Acquisition of auditory discrimination mediated by different processes through two distinct circuits linked to the lateral striatum

Reviewer #1 (Public review):

Summary:

In this study, Setogawa et al. employ an auditory discrimination task in freely moving rats, coupled with small animal imaging, electrophysiological recordings, and pharmacological inhibition/lesioning experiments to better understand the role of two striatal subregions: the anterior Dorsal Lateral Striatum (aDLS) and the posterior Ventrolateral Striatum (pVLS), during auditory discrimination learning. Attempting to better understand the contribution of different striatal subregions to sensory discrimination learning strikes me as a highly relevant and timely question, and the data presented in this study are certainly of major interest to the field. The authors have set up a robust behavioral task, systematically tackled the question about a striatal role in learning with multiple observational and manipulative techniques. Additionally, the structured approach the authors take by using neuroimaging to inform their pharmacological manipulation experiments and electrophysiological recordings is a strength.

Comments on revisions:

The authors have addressed some concerns raised in the initial review but some remain. In particular it is still unclear what conclusions can be drawn about task-related activity from scans that are performed 30 minutes after the behavioral task. I continue to think that a reorganization/analysis data according to event type would be useful and easier to interpret across the two brain areas, but the authors did not choose to do this. Finally, switching the cue-response association, I am convinced, would help to strengthen this study.

Reviewer #2 (Public review):

The study by Setogawa et al. aims to understand the role that different striatal subregions belonging to parallel brain circuits have in associative learning and discrimination learning (S-O-R and S-R tasks). Strengths of the study are the use of multiple methodologies to measure and manipulate brain activity in rats, from microPET imaging to excitotoxic lesions and multielectrode recordings across anterior dorsolateral (aDLS), posterior ventral lateral (pVLS)and dorsomedial (DMS) striatum.

The main conclusions are that the aDLS promotes stimulus-response association and suppresses response-outcome associations. The pVLS is engaged in the formation and maintenance of the stimulus-response association. There is a lot of work done and some interesting findings however, the manuscript can be improved by clarifying the presentation and reasoning. The inclusion of important controls will enhance the rigor of the data interpretation and conclusions.

Comments on revisions:

The authors have made important revisions to the manuscript and it has improved in clarity. They also added several figures in the rebuttal letter to answer questions by the reviewers. I would ask that these figures are also made public as part of the authors' response or if not, included in the manuscript.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public Review):

Summary:

In this study, Setogawa et al. employ an auditory discrimination task in freely moving rats, coupled with small animal imaging, electrophysiological recordings, and pharmacological inhibition/lesioning experiments to better understand the role of two striatal subregions: the anterior Dorsal Lateral Striatum (aDLS) and the posterior Ventrolateral Striatum (pVLS), during auditory discrimination learning. Attempting to better understand the contribution of different striatal subregions to sensory discrimination learning strikes me as a highly relevant and timely question, and the data presented in this study are certainly of major interest to the field. The authors have set up a robust behavioral task and systematically tackled the question about a striatal role in learning with multiple observational and manipulative techniques. Additionally, the structured approach the authors take by using neuroimaging to inform their pharmacological manipulation experiments and electrophysiological recordings is a strength.

However, the results as they are currently presented are not easy to follow and could use some restructuring, especially the electrophysiology. Also, the main conclusion that the authors draw from the data, that aDLS and pVLS contribute to different phases of discrimination learning and influence the animal's response strategy in different ways, is not strongly supported by the data and deserves some additional caveats and limitations of the study in the discussion.

We appreciate the reviewer’s valuable feedback, which has been beneficial for improvement of our manuscript. In response to the reviewer’s comments, we have revised multiple parts of the manuscript, including explanations of electrophysiological data. We have also provided additional data to support our main conclusion and addressed caveats and limitations related to the data in the Discussion section. For more details, please refer to the responses to each comment.

Comment 1: The authors have rigorously used PET neuroimaging, which is an interesting noninvasive method to track brain activity during behavioral states. However, in the case of a freely moving behavior where the scans are performed ~30 minutes after the behavioral task, it is unclear what conclusions can be drawn about task-specific brain activity. The study hinges on the neuroimaging findings that both areas of the lateral striatum (aDLS and pVLS) show increased activity during acquisition, but the DMS shows a reduction in activity during the late stages of behavior, and some of these findings are later validated with complementary experiments. However, the limitations of this technique can be further elaborated on in the discussion and the conclusions.

As described in our response to the following two comments (a, b) from the reviewer, in the PET imaging study we first analyzed task-related activity by comparing ¹⁸F-FDG uptake on different days of the auditory discrimination task with that on Day 4 of the single lever press task as a control. Next, we analyzed learning-dependent activity by comparing the uptake on different days of the discrimination task with that on Day 2 of the same task. Based on the results of both analyses, we concluded that the activity in the striatal subregions changes during the progress of discrimination learning. The behavioral significance of striatal subregions was tested by excitotoxic lesion and pharmacological blockade experiments. The explanation of imaging data analysis may have been insufficient to fully communicate dynamic changes in the activity of striatal subregions. Therefore, we have clarified our voxel-based statistical parametric analysis method to better explain the dynamic activity changes in the striatal subregions. Please refer to the following responses to comments 1 (a, b).

