Spatially heterogeneous acetylcholine dynamics in the striatum promote behavioral flexibility

Domain: neuroscience
Published: 2025-12-17
Editorial outcome: major_revision

Abstract
Being able to switch from established choices to new alternatives when conditions change – behavioral flexibility – is essential for survival. Cholinergic signaling in the striatum contributes to such flexible behavior, yet the timing and spatial organization of acetylcholine release during contingency changes remain unclear, limiting conceptual understanding of its role in behavioral flexibility. Using a genetically encoded acetylcholine sensor and 2-photon imaging in the dorsal striatum of behaving mice, we visualized acetylcholine dynamics during acquisition and reversal learning in a virtual reality Y-maze. Rewarded outcomes evoked phasic decreases in acetylcholine, whereas unexpected non-reward following reversal triggered widespread increases that predicted lose-shift behavior. Targeted inhibition of cholinergic interneurons reduced this adaptive response. Spatial analysis revealed heterogeneous, temporally distinct signals forming functionally diverse microdomains. These findings suggest that widespread and focal acetylcholine release during unexpected outcomes promotes adaptive response shifts, offering a mechanistic framework for understanding disorders such as addiction and obsessive-compulsive rituals.

Introduction
Flexible behavior is essential for survival in changing environments. It requires ceasing previously reinforced responses to explore alternatives and involves multiple brain regions, such as the prefrontal cortex and the striatum. Here, we define behavioral flexibility as the ability to adjust behavior in response to changes in environmental contingencies. Thus, behavioral flexibility requires adopting a new strategy, which is a step beyond learning under constant rules. Cholinergic interneurons (CINs), the prime source of acetylcholine (ACh) in the striatum, are important for behavioral flexibility. Although striatal cholinergic signaling has been implicated in flexible behaviors, the precise timing and spatial patterns of striatal ACh release during reversal learning, a paradigm of behavioral flexibility, remain uncharacterized. In particular, high-resolution measurements of ACh spatio-temporal dynamics during behavioral tasks requiring flexible responses have been lacking.

To address this gap, we monitored striatal ACh dynamics in awake, head-fixed mice performing a virtual-reality response learning (VR-RL) task during initial acquisition and reversal learning, where reversals introduced unexpected non-reward. Using two-photon microscopy through a GRIN lens implanted over the dorsal striatum (DS) and a genetically encoded fluorescent ACh sensor, we visualized ACh release with high temporal and spatial precision. Following reversal, unexpected non-reward evoked widespread increases in ACh release, characterized by distinct spatiotemporal dynamics that predicted lose-shift behavior. These increases were preferentially more medially distributed and showed a trend toward the anterior portions of the striatum. Spatial tiling analysis of 2-photon images revealed heterogeneous and temporally distinct ACh signals across the dorsal striatum, identifying functionally diverse microdomains with outcome-specific response profiles. We further investigated the role of striatal CINs and found that chemogenetic inhibition of these neurons disrupted the adaptive shift in behavior following reversal. Together, these findings elucidate the spatiotemporal organization of striatal ACh signaling and demonstrate its essential role in adaptive decision making and flexible responses.

Results
Head-fixed mice develop a preference for the rewarded arm in a virtual Y-maze task
We first confirmed that awake, head-fixed mice could learn to choose the rewarded arm in a virtual reality Y-maze. The rewarded arm of the maze was not marked by cues, and mice had to explore both arms of the maze and learn by trial-and-error which arm was rewarded. A correct choice triggered reward delivery immediately after entry into the outcome zone. An incorrect choice triggered a black screen, signifying no reward. Mice were trained in daily sessions of ~40 trials until they reached the acquisition criterion of 80% correct choices in at least one session, after which the rewarded side was reversed.

Over successive sessions preceding the reversal, mice progressively increased the proportion of correct choices, indicating successful acquisition of the rewarded arm contingency. In the late learning phase, defined as the final session before reversal, mice achieved an average correct choice rate of 82.1% ± 0.9% (mean ± S.E.M., P = 3.81 × 10−5, n = 11 mice). Across sessions, mice displayed licking movements while approaching the reward location, suggesting anticipation of the reward. Increases in directional preference in anticipatory licking were positively correlated with advancement in the maze (simple linear regression, Pearson’s r = 0.73, P = 0.04, n = 11), signifying that mice had learned the maze structure and accurately predicted the reward location. Across the population, trained mice showed reduced trial latencies and increased velocity when approaching the maze arms, indicating heightened motivation and reward-driven decision-making. Average running velocity increased from early to late sessions, with a trend toward higher velocities in rewarded trials, suggesting that motivational drive strengthens with task experience (main effect of session (F(1,20) = 11.69, P = 0.0027); main effect of outcome (F(1,20) = 0.17, P = 0.68); session × outcome interaction (F(1,20) = 0.28, P = 0.60), repeated measures ANOVA). Similarly, anticipatory licking was robustly driven by reward outcome across learning, with a trend toward higher overall levels in late sessions (main effect of outcome (F(1,20) = 5.14, P = 0.03); main effect of session (F(1,20) = 3.51, P = 0.076); session × outcome interaction (F(1,20) = 3.78, P = 0.066). The overall increase in locomotor velocity and reduced trial latencies (two-tailed, paired t test, P = 0.001) resulted in a higher reward rate across sessions (two-tailed, paired t test, P = 0.003).

Dorsal striatal ACh transients contain reward-related signals
We observed the time course of ACh transients in response to trial outcomes during maze learning using in vivo two-photon imaging. Adult mice (n = 11) received stereotactic injections of adeno-associated virus (AAV) expressing the ACh sensor iAChSnFR under control of the human synapsin-1 promoter with Tet trans-activator amplification into the DS and had a 1 mm diameter gradient index (GRIN) lens implanted above the injected region (see Methods). iAChSnFR is derived from a microbial periplasmic binding protein (PBP), and like other sensors derived from PBPs has rise and decay kinetics on a millisecond time scale, thus permitting useful imaging at >1 kHz. Previous studies using purified iAChSnFR and stopped-flow experiments confirmed that activation has a time constant of ~140 ms at 10 μM ACh and just a few ms at 1 mM ACh, with kon of 0.62 μM−1s−1 and koff of 0.73 s−1.

Two-photon images revealed expression of iAChSnFR in the striatum, on the surface of somata as well as neuropil. Sensitivity of iAChSnFR to ACh was separately confirmed by fluorescence responses to exogenously added ACh in striatal slices from injected mice. To validate sensor specificity, we first applied exogenous ACh to brain slices in ACSF, followed by application of neostigmine (50 µM), an acetylcholinesterase inhibitor, 15–20 min later, which blocked the response to ACh (n = 4 mice). We utilized in vivo imaging at 30 Hz frame rate to measure the time course of spatially averaged ACh levels during the outcome period (time window, 0-1 s) after rewarded correct or unrewarded incorrect outcomes. Correct choices followed by reward outcomes caused transient decreases in the fluorescence of iAChSnFR, indicating dips in ACh in response to reward. The features of these dips in ACh levels (dip amplitude, −1.64 ± 0.07 z-scored ΔF/F; onset, 0.23 ± 0.01 s; dip latency i.e., time to dip, 0.48 ± 0.03 s, mean ± S.E.M., n = 11) are consistent with pauses in tonic activity of striatal tonically active neurons (TANs, putative CINs) and similar to dip latencies observed in ACh release. This timing is also consistent with the time course of pauses in firing activity of CINs (onset around 0.16–0.20 s and duration 0.20–1.0 s) when combined with the kinetics of ACh diffusion and hydrolysis.

In contrast to rewarded outcomes, unrewarded incorrect outcomes evoked dips significantly smaller than those evoked by reward (P = 1.65 × 10−5; dip amplitude, −0.89 ± 0.10 z-scored ΔF/F; onset, 0.25 ± 0.03 s; and latency, 0.65 ± 0.06 s, mean ± S.E.M.; two-tailed, paired t test, P = 0.03, n = 11). We also noted some trial-to-trial variability in reward responses, which was greater than measurement noise (confirmed by comparison with ACh-insensitive tdTomato signals). Notably, tdTomato was imaged in a subset of trials and did not display the large positive-going transients observed with iAChSnFR, supporting the conclusion that our reported ACh signals reflect genuine cholinergic activity rather than measurement noise. These fluctuations in ACh may encode within-session variations in behavioral contexts as perceived by the mouse, potentially reflecting dynamic recruitment of different cholinergic response modes depending on contextual or internal state factors. As mice approached the outcome point of the maze, spatially averaged ACh signals increased progressively until the mouse reached the outcome zone. The increase was similar in rewarded and non-rewarded outcomes. Across trials, ACh signals were classified according to whether linear regression analysis showed a significant positive or negative correlation during the last 5 s preceding the trial outcome. The greatest proportion of rewarded and unrewarded trials showed positive ramping (52% and 48%, respectively), and less commonly negative (32% and 31%, respectively) or non-significant changes. These distributions were not statistically different across conditions (X2 test: X2(2) = 1.52, P = 0.46).

Spatiotemporal patterns of functionally heterogeneous ACh transients in DS
Spatiotemporal analysis of ACh dynamics from two-photon imaging revealed heterogeneous release patterns linked to distinct behavioral outcomes. Although traditionally considered a volume conducted signal, recent findings indicate more spatially localized release of ACh. By tiling the field of view into a matrix of quadrats (~50×50 μm), we uncovered spatially heterogeneous patterns of task-related ACh transients in the DS, previously unexplored in this context. To characterize the release patterns, we performed hierarchical clustering of feedback responses to reward and no-reward outcomes. Hierarchical clustering provides an unsupervised and unbiased, heuristic framework to capture local similarities and to visualize graded relationships, even in the absence of sharply discrete groups. Four clusters were identified (see Methods for details), of which the response profiles are shown. The clustering approach revealed gradations in signal morphology rather than sharply discrete groups (silhouette coefficient mean 0.21, range, −0.55–+0.64 for reward clusters, and mean 0.15, range, −0.44–+1.0 for no-reward clusters) indicating weak separation between clusters lying along a continuum. This is consistent with the fact that the concentration measures in the analysis are from physically adjacent quadrats along continuous concentration gradients. These clusters provide a heuristic grouping that captures local similarity in response patterns.

The activity patterns of quadrats recorded from the DS were grouped based on their responses and temporal release profiles. Reward delivery commonly caused brief decreases in ACh activity (Cluster 1; 60.1%, 1488/2475). Increases were uncommon: a subset (Cluster 2; 8.2%, 202/2475) showed an increase followed by a decrease, and another subset (Cluster 3; 8.2%, 204/2475) showed a decrease followed by a rebound burst in ACh levels. Activity patterns other than the above were few in proportion (Cluster 4; 2.3%, 56/2475). In addition, quadrats exhibiting decreased (pause) and burst-pause response patterns were more medially distributed (P = 0.05 and P = 0.04, respectively) and showed a trend toward anterior localization (median position, ~0.9 mm from bregma). In contrast, quadrats exhibiting pause-burst response patterns were preferentially distributed in relatively more posterior regions (P = 0.005) of the DS. Notably, while four distinct ACh response patterns were identified during late learning, only two primary response clusters emerged during early learning.

In contrast to reward responses, no-reward outcomes following incorrect choices elicited brief decreases in activity in a moderate subset of quadrats (Cluster 1; 32.9%, 815/2475). Overall, the proportions of the observed response types indicated differential ACh dynamics depending on trial outcome. We further analyzed the reward and no-reward feedback responses of identified quadrats. We found significant differences in response amplitude (z-scored ACh dip; two-tailed, unpaired t test, P = 1.40 × 10−43) and temporal characteristics (time to dip; P = 6.44 × 10−69). As previously noted, iAChSnFR is very fast, so the timing of these dips is reflective of ACh dynamics, not a sensor artifact.

ACh encodes bidirectional signals in response to contingency reversal
Striatal CINs have been implicated in behavioral flexibility. To investigate how ACh activity relates to adaptive switching behavior, the rewarded arm of the maze was reversed after mice achieved at least 80% correct performance in a session. This reversal of contingencies occurred without external cues. Following reversal, mice initially persisted in selecting the previously rewarded arm, resulting in a pronounced decline in correct choices and variability in their reversal learning rates (average percent correct was 46.7% ± 4.6%, mean ± S.E.M.). Across animals, the proportion of perseverative errors was 17.1% ± 2.7% (reversal session, R1; range of 10.0%–39.4%, mean ± S.E.M., n = 409 trials from 11 mice). Across learning (“early” vs “late” stages), mice exhibited marked differences in measures of reward anticipation (licking rate) and goal approach behaviors (i.e., run velocity), which ultimately resulted in gradual increases in selection of the new rewarded arm. These observations indicated that mice successfully adapted to the change in contingencies.

Simultaneous recordings of ACh activity and behavioral responses revealed distinct signaling patterns for rewarded and unrewarded outcomes after reversal. Correct responses – reflecting successful adaptation to the new contingency – were associated with a rapid, phasic decrease in ACh activity following reward delivery. In contrast, incorrect responses were associated with a gradual increase in spatially averaged ACh levels. Across animals, these responses differed significantly (two-tailed, paired t test, P = 1.40 × 10−3, n = 11). We propose that when a previously rewarded action unexpectedly fails to yield a reward, ACh release signals the condition of unexpected non-reward – reflecting a state change in outcome expectation. Thus, the ACh signal in response to non-reward is proposed to be specific to when a previously correct response, without warning, ceases to be rewarded.

ACh transients in DS predict adaptive reversal after unexpected non-reward
We hypothesized that ACh elevations following unexpected non-reward promote adaptive shifts in behavior. To test this, we examined trial-by-trial relationships between ACh activity and subsequent choice patterns. The cholinergic signal evoked by unexpected no-reward was more pronounced on trials that preceded a behavioral switch than on those followed by a stay response. Mice with stronger ACh responses exhibited a greater tendency to switch behavior following unexpected non-reward (lose-shift), whereas those with comparably weaker ACh responses were more likely to repeat the same choice (lose-stay) (Pearson’s r = 0.67, P = 0.03, n = 11). To rule out potential confounds related to trial timing after reversal, we compared lose-shift and lose-stay trials within matched post-reversal trial windows (first and last five trials for each condition). ACh responses remained heightened for lose-shift trials, indicating that this effect was not solely driven by when the trials occurred. We further observed that certain response profiles—particularly those with delayed peaks—occurred more frequently during early reversal trials. Together, these findings indicate that both the direction and magnitude of ACh activity during non-reward predict future behavioral shifts.

Comparison of ACh responses to non-reward before and after reversal within the same animals revealed a significant increase in the spatially averaged ACh signal during reversal relative to non-reward in task acquisition phases (early learning and pre-reversal periods). This enhancement suggests that ACh signaling becomes more spatially widespread and pronounced when behavioral switching is required. Notably, modest ACh increases observed during non-reward in the acquisition phase, localized to subsets of quadrats, may represent a preexisting substrate for outcome-related cholinergic modulation that is amplified and more broadly recruited under heightened demands for behavioral flexibility.

The increase in ACh activity following unexpected non-reward could, in principle, reflect motor-related orienting responses. In our setup, however, mice were head-fixed and therefore unable to perform the postural or head movements typical of orienting behaviors. Although limited orienting could be expressed through locomotor changes, analysis of locomotor velocity on a trial-by-trial basis revealed no significant correlation between mean ACh signals and velocity during the outcome period. Mice typically decelerated or stopped during this period, with near-zero velocity in the same time window when ACh levels decreased during task acquisition but increased after reversal. We also observed a gradual decline in anticipatory licking following unexpected non-reward, suggesting reduced reward expectation (on tries to the previously rewarded arm) as mice adapted to the new contingency. These behavioral adjustments followed the elevated ACh activity preceding lose-shift decisions, arguing against movement as a primary driver and supporting a role for ACh in feedback-driven behavioral updating, rather than a reactive motor signal.

It is also possible that the long-latency ACh increases reflect attentional shifts rather than learning-related feedback. However, if primarily driven by attention, such signals would be expected to occur more uniformly across task epochs, particularly during initial learning when attentional load is high. Instead, the magnitude and timing of these responses correlated specifically with behavioral switching after unexpected non-reward, particularly during lose-shift trials. This relationship between ACh activity and adaptive switching supports a role in learning from outcome violations, as indicated by the pre-outcome anticipatory responses to the now-unrewarded arm. Moreover, the ACh increases emerged selectively in the context of contingency reversals and were absent during earlier learning phases. Together, these findings indicate that the enhanced ACh responses to unexpected non-reward convey a teaching signal that promotes adaptive behavioral updating, rather than merely reflecting motor or attentional engagement.

Spatially resolved representations of future switch/stay responses
The full-field ACh responses described above represent spatially localized variations in ACh, i.e., combining all quadrats within the imaging field into an average measure. To resolve and characterize local spatiotemporal dynamics, we next examined ACh activity at the level of individual quadrats and how these were modulated by trial outcomes after reversal. Local ACh responses during the outcome period were heterogeneous, with most quadrats exhibiting bidirectionally distinct activities for reward and non-reward outcomes.

Following unexpected non-reward after reversal, ACh activity increased in the majority of quadrats (NR+; 85.3%, 2112/2475). A smaller subset showed decreases (NR-; 4.5%, 111/2475) or no significant change (non-selective, 10.2%, 252/2475). In contrast, rewarded outcomes primarily evoked decreases in ACh (R-; 80.6%, 1996/2475), whereas only a fraction showed increases (R+; 2.7%, 66/2475) or no response (16.7%, 413/2475). Overall, ACh increases were predominantly associated with post-reversal unrewarded outcomes, reinforcing the conclusion striatal ACh release encodes negative outcomes during contingency changes.

Spatial mapping of quadrat responses along the mediolateral and anteroposterior axes revealed distinct topographical organization during unexpected non-reward. Quadrats with ACh increases were significantly more medially distributed (simple linear regression, r = −0.55, P = 0.03; median position, 1.4 mm from midline) and showed a trend toward anterior portions (median position, 0.9 mm from bregma). Conversely, quadrats exhibiting decreases were relatively more laterally distributed within the dorsal striatum.

To assess how individual quadrats encoded both outcomes, we further classified them by their responsiveness to reward and non-reward events. This analysis revealed marked spatial heterogeneity. A large proportion of quadrats displayed opposing responses to the two outcomes (R- x NR+; 69.7%), while smaller fractions were selectively responsive to reward (R- only; 7.5%) or to no-reward (NR+ only; 13.0%). These findings demonstrate that striatal ACh activity encodes multiple reinforcement contingencies within spatially distinct microdomains, consistent with prior observations that cholinergic signaling is strongly context-dependent.

Given our finding that ACh fluctuations predicted behavioral switching after reversal, we next examined whether similar patterns were evident at the quadrat level. Indeed, microscale analysis revealed comparable effects: a substantial fraction of quadrats significantly encoded future switch responses following non-reward, in line with our observation of future switch encoding after contingency change (for definition of switch and stay encoding quadrats, see Methods). Most quadrats exhibited greater increases in activity preceding future switching (switch+, 62.1%) than staying (stay+, 44.8%). A notable subset was active preceding both switch and stay trials (34.0%; switch+ x stay+), while smaller populations were selectively active preceding switching (16.0%; switch+ only) or staying (7.6%; stay+ only). The responses of these functional subtypes are shown, where certain quadrats differentially encoded future choice direction, whereas others showed comparable activity regardless of the anticipated choice.

To further characterize the organization of these functionally distinct populations, we computed the mean activity of individual quadrats and projected them onto a two-dimensional space according to the magnitude and sign of their outcome responses. This analysis revealed distinct populations of quadrats selectively tuned to future behavioral responses following non-rewarded outcomes, underscoring the multifaceted and spatially heterogeneous nature of striatal cholinergic signaling. Collectively, these results indicate that, at the microscale level, ACh activity encodes information predictive of both future switching and staying behaviors. Such heterogeneous response patterns may represent a substrate through which ACh modulates adaptive decision-making.

To correct for spherical aberrations inherent in GRIN lens imaging, which results in a lower baseline fluorescence signal (Fo) at the edges of the field of view compared to the center, we used ΔF/F normalization, which scales the signal relative to Fo. To ensure that this normalization effectively accounts for baseline intensity differences and that our main findings are not simply driven by expression gradients or brightness alone, we assessed whether ΔF/F values were dependent on Fo. We found no significant correlation between the mean of the behavior-linked signals and baseline fluorescence across quadrats. Thus, regions with lower overall brightness still exhibited robust ΔF/F responses. We also verified that quadrats in peripheral and central areas show similar patterns of behavioral modulation. These analyses indicate that our results are not biased by location within the field of view.

Chemogenetic inhibition of ACh-releasing neurons in DS disrupts lose-shift behavior
To test whether the increased acetylcholine we observed after unexpected non-reward caused the associated lose-shift behavior, we inhibited CIN activity using the designer receptor exclusively activated by designer drug (DREADD) approach. Injection of AAV expressing Cre-dependent hM4D(Gi)-mCherry bilaterally into the DS of ChAT-Cre mice produced hM4D(Gi)-mCherry expression in cell bodies of CINs. After mice reached an acquisition criterion of 80% correct performance, each mouse received an injection of either JHU37160 dihydrochloride (J60; a high-affinity DREADD agonist, n = 5 mice) or vehicle (buffered saline, n = 5 mice), and then commenced reversal learning.

CIN inhibition caused a significant decrease in the percentage of correct choices during early reversal sessions, with no effect observed in late sessions (P = 0.028, Cohen’s d = (−)1.54, 95% CI [−2.98, −0.09], early and P = 0.11, Cohen’s d = (−)1.01, 95% CI [−2.35, 0.32], late, two-tailed, unpaired t test). Following the reversal, saline-treated mice displayed an accelerated increase in correct choice performance. Cumulative errors over the first 80 trials post-reversal were higher in CIN-inhibited mice, reflecting impaired overall task adaptation beyond specific error types. Lose-shift behavior was significantly reduced by CIN inhibition (two-tailed, unpaired t test, P = 0.015, Cohen’s d = (−)1.77, 95% CI [−3.27, −0.26]). However, CIN inhibition effects on win-stay probability following the reversal were not significant (P = 0.14, Cohen’s d = (−)0.94, 95% CI [−2.27, 0.38]). The impairment in lose-shift behavior led to an increased number of trials required to reach the performance criterion (63 ± 14 and 115 ± 11 trials, mean ± S.E.M.; saline and J60-treated mice, respectively, P = 0.019, Cohen’s d = 1.67, 95% CI [0.19, 3.15]). While the difference in perseverative errors was not significant (P = 0.47, Cohen’s d = 0.43, 95% CI [−0.82, 1.69]), a significant difference in the proportion of regressive errors was observed between the two groups (P = 0.007, Cohen’s d = 2.04, 95% CI [0.45, 3.62]).

Previous studies have proposed a role for striatal CINs in locomotion. Therefore, we sought to determine whether the observed differences were attributable to locomotor impairments. Our findings revealed that CIN inhibition did not affect running velocity (P = 0.72, Cohen’s d = 0.21, 95% CI [−1.03, 1.45]). Although our inhibition protocol lacked temporal specificity, the behavioral findings are consistent with the observed changes in ACh associated with lose-shift behavior. Combined, these results demonstrate that dorsal striatal ACh is essential for timely behavioral adaptation to changing reinforcement contingencies and suggest a role for ACh in accelerating the rate of learning during new learning processes.

Methods
Animals
All experimental protocols were performed in an AAALAC (Association for Assessment and Accreditation of Laboratory Animal Care) certified facility, reviewed and approved by the Animal Care and Use Committee (ACUC; protocol numbers ACUP-2021-018 and ACUP-2021-011-2) of Okinawa Institute of Science and Technology Graduate University (OIST). Male and female ChAT-IRES-Cre::SV40pA::Δneo (Chattm1(cre)Lowl; Stock No. 031661; Jackson Laboratories, Bar Harbor, ME, USA) mice (20–35 g, postnatal 3–4 months at the start of surgery) were used in all experiments. Until the start of surgical procedures, all mice were group-housed (3–5 mice/cage) and provided with food and water ad libitum under standard laboratory conditions (22–24°C, 40–60% humidity). To prevent damage to GRIN lens implants, experimental mice were housed individually after surgical implantations. All behavioral experiments were performed during the dark phase of a reverse 12 hr light/dark cycle, with darkness from 7:00 am to 7:00 pm. A week before the start of the virtual-reality experiments, mice were water-restricted and maintained at 85–90% of ad libitum weight. All mice were weighed and handled daily to ensure weight maintenance. For all behavioral sessions, the mice were transferred to the behavior testing room at least 30 min before the tests to allow them to acclimatize to the environment. The mice were given free access to water at least 1 h after the end of behavioral training. All experiments in this study were carried out using both male (n = 17) and female (n = 8) mice, but statistical analyses for sex differences were not performed because of limited sample sizes. Sex was not considered as a biological variable.

