC. This enabled detailed consideration of the change in behavior (i.e., defining epochs of learning, with an 'early phase' when responding is at chance being compared to a 'later phase' when the behavioral switch has occurred) and how these are reflected in neuronal activity in the mPFC, with and without LC-NE input.

    Comments on revised version:

    In their response to reviewers, the authors say "We report p values using 2 decimal points and standard language as suggested by this reviewer". However, no changes were made in the manuscript: for example, "P = 4.2e-3" rather than "p = 0.004".

    In their response to the reviewers, they wrote: "Upon closer examination of the behavioral data, we exclude several sessions where more trials were taken in IDS than in EDS." If those sessions in which EDSIDS. Most problematic is the fact that the manuscript now reads "Importantly, control mice (pooled from Fig. 1e, 1h, Supp. Fig. 1a, 1b) took more trials to complete EDS than IDS (Trials to criterion: IDS vs. EDS, 10 {plus minus} 1 trials vs. 16 {plus minus} 1 trials, P < 1e-3, Supp. Fig. 1c), further supporting the validity of attentional switching (as in Fig. 1c)" without mentioning that data has been excluded.

  3. eLife

    Reviewer #3 (Public review):

    Summary:

    Nigro et al examine how the locus coeruleus (LC) influences the medial prefrontal cortex (mPFC) during attentional shifts required for behavioral flexibility. Specifically, the propose that LC-mPFC inputs enable mice to shift attention effectively from texture to odor cues to optimize behavior. The LC and its noradrenergic projections to the mPFC have previously been implicated in this behavior. The authors further establish this by using chemogenetics to inhibit LC terminals in mPFC and show a selective deficit in extradimensional set shifting behavior. But the study's primary innovation is the simultaneous inhibition of LC while recording multineuron patterns of activity in mPFC. Analysis at the single neuron and population levels revealed broadened tuning properties, less distinct population dynamics, and disrupted predictive encoding when LC is inhibited. These findings add to our understanding of how neuromodulatory inputs shape attentional encoding in mPFC and are an important advance. There are some methodological limitations and/or caveats that should be considered when interpreting the findings, and these are described below.

    Strengths:

    The naturalistic set-shifting task in freely-moving animals is a major strength and the inclusion of localized suppression of LC-mPFC terminals is builds confidence in the specificity of their behavioral effect. Combining chemogenetic inhibition of LC while simultaneously recording neural activity in mPFC with miniscopes is state-of-the-art. The authors apply analyses to population dynamics in particular that can advance our understanding of how the LC modifies patterns of mPFC neural activity. The authors show that neural encoding at both the single cell level and the population level are disrupted when LC is inhibited. They also show that activity is less able to predict key aspects of the behavior when the influence of LC is disrupted. This is quite interesting and adds to a growing understanding of how neuromodulatory systems sharpen tuning of mPFC activity.

    Weaknesses:

    Weaknesses are mostly minor, but there are some caveats that should be considered. First, the authors use a DBH-Cre mouse line and provide histological confirmation of overlap between HM4Di expression and TH immunostaining. While this strongly suggests modulation of noradrenergic circuit activity, the results should be interpreted conservatively as there is no independent confirmation that norepinephrine (NE) release is suppressed and these neurons are known to release other neurotransmitters and signaling peptides. In the absence of additional control experiments, it is important to recognize that effects on mPFC activity may or may not be directly due to LC-mPFC NE.

    Another caveat is that the imaging analyses are entirely from the extradimensional shift session. Without analyzing activity data from the intradimensional shift (IDS) session, one cannot be certain that the observed changes are to some feature of activity that is specific to extradimensional shifts. Future experiments should examine animals with LC suppression during the IDS as well, which would show whether the observed effects are specific to an extradimensional shift and might explain behavioral effects.

  4. eLife

    Author response:

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

    Public Reviews:

    We thank the reviewers and editors for this peer review. Following the editorial assessment and specific review comments, in this revision we have included new analysis to support the validity of the behavioral task (Reviewer #2). We have improved data presentation by including 1) data points from individual animals (Reviewer #1, #3), 2) updated histology showing the expression of hM4Di in LC neurons as well as LC terminals in the mPFC (Reviewer #3), and 3) more detailed descriptions of methodology and data analysis (Reviewer #1, #2, #3).

    Recommendations for the authors:

    Reviewer #2 (Recommendations for the authors):

    (1) Planned t-tests should be performed in both control and experimental animals to determine if the number of trials needed to reach criterion on the ID is lower than on the ED. Based on the data analyses showing no difference among the control group, the data could be pooled to demonstrate that the task is valid. Reporting all p-values using 2 decimal points and standard language e.g., p < 0.001 would greatly improve the readability of the data.