Comment 1 (a): In commenting on the unilateral shifts in brain striatal activity during behavior, the authors use the single lever task as a control, where many variables affecting neuronal activity might be different than in the discriminatory task. The study might be better served using Day 2 measurements as a control against which to compare activity of all other sessions since the task structures are similar.

We initially analyzed task-related activity by comparing ¹⁸F-FDG uptake on one of Days 2, 6, 10, or 24 of auditory discrimination task with that on Day 4 of the single lever press task. This task was used as a control that does not require a decision process based on the auditory stimulus. We observed significant increases in the activity of the unilateral aDLS on Day 6 and in that of the bilateral pVLS on Day 10 of the discrimination task. We also observed a significant decrease in the unilateral DMS on Day 24 (see Figures 2F and 2G). Next, as suggested, we compared the uptake on one of Days 6, 10, or 24 with that on Day 2 as a control to evaluate learning-dependent activity. The activity showed significant increases in the bilateral aDLS on Day 6 and in the unilateral pVLS on Day 10, and a significant decrease in the bilateral DMS on Day 24 (see Figures 2H).

The reviewer has suggested a discrepancy in the activity of the unilateral or bilateral striatal subregions under certain conditions between the image data (shown in Figures 2F–H) and plot data (Figures 2J–L). This discrepancy is also suggested in the following Comment 1 (b). For example, in the image data the brain activity was increased in the unilateral (left) aDLS on Day 6 of the discrimination task as compared to Day 4 of the single lever task (Figure 2F). In the plot data, ¹⁸F-FDG uptake reached a peak on Day 6 in both the left and right sides of the aDLS (Figure 2J), and the uptake in the left aDLS on Day 6 significantly increased relative to the value of the single lever press, whereas the value in the right aDLS on Day 6 tended to increase relative to that of the single lever press with no significant difference. The plot data showing the unilaterality in the aDLS activation relative to the single lever press are consistent with the image data. On the other hand, the ¹⁸F-FDG uptake in the aDLS on Day 6 compared to the value on Day 2 was significantly increased in both sides. Similar observations were made in the activity in the pVLS on Day 10 compared to that on Day 2, as well as in the DMS activity on Day 24 relative to that of the single lever press.

Our analysis of both task-related and learning-dependent activities revealed dynamic changes in striatal subregions during discrimination learning. We investigated the brain regions in which ¹⁸F-FDG uptake significantly increased or decreased during the learning processes, applying a statistical significance threshold (p < 0.001, uncorrected) and an extent threshold, by using a voxel-based statistical parametric analysis. In the image data, the voxels showing significant differences between two conditions are visualized on the brain template. The plot data show the amount of ¹⁸FFDG uptake in the voxels, which was detected by the voxel-based analysis. The insufficient explanation of the data analysis of PET imaging in the initial manuscript may have led to a misunderstanding regarding the activity in the unilateral or bilateral striatal subregions. Therefore, we have revised the explanation for voxel-based statistical parametric analysis, adding a more detailed description of the thresholds in the text (page 7, lines 143–145) and Methods (page 27, lines 672–675).

Comment 1 (b): From the plots in J, K, and L, it seems that shifts in activity in the different substructures are not unilateral but consistently bilateral, in contrast to what is mentioned in the text. Possibly the text reflects comparisons to the single lever task, and here again, I would emphasize comparing within the same task.

Please see our response to the first comment (a) regarding our explanation of the consistency in the activity of the unilateral or bilateral striatal subregions between the image and plot data. We have also revised the explanation in the corresponding sections of the manuscript, as described above.

Comment 2: In Figure 2, the authors present compelling data that chronic excitotoxic lesions with ibotenic acid in the aDLS, pVLS, and DMS produce differential effects on discrimination learning. However, the significant reduction in success rate of performance happens as early as Day 6 in both IBO groups in both aDLS and pVLS mice. This would seem to agree with conclusions drawn about the role of aDLS in the middle stages of learning in Figure 2, but not the pVLS, which only shows an increased activity during the late stages of the behavior.

Figure 3 shows the behavioral effects of ibotenic acid injections into striatal subregions in rats. For the aDLS injection, we performed two-way repeated ANOVA, which revealed a significant main effect of group or day and a significant interaction of group × day, and added the simple main effects between the treatments to the figure (Figure 3G). We observed significant differences in the success rate mainly at the middle stage of learning. In contrast, for the pVLS injection there was no significant interaction for group × day, although the main effects of group or day was significant by two-way repeated ANOVA (Figure 3H). Consequently, it was unclear as to when exactly the significant reduction occurred. These results indicate that the aDLS and pVLS are necessary for the acquisition of auditory discrimination, and that the aDLS is mainly required for the middle stage. Similar results were observed in the win-shift-win strategy in the aDLS and pVLS (Figures 3J and 3K).