Stereotaxic surgery and viral vector injections
All surgical procedures were conducted under general anesthesia using isoflurane (3–4% for induction, 1–1.5% for maintenance; flow rate, 1.5 L/min). For acetylcholine imaging in the dorsal striatum, surgeries were conducted on adult mice under aseptic conditions. First, the mice were weighed and then placed in a plexiglas chamber filled with gaseous isoflurane to induce anesthesia. Anesthetized mice were immobilized in a stereotaxic frame (Kopf Instruments) with their heads secured by non-rupture earbars and their noses placed into a specialized nose cone for continuous inhalation of isoflurane. The body temperature was monitored and maintained at 36°C with a heating pad. The scalp was shaved, and a local anesthetic, lidocaine, was applied, after which a small incision was performed along the midline using a sterile scalpel. Then, the skull was exposed and the periosteum removed using a delicate bone scraper to assure clear viewing of the skull sutures. Afterwards, the bregma and lambda were then leveled to within 0.1 mm. A 0.8–1.2 mm diameter craniotomy was then drilled (centered around target coordinates for virus injection and GRIN lens implantation) on the skull above the dorsal striatum using a motorized drill. To achieve non-specific viral expression, an adeno-associated virus (AAV) encoding iAChSnFR under control of the human synapsin-1 promoter with Tet-Off amplification, AAV1-ihSyn-tTa-sv40/TRE-iAChSnFR-minWPRE (titer 3×1012 genome copies/mL, 200 nL volume) was injected unilaterally into the dorsal striatum, in combination with the static red fluorescent label AAV1-CAG-DIO-tdTomato (Addgene #104112; titer 4×1013 gc/mL, 100 nL volume) as control. Stereotactic coordinates for the DS were determined using the mouse brain atlas. Two injections were made at stereotactic coordinates (relative to the skull surface at bregma): +1.0 anteroposterior (AP), ±1.6 and 1.9 mediolateral (ML), and −2.9 dorsoventral (DV). To minimize backflow and to allow spread of the virus, the solution was slowly injected, and the pipette was left in place for 10–15 min after viral infusion before being slowly withdrawn. Viral infusions were carried out at a rate of 1 nL/sec using an nanoliter injector (Nanoject III, Drummond) through a pulled glass micropipette filled with mineral oil (MP Biomedicals). Following viral injection, a metal head-plate (CF-10, Narishige) was secured to the skull (using a 3D-printed custom tool) with adhesive cement (Super-Bond C&B, Sun Medical) and a self-curing dental acrylic resin (Unifast II, GCCorporation). Mice were given a carprofen injection (for post-operative treatment, 5 mg/kg, s.c.) following surgery, and daily carprofen tablets for three subsequent days. Animals were returned to their home cages and allowed to recover for a minimum of 7 days before lens implantation.

GRIN lens implantation
All surgical procedures were performed under aseptic conditions as above. About 1 week after viral injection, mice underwent a second stereotaxic survival surgery to implant a 1 mm diameter gradient index (GRIN) lens (2P doublet probes; NEM-100-25-10-860-DS, GRINTECH) to the DS. Before implantation, dexamethasone (dissolved in 0.9% saline, 2 mg/kg, s.c.) was administered to reduce brain edema. To facilitate GRIN lens insertion, a 25-gauge needle was attached to the micromanipulator of the stereotaxic frame and inserted from the brain surface to a depth of about 200 μm above the GRIN lens target position. Then, the needle was slowly withdrawn, and the lens was lowered (centered at +0.9 AP and ±1.8 ML) to approximately 200 μm directly above the injection site using a lens holder (Inscopix), at a slow and constant rate of ~200 μm/min. A thin layer of Kwik Sil (WPI) was applied to fix the lens to the craniotomy site. The lens was then affixed to the skull using a thick layer of dental acrylic resin, extending up the sides of the GRIN lens to build a head cap (coated with black nail polish), after which the lens holder grip was loosened. Silicone sealant was applied over the lens to prevent any damage. Analgesia (carprofen, 5 mg/kg, s.c.) was given for post-operative treatment, and daily carprofen tablets was administered for three subsequent days after surgery. All animals were monitored over a recovery period of 1–2 weeks, after which they were habituated to head restraint before behavioral testing and imaging commenced.

Fluorescence imaging of iAChSnFR in brain slices
For the slice recording experiments, we first expressed iAChSnFR in the DS of mice by injecting 200 nL of AAV1-ihSyn-tTa-sv40/TRE-iAChSnFR-minWPRE at the following coordinates: AP, +1.0 mm; ML, ±1.7 mm; DV, −2.9 mm from bregma. After 3-4 weeks of viral expression, the animals were deeply anesthetized with 5% isoflurane and then perfused. The brains were rapidly extracted and placed directly in cold slicing buffer containing (in mM): 2.5 KCl, 1.2 NaHPO4, 30 NaHCO3, 20 HEPES, 25 glucose, 2 thiourea, 5 Na-ascorbate, 3 Na-pyruvate, 5 MgCl2·6H2O, and 2 CaCl2·2H2O. They were then sectioned into 300 μm thick coronal slices with a vibratome (VT1200, Leica Biosystems) and quickly transferred into a container water-bathed at 34°C with same buffer solution and then Na+ spike-in procedure was performed according to the protocol provided in Ting et al.’s study. Thereafter, all slices were transferred to a holding chamber (BSK4, Scientific Systems Design, Inc.) with holding solution containing (in mM): 92 NaCl, 2.5 KCl, 1.2 NaH2PO4, 30 NaHCO3, 20 HEPES, 25 glucose, 2 thiourea, 5 Na-ascorbate, 3 Na-pyruvate, 2 CaCl2·4H2O, 2 MgSO4·6H2O), which was continuously bubbled with 95/5% O2/CO2 for at least 15 minutes at room temperature until recordings were performed. The brain-slice recordings were performed at 32–34°C. Imaging was performed using a sCMOS camera (PCO.Panda 4.2, Excelitas Technologies Co.), equipped with an Olympus 60x/0.9 NA water-immersion objective, at a frame rate of 20 Hz and synchronized with the puffing via TTL triggers. ACh sensor fluorescence was excited with an LED (Solis LED, Thorlabs) at a wavelength of 470 nm and collected with a 525/50 emission filter on an upright epifluorescence microscope (BX-51 WI, Olympus). To confirm that tissue expressing iAChSnFR was responsive to ACh, a glass pipette filled with recording ACSF containing 50 μM ACh was placed above the DS, and slight positive pressure (12 psi ≈ 80 kPa) was briefly applied. The duration of each puff was set at 30 ms, and time-locked fluorescence responses were recorded. Fluorescence responses to puffs of ACh was later blocked with recording ACSF containing 50 μM of the acetylcholinesterase inhibitor, neostigmine, which was bath applied by perfusion into the recording chamber. The image processing including, downsampling (to reduce file size and processing time), quadrat selection (to generate 1-D temporal signals), background correction and peak finding, were performed using custom scripts in Python. Changes in fluorescence DF/F was computed as (F − F0)/F0, where F is the pixel-wise fluorescence value at each time point and F0 is the mean of the 4.5 s baseline fluorescence prior to puffing onset. The effect of photobleaching was removed by subtracting a B-Splines fit to the periods 0.5–4.5 s (initial parts) and 14.5–19.5 s (tail part) using Python (note that puffing onset and responses in the period of 4.5–19.5 s are masked). Peak responses were calculated as the maximum z-scored ΔF/F response recorded for each slice.

Virtual-reality behavioral system
We designed a virtual reality (VR) task for head-fixed mice using the Jet Ball system (PhenoSys GmbH, Germany). The Jet Ball system is a VR experimental system for rodents, which is based on an air-supported styrofoam ball allowing a restrained animal to navigate a two-dimensional linear corridor in a virtual environment. The VR-Jet Ball setup comprised of two sub-units: the Jet Ball Unit and the Control Unit. The Jet Ball unit consisted of six 19” TFT surround monitors (270°, in octagonal arrangement) running at 75 Hz, and a styrofoam ball (20 cm diameter) that was floated using a pressurized air jet, enabling movements with minimal friction. The styrofoam ball sat in an aluminum bowl (ball holder), which contained multiple outlets from which a stream of compressed air was directed. Two X/Y motion sensors attached to the bowl measured ball rotations, i.e., mouse position and running speed, such that any movement of the ball was fed into a computer that generates and updates the virtual environment. The ball holder is also equipped with a liquid reward system mounted on a retractable operant device with a peristaltic pump from which reward (10% sucrose solution) was given to the mouse. The reward system consisted of the lick sensor (Piezosensor) integrated into a cannula, a potentiometer for adjusting the sensitivity, and an articulated arm to adjust the position of the operant device in relation to the animal. Mice were positioned on top of the ball, allowing a slightly uphill run and head-fixed for behavioral training under a two-photon microscope. The Jet Ball unit was controlled via the control unit consisting of a control monitor that was situated on a movable rack with computer, power supply unit, flow meter (a pressure regulator to adjust airflow to the ball, 10–15 L/min), etc. The Y-maze consisted of a closed loop two-dimensional linear corridor that was 350 cm long and 30 cm wide (start region, −15–0 cm; stem, 0–280 cm; pre-outcome, 280–320 cm; outcome location, 320 cm). The walls of the maze (20 cm high) were textured with gray/black stripe patterns (start region up to the junction) and black dots (from junction to the reward location). The behavioral task was designed from the control unit using the program ‘PhenoSoft Schedule’, which offers a graphical interface for creating experimental schedules using state diagrams or flowcharts. Signals from all modules mounted on the Jet Ball unit were transferred simultaneously from the ball holder controller to the control unit computer and analyzed.

Mouse behavioral training: Virtual-Reality Response Learning (VR-RL) task
The behavioral training consisted of four phases, including the habituation, shaping (familiarization), acquisition, and reversal phases. To reduce stress, all mice were handled and habituated to the experimenter two days before head-fixation. During this period, each mouse was gently picked up from the cage, handled for a few minutes and allowed to return to their cages. Afterwards, all mice were habituated to head restraint for an additional 2–3 days. On the first day, each mouse was gently placed on the styrofoam ball and allowed to move freely for about 5–10 mins, without head fixation, and then returned to their cages. This was repeated 2–3 times. On the following days, mice were head-fixed on a metal head-holder (Thorlabs) for 10–15 min and given unpredicted drops of 10% sucrose solution (15 μL), at random intervals, through a reward spout placed directly in front of the mouth. This enabled mice to become habituated to the head-fixed condition and to interact with the VR system. Once mice began actively licking to consume rewards, they were subjected to successive stages of shaping. In this phase, mice were trained to run along a unidirectional, infinitely long virtual linear track (infinity corridor) with regularly spaced rewards. With this approach, mice adapted quickly to the VR, running for long distances within 1-2 weeks of initial exposure. In our experience, male mice adapted quicker and started free runs on the ball earlier than female mice. After these training sessions, mice were briefly introduced to the Y-maze task for a day, where each mouse was placed inside the maze and allowed to move freely and get rewards (warm-up trials), as used in previous studies. These trials were not included in the analyses presented in the paper. Overall, it required 2-3 weeks of handling and pretraining for the mice to move freely on the ball and efficiently perform the VR task. Mice were then subjected to the initial task contingencies, i.e., acquisition phase. In this phase, the mice were trained on a self-initiated adaptive decision-making task requiring choice of the left or right arm of the Y-maze for reward. Mice were trained one session per day, with each session consisting of ~40 trials for a maximum duration of 1 h. Each trial began with a 3-kHz auditory tone (1 s; 75 dB) with 60 frames (2 s) before tone presentation serving as a baseline period, during which the virtual environment remained unresponsive. The mice learned through trial and error to identify which arm was rewarded. Entry into the outcome zone (320 cm from start) was detected by the VR function WaitForVrPositionInCompensation, which immediately activated the delivery of the chosen outcome. In rewarded outcomes, ~25 μL of sucrose solution was delivered for 1 s through a reward port, and mice had to extend their tongue out of the mouth to register a lick. The reward delivery schedule was accomplished by applying the HandleDigOut function, which controlled the peristaltic pump via a TTL pulse from the control unit. In no-reward outcomes, the VR was deactivated and the visual scene immediately turned black, signifying trial failure. The StopMode function was used to freeze the VR environment at the time of the trial outcome. At the end of each trial, mice were teleported to the VR start position to initiate the next trial. Mice typically reached task proficiency (≥80% correct choices in at least one session) within 4–5 days. We defined choice as the percentage of the number of times the mouse chose the reward (correct) arm in a session. Mice were free to control the pace of each trial without time limits. The average duration from trial start to outcome decreased from 71.1 ± 10.4 s for early sessions to 28.1 ± 2.6 s for late sessions, over the 350 cm length of virtual corridors in the maze. This trial duration includes natural variations in approach speed, exploration and grooming.

After successfully learning the initial task contingencies, mice were then subjected to the reversal learning procedure, i.e., reversal phase. In the reversal session, we first subjected mice to 10 trials of the initial task contingencies, after which mice were immediately subjected to the reversal procedure. During this phase, the reinforcement contingencies were reversed (switched), without any explicit signal or cue, such that the arm initially associated with reward became associated with no-reward (the reward-to-no reward reversal), and the arm associated with no-reward became associated with reward (the no reward-to-reward reversal). Before each training session, the styrofoam ball was wiped clean with 70% ethanol. All behavioral training and imaging were done in a dark environment with a constant background noise of ~50 dB.

Two-photon imaging and data acquisition
About 4-5 weeks after the GRIN lens implantation, imaging experiments began. This was performed using a custom-built combined widefield and 2-photon microscope (INSS) integrated with the VR system. The 2-photon microscope was equipped with a tunable Ti:Sapphire excitation laser with a pulse duration of 100 fs and a repetition rate of 80 ± 0.5 MHz (Chameleon Discovery, Coherent) tuned to a wavelength of 930 nm. Scanning was performed using a resonant scanner (Thorlabs). Fluorescence photons were split with a dichroic mirror (DIC-495LP, Thorlabs). Green iAChSnFR fluorescence was isolated using a bandpass filter (525/50, Thorlabs) and detected using a GaAsP photomultiplier tube (H11706P-40, Hamamatsu Photonics). The signal was amplified using transimpedance amplifiers (TIA60, Thorlabs). Data were acquired and collected on a separate computer using ScanImage software (Vidrio Technologies) that also controlled the microscope. Images were acquired at 30 Hz at a resolution of 512×512 pixels with a pixel dwell time of 87.9 ns. Imaging time series were synchronized with behavioral data logs via a TTL pulse from the control unit of the VR system. Laser power measured at the front of the objective (Plan Fluorite 20×, 0.5 NA, Nikon) ranged from 40–60 mW depending on the level of iAChSnFR expression. Optical power measurements were obtained prior to the start of the behavioral study using a thermal power detector (S370C, Thorlabs) connected to a meter console (PM100D, Thorlabs). At the start of each imaging session, we briefly employed widefield imaging to identify the imaging plane using images of the blood vessel pattern, and the focal plane was adjusted slowly until vascular structures and landmarks were clearly observed. Widefield imaging was accomplished with a CCD camera (Retiga Electro, Teledyne QImaging) in the green spectral range. Afterwards, we immediately switched to 2P imaging without moving the animal. On subsequent days, all imaging planes were determined relative to a reference location, which was obtained on the first day of imaging in all mice. Light contamination in optical recordings was reduced by covering the VR monitors with a color filter (#342 Rose Pink, Rosco Laboratories Inc.). The 2P/VR systems were seated on an air-spring vibration isolation table (Physio-Tech) and housed inside a frame box covered with a black curtain.

Chemogenetic inhibition of cholinergic interneurons
For CIN inhibition experiments, we bilaterally injected a Cre-dependent hM4Di-mCherry, AAVDJ/8-hSyn-DIO-hM4D(Gi)-mCherry (AddGene #44362) into the DS of adult ChAT-Cre mice. During surgery, 300 nL of the viral construct was injected per hemisphere. Although injections were performed bilaterally into the dorsal striatum, they were centered primarily into the dorsomedial striatum (DMS), guided by both our imaging findings of enhanced medial ACh responses to non-reward and prior reports implicating the medial striatum in reversal learning and behavioral flexibility. Virus expression was achieved for a minimum of 2 weeks before behavioral training commenced. Following recovery, the mice were tested on the VR-RL task. Mice were first trained in task acquisition until they reached an acquisition criterion of 80% correct choice performance, after which the reversal phase began. A total of 10 mice successfully completed the initial task acquisition, and each mouse was assigned to one of two groups: experimental and control groups. On the test day, the animals were weighed to determine the volume of solution that will be injected. Intraperitoneal injection of 0.3 mg/kg of DREADD-activating ligand, JHU37160 dihydrochloride (J60; HelloBio) preceded reversal tests in experimental mice by ~30 minutes. JHU37160 dihydrochloride was first dissolved in sterile saline (0.9% NaCl), and stock solutions were prepared in advance. Control mice received the same amount of 0.9% sodium chloride (vehicle) before each test session. For behavioral testing, the required dosages were typically prepared and used on the same day. Any remaining solutions were stored at 4 oC and allowed to equilibrate to room temperature for at least one hour before using again.

Quantification and statistical analysis
Analysis of behavioral performance
Following multiple training sessions, mice behaviorally discriminated between the maze arms by displaying improved choice performance and a directional preference in anticipatory licking, particularly on approach to the reward arm. Using a selectivity index, choice performance was computed as: Choiceselectivityindex = (correctchoices - incorrectchoices) / (correctchoices + incorrectchoices). We classified training sessions as ‘early’ or ‘late’ in learning based on both selectivity score and day of training. A session was defined as ‘early’ in learning if the selectivity score was below 0.3, and the session was on day 2 or earlier. Sessions were defined as ‘late’ in learning if the selectivity score was ≥0.6, and the session was on day 4 or later. Similarly, anticipatory licking was used as a measure of appetitive learning. We quantified anticipatory licking behavior in early and late sessions using a lick index, computed as: Lickfrequencyindex = (lickstorewardarm - lickstono-rewardarm) / (lickstorewardarm + lickstono-rewardarm). To further confirm that mice had learned the task, we collected and analyzed the animals’ velocities at which they completed trials, as a measure of adaptive motivation. The velocity data was obtained from the motion sensors that recorded all movements of the animal and translated it into VR coordinates. The mouse velocity was estimated as the moment-by-moment change in displacement, and computed as dY/dt, where dY was the change in Y-position displacement in the virtual environment, and dt was the elapsed time. To do this, we analyzed data in 40 cm bins for positions from 0 to 320 cm and averaged across trials for each mouse.

Two-photon image processing
Offline data analysis and visualization of time-lapse recordings were performed with Fiji software (NIH; http://imagej.net/Fiji), MATLAB (MathWorks), and Prism 10 (GraphPad). First, raw movies acquired on the 2P system were preprocessed in Fiji using a custom macro to automate the process. Time-lapse images were processed for x-y motion correction using a standard normalized cross-correlation method to eliminate any spatially uniform motion and movement artifact. This was accomplished with the ‘Template Matching’ plugin (‘matchTemplate’ function in the openCV library) in Fiji. The movies were then visually inspected to confirm drift correction. The movies were further processed by cropping out the edges of the imaging field to avoid artifacts due to lateral motion. Motion correction was carried out for each day of recording. Movies were denoised by applying a spatiotemporal Gaussian filter (sigma = 2 pixels in x, y, and t) to each frame of the field-of-view (FOV). Representative fluorescence (mean fluorescence projection) images were created by averaging the intensity Z projection of the motion-corrected image sequences. Our imaging and data analysis pipeline minimized technical sources of noise by employing motion correction and baseline normalization.

Selection of individual quadrats and calculation of ΔF/F. To achieve this, we used a custom Fiji script to select individual quadrats and extract their corresponding fluorescence traces. We note that the iAChSnFR sensor was expressed in a non-cell-type-specific manner, labeling all neuronal elements, including somata, dendrites, and neuropil. As a result, the structures observed likely reflect a mixture of cell types and compartments, which can vary in size. Due to the diffuse expression of iAChSnFR sensor, the FOV was overlaid with equally sized quadrats (~50×50 μm) for selection and trace extraction. The use of this grid size was informed by several considerations beyond convenience. First, the choice of 50×50 μm quadrats was based on a practical compromise between spatial resolution and signal reliability. This size was chosen to ensure sufficient signal-to-noise ratio within each quadrat, while also capturing meaningful local variations in ACh dynamics across the FOV. Second, CINs, which are the primary source of striatal ACh, are sparsely distributed but possess extensive axonal arborizations, spanning several hundred micrometers. These broad arbors form spatially diffuse neuromodulatory fields that overlap and influence local microcircuits. However, 2-photon microscopy optically sections the image in the dorsoventral plane, and thus samples only a fraction of the arborization of any particular neuron. In addition, some spatial averaging is required to achieve a meaningful signal-to-noise ratio on a trial-by-trial basis. To ensure that our observations were not biased by our choice of quadrat size, we also conducted control analyses using different quadrat sizes. Based on these, we divided the FOV into 15×15 grids and obtained a total of 225 individual quadrats in each mouse. The fluorescence trace of individual quadrats over time was calculated by averaging the corresponding pixel values within each specified region. Time-series traces of all processed movies were extracted and imported into MATLAB using custom-written scripts. Fluorescence time series were converted to ΔF/F by normalizing signals to the baseline fluorescence such that for each quadrat, the relative fluorescence change (referred to as ΔF/F) was calculated as: ΔF/F(t) = (F(t) - F0(t)) / F0(t), where F(t) is the instantaneous fluorescence from the raw time-series data and F0(t) is the mean of the baseline fluorescence. Mean full-field fluorescence was calculated by averaging the mean fluorescence change of all quadrats in the FOV. iAChSnFR signals were then extracted around relevant behavioral events (e.g., trial outcome) and averaged across trials. For analysis of ACh responses in the outcome period during the acquisition phase, traces of ΔF/F from 1-s before to 1-s after trial outcome were used for the analyses. To quantify the changes in iAChSnFR signals across multiple animals or conditions, the ΔF/F time-series signal was further normalized using z-score (subtraction of mean and division by standard deviation calculated from a 1-s baseline period, such that zscore = (ΔF/F – μ)/SD) to account for potential differences in signal variance across animals and sessions. Mean iAChSnFR traces were calculated using session-averaged traces from individual mice. The dataset of 425 trials (reward trials, 291 and no-reward, 134; acquisition phase) and 416 trials (reward trials, 213 and no-reward, 203; reversal phase) from 11 mice was used for the core of this study.

Hierarchical clustering of quadrat responses and analysis of temporal dynamics. Using a custom MATLAB program, we analyzed the spatiotemporal characteristics of ACh responses during the outcome period. For this purpose, we classified the response patterns of all recorded quadrats (2475 quadrats from 11 mice) using principal component analysis (PCA) to reduce dimensionality, followed by a hierarchical clustering algorithm. Principal components explaining 90% of the total variance were retained to preserve key features. Hierarchical clustering was then applied to the PCA-transformed data using the Euclidean distance metric and the complete linkage method, which resulted in a clear separation of response clusters.

The optimal number of clusters was determined using the Elbow Method, based on the within-cluster sum of squares (WCSS). Finally, the clustered data was visualized in three-dimensional space using the first three principal components. Using this, the hierarchical clustering approach identified four clusters based on response kinetics, with distinct dynamics. Across the population, clusters were unequally represented, ranging from 2.3–60.1% for rewarded outcomes and 0.04–32.9% for unrewarded outcomes. The observed clusters displayed differences in response amplitudes and temporal kinetics. Thus, the four clusters were accordingly labeled as: transient decrease in ACh (Cluster 1), transient increase followed by a decrease (Cluster 2), transient decrease followed by a rebound burst (Cluster 3), and others consisting of an increase in ACh (Cluster 4). Following hierarchical clustering, we applied strict post hoc classification criteria to validate the functional significance of each response type using statistical thresholds (±2SD). The 2SD threshold applied here is a standard criterion for defining reliable responses in neural imaging. A quadrat was classified as “decrease type” if its activity did not exceed 1SD above baseline for any time steps from 0 to 1000 ms of reward delivery, and the minimum activity was less than 2SD below baseline. A quadrat was identified as “increase-decrease” if its activity exceeded 2SD above baseline for at least one time step from 0 to 400 ms of reward delivery, followed by a pause. A rebound burst after the decrease was defined as maximum activity exceeding 2SD for at least one time step from 500 to 1000 ms of reward delivery. A quadrat was classified as “increase type” if the activity exceeded 2SD above baseline during the time of reward delivery. To determine the temporal profiles of a quadrat, we calculated the time-to-dip as well as the dip amplitude (minimal change of iAChSnFR signal, 0-1 s after R/NR onset) of the z-scored ΔF/F response. Similarly, the time-to-peak and peak amplitude (maximal change) were estimated. While four distinct ACh response patterns were identified during late learning (i.e., pause, burst-pause, pause-burst, and increase), only two primary response clusters emerged during early learning, which we classify as long-latency pause (~21%) and short-latency pause (79%).

Identification of switch and stay quadrats. For the analysis of switch/stay encoding quadrats, we first grouped outcome responses on the basis of whether the animal would, in the upcoming trial(s), return to the previously incorrect arm (defined as “stay” trials) or select the now correct arm (defined as “switch” trials). To estimate the trial-by-trial fluctuations in ACh activity, we then calculated the mean evoked activity for individual quadrats by averaging the fluorescence signals across the corresponding trial type. For each quadrat, whether an outcome-evoked response was significantly different was determined by comparing the mean evoked fluorescence change (time window: 0.3–1.3 s after NR) against the baseline, which was the 1-s period preceding trial outcome. A quadrat was considered to encode “switch” or “stay” if it exhibited a significant change in mean activity (two-tailed, paired t test, P < 0.05) relative to the baseline activity, preceding future switching (switch-encoding quadrats) or staying (stay-encoding quadrats), respectively. Non-selective quadrats showed no significant changes from the baseline (P > 0.05). Afterwards, individual quadrats were grouped as switch+ (increased activity preceding future switching and non-responsive to future staying), switch- (decreased activity preceding future switching and non-responsive to future staying), stay+ (increased activity preceding future staying and non-responsive to future switching), stay- (decreased activity preceding future staying and non-responsive to future switching), etc. Thus, we identified nine distinct groups post hoc based on the magnitude and direction of modulation. Color-coded maps of all quadrats were constructed by projecting the trial-averaged values of individual quadrats onto a two-dimensional space for data visualization.