    Thank you for this suggestion. As pointed out by this reviewer, more trials to reach performance criterion in EDS than IDS is indicative of successful acquisition and switching of the attentional sets. Upon closer examination of the behavioral data, we exclude several sessions where more trials were taken in IDS than in EDS, and our conclusions that DREADD inhibition of the LC or LC input to the mPFC impaired rule switching in EDS remain robust (e.g., new Fig. 1e, 1h). We also pool control and test data (Fig. 1e, 1h, new Supp. Fig. 1a, 1b) to demonstrate the validity of this task (new Supp. Fig. 1c, IDS vs. EDS in the control group, 10 ± 1 trials vs. 16 ± 1 trials, P < 1e-3). The validity of set shifting is also supported by the new Fig. 1c.

    We report p values using 2 decimal points and standard language as suggested by this reviewer.

    Relevant to the comments from Reviewer #1 in the public review, we now show individual data points on the bar charts (new Fig. 1e, 1h).

    (2) It may also be helpful to provide the average time between CNO infusion and onset of the ED as well as information about when maximal effects are expected after these treatments.

    Systemic CNO injections were administered immediately after IDS, and we waited approximately one hour before proceeding to EDS. Maximal effects of systemic CNO activation were reported to occur after 30 minutes and last for at least 4-6 hours. Both control and test groups received the CNO injections in the same manner. This is now better described in Methods.

    Reviewer #3 (Recommendations for the authors):

    (1) Add better histology images showing colocalization of TH and HM4Di. Quantification of colocalization would be optimal.

    We now include better histology images (new Fig. 1d) and have quantified the colocalization of TH and HM4Di in the main text (line 115-116).

    (2) If possible, images showing HM4Di expression in mPFC axon terminals would be useful. If these are colocalized with TH immunostaining, that would increase confidence in their identity. This would be much more useful than the images provided in Figure 1C.

    We now include new image to show hM4Di expression (mCherry) in LC terminals in the mPFC (new Fig. 1f). However, due to technical limitations (species of the primary antibody), we did not co-stain with TH.

    (3) Include behavior of mice from the miniscope experiment in Figure 2 to show they are similar to those from Figure 1.

    This is now included in Supp. Fig. 1b.

    (4) More details about the processing and segmentation of miniscope data would be helpful (e.g., how many neurons were identified from each animal?).

    We use standard preprocessing and segmentation pipelines in Inscopix data processing software (version 1.6), which includes modules for motion correction and signal extraction. Briefly, raw imaging videos underwent preprocessing, including a x4 spatial down sampling to reduce file size and processing time. No temporal down sampling was performed. The images were then cropped to eliminate post-registration borders and areas where cells were not visible. Prior to the calculation of the dF/F0 traces, lateral movement was corrected. For ROI identification, we used a constrained non-negative matrix factorization algorithm optimized for endoscopic data (CNMF-E) to extract fluorescence traces from ROIs. We identified 128 ± 31 neurons after manual selection, depending on recording quality and field of view. Number of neurons acquired from each animal are now included in Methods. This is now further elaborated in Methods (line 405415).

    (5) Add more methodological detail for how cell tuning was analyzed, including how z-scoring was performed (across the entire session?), and how neurons in each category were classified.

    We have expanded the Methods section to clarify how cell tuning was analyzed (line 419430). Calcium traces were z-scored on a per-neuron basis across the entire session. For each neuron, we computed trial-averaged activity aligned to specific task events (e.g., digging in one of the two ramekins available). A neuron was classified as responsive if its activity showed a significant difference (p < 0.05) between two conditions within the defined time window in the ROC analysis.

    (6) For data from Figure 2F it would be very useful to plot data from individual mice in addition to this aggregated representation.

    We now include data from individual mice in Supp. Table 1.

    (7) I think it would be helpful to move some parts of Figure S1 to the main Figure 1, in particular the table from S1A.

    Fig. S1 is now part of the new Fig. 1.

    (8) Clarify whether Figure S2 is an independent replication, as implied, or whether the same test data is shown twice in two separate figures (In Figure 1b and Supplementary Figure 2).

    The test group in Fig. S2 (new Fig. S1) is the same as the test group in Fig. 1b (new Fig. 1e), but the control group is a separate cohort. This is now clarified in the figure legends.

    (9) The authors should add a limitations section to the discussion where they specifically discuss the caveats involved in relating their results specifically to NE. This should include the possible involvement of co-transmitters and off-target expression of Cre in other populations.