Next, we performed temporal inhibition of neuronal activity in striatal subregions by muscimol treatment in order to examine whether the activity in the subregions is linked with learning processes at different stages. In this experiment, muscimol was injected into the aDLS or pVLS at the middle or late stage, and the resultant effects on the success rate were investigated. The success rate in the muscimol-injected groups into the aDLS significantly decreased at the middle stage, but not at the early and late stages (Figure 4C). In contrast, the rate in the muscimol groups into the pVLS significantly decreased at the late stage, but not at the early or middle stages (Figure 4D). The results indicate that the aDLS and pVLS are mainly involved in the processes at the middle and late stages, respectively, and support the PET imaging data showing the activation of two striatal subregions at the various stages.

We have now provided the results of simple main effects analysis for the aDLS lesion (Figures 3G and 3J) and revised the description of the Results section (page 8, lines 174–178, page 8, lines 186–188, and page 9, line 205-206) and Figure legend (page 44, lines 1000‒1003, and page 44, lines 1010–1013). We have also added the results of simple main effects analysis in Figure 3J.

Comment 3: In Figure 4, the authors show interesting data with transient inactivation of subregions of the striatum with muscimol, validating their findings that the aDLS mediates the middle and the pVLS the late stages of learning, and the function of each area serves different strategies. However, the inference that aDLS inactivation suppresses the WSW strategy "moderately" is not reflected in the formal statistical value p=0.06. While there still may be a subtle effect, the authors would need to revise their conclusions appropriately to reflect the data. In addition, the authors could try a direct comparison between the success rate during muscimol inhibition in the mid-learning session between the aDLS and pVLS-treated groups in Figure 4C (middle) and 4D (middle). If this comparison is not significant, the authors should be careful to claim that inhibition of these two areas differentially affects behavior.

In Figure 4E, aDLS inhibition showed a tendency to reduce slightly win-shift-win strategy at the middle stage (t[14] = 2.038, p = 0.061, unpaired Student’s t-test). In accordance with the reviewer’s comment, we changed the word “moderate” to “subtle” (page 12, line 272).

In the temporal inhibition of the striatal subregions, the aDLS and pVLS experiments (panels C and D, respectively) were conducted separately. Since it is difficult to directly compare the data obtained from different experiments, we did not carry out a direct comparison of the success rate between the aDLS and pVLS injections.

Comment 4: The authors have used in vivo electrophysiological techniques to systematically investigate the roles of the aDLS and the pVLS in discriminatory learning, and have done a thorough analysis of responses with each phase of behavior over the course of learning. This is a commendable and extremely informative dataset and is a strength of the study. However, the result could be better organized following the sequence of events of the behavioral task to give the reader an easier structure to follow. Ideally, this would involve an individual figure to compare the responses in both areas to Cue, Lever Press, Reward Sound, and First Lick (in this order).

We first showed changes in the proportion of event-related neurons during the acquisition phase (Figure S5). Next, we conducted a detailed analysis of the characteristics of aDLS and pVLS neuronal activity. Specifically, we found several types of event-related neurons, including: (1) reward sound-related neurons representing behavioral outcomes in the aDLS; (2) first licking-related neurons showing sustained activity after the reward in the aDLS and pVLS; and (3) cue-onset and cue-response neurons associated with the beginning and ending of a behavior in the pVLS.

Descriptions of the characteristics of event-related neurons according to the sequence of events in a trial, as the reviewer has suggested, is another way to provide an easy structure for understandings on the electrophysiological data. However, we focused on the characteristics of aDLS neurons at the middle stage and pVLS neurons at the late stage of discrimination learning. Therefore, we explained the electrophysiological data based on the order of learning stages rather than the sequence of events in the trial, as described above.

Comment 5: An important conceptual point presented in the study is that the aDLS neurons, with learning, show a reduction in firing rates and responsiveness to the first lick as well as the behavioral outcome, and don't play a role in other task-related events such as cue onset. However, the neuroimaging data in Figure 2 seems to suggest a transient enhancement of aDLS activity in the mid-stage of discriminatory learning, that is not reflected in the electrophysiology data. Is there an explanation for this difference?