3D spatial cluster mapping of imaged quadrats
To enable cross-subject comparison of quadrat spatial distribution, all fields of view were spatially registered onto a common reference framework. For each animal, anatomical coordinates of GRIN lens implantation locations were estimated using the nearest corresponding coronal section of the mouse brain atlas, based on histological verification. The mediolateral (ML) and anteroposterior (AP) center of each imaging field was defined by the implant location, serving as the spatial origin for quadrat transformation.

Within each FOV, the spatial position of each quadrat was mapped onto a standardized 15×15 grid representing a fixed imaging field (typically 225 quadrats), with each grid square corresponding to a constant physical spacing between quadrats. Quadrat positions were computed relative to the ML and AP implant center, scaled by a fixed spacing constant (~50 μm) and adjusted by a uniform scaling factor of 1. Coordinates were also mirrored along the AP or ML axis if the orientation of the GRIN lens required anatomical inversion, as specified in the metadata for each mouse (described below). This normalization approach ensured that quadrat locations across animals were anatomically aligned in a common reference plane, allowing for spatially consistent visualization and comparison of activity patterns. Spatial localization of quadrats within the imaging plane was achieved using a custom script developed in MATLAB. The input data consisted of multiple excel sheets, each containing quadrat indices alongside functional classifications (e.g., cluster 1, cluster 2, cluster 3, cluster 4, and increase, decrease, non-selective). Physical coordinates were centered using recorded ML and AP metadata values extracted from the input sheets. Notably, the script accounted for potential axis inversions resulting from optical properties of the GRIN lens (e.g., mirroring or flipping across ML and AP axes) and image orientation effects introduced by the two-photon imaging system. Each quadrat was visualized as an open circle plotted using scatter and scatter3 functions, with edge colors assigned to each functional category. Two-dimensional and three-dimensional scatter plots were generated per sheet, and an aggregate 2D and 3D map was created by merging all sheets, using the built-in MATLAB plotting engine.

Heatmap z-score visualization: Central vs Peripheral quadrat mapping
To spatially visualize mean z-score activity across quadrats, a custom MATLAB script was employed. Input data for each imaging session consisted of excel sheets containing paired columns for quadrat indices and their corresponding mean z-scores. Each quadrat was assigned to a standard 15×15 spatial grid based on quadrat order, enabling consistent cross-session mapping. Z-score values were visualized using MATLAB’s heatmap function. The imaging field was subdivided into “central” and “peripheral” regions based on quadrat proximity to the center: Quadrats located within grids 3–13 in both ML and AP axes were defined as “central,” while those within the two outermost rows and columns (grids 1–2 and 14–15) were classified as “peripheral”.

Analysis of behavioral measures in reversal learning and chemogenetics experiments
To examine the treatment effects, behavioral measures, including perseverative and regressive errors, were analyzed for the reversal learning session, as reported in previous studies. Perseverative errors are repeated incorrect choices made after the reversal but before the mouse has exhibited learning of the correct response. Specifically, a perseverative error was defined as errors made in a block of four consecutive trials after reversal, of which 3 or more were incorrect. The measure of perseveration was the number of such errors that occurred in sequence. The end of the sequence was defined by the occurrence of a block of four trials in which 2 or more trials were correct. Regressive errors are incorrect choices that occur after learning has occurred, as evidenced by a sequence of correct choices. Here, a regressive error was defined as any block of four trials in which an error occurred after a block in which 2 or more trials were correct. Perseverative and regressive measures are thus distinguished from individual win-stay and lose-shift responses that are trial-by-trial choices made in the context of the outcome of the previous trial. A two-way repeated measures (RM) ANOVA followed by Tukey’s post hoc test was used to further analyze differences in choice performance between treatment groups. Following all behavioral tests, mice were transcardially perfused, brains sectioned, and CIN-specific expression confirmed using mCherry reporter expression, which co-localized exclusively with ChAT immunoreactivity.

Effect size analysis
To complement null-hypothesis significance testing and better quantify the magnitude of observed group differences, we calculated Cohen’s d for all pairwise comparisons. For unpaired t tests, Cohen’s d was computed as the standardized difference in means between treatment groups, using the pooled standard deviation as the denominator. 95% confidence intervals were also estimated to provide an index of precision. Effect sizes were interpreted according to conventional benchmarks (d≈0.2: small; d≈0.5: medium; d≥0.8: large; negative values in figures indicate direction of effect). Reporting effect sizes is particularly important in studies with modest sample sizes, as it provides information about the strength and potential biological relevance of effects beyond p values alone.

Histology
After completion of behavioral experiments, all mice were examined for histology to confirm viral expression and GRIN lens placements. Briefly, mice were anesthetized with 0.3–0.4 ml (i.p.) of thymylal sodium (Nichi-Iko) and transcardially perfused with 20–30 ml of phosphate-buffered saline (PBS, Wako) followed by 10–15 ml of 4% paraformaldehyde (PFA, Nacalai Tesque) in PBS. The brains were dissected and post-fixed in 4% PFA overnight at 4°C, and then cryoprotected in 30% PBS-buffered sucrose solution for about 3–5 days. Afterwards, the brain was embedded into tissue-freezing medium and sectioned coronally on a retoratome (REM-710, Yamato) at 40 μm and stored at 4°C in PBS until use. Histological verification of sensor expression was performed following standard procedures. To label iAChSnFR, slices were first rinsed (3×10 min) in PBS. Next, the sections were blocked with 5% normal goat serum (Jackson ImmunoResearch) in PBST (0.5% Triton X-100 in PBS) (Sigma-Aldrich, Cat# T8787) for 1 hr at room temperature (RT), and then incubated overnight at 4°C with chicken anti-GFP primary antibody (Abcam, Cat# ab13970, 1:500) and 2% normal goat serum in PBST. On the next day, sections were washed in PBS (3×10 min, RT) and incubated with Alexa Fluor 488 goat anti-chicken secondary antibody (Invitrogen, Cat# A32931, 1:500) for 3–4 hr at RT. Then, the slices were washed (3×5 min in PBS) and mounted on glass slides. For ChAT staining, sections were incubated overnight with goat anti-ChAT primary antibody (Millipore, Cat# AB144P, 1:100) after blocking. The sections were then washed with PBS (5×10 min) and then incubated (3–4 hr, RT) with Alexa Fluor 488 donkey anti-goat secondary antibody (Invitrogen, Cat# A11055, 1:500) in PBST and washed again. Nuclei were stained with DAPI (4′,6-diamidino-2-phenylindole; Invitrogen, Cat# R37606; 70 μL/mL) in PBS. Sections were coverslipped using mounting medium (ProLong glass antifade mountant, Invitrogen). Whole sections showing viral expression and GRIN lens placements were imaged using an epifluorescence microscope (BZ9000, Keyence) while cellular resolution images of immunostained slices were acquired using a laser scanning confocal microscope (LSM780, Zeiss).

Graphical and schematic illustration
Schematics of experimental setups and brain outlines depicted in the paper were created using Adobe Illustrator (Adobe Creative Cloud, version 29.8.3; Adobe Inc.). Each element was hand-drawn using the built-in Shape tools (Ellipse, Rectangle, Line Segment) and Symbols (vector icons available within Adobe Illustrator’s default Libraries).