    Thank you for this comment. Previous pharmacology and lesion studies showed that LC input or NE content in the mPFC was specifically required for EDS-type switching processes (Lapiz, M.D. et al., 2006; Tait, D.S. et al. 2007; McGaughy, J. et al. 2008), in light of which we interpret our mPFC neurophysiological effects with LC inhibition as at least partially mediated by the direct LC-NE input. When discussing the limitations of our study, we now explicitly acknowledge the potential involvement of co-transmitters released by LC neurons (line 253-256).

    (10) The authors should provide details about the TH antibody uses for IHC

    We now include more details in immunohistochemistry (line 384-388).

    (11) Throughout, it would be helpful to include datapoints from individual animals - these are included in some supplementary figures, but are missing in a number of the main plots.

    Reviewer #1 made a similar comment, and we now include individual data points in the figures (e.g., Fig. 1e, 1h).

  5. Version published to 10.7554/elife.105911.2 on eLife
  6. Version published to 10.7554/elife.105911 on eLife
  7. eLife

    eLife Assessment

    This study presents a valuable finding on how the locus coeruleus modulates the involvement of medial prefrontal cortex in set shifting using calcium imaging. The evidence supporting the claims was viewed as incomplete, although a more rigorous statistical comparison of intradimensional vs. extradimensional stages of the task, either in behavior or in the calcium imaging data, would help to address this concern. The work is of broad interest to those studying flexible cognition.

  8. eLife

    Reviewer #1 (Public review):

    Summary:

    The authors note that there is a large corpus of research establishing the importance of LC-NE projections to the medial prefrontal cortex (mPFC) of rats and mice in attentional set or 'rule' shifting behaviours. However, this is complex behavior, and the authors were attempting to gain an understanding of how locus coeruleus modulation of the mPFC contributes to set shifting.

    The authors replicated the ED-shift impairment following NE denervation of mPFC by chemogenetic inhibition of the LC. They further showed that LC inhibition changed the way neurons in mPFC responded to the cues, with a greater proportion of individual neurons responsive to 'switching', but the individual neurons also had broader tuning, responding to other aspects of the task (i.e., response choice and response history). The population dynamics were also changed by LC inhibition, with reduced separation of population vectors between early-post-switch trials, when responding was at chance, and later trials when responding was correct. This was what they set out to demonstrate, and so one can conclude they achieved their aims.

    The authors concluded that LC inhibition disrupted mPFC "encoding capacity for switching" and suggest that this "underlie the behavioral deficits."

    Strengths:

    The principal strength is the combination of inactivation of LC with calcium imaging in the mPFC. This enabled detailed consideration of the change in behavior (i.e., defining epochs of learning, with an 'early phase' when responding is at chance being compared to a 'later phase' when the behavioral switch has occurred) and how these are reflected in neuronal activity in the mPFC, with and without LC-NE input.

    Weaknesses:

    Methodologically, some improvement could be made in terms of the statistical descriptions. Supplementary Figure 2: For the peripheral CNO, the 'control group' (saline) was n=4 and the test group (CNO), n=5. For the central CNO, the test group was n = 8 and the control was n = 7. The authors explain that the group sizes were not statistically determined and mice were assigned to groups 'arbitrarily', but why did they not at least make the group sizes equal?

    In Figure 1 (e), given the small sample size, it would be helpful if all the data points were included on the bar charts. As a t-test was performed on only the ED stage of the test, seeing all the data points would reassure that there would not have been a statistically significant 'improvement' in the ID stage in the group given mPFC CNO. It would also be helpful to give effect sizes for all statistical tests.

  9. eLife

    Reviewer #2 (Public review):

    Summary:

    The authors were building on prior research linking cortical norepinephrine in a test of attentional set shifting. They extended prior research by assessing the effects of excitatory or inhibitory DREADDs prior to the test of attentional set shifting.

    Strengths:

    The use of DREADDs in the previously validated test of attentional set shifting improves temporal control of corticopetal, noradrenergic pathways during behavior. While mice typically require multiple intradimensional shifts to form an attentional set, the subjects in the current study perform a variant of the task similar to that used in humans, improving the translational validity of the work.

    Weaknesses:

    A critical piece of evidence needed to support the behavioral claim that mice form an attentional set is a statistically significant difference between the number of trials to reach criterion at the intradimensional vs. the extradimensional stage of the task. Based on prior literature, this could be done as a planned comparison, which would improve the power to detect differences beyond that found using an HSD test. An additional methodological ambiguity is the amount of time between the administration of CNO and the performance of the ED. As reported, it seems likely that the DREADDS were impacting performance at multiple stages of the test.