In the ¹⁸F-FDG PET imaging study, the brain activity in the aDLS reached a peak at the middle stage of the acquisition phase of auditory discrimination (Figure 2J). In the multi-unit electrophysiological recording experiment, the firing activity of the aDLS neuron subpopulations related to the behavioral outcome showed no significant differences among the three stages (Figure 5E), while the proportion of these subpopulations were gradually reduced through the progress of learning stages (Figure 5F). The extent of the firing activity and length of the firing period of other subpopulations showing sustained activation after the reward appeared to show a learning-dependent decrease (Figures 6B and 6C), although the proportion of these subpopulations indicated no correlation with the progress of the learning (Figure 6D). Patterns of the temporal changes in brain activity in striatal subregions across the learning stages did not match completely the time variation in the property or proportion of specific event-related neurons. In our electrophysiological analysis, we identified well-isolated neurons from the striatal subregions during the auditory discrimination task, focusing on putative medium spiny neurons (Figures S4E–S4G). Based on the combinatorial pattern of the tone instruction cue (high tone/H or low tone /L), and lever press (right/R or left/L), we categorized the electrophysiological data into the four trials, including the HR, LL. LR, and HL. We identified HR or LL type neurons showing significant changes in the firing rate related to specific events, such as cue onset, choice response, reward sound, and first licking compared to the baseline firing rate. These neurons were further divided into two groups with increased or decreased activity relative to the baseline firing (Figures S5A and S5B). In the present study, we focused on event-related neurons with increased activity. Because of the analysis limited to neuronal subpopulations related to specific events with the increased activity, it is difficult to fully explain dynamic shifts in the brain activity of striatal subregions dependent on the progress of learning by the time variation of firing activity of individual event-related neurons. The activity of other subpopulations in the striatum may be involved in the shift in brain activity during the learning processes. In addition, recent studies have reported that the activity of glial cells influences the uptake of ¹⁸FFDG (Zimmer et al., Nat Neurosci., 2017) and that these cells regulate spike timingdependent plasticity (Valtcheva and Venance, Nat Commun, 2016). Changes in glial cellular activity, through the control of synaptic plasticity, may partly contribute to the pattern formation of learning-dependent shifts in brain activity.

To explain the difference in the time course between the brain activity and the firing activity of specific event-related neurons, we have added the aforementioned information to the Limitations section (pages 21 to 22, lines 512–539).

Comment 6: A significant finding of the study is that CO-HR and CO-LL responses are strikingly obvious in the pVLS, but not in the aDLS, in line with the literature that the posterior (sensory) striatum processes sound. This study also shows that responses to the highfrequency tone indicating a correct right-lever choice increase with learning in contrast to the low-frequency tone responses. To further address whether this difference arises from the task contingency, and not from the frequency representation of the pVLS, an important control would be to switch the cue-response association in a separate group of mice, such that high-frequency tones require a left lever press and vice versa. This would also help tease apart task-evoked responses in the aDLS, as I am given to understand all the recording sites were in the left striatum.

We did not conduct an experiment switching cue-response association in the auditory discrimination task. However, the transient activity of cue onset-related neurons in the pVLS, as the reviewer has suggested, did not appear at the early stage of learning, but was observed in a learning-dependent manner (Figures 7A and S8E). In addition, the cue onset-HR activity showed a slight but notable difference between the HR and LL trials at the middle and late stages (Figure 7B), but there was no difference in activity in the HL and LR incorrect trials at the corresponding stages (Wilcoxon signed rank test; early, p = 0.375, middle, p = 0.931, and late, p = 0.668). These results suggest that the activity of cue onset-related neurons in the pVLS is associated with the stimulus and response association (task contingency) rather than the tone frequency.

Reviewer #1 (Recommendations For The Authors):

Minor comment 1: The readability and appeal of this study would be improved by explaining the various neuronal response types, and task-related events in slightly more detail in the results section, and minimizing the use of non-standard abbreviations wherever possible.

As suggested, we have replaced the abbreviations related to electrophysiological events (CO, CR, RS, and FL) with the original terms, and improved the explanation for neuronal response types and event-related neurons.

Minor comment 2: It would be helpful to label DLS and VLS recordings more clearly on the figures instead of only in the figure caption.

Thank you for pointing this out. The terms “aDLS” and “pVLS” have now been added to the panels showing firing pattern of neurons: “aDLS” in Figures 5D, 6A, S6A, S7A, S8A, S8B. S8C, and S8D; and “pVLS” in Figures 6F, 7A, 7D, S6D, S6E, S7F, S8E, and S8F.

Minor comment 3: The authors suggest that aDLS HR- and LL- neurons are more sensitive to the behavioral outcome than those in pVLS (Fig 5 and S5). However, their conclusions are based on sample sizes as low as n=3 for each response type.

We identified event-related neurons from single neurons detected in both the aDLS and pVLS using the same criteria. In the pVLS, we found a small number of neurons that increased their activity during the period when the reward sound is presented (Figures S6D and S6E) (6, 4, and 17 HR type neurons at the early, middle, and late stages, respectively; 3, 5, and 15 LL type neurons at the early, middle, and late stages, respectively). The number of LL type neurons at the early stage was particularly lower, as the reviewer has suggested. However, when we plotted the firing rates of these neurons around the event, their activity did not reflect behavioral outcome. In the aDLS, we detected a large number of reward sound-related neurons representing behavioral outcome (Figures 5 and S6A) (43, 37, and 44 HR type neurons at the early, middle, and late stages, respectively; 49, 62, and 59 LL type neurons at the early, middle, and late stages, respectively). These observations suggest that aDLS neurons are more sensitive to behavioral outcomes than pVLS neurons.