Statistical analyses
All statistical analyses were performed using GraphPad Prism 10. Significance for comparisons were set to *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001. We used two-tailed, paired or unpaired Student’s t test for two-group comparisons, and Two-way RM ANOVA followed by Tukey’s post hoc test for multiple comparisons. In boxplots, center lines depict the median, box limits represent the 25th and 75th percentiles, and whiskers, data range. Correlation analyses were performed using Pearson’s correlation coefficients (P < 0.05).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Sarpong et al\nThis is a very interesting paper that examines changes in acetycholine (Ach) release in the dorsal striatum of mice during\nperformance on a Y maze discrimination and after the reversal of the discrimination. The protocol uses head fixed mice on a\nvirtual maze allowing clear measures of ACh dynamics and localised changes in identified clusters of regionally defined\nsubregions with the field of view in the striatum divided into a 15 x 15 grid. This provided further detail by providing an\nassessment of the heterogeneity of changes in dynamic responses. The authors focus their assessment of ACh activity to\nthe period during which reward was delivered after a correct response in the maze or during a comparable period of non-\nreward after an incorrect response. This provides a relevant but limited period of assessment. Thus, for example, ACh\nactivity during the anticipation of reward was not reported, either pre- or post-choice as the animal approach the choice point\nand after it had selected an arm in the maze. This could have been informative. Nevertheless, the authors report\nconsiderable behavioural data from this period, including measure of anticipation, such as anticipatory licking. The findings\nare reasonably clear, showing large differences in responding to reward and non-reward during initial training and after\nreversal. Interestingly, reward-related activity was found to be heterogeneous across regions: in most clusters showing a\npause, sometimes after a preceding increase in activity, but also, in other clusters, a rebound excitation after the pause\nwithin the field of view. Even more interesting was the finding that an increase in ACh activity after non-reward predicted a\nshift in arm selection (i.e., lose-shift performance) but only after reversal suggesting increased ACh release is relevant to\nacquiring the reversed contingencies. Finally, to establish the causal relevance of ACh relese to reversal performance, a\nCre-dependent inhibitory DREADD was infused into the dorsal striatum in Chat-Cre mice. Inhibition of ACh reduced reversal\nperformance as well as lose-shift probability.\nOverall, this is a nice set of findings. Nevertheless, I had a few issues with the presentation and some questions regarding\nthe interpretation of the findings.\n1. The paper is framed in terms of the role of ACh in cognitive flexibility and, indeed, a large number of variables were\nanalysed from the behavior of the mice during the acquisition of maze learning. However, monitoring of ACH activity was\nlimited to reward delivery or non-delivery period only. Despite the framing in terms of flexibility, therefore, the relationship\nbetween ACh release and performance is difficult to draw. The first 3 figures focus on ACh release during initial acquisition\nof performance on the Y maze. However, although performance is differentially rewarded, and there are both rewarded and\nunrewarded movements, at this stage, according to the release dynamics during reward/non-reward (without consideration\nof the degree to which its anticipated) ACh appears to be responding solely to reward. When no reward is delivered there is\nno, or at least a much diminished, response. So even when the animal makes an incorrect response its hard to see that Ach\nis signalling anything useful to the animal to alter its behaviour. How does this accord with the behavioral flexibility\nhypothesis? Surely, acquiring performance on the maze requires learning and so some degree of flexibility?\n2. Along these lines, what do the animals actually do when unrewarded? I was interested in two things here: (i) Is the degree\nof ACh response to non-reward at all modulated by ACh release in anticipation of reward? Does it differ according to the\ndegree the animals are anticipating reward prior to non-reward? (ii) Is the ACh response influenced by the animals’\nbehavioral responses after non-reward at all - e.g., degree of orienting/search or activity perhaps related to frustration? We\nare not told. There is considerable detail regarding the animals’ behaviour up to reward delivery but not afterwards.\n3. Relatedly, I found myself wondering if the change in ACH response after reversal in the presence of non-reward is in\nanyway related to motor activity/orienting/attentional changes and whether these can be detected in animals’ behavior as a\nchange in activity after reversal. The increase in Ach after an incorrect response during reversal is interesting but, within-\nanimals, how different is this response to that observed to non-reward during training and during the period immediately prior\nto reversal? The authors state: “Thus, the direction and magnitude of ACh signals during the no-reward outcome period\npredicted future behavioral shifts.” Do they predict this because they are indicative of learning or because of some reaction\nthe animals have to non-reward? There is a quite clear analysis of behavioral changes associated with reward. What would\na similarly complete analysis of behavioral responses to non-reward look like and could this anticipate the responses after\nreversal? I suppose ot is worth also considering here the role of shifts in ‘mood’ – by which I mean responses to frustrative\nnon-reward, and in orienting rather than simply changes in behavior generally.\n4. Where changes in ACh signals observed in anticipation of reward? One assumes that the signals would have been\nsimilar prior to correct or incorrect choices if the animals were anticipating reward similarly prior to choice. However, only 1s\nof baseline was used prior to reward signals. Does df/f change with a longer baseline?", "reviewer_2": "The manuscript describes findings from a series of experiments that demonstrates the dynamic changes in dorsal striatal\nacetylcholine efflux during learning and reversal learning. There are several innovations carried out in these studies that\nprovide significant insights of how actions of striatal cholinergic interneurons underlie learning and behavioral flexibility.\nAt the core there are three main experiments. The first combining a genetically encoded fluorescent acetylcholine sensor\nwith 2-photon microscopy to determine how acetylcholine within subregions of the dorsal striatum changes during learning\nand reversal learning. A second study was conducted in striatal slices demonstrating that the fluorescent sensor was\nactually measuring acetylcholine. A third experiment employing chemogenetics selectively reduced striatal acetylcholine\nactivity and showed that this manipulation impaired reversal learning consistent with what would be predicted from the\nfindings in their first experiment.\nWhat stands out about this manuscript is the creativity in carrying out their first and main experiment investigating striatal\nacetylcholine changes during learning and reversal learning. Integrating modern techniques the main experiment is able to\nreveal how dynamic changes in dorsal striatal acetylcholine output supports learning and behavioral flexibility at a\nspatiotemporal scale that has not been achieved before. Further, the breadth and depth of their analyses really provides a\ncomprehensive picture of the how dynamic changes in acetylcholine occur in striatal subregions. Overall, the findings from\nthese experiments make significant advances in revealing how striatal cholinergic interneuron activity facilitates learning\nand behavioral flexibility. These findings will have a broad interest for anyone interested in the brain mechanisms that\nsupport learning and behavioral flexibility.\nI have two main comments related to the manuscript:\n1. The authors use the term \"disappointment\" referring to when a mouse makes an incorrect choice that does not lead to a\nreward. This term really seems like a projection of a human experience onto the mouse that is not necessary. The results are\nextremely strong and convincing so using an alternative term that more simply captures the behavior would suffice, i.e.\nunexpected non-reward.\n2. A real strength of the manuscript is the hierarchical clustering of acetylcholine responses within subregions. I was not\nclear whether the data included in this analysis was for all trials in all sessions mice were tested or only for a subset of trials,\ni.e. late learning? Related, did the same clustering pattern occur throughout testing or was it more prominent in a particular\nstage of learning, i.e. late vs. early.", "reviewer_3": "Sarpong et al. investigate the spatiotemporal dynamics of acetylcholine (ACh) in the dorsal striatum, using a genetically\nencoded sensor in head-fixed mice choosing between left and right paths in a virtual environment. This is an important topic\nand they observe some interesting results. The primary finding is that under reversal conditions (reward location switches\nfrom right to left) arrival at the no-longer-rewarded location causes an increase in ACh, rather than the dip seen with rewards.\nThe size of this no-reward increase is predictive of behavioral switching, and suppressing ACh interneurons makes\nswitching slower. They also find limited evidence for spatial heterogeneity in the ACh response. Overall there are some\nnoteworthy observations here, but the key claims lack adequate support and/or are over-interpreted.\nOne major concern is about the claimed spatial heterogeneity of ACh (which is the primary novel aspect of this work). To\ninvestigate this they divide their field of view into 50x50 micron regions of interest, and analyze them separately. They\nperform cluster analysis on the ACh response and claim distinct functional clusters. This was not very convincing for several\nreasons:\n- First, there’s not a clear separation between clusters. It appears more like a continuum with subtle differences between the\nrelative heights of the increases and decreases. There’s no clear indication of statistical significance in the difference\nbetween response types, which is not surprising given that a) the response is highly variable from one trial to the next, and b)\nthere are very few trials per day (~40), and that includes both rewarded and unrewarded, so the number for one category or\nthe other in a given recording session may be very small indeed). How can the authors rule out the alternative possibility that\nthe underlying ACh signal is exactly the same throughout the FOV, but with spatially variable noise?\n- Second, why 50x50 microns? This seems like an arbitrary choice that has nothing to do with the circuit organization of the\nstriatum. Are the results different if this choice is different? It’s not reasonable to refer to these arbitrary zones as striatal\n“subregions”. And without some reason to choose these particular 50x50 zones, they remain a “bulk” measure of\nneuromodulator signaling like fiber photometry, and the approach is less useful than it should be (less useful than e.g. recent\nmicro-fiber array work such as the cited Bouabid et al reference).\n- Third, the images shown show clear spatial structure in the fluorescence signal. As expected for a GRIN lens the signal is\nmuch fainter at the edges - yet the edges are equally included in the analysis and seem to show similar results. Furthermore,\nthe center of the image shows clear structure of some form… are these dendrites or what? What happens if the ROI is\nchosen to match the locations of expression - does the behavior-linked signal get stronger, as might be expected? I.e.\nhow does deltaF/F depend on F? It was also a concern that the apparent neurites visible in the images don’t seem to scale in\nsize in a way consistent with the scale bars (e.g. comparing Fig. 2b to 2d).\nMany prior studies have shown the distinct properties of different dorsal striatal subregions (defined especially along the\nmedial-lateral axis, also the anterior-posterior axis). This aspect is not adequately considered here in the experimental\ndesign and analyses. E.g.the GRIN lens covers a fairly wide medial-lateral extent of the dorsal striatum, and based on prior\nwork we might expect more medial parts to be more involved in reversal learning / behavioral flexibility - this should be\nexamined.\nThe behavioral task is problematic in several aspects. Besides the low number of trials, just a single reversal further reduces\nthe amount of data available for analysis. Some analyses have notable confounds - e.g. in Fig 4j. it’s not reasonable to\ndirectly compare the lose-shift and lose-stay trials in this way, because (on average) they will be happening at different\ntimes after reversal (later and earlier respectively). Each trial is surprisingly long - around ~35s even in the “late” phase of learning.\nWhy are the mice so slow, and what are they doing in all this time? Overall there seems to be a poor match between the\nbehavioral task design, recording methodology and the analysis approach that precludes strong novel conclusions about\nstriatal ACh function.\nOther points:\nTo assess the imaging conditions it would also be helpful if the sensor used here was better characterized - is the preprint\nfrom 2020 still the most up-to-date reference?\ne.g. line 131: “ iAChSnFR is very fast, so the timing of these dips is reflective of ACh, not a sensor artifact.”\n- we need specific support for these assertions.\nDiscussion is often repetitive and/or confusingly written. Some examples:\n- line 270 “subregions responded to various combinations of expected and unexpected rewards and non-rewards associated\nwith both shift and stay responses. “\n- line 285 “However, these behavioral tasks did not provide a reward alternative.”\n- the two sentences on lines 293-297 seem to say the same thing.\n- too many mentions of “the causal relationship” between ACh and lose-shift behavior.\n- mention of travelling v standing waves not clear enough for most readers, especially if they haven't read the cited paper.\nIn terms of interpreting the results…\n- how much of the ACh increase is about the omission of an expected reward, and how much about the active presentation\nof unexpected non-reward cue?\n- line 314 “Thus, we propose that neural pathways exposed to increased ACh on unexpected non-reward may be preserved,\nprotecting old learning” - wouldn’t that lead to *lack* of behavioral change, rather than enhanced change (switching)?\nline 63: “mice were trained in daily sessions of ~40 trials until they reached acquisition criterion (80% correct choices)”\n- over what … a session?\n“A1-A5” not explained - presumably the acquisition sessions 1-5?\nline 71: “Increases in anticipatory licking were positively correlated with advancement in the maze“\n- actually what is shown is “lick index” - the proportion of licks that are to the rewarded side (if I understood correctly). So this\nis not quite the same as an increase in anticipatory licking.\nline 81: “reduced trial latency”\n- latency here seems to mean duration - it would be better to just say duration, as latency is used in other work to mean e.g.\ntime to begin a trial, rather than complete it.\nline 93: “The response to ACh was later blocked with neostigmine.”\n- define “later”.\n- why is the neostigmine trace noisier, and why does it decrease with ACh puff?\nFig. 2d: ROIs very hard to see.\nFigs 2d, e, etc.. state the number of individual trials included in each average.\nline 99: “The features of these dips in ACh levels (Fig. 2f,g; dip amplitude, -1.64 ± 0.07 z-scored ΔF/F; onset, 0.23 ± 0.01 s;\ndip latency i.e., time to dip, 0.48 ± 0.03 s, mean ± s.e.m., n = 11) are consistent with pauses in tonic activity”\n- what exactly makes these numbers consistent with pauses?\nline 105: “We also noted some variability in reward responses across trials (example mouse in Fig. 2e, left), as previously\nreported”\n- how do the authors distinguish real biological variability from trial to trial, from measurement noise?\nFig.3bii error bars are very small for these clusters - looks like simulaneously recorded ROIs from the same FOV are being\ntreated as independent? would make more sense to average within clusters from the same experiment first, then calculate\nerror bars at the animal level.\nFig3D is hard to understand - e.g. does the cluster 2 NR 100% bar actually indicate that none of the subregions had a dip\nwithin 1 second? It would be useful to show the mean NR responses for each cluster.\nFig. 4b: could be helpful to include trials before reversal, as is standard.\nline 141: “The proportion of perseverative errors was 17.1% ± 2.7%”\n- here and elsewhere, need to specify exactly which trials/sessions are included.\nline 152: “ Incorrect responses, however, were associated with a delayed, progressive increase in the spatially averaged\nlevels of ACh”\n- what exactly makes it “progressive”? Not clear enough.\n- how does the high variability shown in 4i relate to the outcome of the previous (and earlier) trials?\n- where in the FOV are the 4i examples taken from?\nline 239: “Additionally, cumulative errors over the first 80 trials post-reversal were significantly higher in J60-treated mice\ncompared to saline-treated controls (Fig. 6f).”\n- not clear what this additional analysis adds to the story.\nline 245: “While the difference in perseverative errors was not significant (P = 0.47, unpaired t-test), a significant difference in\nthe proportion of regressive errors was observed between the two groups”\n- need to explain “perserverative” vs “regression” and contrast to lose-stay v win-shift (seems different given Fig. 6g)\nline 253: “Although our inhibition protocol lacked temporal specificity, our findings align with our initial observation of\ndecreased switch probability in mice with reduced ACh levels.”\n- the initial observation doesn’t measure ACh \"levels”.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "We thank the reviewers for their detailed and thoughtful assessment of our work and for\nrecognizing the importance and broader implications. Their comments have been invaluable in\nimproving the quality of our manuscript. We have addressed all comments as follows. Changes to\nthe manuscript text are highlighted in red.\nReviewer #1 (Remarks to the Author):\nSarpong et al\nReviewer comment\nThis is a very interesting paper that examines changes in acetycholine (Ach) release in the dorsal\nstriatum of mice during performance on a Y maze discrimination and after the reversal of the\ndiscrimination. The protocol uses head fixed mice on a virtual maze allowing clear measures of\nACh dynamics and localised changes in identified clusters of regionally defined subregions with\nthe field of view in the striatum divided into a 15 x 15 grid. This provided further detail by\nproviding an assessment of the heterogeneity of changes in dynamic responses. The authors focus\ntheir assessment of ACh activity to the period during which reward was delivered after a correct\nresponse in the maze or during a comparable period of non-reward after an incorrect response.\nThis provides a relevant but limited period of assessment. Thus, for example, ACh activity during\nthe anticipation of reward was not reported, either pre- or post-choice as the animal approach the\nchoice point and after it had selected an arm in the maze. This could have been informative.\nNevertheless, the authors report considerable behavioural data from this period, including\nmeasure of anticipation, such as anticipatory licking. The findings are reasonably clear, showing\nlarge differences in responding to reward and non-reward during initial training and after\nreversal. Interestingly, reward-related activity was found to be heterogeneous across regions: in\nmost clusters showing a pause, sometimes after a preceding increase in activity, but also, in other\nclusters, a rebound excitation after the pause within the field of view. Even more interesting was\nthe finding that an increase in ACh activity after non-reward predicted a shift in arm selection\n(i.e., lose-shift performance) but only after reversal suggesting increased ACh release is relevant\nto acquiring the reversed contingencies. Finally, to establish the causal relevance of ACh relese\nto reversal performance, a Cre-dependent inhibitory DREADD was infused into the dorsal\nstriatum in Chat-Cre mice. Inhibition of ACh reduced reversal performance as well as lose-shift\nprobability.\nOverall, this is a nice set of findings. Nevertheless, I had a few issues with the presentation and\nsome questions regarding the interpretation of the findings.\n1. The paper is framed in terms of the role of ACh in cognitive flexibility and, indeed, a large\nnumber of variables were analysed from the behavior of the mice during the acquisition of maze\nlearning. However, monitoring of ACH activity was limited to reward delivery or non-delivery\n1\nperiod only. Despite the framing in terms of flexibility, therefore, the relationship between ACh\nrelease and performance is difficult to draw\nAuthor response\nWe agree that it would be useful to add information about the ACh activity during the approach to\nthe choice point. We have added data and further analyses of ACh signaling during approach\n(Extended data Fig 3) and report this in the manuscript as follows (line 129):\n“As mice approached the outcome point of the maze, spatially averaged ACh signals increased\nprogressively until the mouse reached the outcome zone (Extended Data Figure 3a). The increase\nwas similar in rewarded and non-rewarded outcomes. ACh signals across trials were classified\naccording to whether linear regression analysis showed significant positive or negative slope\nduring the last 5 s preceding trial outcome. The greatest proportion of rewarded and unrewarded\ntrials showed positive ramping (52% and 48%, respectively), and less commonly negative (32%\nand 31%, respectively) or non-significant changes (Extended Data Figure 3b, c). These\ndistributions were not statistically different across conditions (X2(2) = 1.52, P = 0.46).”\nReviewer comment\nThe first 3 figures focus on ACh release during initial acquisition of performance on the Y maze.\nHowever, although performance is differentially rewarded, and there are both rewarded and\nunrewarded movements, at this stage, according to the release dynamics during reward/non-\nreward (without consideration of the degree to which its anticipated) ACh appears to be\nresponding solely to reward. When no reward is delivered there is no, or at least a much\ndiminished, response. So even when the animal makes an incorrect response its hard to see that\nAch is signalling anything useful to the animal to alter its behaviour.\nAuthor response\nWe did not explain what we meant by “behavioral flexibility” very well in the original manuscript.\nBy “behavioral flexibility” we mean “the ability to adjust behavior in response to changes in the\nenvironment”. Thus, we consider behavioral flexibility to be a step beyond adapting to fixed\ncontingencies in a constant environment, toward adopting a new strategy when contingencies\nchange. By this definition, learning the initial maze is not behavioral flexibility because the\ncontingencies (i.e. the rewarded side) are kept constant during the initial learning. We now define\nwhat we mean more clearly in the manuscript, as follows (line 36):\n“More formally, we define behavioral flexibility as the ability to adjust behavior in response to\nchanges in the environmental contingencies. Thus, behavioral flexibility requires adopting a new\nstrategy, which is a step beyond learning under constant rules.”\nHaving clarified our intended meaning, we agree with the reviewer that during the initial learning\nunder constant reward conditions, ACh appears to be responding solely to reward. When no reward\n2\nis delivered there is a diminished response, in the same direction as when reward is delivered, but\nmuch smaller. We think it is consistent with our hypothesis that non reward due to incorrect\nresponding in a constant environment does not engage ACh signalling. We have added the\nfollowing comment to the manuscript to clarify this point (line 442):\n“During initial learning ACh appears to be responding solely to reward. When no reward is\ndelivered due to an incorrect choice there is a much diminished response in the same direction.\nThis suggests that when the animal makes an incorrect response in a constant environment ACh\nsignalling is not engaged and behavioral alterations are driven by other neuromodulators.”\nReviewer comment\nHow does this accord with the behavioral flexibility hypothesis?\nAuthor response\nOur hypothesis is that when the animal makes a previously rewarded response, but does not receive\na reward, ACh signals the condition of “unexpected non-reward”. Thus, the ACh signal in response\nto non-reward is proposed to be specific to when a “previously correct” response is, without\nwarning, suddenly not rewarded. In this sense “expected” is defined statistically by the history of\nprevious reward or non-reward. We do not impute a subjective state of the animal. We have added\na sentence to the manuscript to explain this point more clearly in the discussion (line 203):\n“We propose that when the mouse makes a previously rewarded response, but does not receive a\nreward, ACh signals the condition of “unexpected non-reward”. Thus, the ACh signal in response\nto non-reward is proposed to be specific to when a “previously correct” response, without warning,\nceases to be rewarded.”\nReviewer comment\nSurely, acquiring performance on the maze requires learning and so some degree of flexibility?\nAuthor response\nWe thank the reviewer for this comment. To be clear, we are not hypothesizing that CINs are\nessential for all learning but rather that they play a specific role in behavioral flexibility when the\nenvironmental contingencies change. Previously we1 and others2 have shown that a lesion of CINs\ndoes not reduce initial learning but slows switching to new responses after a rule change. We\nacknowledge that we did note explain this clearly in the original manuscript, and we have added\nthe following sentence to the discussion to explain this point more clearly (line 446):\n“Thus, we suggest that CINs are not essential for all learning but play a specific role in behavioral\nflexibility when the environmental contingencies change. This is consistent with previous work1,2\n3\nshowing that a lesion of CINs does not reduce initial learning but slows switching to new responses\nafter a rule change.\nReviewer comment\n2. Along these lines, what do the animals actually do when unrewarded? I was interested in two\nthings here: (i) Is the degree of ACh response to non-reward at all modulated by ACh release in\nanticipation of reward? Does it differ according to the degree the animals are anticipating reward\nprior to non-reward?\nAuthor response\nWe agree that this is an interesting question, and we have undertaken additional analyses of ACh\nactivity preceding outcome, as previously described. These analyses show there is a modest\nramping in ACh levels prior to both rewarded and non-rewarded outcomes. However, a\ncomparison of ramping between rewarded and non-reward outcomes showed no significant\ndifference between these outcomes, suggesting that the differences in ACh responses are not\nmodulated by anticipation of reward. We have added the additional analysis to the manuscript and\nreported it as follows (line 129):\n“As mice approached the outcome point of the maze, spatially averaged ACh signals increased\nprogressively until the mouse reached the outcome zone (Extended Data Figure 3a). The increase\nwas similar in rewarded and non-rewarded outcomes. ACh signals across trials were classified\naccording to whether linear regression analysis showed significant positive or negative slope\nduring the last 5 s preceding trial outcome. The greatest proportion of rewarded and unrewarded\ntrials showed positive ramping (52% and 48%, respectively), and less commonly negative (32%\nand 31%, respectively) or non-significant changes (Extended Data Figure 3b, c). These\ndistributions were not statistically different across conditions (X2(2) = 1.52, P = 0.46).”\nWe address this again below (page 8) in response to an overlapping comment.\nReviewer comment\n(ii) Is the ACh response influenced by the animals’ behavioral responses after non-reward at all -\ne.g., degree of orienting/search or activity perhaps related to frustration? We are not told. There\nis considerable detail regarding the animals’ behaviour up to reward delivery but not afterwards.\n3. Relatedly, I found myself wondering if the change in ACH response after reversal in the presence\nof non-reward is in anyway[sic] related to motor activity/orienting/attentional changes and\nwhether these can be detected in animals’ behavior as a change in activity after reversal.\nAuthor response\nparticular, we have expanded our analysis of trials after unexpected non-reward by including the\nfollowing additional measures: trial duration, locomotor speed, and anticipatory licking following\nunrewarded trials, particularly focusing on early (lose-stay) vs. late (lose-shift) transitions. We also\nassessed post-outcome differences in locomotor velocity between rewarded and unrewarded trials\nduring the reversal, as follows.\nVelocity and trial duration\nWe assessed post-outcome differences in locomotor velocity between rewarded and unrewarded\ntrials during the entire reversal session and found no significant differences between correct and\nincorrect trials relative to pre-reversal phase (Extended Data Fig. 5g). However, during the first\n15-20 trials after reversal, we did observe slightly longer durations on lose-shift trials (i.e. entries\nto the new reward arm) compared to pre-reversal (correct) trials (Extended Data Fig. 5h), possibly\nindicating increased “deliberation” or “search-and-evaluate process” in adaptive decision-making\n(Redish, Nat Rev Neurosci 2016).\nLocomotor speed\nTo address whether ACh responses were due to outcome-related motor activity, we analyzed trial-\nby-trial velocity during the outcome period (Fig. 4k and Extended data Fig. 6c). Our observations\nshow that mice typically decelerated or stopped during the outcome period, with velocity near zero\nduring the same time window in which ACh levels decreased during task acquisition (Fig. 2e) but\nincreased during reversals (Fig. 4g). These findings argue against movement as a primary driver\nof the ACh signal. Importantly, the enhanced ACh signals we observe after reversal appear to be\npredictive of subsequent behavioral adaptation, suggesting that they may reflect more than just a\nreactive motor signal.\nAnticipatory licking\nWe found a trend toward reduced anticipatory licking over time in trials following unexpected\nnon-reward trials (Extended data Fig. 5f), suggesting a gradual decrease in reward expectation (on\nentries to the previously rewarded arm) as mice adapted to the new contingency.\nThese behavioral adjustments follow the heightened ACh increase observed prior to lose-shift\ndecisions, supporting a potential link between ACh release and adaptive behavioral updating after\nfeedback.\nIn relation to reactions immediately after unexpected non-reward we agree that linking ACh\nresponses to behavioral state variables such as orienting, attentional engagement, or motor activity\nis interesting and important. However, our ability to do this is limited because of the head-fixed\nconfiguration used in our study. While we can measure locomotor activity on the ball, the mouse\ncannot make the head movements and postural orientations that are typical of the orienting\n5\nresponse. Although in principle it would be possible to measure eye movements, we are not\nequipped for eye-tracking and need to avoid contamination of the 2-photon signal by extraneous\nlight sources. We have nevertheless undertaken additional analyses of locomotor activity during\nthe outcome period and added these to the manuscript.\nWe have clarified these findings in the revised manuscript as follows (line 236).\n“The change in ACh response after unexpected non-reward could, in theory, be related to motor\nactivity due to orienting responses. To address this, we analyzed the trial-by-trial velocities during\nthe outcome period and found no significant correlation between mean ACh signals and velocity\nat the time of no-reward (Fig. 4k). Our analysis showed that mice typically decelerated or stopped\nduring the outcome period, with velocity near zero during the same time window in which ACh\nlevels decreased during task acquisition (Fig. 2e) but increased during reversal (Fig. 4g and\nExtended Data Fig. 6c). As previously described, we also found a trend toward reduced\nanticipatory licking over time in trials following unexpected non-reward (Extended Data Fig. 5f),\nsuggesting a gradual decrease in reward expectation (on entries to the previously rewarded arm)\nas mice adapted to the new contingency. These behavioral adjustments follow the heightened ACh\nincrease observed prior to lose-shift decisions, supporting a potential link between ACh release\nand adaptive behavioral updating after feedback. These observations argue against movement as a\nprimary driver of the ACh signal. Importantly, the enhanced ACh signals we observed after\nreversal appear to be predictive of subsequent behavioral adaptation, rather than just a reactive\nmotor signal.