    Overall, the authors seem to have achieved their aims, but have failed to provide critical statistical support for claims made.

    The work is likely to be of interest to the burgeoning number of scientists investigating the role of cortical norepinephrine in cognitive flexibility.

  10. eLife

    Reviewer #3 (Public review):

    Summary:

    Nigro et al examine how the locus coeruleus (LC) influences the medial prefrontal cortex (mPFC) during attentional shifts required for behavioral flexibility. Specifically, they propose that LC-mPFC inputs enable mice to shift attention effectively from texture to odor cues to optimize behavior. The LC and its noradrenergic projections to the mPFC have previously been implicated in this behavior. The authors further establish this by using chemogenetics to inhibit LC terminals in mPFC and show a selective deficit in extradimensional set-shifting behavior. However, the study's primary innovation is the simultaneous inhibition of LC while recording multineuron patterns of activity in mPFC. Analysis at the single neuron and population levels revealed broadened tuning properties, less distinct population dynamics, and disrupted predictive encoding when LC is inhibited. These findings add to our understanding of how neuromodulatory inputs shape attentional encoding in mPFC. However, several issues somewhat limit the overall impact and interpretation of the results.

    Strengths:

    The more naturalistic set-shifting task used in the study is a major strength, and its implementation in freely-moving animals is very useful. The inclusion of localized suppression of LC-mPFC terminals is also a strength that builds confidence in the specificity of their behavioral effect. Moreover, the combination of chemogenetic inhibition of LC while simultaneously recording neural activity in mPFC with miniscopes is state-of-the-art. The authors apply analyses to population dynamics, in particular, that can advance our understanding of how the LC modifies patterns of mPFC neural activity. The authors show that neural encoding at both the single-cell level and the population level is disrupted when LC is inhibited. They also show that activity is less able to predict key aspects of the behavior when the influence of LC is disrupted. This is quite interesting and adds to a growing understanding of how neuromodulatory systems sharpen the tuning of mPFC activity.

    Weaknesses:

    There are some concerns about tying the results to noradrenergic circuit activity. The authors use a DBH-Cre mouse line, but the histology images provided are low resolution, and surprisingly, there appears to be little overlap between HM4Di expression and TH immunostaining. It is unclear what explains this, but without further confirmation, it is hard to be sure whether the manipulation selectively impacts a specific LC population. While the authors are generally conservative in relating their findings to norepinephrine (NE) signaling, it is still implied that this is likely. But even if HM4Di is expressed specifically in DBH+ LC neurons, there is no confirmation that NE release is suppressed, and these neurons may release other neurotransmitters, including glutamate and dopamine. In the absence of careful controls, it is important to recognize that effects may or may not be due to LC-mPFC NE.

    Another weakness is that the behavior of miniscope mice is not shown. These experiments make up the bulk of the study, including the most significant results (Figures 2-4). Interpreting the chemogenetics + imaging results without this data is more challenging and relies on the assumption that they were affected similarly to an animal from Figure 1. More fundamentally, the imaging analyses are entirely from the extradimensional shift session. Showing similar analyses from the intradimensional shift (IDS) session would confirm that test group mice do not exhibit broadened tuning prior to injecting CNO and would help to establish whether the observed changes are to some feature of activity that is specific to extradimensional shifts. The ideal experiment would also include a separate group of animals with LC suppression during the IDS, which would show whether the observed effects are specific to an extradimensional shift and might explain behavioral effects.

    There are also some weaknesses in how the single neuron encoding data is analyzed and presented. First, the corresponding methods section is insufficient to fully understand how selectively tuned neurons were classified. The authors perform ROC analysis for the period 0 - 5s before choice to reveal choice-tuned neurons. It would be useful to know what proportion of the total neurons this represents, and whether this includes neurons with activity that is significantly increased, decreased, or both. Further, insufficient detail is provided to be able to understand how neurons are further classified into 'choice', 'history', and 'switch' categories, or what percentage of ROC-identified neurons fall into each category (only % of total neurons is provided).

    Finally, there are some concerns about lumping all the identified neurons together (as in Figure 2F). The miniscope experiments include very few mice (n=4 controls, n=5 test), and effects may be driven by only 1 or 2 subjects. Also, plotting the data on a per-animal basis would help to better understand the effects in greater detail. Overall, the results are interesting, but these weaknesses limit the strength and specificity of the claims that can be made.

  11. Version published to 10.7554/elife.105911.1 on eLife
  12. Version published to 10.1101/2023.12.13.571356 on bioRxiv