Minor comment 4: Typo in Figure 4C and D, right plots, y-axis label: "subtracted".

The typographic errors in Figures 4C–4H have now been corrected to “subtracted”.

Reviewer #2 (Public Reviews):

The study by Setogawa et al. aims to understand the role that different striatal subregions belonging to parallel brain circuits have in associative learning and discrimination learning (S-O-R and S-R tasks). Strengths of the study are the use of multiple methodologies to measure and manipulate brain activity in rats, from microPET imaging to excitotoxic lesions and multielectrode recordings across anterior dorsolateral (aDLS), posterior ventral lateral (pVLS)and dorsomedial (DMS) striatum. The main conclusions are that the aDLS promotes stimulus-response association and suppresses response-outcome associations. The pVLS is engaged in the formation and maintenance of the stimulus-response association. There is a lot of work done and some interesting findings however, the manuscript can be improved by clarifying the presentation and reasoning. The inclusion of important controls will enhance the rigor of the data interpretation and conclusions.

We appreciate the reviewer’s valuable feedback, which has been beneficial in our endeavor to improve our manuscript. In response to the comments, we have revised the description of the experimental methods and underlying rationale, as well as the Results section. We have also provided additional data for some of the experiments that support the conclusions. For more details, please refer to the responses to each comment, included below.

Reviewer #2 (Recommendations For The Authors):

Comment 1: Generally, the manuscript is hard to read because of the cumbersome sentence structure, overuse of poorly defined acronyms, and lack of clarity on the methods used.

According to the following comments (a)–(d), we have revised the corresponding text in the manuscript to clarify the sentence structure, definitions of terms, and methodology.

Comment 1 (a): For example, the single lever task used as a control for the auditory discrimination task could be introduced better, explaining the reasoning and the strategy for subtracting it from the images obtained during the discrimination phase at the start of the section.

We analyzed task-related activity by comparing ¹⁸F-FDG uptake on Days 2, 6, 10, or 24 of auditory discrimination task with that on Day 4 of the single lever press task. This task was used as a control that does not require a decision process based on the auditory stimulus. For clarification, we have provided a more detailed explanation of the flow of the single lever press task used in the PET experiment, including the rationale for employing this task as a control (page 6, lines 129–135). We have also revised the explanation of voxel-based statistical parametric analysis, adding a more detailed description of the thresholds (page 7, lines 143–145).

Comment 1 (b): Another example is that important methodological information is buried deep in the text and complicates the interpretation of the results.

We have revised the following sentences in the manuscript in order to provide clearer methodological information.

(1) As described above, explanations for the single lever task (page 6, lines 129–135) and voxel-based statistical parametric analysis were added (page 7, lines 143–145).

(2) Definition of the early, middle, and late stages were described in the initial behavioral experiment (page 6, lines 113–119).

(3) Abbreviations related to behavioral strategies (WSW and LSL) and electrophysiological events (CO, CR, RS, and FL) were replaced with the original terms.

Comment 1 (c): The specie being studied is not stated in the abstract, nor the introduction, and only in the middle of the result section. Please include the specie in the abstract and the first part of the result also for clarity.

We included the name of the species (rats) in the Abstract (page 3, line 47), at the end of the Introduction (page 5, lines 87–88) and at the beginning of the Results (page 5, line 109).

Comment 1 (d): The last part of the intro is copied/pasted from the abstract. Please revise.

The last part of the Introduction was revised accordingly (page 5, lines 97–104).

Comment 2: The glucose microPET imaging is carried out 30 mins after the rats performed the task and it is expected to capture activation during the task. Is this correct? This assumption has to be validated with an experiment, which is a control showing a validation of the microPET approach used, and this way can report activation of brain areas during the task completed 20-30 minutes before. For example, V1 or A1 would be a control that we would expect to be activated during the task.

Our PET experiment was conducted in accordance with previously established methods (Cui et al, Neuroimage, 2015), where rats received intravenous administration of ¹⁸FFDG solution just before the start of the behavioral session, which lasted for 30 min. The ¹⁸F-FDG uptake in the brain starts immediately and reaches the maximum level until 30 min after the administration, and the level is kept at least for 1 h (Mizuma et al., J Nucl Med, 2010). The rats were returned to their home cages, and a 30-min PET scan started 25 min after the session. The start time of the scan was chosen to allow for sufficient reduction of 18F radioactivity in arterial blood to increase the S/N ratio of the radioactivity (Mizuma et al., J Nucl Med, 2010). As shown in Table S1, we confirmed that the brain activity in the medial geniculate body (auditory thalamus) was increased on Days 6 and 10 in the acquisition phase, although the activity in the auditory cortex was not changed, which is consistent with the results of a previous study reporting that the auditory cortex does not show the causality for the pure-tone discrimination task (Gimenez et al., J Neurophysiol., 2015).