\nIt is also theoretically possible that the long-latency ACh increases in response to unexpected non-\nreward were related to attentional shifts. If these signals were primarily driven by attentional\nengagement with task-relevant cues or trial events, one would expect them to be present more\nuniformly across different task epochs (Extended Data Fig. 6d), particularly during initial learning\nphases when task demands and attentional load are also high. However, our behavioral data\ndemonstrated that the magnitude and timing of these long-latency ACh responses correlate with\nbehavioral adaptation following unexpected non-reward, particularly during lose-shift decisions\n(Fig. 4i). This link between ACh dynamics and adaptive switching supports a role in learning from\noutcome violations as indicated by the pre-outcome anticipatory responses (Extended Data Fig.\n6e), rather than purely attentional tracking of task features. In addition, the long-latency ACh\nincreases occurred specifically in the context of contingency reversals and were absent during\nearlier phases of learning.”\nReviewer Comment\nThe increase in Ach after an incorrect response during reversal is interesting but, within-animals,\nhow different is this response to that observed to non-reward during training and during the period\nimmediately prior to reversal?\nFollowing the Reviewer’s suggestion, we compared ACh responses to non-reward before and after\nreversal within the same animals, using per-mouse averages. We report these in the revised result\nsection as follows (line 226):\n“Comparison of ACh responses to non-reward before and after reversal within the same animals,\nusing per-mouse averages, revealed a significant increase in the spatially averaged ACh response\nto non-reward during reversal compared to non-reward during task acquisition (training i.e., early\nand pre-reversal periods i.e., late), confirming that this signal is not only present but more spatially\nwidespread during behavioral switching (Extended Data Fig. 6d). Notably, the ACh increases\nobserved in response to non-reward during acquisition, present in a subset of individual quadrats\n(cluster 4 in Fig. 4b-d), may reflect a preexisting substrate for outcome-related ACh modulation.\nThis modulation appears to be amplified and more broadly recruited during reversal, when\nbehavioral flexibility demands are heightened”\nReviewer comment\nThe authors state: “Thus, the direction and magnitude of ACh signals during the no-reward\noutcome period predicted future behavioral shifts.” Do they predict this because they are\nindicative of learning or because of some reaction the animals have to non-reward? There is a\nquite clear analysis of behavioral changes associated with reward. What would a similarly\ncomplete analysis of behavioral responses to non-reward look like and could this anticipate the\nresponses after reversal? I suppose ot is worth also considering here the role of shifts in ‘mood’ –\nby which I mean responses to frustrative non-reward, and in orienting rather than simply changes\nin behavior generally.\nAuthor Response\nWe thank the reviewer for this insightful question. Our interpretation is that the ACh increases\nobserved following unexpected non-reward reflect an early component of behavioral adaptation,\nlikely serving as a learning signal (i.e. the cholinergic system has learned this outcome is no longer\nvalid) rather than a direct consequence of a change in mood. However, we agree that distinguishing\nwhether ACh changes reflect learning per se or more immediate affective/motivational reactions\n(e.g. frustration) that may coincide or be influenced by changes in reward prediction, is critical.\nTo address this comment we discuss this in the revised discussion as follows (line 451):\n“We speculate that the ACh increases observed following unexpected non-reward reflect an early\ncomponent of behavioral adaptation, serving as a learning signal in which the CINs are signalling\nthat the expected outcome did not occur. However, distinguishing whether ACh changes reflect\nlearning per se or more immediate affective/motivational reactions (e.g. frustration) that may\ncoincide or be influenced by changes in reward prediction, is critical. These mechanisms cannot\n7\nbe separated with the current dataset, and further experiments will be required to investigate these\naspects.”\nSince our response overlaps with an earlier response made above, we note here that we also\nexpanded our analysis of behavioral responses to non-rewarded outcomes. Specifically, we now\ninclude measures of trial duration, locomotor speed, and anticipatory licking following unrewarded\ntrials (Extended Data Fig. 5f-h).\nReviewer Comment\n4. Where[sic] changes in ACh signals observed in anticipation of reward? One assumes that the\nsignals would have been similar prior to correct or incorrect choices if the animals were\nanticipating reward similarly prior to choice. However, only 1s of baseline was used prior to\nreward signals. Does df/f change with a longer baseline?\nAuthor Response:\nTo address this point, we extended our analysis window to include a 5-second pre-outcome period.\nAcross the population, this revealed similar dF/F ramping signals during the approach phase for\nboth correct and incorrect trials (Extended Data Fig. 3). These data indicate that outcome-evoked\nACh transients were not preceded by outcome-specific anticipatory activity. We have added this\nobservation to the revised manuscript as follows (line 129):\n“As mice approached the outcome point of the maze, spatially averaged ACh signals increased\nprogressively until the mouse reached the outcome zone (Extended Data Figure 3a). The increase\nwas similar in rewarded and non-rewarded outcomes. ACh signals across trials were classified\naccording to whether linear regression analysis showed significant positive or negative slope\nduring the last 5 s preceding trial outcome. The greatest proportion of rewarded and unrewarded\ntrials showed positive ramping (52% and 48%, respectively), and less commonly negative (32%\nand 31%, respectively) or non-significant changes (Extended Data Figure 3b, c). These\ndistributions were not statistically different across conditions (X2(2) = 1.52, P = 0.46).”\nReviewer #2 (Remarks to the Author):\nReviewer Comment\nThe manuscript describes findings from a series of experiments that demonstrates the dynamic\nchanges in dorsal striatal acetylcholine efflux during learning and reversal learning. There are\nseveral innovations carried out in these studies that provide significant insights of how actions of\nstriatal cholinergic interneurons underlie learning and behavioral flexibility.\nAt the core there are three main experiments. The first combining a genetically encoded\n8\nfluorescent acetylcholine sensor with 2-photon microscopy to determine how acetylcholine within\nsubregions of the dorsal striatum changes during learning and reversal learning. A second study\nwas conducted in striatal slices demonstrating that the fluorescent sensor was actually measuring\nacetylcholine. A third experiment employing chemogenetics selectively reduced striatal\nacetylcholine activity and showed that this manipulation impaired reversal learning consistent\nwith what would be predicted from the findings in their first experiment.\nWhat stands out about this manuscript is the creativity in carrying out their first and main\nexperiment investigating striatal acetylcholine changes during learning and reversal learning.\nIntegrating modern techniques the main experiment is able to reveal how dynamic changes in\ndorsal striatal acetylcholine output supports learning and behavioral flexibility at a\nspatiotemporal scale that has not been achieved before. Further, the breadth and depth of their\nanalyses really provides a comprehensive picture of the how dynamic changes in acetylcholine\noccur in striatal subregions. Overall, the findings from these experiments make significant\nadvances in revealing how striatal cholinergic interneuron activity facilitates learning and\nbehavioral flexibility. These findings will have a broad interest for anyone interested in the brain\nmechanisms that support learning and behavioral flexibility.\nI have two main comments related to the manuscript:\n1. The authors use the term \"disappointment\" referring to when a mouse makes an incorrect choice\nthat does not lead to a reward. This term really seems like a projection of a human experience\nonto the mouse that is not necessary. The results are extremely strong and convincing so using an\nalternative term that more simply captures the behavior would suffice, i.e. unexpected non-reward.\nAuthor Response\nThank you for the suggestion. We have replaced “disappointment” with “unexpected non-reward”.\nReviewer Comment\n2. A real strength of the manuscript is the hierarchical clustering of acetylcholine responses within\nsubregions. I was not clear whether the data included in this analysis was for all trials in all\nsessions mice were tested or only for a subset of trials, i.e. late learning? Related, did the same\nclustering pattern occur throughout testing or was it more prominent in a particular stage of\nlearning, i.e. late vs. early.\nAuthor Response\nThe clustering analysis presented in the manuscript was based on session averages of all mice in\nthe late phase of learning, specifically when animals had reached criterion performance (80%\ncorrect). To further explore how these patterns may evolve over learning, we have performed\nadditional analyses (and clustering) on sessions from the early acquisition phase, while also\nacknowledging the unstable nature of learning at such an early phase. Nonetheless, while four\n9\ndistinct ACh response patterns were identified during late learning (i.e., pause, burst-pause, pause-\nburst, and increase) (Fig. 3b-d), only two primary response clusters emerged during early learning,\nwhich we classify as long-latency pause (~21%) and short-latency pause (79%). We now include\nthis additional analysis in Extended data Fig. 4g,h and address the reviewer’s comments as follows\n(line 1349):\n“… we classified the response patterns of all recorded quadrats (2475 quadrats from 11 mice)\nusing principal component analysis (PCA) to reduce dimensionality followed by hierarchical\nclustering algorithm.”\nand as follows (line 1376):\n“While four distinct ACh response patterns were identified during late learning (i.e., pause, burst-\npause, pause-burst, and increase) (Fig. 3b-d), only two primary response clusters emerged during\nearly learning, which we classify as long-latency pause (~21%) and short-latency pause (79%)\n(Extended Data Fig. 4g,h).”\nReviewer #3 (Remarks to the Author):\nSarpong et al. investigate the spatiotemporal dynamics of acetylcholine (ACh) in the dorsal\nstriatum, using a genetically encoded sensor in head-fixed mice choosing between left and right\npaths in a virtual environment. This is an important topic and they observe some interesting results.\nThe primary finding is that under reversal conditions (reward location switches from right to left)\narrival at the no-longer-rewarded location causes an increase in ACh, rather than the dip seen\nwith rewards. The size of this no-reward increase is predictive of behavioral switching, and\nsuppressing ACh interneurons makes switching slower. They also find limited evidence for spatial\nheterogeneity in the ACh response. Overall there are some noteworthy observations here, but the\nkey claims lack adequate support and/or are over-interpreted.\nOne major concern is about the claimed spatial heterogeneity of ACh (which is the primary novel\naspect of this work). To investigate this they divide their field of view into 50x50 micron regions\nof interest, and analyze them separately. They perform cluster analysis on the ACh response and\nclaim distinct functional clusters. This was not very convincing for several reasons:\n- First, there’s not a clear separation between clusters. It appears more like a continuum with\nsubtle differences between the relative heights of the increases and decreases.\nAuthor Response\nTo emphasize, hierarchical clustering often reveals gradations in signal morphology rather than\nsharply discrete groups in complex biological datasets. To quantify separation between clusters,\n10\nwe have further computed the silhouette coefficient for the hierarchical clustering using Euclidean\ndistance, giving a mean of 0.21 and 0.15 with a range of -0.55 – +0.64 and -0.44 – +1.0 for reward\nand no-reward clusters, respectively. This value for the silhouette coefficient suggests that the\nseparation between clusters is weak and there may be an overlap. We have added this analysis to\nthe revised manuscript as follows (line 149):\n“The clustering approach revealed gradations in signal morphology rather than sharply discrete\ngroups (silhouette coefficient mean 0.21, range, -0.55 – +0.64 for reward clusters, and mean 0.15,\nrange, -0.44 – +1.0 for no-reward clusters) indicating weak separation between clusters lying along\na continuum (Extended Data Fig. 4d). This is consistent with the fact that the concentration\nmeasures in the analysis are from physically adjacent quadrats along continuous concentration\ngradients. These clusters provide a heuristic grouping that captures local similarity in response\npatterns.”\nReviewer Comment\nThere’s no clear indication of statistical significance in the difference between response types,\nwhich is not surprising given that a) the response is highly variable from one trial to the next, and\nb) there are very few trials per day (~40), and that includes both rewarded and unrewarded, so\nthe number for one category or the other in a given recording session may be very small indeed).\nHow can the authors rule out the alternative possibility that the underlying ACh signal is exactly\nthe same throughout the FOV, but with spatially variable noise?\nAuthor Response\nWe have taken several steps to ensure that our clustering reflects meaningful functional differences.\nFollowing unsupervised hierarchical clustering, we applied strict post hoc classification criteria to\nvalidate the functional relevance of each response type (Dorst et al., Nat Commun 2020). For\ninstance, a pause was deemed significant if the decrease in activity exceeded 2 standard deviations\n(SD) below baseline; a burst-pause required a preceding increase >2 SD above baseline followed\nby a pause; a pause-burst required a >2 SD burst following a pause; and an increase if the activity\n>2 SD above baseline. This approach ensured that cluster identities were not arbitrary but reflected\nstatistically defined signal changes. We have explained this in the methods section as follows (line\n1365):\n“… we applied strict post hoc classification criteria to validate the functional significance of each\nresponse type using statistical thresholds (±2 SD). A quadrat was classified as ‘decrease type’ if\nits activity did not exceed 1 SD above baseline for any time steps from 0 to 1000 ms of reward\ndelivery, and the minimum activity was less than 2 SD below baseline. A quadrat was identified\nas increase-decrease if its activity exceeded 2 SD above baseline for at least one time step from 0\nto 400 ms of reward delivery followed by a pause. A rebound burst after the decrease was defined\nas maximum activity exceeding 2 SD for at least one time step from 500 to 1000 ms of reward\n11\ndelivery. A quadrat was classified as ‘increase type’ if the activity exceeded 2 SD above baseline\nduring the time of reward delivery.”\nFollowing this criteria, ~79% and 45% of quadrats were classified as reward and no-reward\nresponsive, respectively (n = 2475 qudrats from 11 mice). Our revised manuscript specifies more\nexplicitly that the differences in distribution of response types were tested with the Chi-square test,\nwhich revealed a significant difference between reward and no-reward conditions (Chi-square:\nX2(3) = 232.7, P < 0.0001). We have reported these differences in the revised text and updated\nFigure 3 as follows (line 172):\n“Overall, the proportions of the observed response types were significantly different between\nreward and no-reward outcomes (Chi-square test: X2(3) = 232.7, P < 0.0001), indicating\ndifferential ACh dynamics depending on trial outcome.”\nAnd (line 858):\n“A total of 2475 quadrats were imaged in 11 mice. Of these, ~79% and 45% were classified as\nreward and no-reward responsive, respectively. The response types between R and NR outcomes\nwere significantly different in proportion: X2(3) = 232.7, P < 0.0001.”\nWe also observed that certain response types (e.g., pause, pause-burst) were spatially organized\nwithin the dorsal striatum — more so than expected by chance, showing that clusters were not\nrandomly distributed across the field of view. These findings align with emerging evidence of\ntopographic variation in cholinergic microcircuits 3,4,5. In brief, quadrats exhibiting decreased\n(pause) and burst-pause response patterns were more medially distributed (P = 0.05 and P = 0.04,\nrespectively) and showed a trend toward anterior localization (median position, ~0.9 mm from\nbregma). In contrast, quadrats classified as pause-burst were preferentially located in relatively\nmore posterior regions (P = 0.005) of the dorsal striatum. We have added this data to Figure 3f\nand to the results section as follows (line 162):\n“Quadrats exhibiting decreased (pause) and burst-pause response patterns were more medially\ndistributed (P = 0.05 and P = 0.04, respectively) and showed a trend toward anterior localization\n(median position, ~0.9 mm from bregma). In contrast, quadrats exhibiting pause-burst response\npatterns were preferentially distributed in relatively more posterior regions (P = 0.005) of the DS\n(Fig. 3f).”\nFor clustering, ACh responses were averaged across multiple trials within each phase (i.e., late\nlearning), to reduce noise and focus on consistent patterns. We believe this approach enhanced the\ninterpretability of the observed ACh responses by focusing on sessions in which animals had\nreached behavioral criterion, thereby ensuring that the signals were more reliably associated with\n12\nwell-learned task performance. The dataset of 425 trials (reward trials, 291 and no-reward, 134)\nwas used for the core of this analysis.\nWe discuss the additional analyses as follows (line 459):\n“The distinct response clusters we observed during task acquisition—particularly decrease,\nincrease, and biphasic patterns—were not only defined by statistical thresholds but also displayed\nnon-random spatial organization within the DS. This supports the existence of spatial\nheterogeneity in cholinergic signaling, consistent with recent anatomical and functional studies\ndemonstrating topographical variation in cholinergic interneuron activity and receptor\ndistribution4,6,7. These findings suggest that different microdomains within the striatum may\ndifferentially process action outcomes, potentially contributing to the flexible updating of\nbehavior.”\nReviewer Comment\n- Second, why 50x50 microns? This seems like an arbitrary choice that has nothing to do with the\ncircuit organization of the striatum. Are the results different if this choice is different? It’s not\nreasonable to refer to these arbitrary zones as striatal “subregions”. And without some reason to\nchoose these particular 50x50 zones, they remain a “bulk” measure of neuromodulator signaling\nlike fiber photometry, …\nAuthor Response:\nWe agree that it is important to justify the choice of quadrat size. We have added text to the\nmethods section to address concerns about the arbitrariness of ROI size, and conducted control\nanalyses using different ROI sizes (50×50 µm vs 150×150 µm), as follows (line 1313):\n“The use of this grid size was informed by several considerations beyond convenience. First, the\nchoice of 50×50 µm quadrats was based on a practical compromise between spatial resolution and\nsignal reliability. This size was chosen to ensure sufficient signal-to-noise ratio within each quadrat,\nwhile also capturing meaningful “local” variations in ACh dynamics across the FOV (Extended\nData Fig. 4e). Second, CINs, which are the primary source of striatal ACh, are sparsely distributed\nbut possess extensive axonal arborizations, spanning several hundred micrometers8,9,10. These\nbroad arbors form spatially diffuse neuromodulatory fields that overlap and influence local\nmicrocircuits. However, 2-photon microscopy optically sections the image in the dorsoventral\nplane, and thus samples only a fraction of the arborization of any particular neuron. In addition,\nsome spatial averaging is required to achieve meaningful signal-to-noise ratio on a trial-by-trial\nbasis. To ensure that our observations were not biased by our choice of quadrat size we also\nconducted control analyses using different quadrat sizes (Extended Data Fig. 4e).\nReviewer Comment\n13\n“…and the approach is less useful than it should be (less useful than e.g. recent micro-fiber array\nwork such as the cited Bouabid et al reference)”.\nAuthor Response\nWe respectfully but firmly disagree that 2-photon imaging is “less useful” than micro-fiber array\nrecordings in general, and in particular for our question. Different approaches measure different\nthings and address different questions. Individual fibers in micro-fiber array recordings sample\nunknown volumes because the spread of excitation is unknown, and falls off with distance from\nthe fiber tip, while the volume of collection of fluorescence also falls off with distance from the\ntip. In the paper the reviewer cites, Bouabid et al estimated a tapered collection volume extending\n100 µm axially and 25 µm radially from each fiber (thus sampling a circle of varying diameter –\ndepending on distance from the tip – up to 50 µm in the horizontal plane). According to their\nestimate of location, the minimum separation of fiber tips in their arrays was 220 µm radially and\n250 µm axially (i.e. a Euclidean distance of at least 330 µm between tips). Thus, the fiber tips do\nnot sample contiguous volumes (by design) but rather cones of estimated maximum radius 25 µm\nseparated by at least 330 µm. Because the tips are scattered in three dimensions throughout the\nvolume of the striatum, these microfiber arrays are good for detecting regional differences but not\ngood for measuring the spatial dimensions of ACh distribution. On the other hand, 2-photon\nmicroscopy provides gap-free measurements across a narrower field of view, which is useful for\nspatially continuous mapping of temporospatial dynamics with high spatial resolution in defined\noptical sections of known dimensions. This complements the discontinuous measurements\nobtained with microfiber techniques by revealing the areas that go undetected between fiber tips,\nand providing absolute spatially defined and localized measurements.\nReviewer Comment\nThird, the images shown show clear spatial structure in the fluorescence signal. As expected for a\nGRIN lens the signal is much fainter at the edges - yet the edges are equally included in the analysis\nand seem to show similar results. Furthermore, the center of the image shows clear structure of\nsome form… are these dendrites or what? What happens if the ROI is chosen to match the\nlocations of expression - does the behavior-linked signal get stronger, as might be expected? I.e.\nhow does deltaF/F depend on F?\nAuthor Response\nWe agree that the baseline fluorescence signal (Fo) is fainter at the edges of the field of view, as\nexpected due to spherical aberrations in GRIN lens imaging. However, in our analysis, ROIs were\nselected based on functional responsiveness, not by Fo. Furthermore, although we used a 1 mm\nGRIN lens for implantation, our imaging field (set by the objective lens) covered between 750-\n800 µm, and we limited our ROI selection to this central area. Thus, a large portion of the GRIN\nlens periphery — where optical quality is typically degraded — was not included in our analysis.\nAlthough Fo drops off at the image periphery this does not indicate a lack of behavior-linked\n14\nactivity. Since our analyses are based on ΔF/F, which normalizes the signal relative to Fo, regions\nwith lower overall brightness can still exhibit robust ΔF/F responses. We have clarified this in the\ntext as follows (line 331):\n“To correct for spherical aberrations inherent in GRIN lens imaging, which results in lower\nbaseline fluorescence signal (Fo) at the edges of the field of view compared to the center, we used\nΔF/Fo normalization, which scales the signal relative to Fo. To ensure that this normalization\neffectively accounts for baseline intensity differences and that our main findings are not simply\ndriven by expression gradients or brightness alone, we assessed whether ΔF/F values were\ndependent on Fo. We found no significant correlation between the mean of the behavior-linked\nsignals and baseline fluorescence across quadrats (Extended Data Fig. 8d,e). Thus, regions with\nlower overall brightness still exhibited robust ΔF/F responses. We also verified that quadrats in\nperipheral and central areas show similar patterns of behavioral modulation (Extended Data Fig.\n8f,g). These analyses indicate that our results are not biased by location within the field of view.”\nRegarding the reviewers comment:\nFurthermore, the center of the image shows clear structure of some form… are these dendrites or\nwhat?\nThis is addressed together with the following comment.\nReviewer comment\nIt was also a concern that the apparent neurites visible in the images don’t seem to scale in\nsize in a way consistent with the scale bars (e.g. comparing Fig. 2b to 2d).\nAuthor response\nWe appreciate the reviewer’s careful observation. The visible structure in the center of the image\nlikely reflects neuropil and fine, unresolved processes rather than individually distinguishable\ndendrites or somata. This is consistent with the widespread expression pattern of iAChSnFR sensor\nwhich labels all neuronal compartments within the striatum, including somata, dendrites, axons,\nand neuropil. The observed features may arise from densely arborized dendritic and axonal\nprocesses of both cholinergic interneurons and other neuron types. The apparent discrepancy in\nneurite size between Figures 2b and 2d likely arises from differences in zoom factor and imaging\npurpose. Figure 2b shows a representative image with a higher magnification mainly for\nvisualization purposes, while Figure 2d reflects the actual zoom levels used during functional\nimaging. The scale bars in each panel are accurate for their respective magnifications. Additionally,\nwe note that the iAChSnFR sensor was expressed in a non-cell-type-specific manner, labeling all\nneuronal elements, including somata, dendrites, and neuropil. As a result, the structures observed\nlikely reflect a mixture of cell types and compartments, which can vary in size. Therefore, direct\ncomparison of apparent neurite dimensions across fields should be interpreted with caution,\n15\nparticularly when the underlying cellular composition may differ. We have revised the figure\nlegend to clarify these points, adding text as follows (line 803):\n“Differences in apparent feature size across panels reflect varying zoom levels; all scale bars are\naccurate to the respective imaging conditions. Additionally, we note that the iAChSnFR sensor\nwas expressed in a non-cell-type-specific manner, labeling all neuronal elements, including somata,\ndendrites, and neuropil. As a result, the structures observed likely reflect a mixture of cell types\nand compartments, which can vary in size. Therefore, direct comparison of apparent neurite\ndimensions across fields should be interpreted with caution”\nReviewer comment\nMany prior studies have shown the distinct properties of different dorsal striatal subregions\n(defined especially along the medial-lateral axis, also the anterior-posterior axis). This aspect is\nnot adequately considered here in the experimental design and analyses. E.g.the GRIN lens covers\na fairly wide medial-lateral extent of the dorsal striatum, and based on prior work we might expect\nmore medial parts to be more involved in reversal learning / behavioral flexibility - this should be\nexamined.\nAuthor Response:\nWe agree regional differences in function are of interest. Although our experiments were not\ndesigned to look for regional differences, in light of the reviewer’s comment, we have undertaken\nadditional analyses to test for differences along the medial-lateral and anterior-posterior axes, to\nthe extent possible with our limited field of view. We have added these results to the manuscript\nas follows (line 285):\n“Examination of quadrats along the medial-lateral and anterior-posterior axes in response to\nunexpected non-reward revealed functional differences. In particular, quadrats with increased\nACh responses to non-reward were significantly more medially distributed (P = 0.03; median\nposition, 1.4 mm from midline) and showed a trend toward anterior portions of the imaging field\n(median position, 0.9 mm from bregma). Conversely, quadrats with decreased responses tended to\nbe relatively more laterally distributed (P = 0.046; median position, 1.6 mm from midline) in the\ndorsal striatum (Figure 5d).”\nWhile this anatomical differentiation was not a central focus of the current study, it offers an\nimportant layer of interpretation and has been fully incorporated into the revised discussion as\nfollows (line 469):\n“Our analyses showed that quadrats showing increased ACh responses to no-reward outcomes\nwere predominantly located in more medial and anterior regions of the imaging field. These results\nalign well with prior findings suggesting a stronger role for the medial striatum in feedback\n16\nprocessing and behavioral adaptation11,12,13. This pattern is consistent with previous studies\nhighlighting the role of the dorsomedial striatum in behavioral flexibility and outcome monitoring.\nAlthough regional differences were not the primary focus of our study, these findings suggest that\nthe enhanced ACh responses observed during reversal may arise, at least in part, from functional\nsubpopulations within the DS. Future studies with more targeted imaging or manipulations could\nfurther delineate these regional contributions.”\nReviewer Comment\nThe behavioral task is problematic in several aspects. Besides the low number of trials, just a\nsingle reversal further reduces the amount of data available for analysis.\nAuthor Response\nWe acknowledge a need to better justify our choice of behavioral task, and have added the\nfollowing text to the manuscript to the discussion to address the reviewer’s concern (line 502):\n“Two-photon imaging imposes practical limitations on session duration due to photobleaching risk,\nlaser heating, and imaging stability — especially in awake, behaving animals14,15. Unlike brief\nPavlovian trials, our VR task produces longer, continuous ACh recordings during decision-making.\nWe therefore used fewer, longer trials, consistent with maze reversal studies that often use just two\nsessions: acquisition and reversal16,17,18. Our task extended this by using multiple acquisition\nsessions, with reversal triggered only after mice reached 80% accuracy. This ensured learning was\ncomplete before reversal, thereby providing a clear contrast at the moment of contingency change.\nAlthough this design led to fewer trials per session, it enhanced the interpretability of ACh\nresponses specifically tied to well-learned behavior. We used a single reversal to elicit responses\nto an unexpected change in reinforcement contingencies under well-learned conditions. We elected\nnot to use repeated reversals, which diminish error-driven responses due to growing expectation\nof change19,20, potentially reducing the salience of non-reward. While this limits trial count, it\nstrengthens the relevance of the observed ACh dynamics to unanticipated outcome shifts requiring\nbehavioral flexibility. In general, a lower number of trials (40 per session across five sessions)\ndoes not impair learning if there is sufficient interval between rewards21 (average of ~50 s in our\ntask).”\nReviewer Comment\nSome analyses have notable confounds - e.g. in Fig 4j. it’s not reasonable to directly compare the\nlose-shift and lose-stay trials in this way, because (on average) they will be happening at different\ntimes after reversal (later and earlier respectively).\nAuthor Response\nWe agree that the comparison of lose-stay vs. lose-shift trials may be confounded by the timing of\nthese trials post-reversal. To address this, we reanalyzed the data by matching trials across a\n17\ncommon post-reversal trial window i.e., first and last 5 trials for each condition. We found that\nACh responses remained relatively greater for lose-shift trials suggesting that this difference is not\nsolely driven by when the trials occurred. Hopefully, this approach emphasizes the distinction\nbetween a behavioral effect and a time-based confound. We have revised the relevant figure and\ndiscussion to acknowledge the temporal structure and interpret the findings with appropriate\ncaution, as follows (Figure 4i), line 217):\n“To avoid potentially confounding the effect of time with the choice of strategy after reversals, we\nalso analyzed the data by matching trials across a common post-reversal trial window i.e., first and\nlast 5 trials for each condition. We found that ACh responses remained relatively greater for lose-\nshift trials suggesting that this difference is not solely driven by when the trials occurred (Fig. 4i,\nright).”\nReviewer Comment\nEach trial is surprisingly long - around ~35s even in the “late” phase of learning. Why are the\nmice so slow, and what are they doing in all this time?.\nAuthor Response\nThe virtual reality Y-maze used in our task spans ~350 cm, and mice control the pace of each trial.\nWe intentionally avoided imposing hard time constraints so as not to artificially shape behavior or\nexclude natural variability in approach speed, exploration or grooming. We have added further\nexplanation and discussion of this point as follows (line 1210):\n“Mice were free to control the pace of each trial without time limits. The average duration from\ntrial start to outcome decreased from 71.1 ± 10.4 s for early sessions to 28.1 ± 2.6 s for late sessions\n(Fig 1h), over the 350 cm length of virtual corridors in the maze. This trial duration includes natural\nvariations in approach speed, exploration and grooming.”\nReviewer Comment\nOverall there seems to be a poor match between the behavioral task design, recording\nmethodology and the analysis approach that precludes strong novel conclusions about\nstriatal ACh function\nAuthor Response\nWe thank the reviewer for their generally thoughtful critique and helpful comments. We believe\nwe have addressed the all the specific concerns underlying this overall comment in full, and added\nclarification of the reasons for the behavioral task design and choice of the recording methodology.\nWe have undertaken additional analyses as suggested. We feel these responses have strengthened\nthe conclusions of the paper.\nReviewer Comment\nTo assess the imaging conditions it would also be helpful if the sensor used here was better\ncharacterized - is the preprint from 2020 still the most up-to-date reference?\ne.g. line 131: “ iAChSnFR is very fast, so the timing of these dips is reflective of ACh, not a sensor\nartifact.”\n- we need specific support for these assertions.\nAuthor Response\nWe have added more details, supporting the fast response time of the sensor, as follows (line 93)\n“iAChSnFR is derived from a microbial periplasmic binding protein (PBP), and like other sensors\nderived from PBPs22 has rise and decay kinetics on a millisecond time scale thus permitting useful\nimaging at >1 kHz. Previous studies using purified iAChSnFR and stopped-flow experiments\nconfirmed that activation has a time constant of ~140 ms at 10 μM ACh and just a few ms at 1 mM\nACh, with k of 0.62 μM-1s-1 and k of 0.73 s-1 23,24.\non off\nReviewer Comment\nDiscussion is often repetitive and/or confusingly written.\nAuthor Response:\nWe agree and we have revised the discussion to reduce repetition and clarify conceptual points.\nReviewer Comment\nSome examples:\n- line 270 “subregions responded to various combinations of expected and unexpected rewards\nand non-rewards associated with both shift and stay responses. “\nAuthor Response:\nWe have reworded the sentence as follows (line 385):\n“Dividing the field of view into a matrix of quadrats revealed non-uniform spatiotemporal\ndynamics of ACh release. Different quadrats showed differential responses to rewards, non-\nrewards after errors, and unexpected non-rewards.”\nReviewer Comment\n- line 285 “However, these behavioral tasks did not provide a reward alternative\nAuthor Response:\nchoices, in order to better highlight the importance of behavioral flexibility, as follows (line 401):\n“However, unlike Pavlovian conditioning paradigms, our task incorporates two active choice\noutcomes, allowing direct assessment of behavioral flexibility through action selection.”\nReviewer Comment\n”- the two sentences on lines 293-297 seem to say the same thing.\n- too many mentions of “the causal relationship” between ACh and lose-shift behavior\nAuthor Response\nThese sentences have been rewritten to avoid redundancy and to streamline the interpretation of\nACh modulation, as follows (line 436)\n“Our observation that ACh increase predicted lose-shift switching suggests the increase not only\nsignals the mismatch implicit in unexpected non-reward but also contributes to the behavioral\nchange. This hypothesis is supported by our finding that lose-shift behavior was reduced after CIN\ninhibition, indicating a direct causal relationship between CIN activity and lose-shift behavior.”\nReviewer Comment\n- mention of travelling v standing waves not clear enough for most readers, especially if they\nhaven't read the cited paper.\nAuthor Response\nThe section discussing traveling versus standing waves has been expanded slightly and reworded\nfor clarity, as follows (495):\n“Further, the spatially heterogeneous ACh responses we observed—occurring without clear\npropagation across the imaging field—may resemble a ‘standing wave’ profile25 in which\nacetylcholine locally activates dopamine fibers without inducing a traveling wave of activation.\nThis suggests that local ACh dynamics can generate spatially confined patterns of neuromodulator\nrelease, potentially contributing to functional compartmentalization within the striatum.\nReviewer Comment\nIn terms of interpreting the results…\n- how much of the ACh increase is about the omission of an expected reward, and how much about\nthe active presentation of unexpected non-reward cue?\nAuthor Response:\nrewarded outcomes were signaled by an abrupt black screen, serving as an explicit cue for non-\nreward. While this visual signal could contribute to the observed ACh response, we believe it is\nunlikely to fully account for the increase. We have detailed these reasons in the revised discussion\nas follows (line 417):\n“An alternative interpretation of the ACh response we observed in response to unexpected non-\nreward is the possibility that the signal indicating non-reward could act as a salient sensory cue\nand contribute to the ACh signal under certain contexts. However, the ACh increase we observed\nfollowing non-reward was specific to the reversal phase and not present during initial acquisition\n— even though the same cue was used throughout. If the ACh response were primarily driven by\nthe non-reward cue, we would expect a similar signal during task acquisition. The fact that this\nincrease emerges specifically after reversal suggests that it reflects a mismatch between expected\nand actual outcomes, rather than mere cue salience. Additionally, the long-latency nature of the\nACh signal (following anticipatory licking) suggests it encodes a violation of reward expectation,\nconsistent with a prediction error-like signal. The response latency is inconsistent with a purely\nsensory response to the cue, which would occur more immediately after the screen goes black.\nThus, it may reflect an internal (cognitive) state of expectant waiting, disrupted by its violation,\nwhich is more characteristic of a prediction error signal than a cue-driven sensory response.\nFurthermore, several studies of electrophysiological recordings from presumed CINs have\nreported a brief decrease in activity after the presentation of a visual stimulus, usually referred to\nas the pause response. In some cases, a rebound activation often occurred immediately after the\npause26,27,28,29. The timing of these responses suggests that the non-reward cue does not account\nfor the increased ACh activity we observed in our study.”\nReviewer Comment\n- line 314 “Thus, we propose that neural pathways exposed to increased ACh on unexpected non-\nreward may be preserved, protecting old learning” - wouldn’t that lead to *lack* of behavioral\nchange, rather than enhanced change (switching)?\nAuthor Response:\nWe agree this is unclear and have rewritten this part of the discussion as follows (line 487):\n“Recent work shows that long-term potentiation of cortical inputs to striatal projection neurons\nrequires coincidence of pauses in CINs with phasic dopamine activation57. Thus, we propose that\nneural pathways exposed to decreased ACh on reward delivery – causing a pause after successfully\nshifting – would experience synaptic potentiation. Conversely, pathways exposed to increased\nACh on unexpected non-reward would not be potentiated, preventing reinforcement of incorrect\nresponses. This would ultimately lead to a reduction in interference from pre-existing memory16.\nparallel in different quadrats. Thus ACh activity in the striatum supports diverse adaptive\nresponses, because of its spatiotemporal heterogeneity.”\nReviewer Comment\nline 63: “mice were trained in daily sessions of ~40 trials until they reached acquisition criterion\n(80% correct choices)”\n- over what … a session?\nAuthor Response\nYes, that is correct. We have added text to clarify that point (line 64):\n“…until they reached acquisition criterion of 80% correct choices in at least one session”\nReviewer Comment\n“A1-A5” not explained - presumably the acquisition sessions 1-5?\nAuthor Response\nThanks for spotting this oversight. Yes, A1-A5 refers to acquisition sessions 1-5. We have clarified\nthis in the revised text (line 765):\n“A1-A5, acquisition sessions 1-5”\nReviewer Comment\nline 71: “Increases in anticipatory licking were positively correlated with advancement in the\nmaze“\n- actually what is shown is “lick index” - the proportion of licks that are to the rewarded side (if I\nunderstood correctly). So this is not quite the same as an increase in anticipatory licking.\nAuthor Response\nThank you for this helpful clarification. We agree that our metric reflects a bias in anticipatory\nlicking toward the ultimately rewarded side, rather than an absolute increase in licking frequency.\nWe have revised the text to clarify this distinction, as follows (line 1274):\n“a directional preference in anticipatory licking”\nReviewer Comment\nline 81: “reduced trial latency”\n22\n- latency here seems to mean duration - it would be better to just say duration, as latency is used\nin other work to mean e.g. time to begin a trial, rather than complete it.\nAuthor Response\nThank you. We have replaced “latency” with “duration”.\nReviewer Comment\nline 93: “The response to ACh was later blocked with neostigmine.”\n- define “later”.\n- why is the neostigmine trace noisier, and why does it decrease with ACh puff?\nAuthor Response\nWe have defined “later” in the text as follows (line 102):\n“To validate sensor specificity, we first applied exogenous ACh to brain slices in ACSF, followed\nby application of neostigmine (50 µM), an acetylcholinesterase inhibitor, 15-20 minutes later,\nwhich blocked the response to ACh (Fig. 2c, n = 4 mice).”\nWe agree that in the originally submitted manuscript the trace showing the response to ACh in the\npresence of neostigmine appeared noisier and showed a small decrease in fluorescence following\nACh puffing. On re-examination of the figure we found that the calculation of the Z-scores for\nthese traces was based on the entire trace. Including the large response to ACh in the ACSF\ncondition resulted in a larger standard deviation in that condition, which after conversion to a Z-\nscore, scaled the ACSF signal down relative to the neostigmine condition, where the response was\nblocked, giving the appearance of more noise in the neostigmine condition. We recalculated the Z\nscores using the standard deviation from the mean of the baseline before ACh puff, which is the\ncorrect way to compare the two conditions. When this is done it can be seen that the noise levels\nare the same in the neostigmine and ACSF traces, and the decrease with the ACh puff in the\nneostigmine is a small deflection presumably due to a movement artifact that is hidden by the large\namplitude positive response in the ACSF condition. We have corrected the figure as shown (Fig\n1c).\nReviewer Comment\nFig. 2d: ROIs very hard to see.\nAuthor Response\nThank you. ROIs have been color-coded and magnified.\nReviewer Comment\nFigs 2d, e, etc.. state the number of individual trials included in each average.\nWe have included the number of trials accordingly.\nReviewer Comment\nline 99: “The features of these dips in ACh levels (Fig. 2f,g; dip amplitude, -1.64 ± 0.07 z-scored\nΔF/F; onset, 0.23 ± 0.01 s; dip latency i.e., time to dip, 0.48 ± 0.03 s, mean ± s.e.m., n = 11) are\nconsistent with pauses in tonic activity”\n- what exactly makes these numbers consistent with pauses?\nAuthor Response\nWe have added some text to the manuscript to clarify why we consider these numbers to be\nconsistent with pauses, as follows (line 113):\n“…the time to dip (0.48 s) is comparable to the timecourse of physiological action of ACh after a\npause in firing, which has a latency of 0.40 s30. This timing is also consistent with the timecourse\nof pauses in firing activity of CINs (onset around 0.16 – 0.20 s and duration 0.20 – 1.0 s)28,31,32,33\nwhen combined with the kinetics of ACh diffusion and hydrolysis34.”\nReviewer Comment\nline 105: “We also noted some variability in reward responses across trials (example mouse in\nFig. 2e, left), as previously reported”\n- how do the authors distinguish real biological variability from trial to trial, from measurement\nnoise?\nAuthor Response:\nWe agree that it is important to distinguish real biological variability from trial to trial from\nmeasurement noise. To do so we have shown the variability observed in the ACh signal compared\nthat with the noise measured from an ACh insensitive fluorophore (tdTomato) expressed in the\nsame region and observed under similar conditions (Extended Data Figure 2a). We have added the\nfollowing text to the manuscript (line 122):\n“We also noted some trial-to-trial variability in responses to rewards (example mouse in Fig 2e,\nleft; Extended Data Figure 2a) as previously reported36, which was greater than measurement noise\n(confirmed by comparison with ACh insensitive signals recorded under the same conditions).\nThese fluctuations in ACh may encode within session variations in behavioral contexts as\nperceived by the mouse, potentially reflecting dynamic recruitment of different cholinergic\nresponse modes depending on contextual or internal state factors.”\nReviewer Comment\nthe same FOV are being treated as independent? would make more sense to average within\nclusters from the same experiment first, then calculate error bars at the animal level.\nAuthor Response\nFollowing the Reviewer’s suggestion, we averaged the responses within clusters and calculated\nthe mean and error bars at the animal level (Figure 3c). In addition, we also show the mean\nresponses of each cluster and an activity heat map of all clusters (Figure 3d).\nReviewer Comment\nFig3D is hard to understand - e.g. does the cluster 2 NR 100% bar actually indicate that none of\nthe subregions had a dip within 1 second? It would be useful to show the mean NR responses for\neach cluster.\nAuthor Response\nFollowing the statistical threshold we applied for outcome responsiveness, only 1 ROI met the\ncriteria, showing a relatively long latency dip. We have included the mean NR responses, as\nsuggested by the Reviewer (Extended Data Fig. 4a,b).\nReviewer Comment\nFig. 4b: could be helpful to include trials before reversal, as is standard.\nAuthor Response\nWe thank the Reviewer for pointing this out. We have included trials before reversal (Figure 4b).\nReviewer Comment\nline 141: “The proportion of perseverative errors was 17.1% ± 2.7%”\n- here and elsewhere, need to specify exactly which trials/sessions are included.\nAuthor Response\nWe have added information on which the trials/sessions are included (line 188).\nReviewer Comment\nline 152: “ Incorrect responses, however, were associated with a delayed, progressive increase in\nthe spatially averaged levels of ACh”\n- what exactly makes it “progressive”? Not clear enough.\nAuthor Response:\nWe have clarified this point as follows (line 199):\n25\n“Incorrect responses, however, were associated with an increase in spatially averaged ACh levels\nthat occurred gradually over seconds, in contrast to the rapid phasic decreases seen on rewarded\ntrials.”\nReviewer Comment\n- how does the high variability shown in 4i relate to the outcome of the previous (and earlier)\ntrials?\nAuthor Response\nFor reference, the previous Figure 4i is now Extended Data Fig. 6a, which shows representative\nexamples of responses to unexpected non-reward across trials. As shown in the updated Figure, in\nthe early trials after reversal, responses often showed a slow onset with a late peak. In contrast,\nresponses in later reversal trials tended to have earlier peaks. We have added a brief clarification\nof this observation in the Results section to help contextualize the variability shown in 4i (line\n221):\n“…This also revealed that certain response profiles—particularly those with delayed peaks—\ntended to occur more frequently during early trials.”\nReviewer Comment\n- where in the FOV are the 4i examples taken from?\nAuthor Response\nThese traces are spatially averaged population responses across the entire imaged region showing\nthe temporal profiles of ACh signals across different trials. They do not represent individual\nquadrats in the FOV. To clarify this we have modified the legend as follows (line 1701):\n“a. …Representative traces of single trial, spatially averaged population responses to no-reward\noutcomes (right). Dashed black line indicates no-reward event.”\nReviewer Comment\nline 239: “Additionally, cumulative errors over the first 80 trials post-reversal were significantly\nhigher in J60-treated mice compared to saline-treated controls (Fig. 6f).”\n- not clear what this additional analysis adds to the story.\nAuthor Response:\nThe cumulative error analysis provides a broader behavioral context by summarizing the overall\nimpairment in reversal performance beyond specific trial types. While individual analyses such as\nlose-shift probability and error subtypes (e.g., regressive errors) offer insight into discrete\nbehavioral strategies, the cumulative error metric captures the overall learning curve post-reversal.\nsignalling supports flexible updating and overall task adaptation. We have clarified this rationale\nin the revised text, as follows (line 355):\n“Cumulative errors over the first 80 trials post-reversal were higher in CIN-inhibited mice,\nreflecting impaired overall task adaptation beyond specific error types (Fig. 6f).”\nReviewer Comment\nline 245: “While the difference in perseverative errors was not significant (P = 0.47, unpaired t-\ntest), a significant difference in the proportion of regressive errors was observed between the\ntwo groups”\n- need to explain “perseverative” vs “regression” and contrast to lose-stay v win-shift (seems\ndifferent given Fig. 6g)\nAuthor Response:\nWe agree that this was unclear in the original manuscript. We edited the relevant section and\nprovided clearer definitions of “perseverative” vs “regression” and also clarified how these\nmeasures are related to lose-shift and win-stay trials, as follows (line 1446):\n“Perseverative errors are repeated incorrect choices made after the reversal but before the mouse\nhas exhibited learning of the correct response. Specifically, a perseverative error was defined as\nerrors made in a block of four consecutive trials after reversal, of which 3 or more were incorrect.\nThe measure of perseveration was the number of such errors that occurred in sequence. The end\nof the sequence was defined by occurrence of a block of four trials in which 2 or more trials were\ncorrect. Regressive errors are incorrect choices that occur after learning has occurred, as evidenced\nby a sequence of correct choices. Here a regressive error was defined as any block of four trials in\nwhich an error occurred after a block in which 2 or more trials were correct. Perseverative and\nregressive measures are thus distinguished from individual win-stay and lose-shift responses that\nare trial-by-trial choices made in the context of the outcome of the previous trial.”\nReviewer Comment\nline 253: “Although our inhibition protocol lacked temporal specificity, our findings align with\nour initial observation of decreased switch probability in mice with reduced ACh levels.”\n- the initial observation doesn’t measure ACh \"levels”.\nAuthor Response\nWe agree with the reviewer. Our initial observation measured changes in levels, not absolute levels.\nWe have now revised the text as follows (line 370),\n“Although our inhibition protocol lacked temporal specificity, the behavioral findings are\nconsistent with the observed changes in ACh associated with lose-shift behavior”\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is\npart of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nAuthor Response\nThank you for co-reviewing the manuscript.\nReferences cited in response to reviewers\n1. Aoki S, Liu AW, Zucca A, Zucca S, Wickens JR. Role of striatal cholinergic interneurons\nin set-shifting in the rat. J Neurosci 35, 9424-9431 (2015).\n2. Bradfield LA, Bertran-Gonzalez J, Chieng B, Balleine BW. The thalamostriatal pathway\nand cholinergic control of goal-directed action: interlacing new with existing learning in\nthe striatum. Neuron 79, 153-166 (2013).\n3. Duhne M, Mohebi A, Kim K, Pelattini L, Berke JD. A mismatch between striatal\ncholinergic pauses and dopaminergic reward prediction errors. Proc Natl Acad Sci U S A\n121, e2410828121 (2024).\n4. Gonzales KK, Smith Y. Cholinergic interneurons in the dorsal and ventral striatum:\nanatomical and functional considerations in normal and diseased conditions. Ann N Y Acad\nSci 1349, 1-45 (2015).\n5. Matamales M, Gotz J, Bertran-Gonzalez J. Quantitative Imaging of Cholinergic\nInterneurons Reveals a Distinctive Spatial Organization and a Functional Gradient across\nthe Mouse Striatum. PLoS One 11, e0157682 (2016).\n6. Duhne M, Mohebi A, Kim K, Pelattini L, Berke JD. A mismatch between striatal\ncholinergic pauses and dopaminergic reward prediction errors. P Natl Acad Sci USA 121,\n(2024).\n7. Matamales M, Skrbis Z, Hatch RJ, Balleine BW, Gotz J, Bertran-Gonzalez J. Aging-related\ndysfunction of striatal cholinergic interneurons produces conflict in action selection.\nNeuron 90, 362-373 (2016).\n8. Takagi H, Somogyi P, Smith AD. Aspiny neurons and their local axons in the neostriatum\nof the rat: A correlated light and electron microscopic study of Golgi-imprgnated material.\nJournal of Neurocytology 13, 239-265 (1984).\n9. Chang HT, Kitai ST. Large neostriatal neurons in the rat: an electron microscopic study of\ngold-toned Golgi-stained cells. Brain Res Bull 8, 631-643 (1982).\n10. Bolam JP, Ingham CA, Smith AD. The section Golgi-impregnation procedure 3.\nCombination of Golgi-impregnation with enzyme histochemistry to characterize\nacetylcholinesterase-containing neurons in the rat neostriatum. Neuroscience 12, 687-709\n(1984).\n11. Ragozzino ME, Mohler EG, Prior M, Palencia CA, Rozman S. Acetylcholine activity in\nselective striatal regions supports behavioral flexibility. Neurobiol Learn Mem 91, 13-22\n(2009).\n28\n12. Inokawa H, Matsumoto N, Kimura M, Yamada H. Tonically Active Neurons in the\nMonkey Dorsal Striatum Signal Outcome Feedback during Trial-and-error Search\nBehavior. Neuroscience 446, 271-284 (2020).\n13. Yin HH, Knowlton BJ. The role of the basal ganglia in habit formation. Nat Rev Neurosci\n7, 464-476 (2006).\n14. Kondo M, Kobayashi K, Ohkura M, Nakai J, Matsuzaki M. Two-photon calcium imaging\nof the medial prefrontal cortex and hippocampus without cortical invasion. eLife 6, (2017).\n15. Podgorski K, Ranganathan G. Brain heating induced by near-infrared lasers during\nmultiphoton microscopy. J Neurophysiol 116, 1012-1023 (2016).\n16. Ragozzino ME, Jih J, Tzavos A. Involvement of the dorsomedial striatum in behavioral\nflexibility: role of muscarinic cholinergic receptors. Brain Res 953, 205-214 (2002).\n17. Floresco SB, Ghods-Sharifi S, Vexelman C, Magyar O. Dissociable roles for the nucleus\naccumbens core and shell in regulating set shifting. J Neurosci 26, 2449-2457 (2006).\n18. Brown HD, Baker PM, Ragozzino ME. The parafascicular thalamic nucleus concomitantly\ninfluences behavioral flexibility and dorsomedial striatal acetylcholine output in rats. J\nNeurosci 30, 14390-14398 (2010).\n19. Costa VD, Tran VL, Turchi J, Averbeck BB. Reversal learning and dopamine: a bayesian\nperspective. J Neurosci 35, 2407-2416 (2015).\n20. Dalton GL, Wang NY, Phillips AG, Floresco SB. Multifaceted Contributions by Different\nRegions of the Orbitofrontal and Medial Prefrontal Cortex to Probabilistic Reversal\nLearning. J Neurosci 36, 1996-2006 (2016).\n21. Burke DA, Taylor A, Jeong H, Lee S, Wu B, Floeder JR, VM KN. Reward timescale\ncontrols the rate of behavioural and dopaminergic learning. bioRxiv, (2024).\n22. Marvin JS, et al. An optimized fluorescent probe for visualizing glutamate\nneurotransmission. Nature Methods 10, 162-170 (2013).\n23. Borden PM, et al. A fast genetically encoded fluorescent sensor for faithful in vivo\nacetylcholine detection in mice, fish, worms and flies. bioRxiv, 2020.2002.2007.939504\n(2020).\n24. Zhu PK, et al. Nanoscopic visualization of restricted nonvolume cholinergic and\nmonoaminergic transmission with genetically encoded sensors. Nano letters 20, 4073-4083\n(2020).\n25. Matityahu L, Gilin N, Sarpong GA, Atamna Y, Tiroshi L, Tritsch NX, Wickens JR,\nGoldberg JA. Acetylcholine waves and dopamine release in the striatum. Nat Commun 14,\n6852 (2023).\n26. Aosaki T, Tsubokawa H, Ishida A, Watanabe K, Graybiel AM, Kimura M. Responses of\ntonically active neurons in the primate's striatum undergo systematic changes during\nbehavioral sensorimotor conditioning. J Neurosci 14, 3969-3984 (1994).\n27. Apicella P, Legallet E, T’rouche E. Responses of tonically discharging neurons in the\nmonkey striatum to primary rewards delivered during different behavioral states. Exp Brain\nRes 116, 456-466 (1997).\n28. Morris G, Arkadir D, Nevet A, Vaadia E, Bergman H. Coincident but distinct messages of\nmidbrain dopamine and striatal tonically active neurons. Neuron 43, 133-143 (2004).\n29. Yamada H, Matsumoto N, Kimura M. Tonically active neurons in the primate caudate\nnucleus and putamen differentially encode instructed motivational outcomes of action. J\nNeurosci 24, 3500-3510 (2004).\n29\n30. Zucca S, Zucca A, Nakano T, Aoki S, Wickens J. Pauses in cholinergic interneuron firing\nexert an inhibitory control on striatal output in vivo. eLife 7, doi: 10.7554/eLife.32510.\n(2018).\n31. Aosaki T, Kimura M, Graybiel AM. Temporal and spatial characteristics of tonically active\nneurons of the primate's striatum. J Neurophysiol 73, 1234-1252 (1995).\n32. Schulz JM, Oswald MJ, Reynolds JN. Visual-induced excitation leads to firing pauses in\nstriatal cholinergic interneurons. J Neurosci 31, 11133-11143 (2011).\n33. Sharott A, Doig NM, Mallet N, Magill PJ. Relationships between the firing of identified\nstriatal interneurons and spontaneous and driven cortical activities in vivo. J Neurosci 32,\n13221-13236 (2012).\n34. Nosaka D, Wickens JR. Striatal cholinergic signaling in time and space. Molecules 27,\n1202 (2022).\nReviewer #1 (Remarks to the Author):\nThe authors have very thoroughly revised the original submission. they have added considerably\nmore data regarding the behavior of their mice during the training trials, after choice and reward\nexposure and, in the process, have addressed the points I raised with regard to their original\nsubmission. I was particularly impressed by how thoroughly they addressed the points regarding\nthe definition of cognitive flexibility here. I do agree that CINs are likely to be centrally involved\nin updating prior learning after contingencies have changed and I think that they have now\nclarified where and why those changes occur in the current task. I was also impressed by the way\nthey have handled alternative explanations of their data. I find my prior comments have been\naddressed and that I have no further comments to make. From my perspective it is acceptable in\nits current form.\nAuthor response\nWe thank the Reviewer for their thoughtful assessment and positive response to our work. We\nare glad that our revisions and additional analyses have addressed all concerns raised.\nReviewer #2 (Remarks to the Author):\nThe manuscript describes findings from a series of experiments that demonstrates the dynamic\nchanges in dorsal striatal acetylcholine efflux during learning and reversal learning. There are\nseveral innovations carried out in these studies that provide significant insights of how actions of\nstriatal cholinergic interneurons underlie learning and behavioral flexibility.\nThe original submission represented a very important set of experiments and results addressing\nan important issue about brain-behavioral relationships. In particular, how does cholinergic\nsignaling in the striatum contribute to behavioral flexibility. In the revised version of the\nmanuscript, the authors have clarified a lot of questions raised by reviewers by conducting\nseveral different analyses. These additional analyses clarify what conditions lead to changes in\nstriatal ACh signaling and what conditions do not change striatal ACh signaling. Overall, the\ncomprehensive analyses conducted provide some real insights and make a strong argument for\nstriatal ACh contributing to behavioral flexibility.\nAuthor response\nThank you for reviewing our manuscript and for the valuable feedback. We are pleased that the\nreviewer found the revised manuscript to be improved and insightful.\nReviewer #3 (Remarks to the Author):\nRe-review of Sarpong et al.\nAs noted in my initial review, this manuscript includes some interesting observations, and I\napplaud the authors’ efforts to use 2p imaging to probe the spatiotemporal organization of ACh\nsignals. At the same time, these efforts, as currently presented, do not seem to have been\nparticularly successful. To see this, look at the title and abstract: essentially none of the results\nmentioned there rely on the imaging - they could more easily have been obtained with simple\nfiber photometry. This feels like a missed opportunity. It’s also a mismatch to how the\nmanuscript is presented, which repeatedly emphasizes the novelty of and “new insight” gained\nfrom the imaging method. When the image is divided into 50um x 50um zones the authors\nobserve some heterogeneity between zones. But beyond the apparent fact of heterogeneity,\nthere’s not much learned about the spatiotemporal scales or patterns of ACh release, and how\nthese relate to anatomy or physiology. Overall the results are somewhat interesting to specialists\nbut represent a modest advance over our prior knowledge of striatal ACh and behavioral\nflexibility.\nAuthor response\nWe thank the reviewer for acknowledging the contribution of our 2-photon approach beyond\nwhat could be obtained with fiber photometry. We have modified the title and abstract to better\nmatch the findings, and hopefully make their significance clearer to the non-specialist, as\nfollows:\nTitle: “Spatially heterogeneous acetylcholine dynamics in the striatum promote behavioral\nflexibility”\nAbstract: (line 28): “Analysis of 2-photon images by tiling the field of view into small quadrats\nrevealed spatially inhomogeneous and temporally distinct acetylcholine signals within the dorsal\nstriatum. Distinct patterns of outcome-related responses were differentially distributed across the\nstriatum, revealing functionally diverse acetylcholine microdomains.”\nReviewer comment\nOther points:\nline 37: “more formally…” - this doesn’t seem especially more “formal” than the prior\nsentences.\nAuthor response\nWe have deleted “more formally” from the text (line 39).\nReviewer comment\nline 65: why was the reversal performed between, rather than within, sessions? Wouldn’t within-\nsession provide more power to detect neural changes (although the low number of trials may\n2\ncancel this out).\nAuthor response\nIn principle, a within-session reversal design might provide more statistical power for detecting\nneural changes. However, in the present study we opted for between-session reversals due to\npractical constraints of two-photon imaging. Specifically, the duration and number of trials per\nsession are limited by exposure (laser heating) and photobleaching of the ACh sensor and by the\nneed to maintain stable optical access and imaging quality across the session. These factors\nreduce the feasibility of including a sufficient number of pre- and post-reversal trials within a\nsingle imaging session. As the reviewer notes, the limited trial count would likely offset the\npotential power gains of a within-session design.