Comment 3: Why are Days 2, 6, 10, and 13 chosen and compared for the behavior? Why aren't these the same days chosen in the other part of the study? It is unclear why authors focused on these days and why the focus changed later.

We conducted daily training of the discrimination task. The success rate reached a plateau on Day 13 and was maintained until Day 24 (Figure 1B). Based on these results, we categorized the learning processes into the acquisition and learned phases, and then divided the acquisition phase into the early (< 60%), middle (60–80%), and late (> 80%) stages. In the PET experiment, we selected Days 2, 6, and 10 as the representatives of each stage during the acquisition phase. In addition, we also selected Day 24 for the learned phase. However, no scan was performed on Day 13 due to the transition between the two phases.

Comment 4: (A) Is the learning and acquisition of the single lever press and discrimination task completed by day 4? Or are rats still learning? The authors claimed no changes in DMS activity between single lever press & discrimination, and therefore DMS isn't involved in learning. But to make this claim we should have measures that the learning has already happened, which I am not sure have been provided. (B) On this same point, the DMS activity is elevated on Day 4 of a single lever press compared to the aDLS and pVLS. So is it possible that the activity in DMS was already elevated on Day 4 of single lever press training? Especially given that DMS is supposedly involved in goal-directed behavior?

(A) In the single lever press task, the number of lever presses plateaued on Day 2 (Figure 1C). In addition, we analyzed response time and its variability, which plateaued from Day 3 and Day 2, respectively (see Author response image 1). These results indicate that the learning in the task was completed by Day 4. In the auditory discrimination task, Day 4 corresponded to the transition period from the early-tomiddle stages of the acquisition phase, suggesting that learning was still progressing.

In the imaging analysis, we examined task-related activity by comparing ¹⁸F-FDG uptake on either day of the discrimination task with that on Day 4 of the single lever press task, and did not find any changes in the brain activity in the DMS. In addition, we investigated learning-related activity, and the DMS activity did not change during acquisition phase. These results suggest that the DMS is not involved in the acquisition phase of learning. Furthermore, comparisons between Days 10 and Day 24 showed a decrease in DMS activity during the learned phase, suggesting that DMS activity was downregulated during the learned phase. In addition, chronic lesion in the DMS indicated that the success rate in the discrimination task was comparable between the control and lesioned groups (Figure 3I), whereas the response time lengthened throughout the learning in the lesioned group compared to the controls (Figure S1C). These results support our notion that the DMS contributes to the execution, but not learning, of discriminative behavior (Figure 3I and S1C).

Author response image 1.

Performance of single lever press task conducted before auditory discrimination task. (A) Number of lever presses. (B) Response time (Kruskal-Wallis test, χ² = 38.063, p = 2.7 × 10^-8, post hoc Tukey–Kramer test, p = 0.047 for Day 1 vs. Day 2; p = 2.3 × 10^-7 for Day 1 vs. Day 3; and p = 4.0 × 10^-6 for Day 1 vs. Day 4; p = 0.019 for Day 2 vs. Day 3; p = 0.082 for Day 2 vs. Day 4; p = 0.951 for Day 3 vs. Day 4). (C) Response time variability (Kruskal-Wallis test, χ² = 28.929, p = 2.3 × ^-6, post hoc Tukey–Kramer test, p = 0.077 for Day 1 vs. Day 2; p = 5.7 × 10^-6 for Day 1 vs. Day 3; and p = 1.3 × 10^-4 for Day 1 vs. Day 4; p = 0.060 for Day 2 vs. Day 3; p = 0.253 for Day 2 vs. Day 4; p = 0.912 for Day 3 vs. Day 4). Data obtained from the task shown in Figure 2C are plotted as the median and quartiles with the maximal and minimal values. *p < 0.05, **p < 0.01, and ***p < 0.001.

(B) We compared ¹⁸F-FDG uptakes among striatal subregions on Day 4 of the single lever press task (334.8 ± 2.86, 299.0 ± 1.71, and 336.8 ± 2.18 for the aDLS, pVLS, and DMS, respectively; one-way ANOVA, F[2,41] = 104.767, p = 2.1 × 10^-16). The uptake was comparable between the aDLS and DMS (post hoc Tukey-Kramer test, p = 0.058), but it was significantly lower in the pVLS compared to either of the other two subregions (post hoc Tukey-Kramer test, aDLS vs. pVLS, p = 5.1 × 10^-9, post hoc Tukey-Kramer test, pVLS vs. DMS, p = 5.1 × 10^-9). However, since we did not measure the brain activity in the single lever task outside of Day 4, it is unclear whether there was an increase in DMS activity during the acquisition of the task. Similarly, since we did not confirm the behavioral modes, which include goal-directed and habitual actions, it is difficult to conclude that the lever presses in the task were controlled by the goaldirected mode. However, our chronic lesion experiment suggests that the DMS is involved in the execution of discrimination behavior (Figure S1C). A clearer understanding of the DMS function in discrimination learning is an important challenge in the future.