\nWe agree that exploring within-session reversals would be valuable, and we view our current\napproach as a first step toward characterizing striatal ACh dynamics during behavioral\nflexibility. We hope that future studies, potentially using complementary methods with higher\nthroughput (e.g., fiber photometry or miniature endoscopes), will build on our findings and test\nwithin-session reversal designs.\nReviewer comment\nline 69: “average percent correct was 82.1%…” when? during the sessions defined as happening\nafter an 80% correct session?\nAuthor response\nWe appreciate the opportunity to clarify this point. The reported value of 82.1% correct\ncorresponds to the average percent correct choices in the late learning phase across mice,\nspecifically during the last session preceding reversal. We have revised the text to explicitly\nstate this to avoid confusion. We now state that as follows (line 74):\n“In the late learning phase, defined as the final session preceding reversal, mice performed with\nan average correct choices across subjects of 82.1% ± 0.9% (mean ± s.e.m, P < 0.0001, n = 11\nmice)”\nReviewer comment\nline 116: “…time to dip (0.48s) is comparable to the timecourse of physiological action of\nACh…” why is the response time of striatal neurons a reasonable comparison, given time taken\nfor GPCR-mediated action of ACh?\nAuthor response\nWe agree this text may cause confusion and we have deleted it.\nline 126: “(confirmed by comparison with ACh insensitive signals…”. The tdTomato signal is\nbarely used in the manuscript - all the main results should include comparison to tdTomato to\nhelp rule out contributions of movement artifacts etc.\nAuthor response\nThe tdTomato experiment was added in response to this reviewer’s previous comment “how do\nthe authors distinguish real biological variability from trial to trial, from measurement noise?”\nTo demonstrate the actual measurement noise for the reviewer we showed the signal from an ACh\ninsensitive fluorophore (tdTomato) expressed in the same region and observed under similar\nconditions. We did not record tdTomato and iAChSnFR simultaneously in all trials because\nexcitation of each fluorophore requires different laser wavelengths and applying both lasers at the\ns\nthe same time increases the power and potential for overexposure, bleaching, and heating of the\ntissue. Therefore, tdTomato was imaged separately in a subset of trials. It should be noted that unlike\nisosbestic emission commonly used in fiber photometry the tdTomato signal is not emitted from\nthe same sensor molecules: tdTomato is expressed in different subcellular locations and therefore\nis not suitable for subtraction from signal. As noted in the Methods, all images were processed for\nx-y motion correction using a standard normalized cross-correlation method to eliminate any\nspatially uniform motion and movement artifact. Being able to see the detailed field of view is one\nof the advantages of two-photon imaging over fiber photometry, and our approach is consistent\nwith prior two-photon imaging studies (e.g., Patriarchi et al., Science, 2018; Sun et al., Nat.\nMethods, 2020). We have clarified this in the revised text as follows (line 131):\n“…(confirmed by comparison with ACh-insensitive tdTomato signals; Extended Data Fig. 2).\ntdTomato was imaged in a subset of trials and did not display the large positive-going transients\nobserved with iAChSnFR, supporting the conclusion that our reported ACh signals reflect\ngenuine cholinergic activity rather than measurement noise1,2.”\nReviewer comment\nline 151: “…indicating weak separation between clusters lying along a continuum (Extended\nData Fig. 4d). This is consistent with the fact that the concentration measures in the analysis are\nfrom physically adjacent quadrats along continuous concentration gradients“\n- if true, this seems like an acknowledgement that clustering doesn’t really make sense here.\n- although, the quadrats within the clusters don’t seem to generally appear physically contiguous,\nat least in the examples shown. Is this because the real spatial scale of ACh signals is comparable\n(or smaller) to 50µm? Or because the statistical criterion - an arbitrarily chosen 2SD - is not\nrigorous enough? Not enough is done to address such concerns.\nAuthor response\nrather than sharply discrete groups. This result does not undermine the validity of the approach;\nrather, it reveals that the ACh signal is inhomogeneous and graded in the dorsal striatum, like the\ntopography of many natural landscapes. Hierarchical clustering is an unsupervised, unbiased\nmethod well-suited for exploring datasets where the true underlying structure is unknown. In\nmany biological systems, activity patterns exist on a continuum rather than as discrete\ncategories, and hierarchical clustering allows the visualization of such graded relationships\nwhile still grouping local similarities (Xu & Wunsch, IEEE Trans Neural Netw., 2005). Thus, the\nfinding of weak separation is an informative result that indicates that ACh signals across\nquadrats are better conceptualized as varying along a gradient, rather than forming categorical\n“types.”\nIt is also important to note that structurally the cell bodies of CINs in the dorsal striatum are not\nclustered but have an inhomogeneous random spatial distribution, as we3,4 and others5 have\nshown. Each CIN produces an estimated 55 mm of axon6 that arborises profusely to fill a\nspheroidal volume with long axis of 500-700 micrometers7,8,9. Therefore, the axonal\narborizations individual CINs extend over multiple 50 µm x 50 µm quadrats. Given that with 2-\nphoton imaging we can reliably detect ACh efflux from single action potentials in individual\nCINs using similar-sized regions of interest10, the sampling spatial frequency obtained by using\n50 x 50 µm quadrats is appropriate 11,12,13.\nAs we have previously suggested, macroscopic features such as travelling and stationary waves\ncan arise from the dynamical interactions of multiple CIN axon arborizations overlapping in the\nsame volumes 10. While we agree that it would be of interesting to characterize the spatial spread,\ncontours and dimensions of these features, our study was not designed to do this but rather\nfocused on the dynamics associated with behavioral flexibility.\nRegarding the reviewer’s concern about spatial contiguity: it is not surprising that quadrats from\nthe same cluster are not always physically adjacent given that interactions among CINs can occur\nover distances greater than 50 µm 14 and corticostriatal afferents typically project to multiple,\nwidely separated striatal locations 15,16.\nRegarding 2SD threshold we applied – this was not “arbitrarily chosen”. It is a standard criterion\nwidely used to assess signal selectivity and confidence in calcium and neurotransmitter imaging\nstudies (e.g. Chen et al., Nature, 2013)17. This threshold ensures that identified responses are\nreliably distinct from baseline fluctuations while avoiding overfitting or false positives.\nIn summary, the clustering objectively reveals local similarities in response morphology and\nshows that ACh release patterns in the striatum exist along a continuum rather than in discrete\ncategories. This aligns with the broader literature, where clustering is often used not only to\n5\ndefine strong categorical differences, but also to explore and describe heterogeneous and\ngraded neural response patterns (Cohen & Kohn, Nat. Neurosci., 2011)18.\nWe have added this clarification as follows:\nLine 156:\n“Hierarchical clustering provides an unsupervised and unbiased method, heuristic framework to\ncapture local similarities and to visualize graded relationships, even in the absence of sharply\ndiscrete groups.”\nLine 1459:\n“The 2SD threshold applied here is a standard criterion for defining reliable responses in neural\nimaging17.”\nLine 481:\nStructurally, the cell bodies of CINs in the dorsal striatum are not clustered but have a random\nspatial distribution3,4,5. The axon of individual CINs arborises profusely to fill a spheroidal\nvolume with long axis of 500-700 micrometers6,7,8,9. Therefore, the axonal arborization of each\nCIN would extend over multiple 50 µm x 50 µm quadrats. This might explain why the clustering\nresults indicate gradations rather than sharply discrete groups. On the other hand, the clusters are\nnot necessarily due to anatomical structures but may be dynamically generated. Macroscopic\nfeatures such as travelling and stationary waves can arise from the dynamical interactions of\nmultiple CIN axon arborizations overlapping in the same volumes 10. Finally, given that\ninteractions among CIN axons can occur over distances greater than 50 µm 14 and corticostriatal\nafferents typically project to multiple striatal locations 15,16 it is not surprising that quadrats from\nthe same cluster are not always physically adjacent.\nReviewer comment\nline 202 “…increase in spatially averaged ACh levels that occurred gradually over seconds…”.\nThis is certainly an interesting observation, although given that the size of the increase is very\nmodest (<1 Z) and ACh can change very fast, it’s harder to have confidence this is a discrete,\nspecific signal being used by the brain.\nAuthor response\nIn addition to the observed signal, the evidence suggesting this signal is used by the brain is that\nit is predictive of the lose-shift behavior, and blocking CIN activity reduces lose-shift behavior.\nWhile we also agree that ACh concentration can change rapidly, that does not preclude slow\nchange and operation on multiple time scales, as we have shown here (Extended Data Fig. 6a).\nReviewer comment\nline 232: “…confirming that this signal is not only present but more spatially widespread during\n6\nbehavioral switching (Extended Data Fig. 6d).”\n- this figure panel is important enough to the authors’ argument to be in the main figures.\nAuthor response\nWe thank the reviewer for this helpful suggestion. We agree that this panel provides important\nsupport for our central argument. A related version of this analysis has been included in the main\nfigures (Fig. 4g,h), which compares ACh dynamics before and after reversal and thus directly\naddresses changes across training phases. The Extended Data panel (Extended Data Fig. 6d) was\nadded in response to Reviewer 1’s comment to illustrate phase-specific differences in greater\ndetail. To avoid redundancy while still highlighting the dynamic changes during, before and after\ntask switching, we have clarified in the text such that the Extended Data Fig. 6d panel\nspecifically emphasizes the differences (line 238). This way, both figures highlight\ncomplementary aspects of the data without duplication.\n“Comparison of ACh responses to non-reward before and after reversal within the same animals,\nusing per-mouse averages, revealed a significant increase in the spatially averaged ACh response\nto non-reward during reversal compared to non-reward during task acquisition (training phase\ni.e., early, and pre-reversal periods i.e., late), confirming that this signal is not only present but\nmore spatially widespread during behavioral switching (Fig. 4g,h and Extended Data Fig. 6d).”\nReviewer comment\nline 233: “ …the ACh increases observed in response to non-reward during acquisition, present\nin a subset of individual quadrats (cluster 4 in Fig. 4b-d), may reflect a preexisting substrate for\noutcome-related ACh modulation”. If this is correct, would it not imply that the specific quadrats\nwith increases during acquisition would also show (bigger) increases during reversal? Is this\ntrue?\nAuthor response\nThe quadrats with increases during acquisition may be the forerunners. We do not imply they\nincrease their activity further during reversal, rather that they show pre-existing increases. As\nshown in Fig. 5b–d, there is a clear population-level shift in quadrat responses from few\nincreases and predominantly decreases during acquisition to predominantly increases during\nreversal, indicating that other quadrats become active later.\nReviewer comment\nMore generally, how consistent are the quadrat-level signals from one imaging day to the next?\nShowing consistency could boost confidence in the quadrat-level results. Determining the spatial\nscale of such consistency might allow the authors to pick a quadrat size in a principled manner.\nAuthor response\ndifferent stages of learning. During early acquisition, only two main response motifs emerged\n(short-latency pause, ~79%; long-latency pause, ~21%), whereas during late learning four\ndistinct patterns were observed (pause, burst-pause, pause-burst, and increase). Comparison on\nthe 2D score plot revealed that the emergence of these additional motifs appears to arise from\nwithin the same space and boundaries of the early-phase responses, where reward-decreased\ncells reduce in proportion to give rise to more differentiated motifs. We speculate that early-\nphase motifs reflect heightened sensitivity to outcome novelty or reward prediction error,\nconsistent with prior reports of strong pause responses to unexpected rewards or salient cues in\nCINs19,20. With learning, the additional motifs likely represent more specialized roles in\nexpectation tracking and behavioral updating. Although we did not explicitly quantify spatial\nconsistency of individual quadrats across imaging days, the reproducibility of these motifs across\nsessions and mice supports the reliability of our quadrat-based approach.\nReviewer comment\nline 239: “The change in ACh response after unexpected non-reward could, in theory, be related\nto motor activity due to orienting responses. To address this, we analyzed the trial-by-trial\nvelocities ...”\n- velocities are not the same as orienting.\nAuthor response\nWhile we agree that locomotor velocity is not equivalent to orienting per se, we included this\ndata in response to a comment from Reviewer 1 who wondered “…if the change in ACh\nresponse after reversal in the presence of non-reward is related to motor activity/ orienting/\nattentional changes?” Because mice were head-fixed in our setup, they could not make postural\nor head- movements typical of orienting responses. Locomotor velocity on the ball was therefore\nthe only measurable motor activity. Reviewer 1 was happy that we report that. However, given\nthe concern raised, we have modified the text as follows:\nLine 248:\n“The change in ACh activity after unexpected non-reward could, in theory, be related to motor\nactivity due to orienting responses. In our setup, mice were head-fixed and therefore unable to\nperform the postural or head movements typical of orienting responses. However, they could\nexpress some degree of orienting through changes in locomotor activity. We analyzed locomotor\nvelocity on the ball on a trial-by-trial basis during the outcome period and found no significant\ncorrelation between mean ACh signals and velocity following non-reward (Fig. 4k).”\nReviewer comment\nline 288-294: The new results about subregions are interesting and relevant and as the authors\nnote they are consistent with prior results, though it’s surprising that these findings barely, or do\n8\nnot, reach significance (and are not visible in the “3D map”) What if “quadrats” are dropped and\nthe authors analyze much broader fields-of-view?\nAuthor response\nOur analysis along the medial–lateral axis did reveal a significant bias for quadrats with\nincreased ACh responses to unexpected non-reward toward the medial striatum (Pearson’s r = -\n0.55, P = 0.03, simple linear regression). These findings are consistent with prior reports linking\nmedial striatum ACh signaling to reversal learning. We therefore do not interpret the results as\n“barely” reaching significance but rather as reflecting a reproducible trend aligned with existing\nliterature. Broader averaging across the entire field of view would obscure the regional\ndifferences we observed. Finally, the GRIN lens positioning provided coverage of the dorsal\nstriatum sufficient to capture the medial–lateral and anteroposterior distributions, giving us\nconfidence in the spatial patterns reported (Fig. 5d).\nReviewer comment\nline 348: “…hM4D(Gi)-mCherry bilaterally into the DS of ChAT-Cre mice...”\nthe “DS” here is actually “DMS”, which makes sense given prior results but the imaging field is\nnot consistently DMS - why?\nAuthor response\nWe thank the reviewer for noting this. Our imaging experiments via the GRIN lens covered\ndorsal striatal regions that included both medial and lateral portions of the DS, whereas the\nchemogenetic manipulations targeted primarily the dorsomedial striatum (DMS). This focus was\nguided by two considerations: first, our imaging analyses revealed that increases in ACh\nresponses to unexpected non-reward were most prominently distributed medially; second, prior\npharamcological and lesion studies have consistently implicated the medial striatum in reversal\nlearning and flexible behavior21,22,23.However, we do not view the term “DS” or the injection\nsites as a discrepancy, but rather as a targeted experimental design choice that complements our\nimaging findings and leverages prior literature to test the functional role of medial striatal ACh\nsignaling.\nWe have added this to the Methods section as follows (line 1347):\n“Although injections were performed bilaterally in the dorsal striatum, they were centered\nprimarily in the dorsomedial striatum (DMS), guided by both our imaging findings of enhanced\nmedial ACh responses to non-reward and prior reports implicating the medial striatum in\nreversal learning and behavioral flexibility.”\nReviewer comment\nline 360: “Cumulative errors over the first 80 trials post-reversal were higher in CIN-inhibited\nmice, reflecting impaired overall task adaptation beyond specific error types (Fig. 6f).” How\n9\ndoes this observation support this conclusion?\nAuthor response\nOur conclusion that cumulative errors over the first 80 trials post-reversal reflect impaired\noverall task adaptation is supported by two complementary observations. First, CIN-inhibited\nmice showed a significant reduction in correct choices specifically during the early reversal\nphase (Fig. 6d,e), when flexible updating is most critical, whereas performance in later sessions\nwas unaffected. This indicates that CIN activity is particularly important for the initial adaptive\nresponse to changing contingencies rather than for late, steady-state performance. Second, our\nimaging results further support this interpretation: unexpected non-reward after reversal elicited\nwidespread increases in acetylcholine signals, especially in lose-shift trials, where animals\nadapted by switching to the opposite arm. Thus, higher ACh levels were predictive of adaptive\nswitching behavior. By contrast, chemogenetic inhibition of CINs reduced this adaptive shift,\nresulting in greater cumulative errors that could not be explained by specific error types alone.\nCollectively, these findings suggest that CIN-dependent ACh release supports early task\nadaptation following contingency change, and its inhibition impairs this process.\nReviewer comment\nline 364: “While CIN inhibition reduced lose-shift behavior (Fig. 6g, left; unpaired t-test, P =\n0.015), it did not alter win-stay probability following the reversal (Fig. 6g, right; P = 0.14).\n“ Judging by Fig. 6g, this is not a convincing result. The analysis appears to lack statistical power\ndue to few, scattered data points.\nAuthor response\nLose-shift behavior showed a robust and statistically significant reduction following CIN\ninhibition. We agree that we cannot exclude an effect on win-stay probability. The sample size (n\n= 5 per group) used in our chemogenetic experiments is within the range (5-9 animals/group)\ncommonly used in CIN manipulation studies (Okada et al., Eur J Neurosci., 2015). To address\nthe reviewer’s concern, we (i) now report exact p-values and included effect sizes (Cohen’s d to\nquantify the magnitude of effect) for all primary comparisons, (ii) have shown individual animal\ndata to display the underlying distribution. These additional analyses indicated that the primary\neffect (e.g., reduced lose-shift probability after CIN inhibition) is consistent across subjects and\nof large magnitude, increasing confidence in the biological effect. For the comparisons reported\nin Fig. 6d,g, the effect sizes were very large (e.g., d = (–)1.54, (–)1.01, (–)1.77, and (–)0.94),\nrespectively, indicating robust group differences despite modest power. Even in the case of a\nsmaller effect (d = 0.43 and 0.21; Ext. Data Fig. 9b,d), the observed direction was consistent with\nour hypothesis. Thus, although the small sample number may limit statistical power, the effect\nsize analysis suggests that the observed differences are biologically meaningful and reproducible.\nThe Methods section has been revised as follows (line 1554):\n“Effect size analysis\nobserved group differences, we calculated Cohen’s d for all pairwise comparisons. For unpaired\nt-tests, d was computed as the standardized difference in means between treatment groups, using\nthe pooled standard deviation as the denominator. 95% confidence intervals were also estimated\nto provide an index of precision. Effect sizes were interpreted according to conventional\nbenchmarks (d ≈ 0.2: small; d ≈ 0.5: medium; d ≥ 0.8: large; negative values in figures indicate\ndirection of effect)24. Reporting effect sizes is particularly important in studies with modest\nsample sizes, as it provides information about the strength and potential biological relevance of\neffects beyond p values alone.”\nThe figure legend has been updated as follows (line 1094):\n“…Cohen’s d = (–)1.54, 95% CI [-2.98, -0.09]) but comparable in Test 5 (P = 0.11, Cohen’s d =\n(–)1.01, 95% CI [-2.35, 0.32]). Data shown as mean ± SEM. Effect sizes (Cohen’s d) was\ncalculated to quantify the magnitude of differences (see Methods for details). These values\nhighlight that, despite the modest sample size, the observed differences represent substantial\nbiological effects in most comparisons.”\nResults section has been revised as follows (line 374):\nLose-shift behavior was significantly reduced by CIN inhibition (Fig. 6g, left; unpaired t-test, P\n= 0.015, Cohen’s d = (–)1.77, 95% CI [-3.27, -0.26]). However, CIN inhibition effects on win-\nstay probability following the reversal were not significant (Fig. 6g, right; P = 0.14, Cohen’s d =\n(–)0.94, 95% CI [-2.27, 0.38]).\nReviewer comment\nline 497: “we propose that neural pathways exposed to decreased ACh on reward delivery –\ncausing a pause after successfully shifting – would experience synaptic potentiation”. It’s not\nfully clear from the figures, but it looks like decrease ACh on reward delivery is strong even at\nthe end of behavioral acquisition - would it really be helpful to keep potentiating synapses at that\npoint?\nAuthor response\nAs noted in the preceding sentence of the discussion, long-term potentiation of cortical inputs to\nstriatal projection neurons requires coincidence of pauses in CINs with phasic dopamine\nactivation. Phasic dopamine release generally occurs in response to unexpected reward but not in\nresponse to reward that is expected. At the end of behavioral acquisition, it is likely that reward\nwill be expected, and dopamine will not be released, so there will be no potentiation. To make\nthis clearer we have revised the text as follows (line 516):\n“Recent work shows that long-term potentiation of cortical inputs to striatal projection neurons\nrequires coincidence of pauses in CINs with phasic dopamine activation25, which occurs in\n11\nresponse to unexpected reward. Thus, we propose that neural pathways exposed to decreased\nACh on unexpected reward delivery – causing a CIN pause and dopamine activation after\nsuccessfully shifting – would experience synaptic potentiation. At the end of behavioral\nacquisition it is likely that reward will be expected, and dopamine will not be released, so there\nwill be no potentiation. Pathways exposed to increased ACh on unexpected non-reward would\nnot be potentiated, preventing reinforcement of incorrect responses. This would ultimately lead\nto a reduction in interference from pre-existing memory26.”\nReviewer comment\nFig. 1c: please show the data divided into the individual sessions.\nAuthor response\nWe have added information about the demarcation of individual sessions, in the figure legend\n(line 842):\n“c, Sample choice behavior in representative trials (200 trials across 5 sessions; 40 trials per\nsession) from an example mouse…”\nReviewer comment\nFig. 1f/g (and later figures): since the point is to compare “early” and “late”, the statistical test\nshould incorporate both these conditions (e.g. as a factor in ANOVA) rather than running\nseparate tests for each condition.\nAuthor response\nResponse:\nWe thank the reviewer for this suggestion. Our original paired t-tests within “early” and “late”\nwere intended to provide simple, within-animal contrasts for each phase. However, we agree that\na model that includes Session (Early vs. Late) and Outcome (Reward vs. No reward) in the same\nanalysis is statistically stronger. Accordingly, we have re-analyzed velocity and licking rate\nusing a within-subject repeated-measures framework with factors Session (2 levels) and\nOutcome (2 levels) in ANOVA.\nFor anticipatory licking, this analysis revealed a significant main effect of Outcome (F(1,20) =\n5.14, P = 0.03), indicating that mice exhibited higher anticipatory licking when expecting reward\nrelative to non-reward outcomes. The main effect of Session did not reach significance (F(1,20)\n= 3.51, P = 0.076), and the Session × Outcome interaction also did not reach significance\n(F(1,20) = 3.78, P = 0.066). Combined, these findings suggest that anticipatory licking is\nrobustly driven by reward outcome across learning, with a trend toward higher overall levels in\nlate sessions. Importantly, this confirms our conclusion that anticipatory behavior reflects\nsensitivity to reward expectation.\n11.69, P = 0.0027), indicating that overall velocities increased from early to late learning. The\nmain effect of outcome did not reach significance (F(1,20) = 0.17, P = 0.68), though the trend\nwas consistent with higher velocities in rewarded trials. The Session × Outcome interaction was\nalso not significant (F(1,20) = 0.28, P = 0.60). Combined, these results indicate that the clearest\neffect was a robust increase in approach velocity across learning, supporting our original\nconclusion that motivational drive strengthens with task experience. Thus, the direction of the\neffect is consistent with our within-phase t-tests, which suggested higher velocity in rewarded\ntrials during late learning. The respective figures (Fig. 1f,g and 4d,e) have been updated.\nReviewer comment\nFig. 4b : why show the orange and blue ticks twice each?\nAuthor response\nThe lower raster plot shows trial-by-trial choices, where blue ticks denote correct trials and\norange ticks denote incorrect trials. Above this, a heatmap depicts the same trial outcomes in\nbinary format (1 = correct, 0 = incorrect), aligned to the raster plot for visual comparison. Thus,\nthe raster + binary heatmap give complementary information: the raster conveys variability\nacross trials, while the heatmap makes the correct/incorrect sequence categorical and easy to\nread.\nReviewer comment\nEx Fig 6b: why are these particular three “representative” mice shown, rather than all of them?\nThe right part of this panel is hard to follow and the argument presented from these individual\nmice is unconvincing.\nAuthor response\nWe appreciate the reviewer’s concern. We note, however, that the individual activity patterns\nand mean responses of individual mice are already presented in Fig. 4g (heatmaps) and Fig. 4j\n(scatter plot of lose-shift correlation). The representative traces in Extended Data Fig. 6b were\nincluded primarily as a visual illustration of how cholinergic responses to unexpected non-\nreward can influence choice behavior across reversal, complementing the full dataset shown in\nFig. 4g,j. This approach, using representative animals to illustrate trial dynamics alongside\ngroup-level statistics, has been employed in previous studies of striatal acetylcholine and TAN\nactivity for example19,27,28. We therefore believe these examples are appropriate and serve as a\nheuristic aid, while the statistical conclusions are drawn from the complete dataset (n = 11).\nReviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is\npart of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nAuthor Response\nThank you for co-reviewing the manuscript.\nReferences cited\n1. Patriarchi T, et al. Ultrafast neuronal imaging of dopamine dynamics with designed\ngenetically encoded sensors. Science 360, 1-8 (2018).\n2. Sun F, et al. Next-generation GRAB sensors for monitoring dopaminergic activity in\nvivo. Nat Methods 17, 1156-1166 (2020).\n3. Carrasco A, Oorschot DE, Barzaghi P, Wickens JR. Three-dimensional spatial analyses\nof cholinergic neuronal distributions across the mouse septum, nucleus basalis, globus\npallidus, nucleus accumbens, and caudate-putamen. Neuroinformatics 20, 1121-1136\n(2022).\n4. Zhang R, Wickens JR, Carrasco A, Oorschot DE. 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Calculating and reporting effect sizes to facilitate cumulative science: a\npractical primer for t-tests and ANOVAs. Front Psychol 4, 863 (2013).\n25. Reynolds JNJ, Avvisati R, Dodson PD, Fisher SD, Oswald MJ, Wickens JR, Zhang YF.\nCoincidence of cholinergic pauses, dopaminergic activation and depolarisation of spiny\nprojection neurons drives synaptic plasticity in the striatum. Nat Commun 13, 1296\n(2022).\n26. Bradfield LA, Bertran-Gonzalez J, Chieng B, Balleine BW. The thalamostriatal pathway\nand cholinergic control of goal-directed action: interlacing new with existing learning in\nthe striatum. Neuron 79, 153-166 (2013).\n27. Morris G, Arkadir D, Nevet A, Vaadia E, Bergman H. Coincident but distinct messages\nof midbrain dopamine and striatal tonically active neurons. Neuron 43, 133-143 (2004).\n28. Krok AC, Maltese M, Mistry P, Miao X, Li Y, Tritsch NX. Intrinsic dopamine and\nacetylcholine dynamics in the striatum of mice. Nature 621, 543–549 (2023).\nReviewer #1 (Remarks to the Author):\nThe authors have very thoroughly revised the original submission. they have added considerably\nmore data regarding the behavior of their mice during the training trials, after choice and reward\nexposure and, in the process, have addressed the points I raised with regard to their original\nsubmission. I was particularly impressed by how thoroughly they addressed the points regarding\nthe definition of cognitive flexibility here. I do agree that CINs are likely to be centrally involved\nin updating prior learning after contingencies have changed and I think that they have now\nclarified where and why those changes occur in the current task. I was also impressed by the way\nthey have handled alternative explanations of their data. I find my prior comments have been\naddressed and that I have no further comments to make. From my perspective it is acceptable in\nits current form.\nAuthor response\nWe thank the Reviewer for their thoughtful assessment and positive response to our work. We\nare glad that our revisions and additional analyses have addressed all concerns raised.\nReviewer #2 (Remarks to the Author):\nThe manuscript describes findings from a series of experiments that demonstrates the dynamic\nchanges in dorsal striatal acetylcholine efflux during learning and reversal learning. There are\nseveral innovations carried out in these studies that provide significant insights of how actions of\nstriatal cholinergic interneurons underlie learning and behavioral flexibility.\nThe original submission represented a very important set of experiments and results addressing\nan important issue about brain-behavioral relationships. In particular, how does cholinergic\nsignaling in the striatum contribute to behavioral flexibility. In the revised version of the\nmanuscript, the authors have clarified a lot of questions raised by reviewers by conducting\nseveral different analyses. These additional analyses clarify what conditions lead to changes in\nstriatal ACh signaling and what conditions do not change striatal ACh signaling. Overall, the\ncomprehensive analyses conducted provide some real insights and make a strong argument for\nstriatal ACh contributing to behavioral flexibility.\nAuthor response\nThank you for reviewing our manuscript and for the valuable feedback. We are pleased that the\nreviewer found the revised manuscript to be improved and insightful.\nReviewer #3 (Remarks to the Author):\nRe-review of Sarpong et al.\nAs noted in my initial review, this manuscript includes some interesting observations, and I\napplaud the authors’ efforts to use 2p imaging to probe the spatiotemporal organization of ACh\nsignals. At the same time, these efforts, as currently presented, do not seem to have been\nparticularly successful. To see this, look at the title and abstract: essentially none of the results\nmentioned there rely on the imaging - they could more easily have been obtained with simple\nfiber photometry. This feels like a missed opportunity. It’s also a mismatch to how the\nmanuscript is presented, which repeatedly emphasizes the novelty of and “new insight” gained\nfrom the imaging method. When the image is divided into 50um x 50um zones the authors\nobserve some heterogeneity between zones. But beyond the apparent fact of heterogeneity,\nthere’s not much learned about the spatiotemporal scales or patterns of ACh release, and how\nthese relate to anatomy or physiology. Overall the results are somewhat interesting to specialists\nbut represent a modest advance over our prior knowledge of striatal ACh and behavioral\nflexibility.