Comment 5: It seems like the procedure of microPET imaging affects performance on the task. The anesthesia used maybe? Figures 2C and D show evidence that the behavior was negatively affected on the days on which microPET imaging was performed after the training. Can the author clarify/comment?

Isoflurane anesthesia may slightly reduce behavioral performance. We carried out anesthesia (median [interquartile range]: 6 [5–8] min) during the insertion of the catheter for FDG injection, and set a recovery period of at least 2 h until the beginning of the behavioral session, to minimize the impact of anesthesia. The performances in Figure 2E were similar to those in the intact rats (compared to Figures 1C–1F), suggesting that the procedure for PET scans does not affect the acquisition of discrimination.

We have added detailed information on the isoflurane anesthesia to the Methods section (page 26, lines 649–653).

Comment 6: More on clarity. Section 3 of the results (muscimol inactivation) refers a lot to "the behavioral strategies" without really clarifying what these are - are they referring to WSW / LSL (which also could use a better introduction) or goal-directed/habitual or stimulus-response/stimulus-outcome?

The dorsal striatum is involved in both behavioral strategies based on stimulus-response association and the response-outcome association during instrumental learning. To assess the impact of striatal lesions on the behavioral strategies, we analyzed the proportion of response attributed to two strategies in all responses of each session. One is the “win-shift-win” strategy, which is considered to reflect the behavioral strategy based on the stimulus-response association. In this strategy, after a correct response in the previous trial, the rats press the opposite lever in the current trial in response to a shift of the instruction cue, resulting in the correct response. Another strategy is the “lose-shift-lose” strategy, which is considered to appear as a consequence of the behavioral strategy based on the response-outcome association. In this strategy, after an error response in the previous trial, the rats press the opposite lever in the current trial despite a shift of the instruction cue, leading to another error response.

We have revised the explanations of the behavioral strategies in the section of the Results section (page 9, lines 192–201).

Comment 7: Related to WSW / LSL needing a better introduction, on lines 192/193 authors describe a result where they saw the WSW and LSL strategies increase and decrease, respectively, in saline-injected mice. Is the change in performance expected or an undesired effect of the saline injection? This is not clear now and it should be clarified.

The explanations of the win-shift-win and lose-shift-lose strategies have been revised in the Results section on excitotoxic lesion experiment (page 9, lines 192–201) as described in our response to Comment 6. Win-shift-win is an indicator of correct responses, while lose-shift-lose indicates errors. Therefore, win-shift-win is predicted to increase, and lose-shift-lose decrease, as discrimination learning progresses. Indeed, in the results of the behavioral experiments, shown in Figure 1, both indicators change in a similar pattern to those in the results of the lesion experiments (Figure 3).

We have added the explanation of the proportions of both strategies in intact rats (page 9, lines 203–204) with a supplementary figure (Figure S2) and accompanying legend (page 56, lines 1173–1177).

Comment 8: Muscimol experiments - two questions/comments. How often do rats receive muscimol?

In this section, muscimol is given on day 2 and on days after the animals hit a 60% or 80% success rate. Can the authors provide a mean and SEM for when are those injections?

The first injection was conducted on Day 2 to target the early stage. The second and third injections were conducted on the days after the success rate had reached 60% and 80% for the first time through the training, respectively, to target the middle and late stage. respectively. These conditions are described in the Results (page 10, lines 234– 237) and Methods (page 26, lines 633–636). The mean and s.e.m. of the injection day at the middle and late stages were not significantly different between the saline and muscimol-injected groups into the aDLS (see Author response image 2A) and pVLS (see Author response image 2B).

Author response image 2.

Injection days during auditory discrimination learning.

Injections with saline (SAL) and muscimol (MUS) into the aDLS (A) or pVLS (B) were performed after the success rate had reached 60% (middle stage) and 80% (late stage) for the first time through the training, respectively (A, Wilcoxon signed rank test, middle, Z = 65, p = 0.772, late, Z = 56.5, p = 0.242 for the aDLS; B, Wilcoxon signed rank test, middle, Z = 39, p = 1.000, late, Z = 43, p = 0.587). Data are indicated as the median and quartiles with the maximal and minimal values.

Comment 9: Muscimol experiments. Can the authors comment on the effects on performance vs learning? What happens on the days after Muscimol? Does performance bounce back or is it still impaired?

We conducted a transient inhibition experiment with muscimol to examine whether the neuronal activity in the striatal subregions is linked with the processes at different stages. In this experiment, to lower the possibility that compensation of learning may occur during a session after the muscimol injection (Day N), we limited the session time to 15 min (45 trials) and evaluated the impact of the injection on the success rate at specific stages. The success rate in the muscimol-injected groups into the aDLS significantly decreased at the middle stage compared to the corresponding salineinjected groups, but not at the early and late stages (Figure 4C), and the rate in the muscimol groups into the pVLS significantly decreased at the late stage compared with the respective saline groups, but not at the early and middle stages (Figure 4D). Our results demonstrated that the aDLS and pVLS mainly function at the middle and late stages of the auditory discrimination task, respectively.

In addition, we here reply to comment 10 as for the comparison of success rates before (Day N-1) and after (Day N+1) the injections (see Author response image 3). We focused on two injections into the aDLS at the middle stage and into the pVLS at the late stage, in which the rate was reduced soon after the muscimol injection on Day N. The success rate for the two injections showed no significant main effect regarding group (saline/muscimol) or day (Days N-1/N+1) and no significant interactions for group × day. Moreover, the success rate was not significantly increased on Day N+1 as compared to Day N-1, even in the saline-injected control group, probably because of the limited session time soon after the injection. Therefore, we consider that it was difficult to define the effects of drug injection on the learning of auditory discrimination in our behavioral protocol for the transient inhibition experiment, and that the reduced rates observed in the muscimol-injected group on Day N mostly reflect the impacts of muscimol at least partly on the performance of discriminative behavior.

Author response image 3.

Comparison of success rate between days before (Day N1) and after (Day N+1) the injections into striatal subregions. Success rate in the saline (SAL)- and muscimol (MUS)-injected groups into the aDLS (A) or pVLS (B) at the early, middle, and late stages of auditory discrimination learning (two-way repeated ANOVA; early, day, F[1,14] = 5.266, p = 0.038, group, F[1,14] = 0.276, p = 0.608, day × group, F[1,14] = 0.118, p = 0.736; middle, day, F[1,14] = 4.110, p = 0.062, group, F[1,14] = 0.056, p = 0.816, day × group, F[1,14] = 1.150, p = 0.302; late, day, F[1,14] = 6.408, p = 0.024, group, F[1,14] = 0.229, p = 0.640, day × group, F[1,14] = 1.277, p = 0.278 for the aDLS; and early, day, F[1,10] = 0.115, p = 0.746, group, F[1,10] = 2.414, p = 0.151, day × group, F[1,10] = 0.157, p = 0.700; middle, day, F[1,10] = 0.278, p = 0.610, group, F[1,10] = 0.511, p = 0.491, day × group, F[1,10] = 4.144, p = 0.069; late, day, F[1,10] = 0.151, p = 0.705, group, F[1,10] = 0.719, p = 0.416, day × group, F[1,10] = 0.717, p = 0.417 for the pVLS). Data are indicated as the mean ± s.e.m.

Comment 10: Muscimol data has a pair before and after, can the authors show this comparison at early, middle, and late training? Not just the subtraction.

The comparison of success rates before and after drug injection is shown in Author response image 3.

Comment 11: Ephys recordings. These are complex figures and include a large number of acronyms. It would help to define them again and help the reader through these figures so the reader can focus on understanding the finding more than the figure presentation.

We replaced the abbreviations related to electrophysiological events (CO, CR, RS, and FL) with the original terms, and improved the explanation in the text and figures.

Comment 12: Figure 7B/E - on correct trials, they see a difference in the cue response to high tone / low tone but no difference in the choice. This is the one that seemed like a topography issue.

The transient activity of cue onset-related neurons in the pVLS did not appear at the early stage of learning, but was observed in a learning-dependent manner (Figures 7A and S8E). In addition, the cue onset-HR activity showed a slight but notable difference between the HR and LL trials at the middle and late stages (Figure 7B), whereas there was no difference between activities in the HL and LR incorrect trials at the corresponding stages (Wilcoxon signed rank test; early, p = 0.375, middle, p = 0.931, and late, p = 0.668). These results suggest that the cue onset-related neurons in the pVLS represents the stimulus and response association (task contingency) rather than the topography of tone frequency.

Comment 13: Animals were normally trained for 60 minutes but on muscimol days only trained for 15 mins. On PET days only trained for 30 minutes. Ephys sessions were 60 mins. Is this correct? Why?

We determined the session time for each experiment by considering both technical and behavioral aspects. In the initial behavioral experiment, the session time was set to 60 min per day. Under this condition, the rats acquired the discrimination learning within 13 days. In the imaging experiment, the session without a PET scan was conducted for 60 min, while the session with a PET scan was carried out for 30 min as described previously (Cui et al, Neuroimage, 2015). This time schedule produced a learning curve similar to that of the initial behavioral experiment. In the transient inhibition experiment, the sessions without drug injections lasted for 60 min. As described in our response to the comment 2, the time of the session soon after the injection was limited to 15 min to lower the possibility of compensation of learning during the session. In the chronic lesion and electrophysiological experiments, all sessions were conducted for 60 min, corresponding to the initial experiment.

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