\nAuthor response\nWe thank the reviewer for acknowledging the contribution of our 2-photon approach beyond\nwhat could be obtained with fiber photometry. We have modified the title and abstract to better\nmatch the findings, and hopefully make their significance clearer to the non-specialist, as\nfollows:\nTitle: “Spatially heterogeneous acetylcholine dynamics in the striatum promote behavioral\nflexibility”\nAbstract: (line 28): “Analysis of 2-photon images by tiling the field of view into small quadrats\nrevealed spatially inhomogeneous and temporally distinct acetylcholine signals within the dorsal\nstriatum. Distinct patterns of outcome-related responses were differentially distributed across the\nstriatum, revealing functionally diverse acetylcholine microdomains.”\nReviewer comment\nOther points:\nline 37: “more formally…” - this doesn’t seem especially more “formal” than the prior\nsentences.\nAuthor response\nWe have deleted “more formally” from the text (line 39).\nReviewer comment\nline 65: why was the reversal performed between, rather than within, sessions? Wouldn’t within-\nsession provide more power to detect neural changes (although the low number of trials may\n2\ncancel this out).\nAuthor response\nIn principle, a within-session reversal design might provide more statistical power for detecting\nneural changes. However, in the present study we opted for between-session reversals due to\npractical constraints of two-photon imaging. Specifically, the duration and number of trials per\nsession are limited by exposure (laser heating) and photobleaching of the ACh sensor and by the\nneed to maintain stable optical access and imaging quality across the session. These factors\nreduce the feasibility of including a sufficient number of pre- and post-reversal trials within a\nsingle imaging session. As the reviewer notes, the limited trial count would likely offset the\npotential power gains of a within-session design.\nWe agree that exploring within-session reversals would be valuable, and we view our current\napproach as a first step toward characterizing striatal ACh dynamics during behavioral\nflexibility. We hope that future studies, potentially using complementary methods with higher\nthroughput (e.g., fiber photometry or miniature endoscopes), will build on our findings and test\nwithin-session reversal designs.\nReviewer comment\nline 69: “average percent correct was 82.1%…” when? during the sessions defined as happening\nafter an 80% correct session?\nAuthor response\nWe appreciate the opportunity to clarify this point. The reported value of 82.1% correct\ncorresponds to the average percent correct choices in the late learning phase across mice,\nspecifically during the last session preceding reversal. We have revised the text to explicitly\nstate this to avoid confusion. We now state that as follows (line 74):\n“In the late learning phase, defined as the final session preceding reversal, mice performed with\nan average correct choices across subjects of 82.1% ± 0.9% (mean ± s.e.m, P < 0.0001, n = 11\nmice)”\nReviewer comment\nline 116: “…time to dip (0.48s) is comparable to the timecourse of physiological action of\nACh…” why is the response time of striatal neurons a reasonable comparison, given time taken\nfor GPCR-mediated action of ACh?\nAuthor response\nWe agree this text may cause confusion and we have deleted it.\nline 126: “(confirmed by comparison with ACh insensitive signals…”. The tdTomato signal is\nbarely used in the manuscript - all the main results should include comparison to tdTomato to\nhelp rule out contributions of movement artifacts etc.\nAuthor response\nThe tdTomato experiment was added in response to this reviewer’s previous comment “how do\nthe authors distinguish real biological variability from trial to trial, from measurement noise?”\nTo demonstrate the actual measurement noise for the reviewer we showed the signal from an ACh\ninsensitive fluorophore (tdTomato) expressed in the same region and observed under similar\nconditions. We did not record tdTomato and iAChSnFR simultaneously in all trials because\nexcitation of each fluorophore requires different laser wavelengths and applying both lasers at\nthe same time increases the power and potential for overexposure, bleaching, and heating of the\ntissue. Therefore, tdTomato was imaged separately in a subset of trials. It should be noted that unlike\nisosbestic emission commonly used in fiber photometry the tdTomato signal is not emitted from\nthe same sensor molecules: tdTomato is expressed in different subcellular locations and therefore\nis not suitable for subtraction from signal. As noted in the Methods, all images were processed for\nx-y motion correction using a standard normalized cross-correlation method to eliminate any\nspatially uniform motion and movement artifact. Being able to see the detailed field of view is one\nof the advantages of two-photon imaging over fiber photometry, and our approach is consistent\nwith prior two-photon imaging studies (e.g., Patriarchi et al., Science, 2018; Sun et al., Nat.\nMethods, 2020). We have clarified this in the revised text as follows (line 131):\n“…(confirmed by comparison with ACh-insensitive tdTomato signals; Extended Data Fig. 2).\ntdTomato was imaged in a subset of trials and did not display the large positive-going transients\nobserved with iAChSnFR, supporting the conclusion that our reported ACh signals reflect\ngenuine cholinergic activity rather than measurement noise1,2.”\nReviewer comment\nline 151: “…indicating weak separation between clusters lying along a continuum (Extended\nData Fig. 4d). This is consistent with the fact that the concentration measures in the analysis are\nfrom physically adjacent quadrats along continuous concentration gradients“\n- if true, this seems like an acknowledgement that clustering doesn’t really make sense here.\n- although, the quadrats within the clusters don’t seem to generally appear physically contiguous,\nat least in the examples shown. Is this because the real spatial scale of ACh signals is comparable\n(or smaller) to 50µm? Or because the statistical criterion - an arbitrarily chosen 2SD - is not\nrigorous enough? Not enough is done to address such concerns.\nAuthor response\nrather than sharply discrete groups. This result does not undermine the validity of the approach;\nrather, it reveals that the ACh signal is inhomogeneous and graded in the dorsal striatum, like the\ntopography of many natural landscapes. Hierarchical clustering is an unsupervised, unbiased\nmethod well-suited for exploring datasets where the true underlying structure is unknown. In\nmany biological systems, activity patterns exist on a continuum rather than as discrete\ncategories, and hierarchical clustering allows the visualization of such graded relationships\nwhile still grouping local similarities (Xu & Wunsch, IEEE Trans Neural Netw., 2005). Thus, the\nfinding of weak separation is an informative result that indicates that ACh signals across\nquadrats are better conceptualized as varying along a gradient, rather than forming categorical\n“types.”\nIt is also important to note that structurally the cell bodies of CINs in the dorsal striatum are not\nclustered but have an inhomogeneous random spatial distribution, as we3,4 and others5 have\nshown. Each CIN produces an estimated 55 mm of axon6 that arborises profusely to fill a\nspheroidal volume with long axis of 500-700 micrometers7,8,9. Therefore, the axonal\narborizations individual CINs extend over multiple 50 µm x 50 µm quadrats. Given that with 2-\nphoton imaging we can reliably detect ACh efflux from single action potentials in individual\nCINs using similar-sized regions of interest10, the sampling spatial frequency obtained by using\n50 x 50 µm quadrats is appropriate 11,12,13.\nAs we have previously suggested, macroscopic features such as travelling and stationary waves\ncan arise from the dynamical interactions of multiple CIN axon arborizations overlapping in the\nsame volumes 10. While we agree that it would be of interesting to characterize the spatial spread,\ncontours and dimensions of these features, our study was not designed to do this but rather\nfocused on the dynamics associated with behavioral flexibility.\nRegarding the reviewer’s concern about spatial contiguity: it is not surprising that quadrats from\nthe same cluster are not always physically adjacent given that interactions among CINs can occur\nover distances greater than 50 µm 14 and corticostriatal afferents typically project to multiple,\nwidely separated striatal locations 15,16.\nRegarding 2SD threshold we applied – this was not “arbitrarily chosen”. It is a standard criterion\nwidely used to assess signal selectivity and confidence in calcium and neurotransmitter imaging\nstudies (e.g. Chen et al., Nature, 2013)17. This threshold ensures that identified responses are\nreliably distinct from baseline fluctuations while avoiding overfitting or false positives.\nIn summary, the clustering objectively reveals local similarities in response morphology and\nshows that ACh release patterns in the striatum exist along a continuum rather than in discrete\ncategories. This aligns with the broader literature, where clustering is often used not only to\n5\ndefine strong categorical differences, but also to explore and describe heterogeneous and\ngraded neural response patterns (Cohen & Kohn, Nat. Neurosci., 2011)18.\nWe have added this clarification as follows:\nLine 156:\n“Hierarchical clustering provides an unsupervised and unbiased method, heuristic framework to\ncapture local similarities and to visualize graded relationships, even in the absence of sharply\ndiscrete groups.”\nLine 1459:\n“The 2SD threshold applied here is a standard criterion for defining reliable responses in neural\nimaging17.”\nLine 481:\nStructurally, the cell bodies of CINs in the dorsal striatum are not clustered but have a random\nspatial distribution3,4,5. The axon of individual CINs arborises profusely to fill a spheroidal\nvolume with long axis of 500-700 micrometers6,7,8,9. Therefore, the axonal arborization of each\nCIN would extend over multiple 50 µm x 50 µm quadrats. This might explain why the clustering\nresults indicate gradations rather than sharply discrete groups. On the other hand, the clusters are\nnot necessarily due to anatomical structures but may be dynamically generated. Macroscopic\nfeatures such as travelling and stationary waves can arise from the dynamical interactions of\nmultiple CIN axon arborizations overlapping in the same volumes 10. Finally, given that\ninteractions among CIN axons can occur over distances greater than 50 µm 14 and corticostriatal\nafferents typically project to multiple striatal locations 15,16 it is not surprising that quadrats from\nthe same cluster are not always physically adjacent.\nReviewer comment\nline 202 “…increase in spatially averaged ACh levels that occurred gradually over seconds…”.\nThis is certainly an interesting observation, although given that the size of the increase is very\nmodest (<1 Z) and ACh can change very fast, it’s harder to have confidence this is a discrete,\nspecific signal being used by the brain.\nAuthor response\nIn addition to the observed signal, the evidence suggesting this signal is used by the brain is that\nit is predictive of the lose-shift behavior, and blocking CIN activity reduces lose-shift behavior.\nWhile we also agree that ACh concentration can change rapidly, that does not preclude slow\nchange and operation on multiple time scales, as we have shown here (Extended Data Fig. 6a).\nReviewer comment\nline 232: “…confirming that this signal is not only present but more spatially widespread during\n6\nbehavioral switching (Extended Data Fig. 6d).”\n- this figure panel is important enough to the authors’ argument to be in the main figures.\nAuthor response\nWe thank the reviewer for this helpful suggestion. We agree that this panel provides important\nsupport for our central argument. A related version of this analysis has been included in the main\nfigures (Fig. 4g,h), which compares ACh dynamics before and after reversal and thus directly\naddresses changes across training phases. The Extended Data panel (Extended Data Fig. 6d) was\nadded in response to Reviewer 1’s comment to illustrate phase-specific differences in greater\ndetail. To avoid redundancy while still highlighting the dynamic changes during, before and after\ntask switching, we have clarified in the text such that the Extended Data Fig. 6d panel\nspecifically emphasizes the differences (line 238). This way, both figures highlight\ncomplementary aspects of the data without duplication.\n“Comparison of ACh responses to non-reward before and after reversal within the same animals,\nusing per-mouse averages, revealed a significant increase in the spatially averaged ACh response\nto non-reward during reversal compared to non-reward during task acquisition (training phase\ni.e., early, and pre-reversal periods i.e., late), confirming that this signal is not only present but\nmore spatially widespread during behavioral switching (Fig. 4g,h and Extended Data Fig. 6d).”\nReviewer comment\nline 233: “ …the ACh increases observed in response to non-reward during acquisition, present\nin a subset of individual quadrats (cluster 4 in Fig. 4b-d), may reflect a preexisting substrate for\noutcome-related ACh modulation”. If this is correct, would it not imply that the specific quadrats\nwith increases during acquisition would also show (bigger) increases during reversal? Is this\ntrue?\nAuthor response\nThe quadrats with increases during acquisition may be the forerunners. We do not imply they\nincrease their activity further during reversal, rather that they show pre-existing increases. As\nshown in Fig. 5b–d, there is a clear population-level shift in quadrat responses from few\nincreases and predominantly decreases during acquisition to predominantly increases during\nreversal, indicating that other quadrats become active later.\nReviewer comment\nMore generally, how consistent are the quadrat-level signals from one imaging day to the next?\nShowing consistency could boost confidence in the quadrat-level results. Determining the spatial\nscale of such consistency might allow the authors to pick a quadrat size in a principled manner.\nAuthor response\ndifferent stages of learning. During early acquisition, only two main response motifs emerged\n(short-latency pause, ~79%; long-latency pause, ~21%), whereas during late learning four\ndistinct patterns were observed (pause, burst-pause, pause-burst, and increase). Comparison on\nthe 2D score plot revealed that the emergence of these additional motifs appears to arise from\nwithin the same space and boundaries of the early-phase responses, where reward-decreased\ncells reduce in proportion to give rise to more differentiated motifs. We speculate that early-\nphase motifs reflect heightened sensitivity to outcome novelty or reward prediction error,\nconsistent with prior reports of strong pause responses to unexpected rewards or salient cues in\nCINs19,20. With learning, the additional motifs likely represent more specialized roles in\nexpectation tracking and behavioral updating. Although we did not explicitly quantify spatial\nconsistency of individual quadrats across imaging days, the reproducibility of these motifs across\nsessions and mice supports the reliability of our quadrat-based approach.\nReviewer comment\nline 239: “The change in ACh response after unexpected non-reward could, in theory, be related\nto motor activity due to orienting responses. To address this, we analyzed the trial-by-trial\nvelocities ...”\n- velocities are not the same as orienting.\nAuthor response\nWhile we agree that locomotor velocity is not equivalent to orienting per se, we included this\ndata in response to a comment from Reviewer 1 who wondered “…if the change in ACh\nresponse after reversal in the presence of non-reward is related to motor activity/ orienting/\nattentional changes?” Because mice were head-fixed in our setup, they could not make postural\nor head- movements typical of orienting responses. Locomotor velocity on the ball was therefore\nthe only measurable motor activity. Reviewer 1 was happy that we report that. However, given\nthe concern raised, we have modified the text as follows:\nLine 248:\n“The change in ACh activity after unexpected non-reward could, in theory, be related to motor\nactivity due to orienting responses. In our setup, mice were head-fixed and therefore unable to\nperform the postural or head movements typical of orienting responses. However, they could\nexpress some degree of orienting through changes in locomotor activity. We analyzed locomotor\nvelocity on the ball on a trial-by-trial basis during the outcome period and found no significant\ncorrelation between mean ACh signals and velocity following non-reward (Fig. 4k).”\nReviewer comment\nline 288-294: The new results about subregions are interesting and relevant and as the authors\nnote they are consistent with prior results, though it’s surprising that these findings barely, or do\n8\nnot, reach significance (and are not visible in the “3D map”) What if “quadrats” are dropped and\nthe authors analyze much broader fields-of-view?\nAuthor response\nOur analysis along the medial–lateral axis did reveal a significant bias for quadrats with\nincreased ACh responses to unexpected non-reward toward the medial striatum (Pearson’s r = -\n0.55, P = 0.03, simple linear regression). These findings are consistent with prior reports linking\nmedial striatum ACh signaling to reversal learning. We therefore do not interpret the results as\n“barely” reaching significance but rather as reflecting a reproducible trend aligned with existing\nliterature. Broader averaging across the entire field of view would obscure the regional\ndifferences we observed. Finally, the GRIN lens positioning provided coverage of the dorsal\nstriatum sufficient to capture the medial–lateral and anteroposterior distributions, giving us\nconfidence in the spatial patterns reported (Fig. 5d).\nReviewer comment\nline 348: “…hM4D(Gi)-mCherry bilaterally into the DS of ChAT-Cre mice...”\nthe “DS” here is actually “DMS”, which makes sense given prior results but the imaging field is\nnot consistently DMS - why?\nAuthor response\nWe thank the reviewer for noting this. Our imaging experiments via the GRIN lens covered\ndorsal striatal regions that included both medial and lateral portions of the DS, whereas the\nchemogenetic manipulations targeted primarily the dorsomedial striatum (DMS). This focus was\nguided by two considerations: first, our imaging analyses revealed that increases in ACh\nresponses to unexpected non-reward were most prominently distributed medially; second, prior\npharamcological and lesion studies have consistently implicated the medial striatum in reversal\nlearning and flexible behavior21,22,23.However, we do not view the term “DS” or the injection\nsites as a discrepancy, but rather as a targeted experimental design choice that complements our\nimaging findings and leverages prior literature to test the functional role of medial striatal ACh\nsignaling.\nWe have added this to the Methods section as follows (line 1347):\n“Although injections were performed bilaterally in the dorsal striatum, they were centered\nprimarily in the dorsomedial striatum (DMS), guided by both our imaging findings of enhanced\nmedial ACh responses to non-reward and prior reports implicating the medial striatum in\nreversal learning and behavioral flexibility.”\nReviewer comment\nline 360: “Cumulative errors over the first 80 trials post-reversal were higher in CIN-inhibited\nmice, reflecting impaired overall task adaptation beyond specific error types (Fig. 6f).” How\n9\ndoes this observation support this conclusion?\nAuthor response\nOur conclusion that cumulative errors over the first 80 trials post-reversal reflect impaired\noverall task adaptation is supported by two complementary observations. First, CIN-inhibited\nmice showed a significant reduction in correct choices specifically during the early reversal\nphase (Fig. 6d,e), when flexible updating is most critical, whereas performance in later sessions\nwas unaffected. This indicates that CIN activity is particularly important for the initial adaptive\nresponse to changing contingencies rather than for late, steady-state performance. Second, our\nimaging results further support this interpretation: unexpected non-reward after reversal elicited\nwidespread increases in acetylcholine signals, especially in lose-shift trials, where animals\nadapted by switching to the opposite arm. Thus, higher ACh levels were predictive of adaptive\nswitching behavior. By contrast, chemogenetic inhibition of CINs reduced this adaptive shift,\nresulting in greater cumulative errors that could not be explained by specific error types alone.\nCollectively, these findings suggest that CIN-dependent ACh release supports early task\nadaptation following contingency change, and its inhibition impairs this process.\nReviewer comment\nline 364: “While CIN inhibition reduced lose-shift behavior (Fig. 6g, left; unpaired t-test, P =\n0.015), it did not alter win-stay probability following the reversal (Fig. 6g, right; P = 0.14).\n“ Judging by Fig. 6g, this is not a convincing result. The analysis appears to lack statistical power\ndue to few, scattered data points.\nAuthor response\nLose-shift behavior showed a robust and statistically significant reduction following CIN\ninhibition. We agree that we cannot exclude an effect on win-stay probability. The sample size (n\n= 5 per group) used in our chemogenetic experiments is within the range (5-9 animals/group)\ncommonly used in CIN manipulation studies (Okada et al., Eur J Neurosci., 2015). To address\nthe reviewer’s concern, we (i) now report exact p-values and included effect sizes (Cohen’s d to\nquantify the magnitude of effect) for all primary comparisons, (ii) have shown individual animal\ndata to display the underlying distribution. These additional analyses indicated that the primary\neffect (e.g., reduced lose-shift probability after CIN inhibition) is consistent across subjects and\nof large magnitude, increasing confidence in the biological effect. For the comparisons reported\nin Fig. 6d,g, the effect sizes were very large (e.g., d = (–)1.54, (–)1.01, (–)1.77, and (–)0.94),\nrespectively, indicating robust group differences despite modest power. Even in the case of a\nsmaller effect (d = 0.43 and 0.21; Ext. Data Fig. 9b,d), the observed direction was consistent with\nour hypothesis. Thus, although the small sample number may limit statistical power, the effect\nsize analysis suggests that the observed differences are biologically meaningful and reproducible.\nThe Methods section has been revised as follows (line 1554):\n“Effect size analysis\nobserved group differences, we calculated Cohen’s d for all pairwise comparisons. For unpaired\nt-tests, d was computed as the standardized difference in means between treatment groups, using\nthe pooled standard deviation as the denominator. 95% confidence intervals were also estimated\nto provide an index of precision. Effect sizes were interpreted according to conventional\nbenchmarks (d ≈ 0.2: small; d ≈ 0.5: medium; d ≥ 0.8: large; negative values in figures indicate\ndirection of effect)24. Reporting effect sizes is particularly important in studies with modest\nsample sizes, as it provides information about the strength and potential biological relevance of\neffects beyond p values alone.”\nThe figure legend has been updated as follows (line 1094):\n“…Cohen’s d = (–)1.54, 95% CI [-2.98, -0.09]) but comparable in Test 5 (P = 0.11, Cohen’s d =\n(–)1.01, 95% CI [-2.35, 0.32]). Data shown as mean ± SEM. Effect sizes (Cohen’s d) was\ncalculated to quantify the magnitude of differences (see Methods for details). These values\nhighlight that, despite the modest sample size, the observed differences represent substantial\nbiological effects in most comparisons.”\nResults section has been revised as follows (line 374):\nLose-shift behavior was significantly reduced by CIN inhibition (Fig. 6g, left; unpaired t-test, P\n= 0.015, Cohen’s d = (–)1.77, 95% CI [-3.27, -0.26]). However, CIN inhibition effects on win-\nstay probability following the reversal were not significant (Fig. 6g, right; P = 0.14, Cohen’s d =\n(–)0.94, 95% CI [-2.27, 0.38]).\nReviewer comment\nline 497: “we propose that neural pathways exposed to decreased ACh on reward delivery –\ncausing a pause after successfully shifting – would experience synaptic potentiation”. It’s not\nfully clear from the figures, but it looks like decrease ACh on reward delivery is strong even at\nthe end of behavioral acquisition - would it really be helpful to keep potentiating synapses at that\npoint?\nAuthor response\nAs noted in the preceding sentence of the discussion, long-term potentiation of cortical inputs to\nstriatal projection neurons requires coincidence of pauses in CINs with phasic dopamine\nactivation. Phasic dopamine release generally occurs in response to unexpected reward but not in\nresponse to reward that is expected. At the end of behavioral acquisition, it is likely that reward\nwill be expected, and dopamine will not be released, so there will be no potentiation. To make\nthis clearer we have revised the text as follows (line 516):\n“Recent work shows that long-term potentiation of cortical inputs to striatal projection neurons\nrequires coincidence of pauses in CINs with phasic dopamine activation25, which occurs in\n11\nresponse to unexpected reward. Thus, we propose that neural pathways exposed to decreased\nACh on unexpected reward delivery – causing a CIN pause and dopamine activation after\nsuccessfully shifting – would experience synaptic potentiation. At the end of behavioral\nacquisition it is likely that reward will be expected, and dopamine will not be released, so there\nwill be no potentiation. Pathways exposed to increased ACh on unexpected non-reward would\nnot be potentiated, preventing reinforcement of incorrect responses. This would ultimately lead\nto a reduction in interference from pre-existing memory26.”\nReviewer comment\nFig. 1c: please show the data divided into the individual sessions.\nAuthor response\nWe have added information about the demarcation of individual sessions, in the figure legend\n(line 842):\n“c, Sample choice behavior in representative trials (200 trials across 5 sessions; 40 trials per\nsession) from an example mouse…”\nReviewer comment\nFig. 1f/g (and later figures): since the point is to compare “early” and “late”, the statistical test\nshould incorporate both these conditions (e.g. as a factor in ANOVA) rather than running\nseparate tests for each condition.\nAuthor response\nResponse:\nWe thank the reviewer for this suggestion. Our original paired t-tests within “early” and “late”\nwere intended to provide simple, within-animal contrasts for each phase. However, we agree that\na model that includes Session (Early vs. Late) and Outcome (Reward vs. No reward) in the same\nanalysis is statistically stronger. Accordingly, we have re-analyzed velocity and licking rate\nusing a within-subject repeated-measures framework with factors Session (2 levels) and\nOutcome (2 levels) in ANOVA.\nFor anticipatory licking, this analysis revealed a significant main effect of Outcome (F(1,20) =\n5.14, P = 0.03), indicating that mice exhibited higher anticipatory licking when expecting reward\nrelative to non-reward outcomes. The main effect of Session did not reach significance (F(1,20)\n= 3.51, P = 0.076), and the Session × Outcome interaction also did not reach significance\n(F(1,20) = 3.78, P = 0.066). Combined, these findings suggest that anticipatory licking is\nrobustly driven by reward outcome across learning, with a trend toward higher overall levels in\nlate sessions. Importantly, this confirms our conclusion that anticipatory behavior reflects\nsensitivity to reward expectation.\n11.69, P = 0.0027), indicating that overall velocities increased from early to late learning. The\nmain effect of outcome did not reach significance (F(1,20) = 0.17, P = 0.68), though the trend\nwas consistent with higher velocities in rewarded trials. The Session × Outcome interaction was\nalso not significant (F(1,20) = 0.28, P = 0.60). Combined, these results indicate that the clearest\neffect was a robust increase in approach velocity across learning, supporting our original\nconclusion that motivational drive strengthens with task experience. Thus, the direction of the\neffect is consistent with our within-phase t-tests, which suggested higher velocity in rewarded\ntrials during late learning. The respective figures (Fig. 1f,g and 4d,e) have been updated.\nReviewer comment\nFig. 4b : why show the orange and blue ticks twice each?\nAuthor response\nThe lower raster plot shows trial-by-trial choices, where blue ticks denote correct trials and\norange ticks denote incorrect trials. Above this, a heatmap depicts the same trial outcomes in\nbinary format (1 = correct, 0 = incorrect), aligned to the raster plot for visual comparison. Thus,\nthe raster + binary heatmap give complementary information: the raster conveys variability\nacross trials, while the heatmap makes the correct/incorrect sequence categorical and easy to\nread.\nReviewer comment\nEx Fig 6b: why are these particular three “representative” mice shown, rather than all of them?\nThe right part of this panel is hard to follow and the argument presented from these individual\nmice is unconvincing.\nAuthor response\nWe appreciate the reviewer’s concern. We note, however, that the individual activity patterns\nand mean responses of individual mice are already presented in Fig. 4g (heatmaps) and Fig. 4j\n(scatter plot of lose-shift correlation). The representative traces in Extended Data Fig. 6b were\nincluded primarily as a visual illustration of how cholinergic responses to unexpected non-\nreward can influence choice behavior across reversal, complementing the full dataset shown in\nFig. 4g,j. This approach, using representative animals to illustrate trial dynamics alongside\ngroup-level statistics, has been employed in previous studies of striatal acetylcholine and TAN\nactivity for example19,27,28. We therefore believe these examples are appropriate and serve as a\nheuristic aid, while the statistical conclusions are drawn from the complete dataset (n = 11).\nReviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is\npart of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nAuthor Response\nThank you for co-reviewing the manuscript.\nReferences cited\n1. Patriarchi T, et al. Ultrafast neuronal imaging of dopamine dynamics with designed\ngenetically encoded sensors. Science 360, 1-8 (2018).\n2. Sun F, et al. Next-generation GRAB sensors for monitoring dopaminergic activity in\nvivo. Nat Methods 17, 1156-1166 (2020).\n3. Carrasco A, Oorschot DE, Barzaghi P, Wickens JR. Three-dimensional spatial analyses\nof cholinergic neuronal distributions across the mouse septum, nucleus basalis, globus\npallidus, nucleus accumbens, and caudate-putamen. Neuroinformatics 20, 1121-1136\n(2022).\n4. Zhang R, Wickens JR, Carrasco A, Oorschot DE. Absolute Number of Thalamic\nParafascicular and Striatal Cholinergic Neurons, and the Three-Dimensional Spatial\nArray of Striatal Cholinergic Neurons, in the Sprague-Dawley Rat. J Comp Neurol 533,\ne70050 (2025).\n5. Matamales M, Gotz J, Bertran-Gonzalez J. Quantitative Imaging of Cholinergic\nInterneurons Reveals a Distinctive Spatial Organization and a Functional Gradient across\nthe Mouse Striatum. PLoS One 11, e0157682 (2016).\n6. Williams SR, Zhou X, Fletcher LN. Compartment-specific dendritic information\nprocessing in striatal cholinergic interneurons is reconfigured by peptide\nneuromodulation. Neuron 111, 1933-1951 e1933 (2023).\n7. Wilson CJ, Chang HT, Kitai ST. Firing patterns and synaptic potentials of identified giant\naspiny interneurons in the rat neostriatum. J Neurosci 10, 508-519 (1990).\n8. Kawaguchi Y. Physiological, morphological, and histochemical characterization of three\nclasses of interneurons in rat neostriatum. J Neurosci 13, 4908-4923 (1993).\n9. Goldberg JA, Wilson CJ. Chapter 7 - The Cholinergic Interneuron of the Striatum. In:\nHandbook of Behavioral Neuroscience (eds Steiner H, Tseng KY). Elsevier (2016).\n10. Matityahu L, Gilin N, Sarpong GA, Atamna Y, Tiroshi L, Tritsch NX, Wickens JR,\nGoldberg JA. Acetylcholine waves and dopamine release in the striatum. Nat Commun\n14, 6852 (2023).\n11. Nyquist H. Certain topics in telegraph transmission theory (Reprinted from Transactions\nof the A. I. E. E., February, pg 617-644, 1928). Proceedings of the Ieee 90, 280-305\n(2002).\n12. Jerri AJ. The Shannon sampling theorem—Its various extensions and applications: A\ntutorial review. Proceedings of the IEEE 65, 1565-1596 (1977).\n13. Shannon CE. Communication in the Presence of Noise. Proceedings of the Institute of\nRadio Engineers 37, 10-21 (1949).\n14. Dorst MC, Tokarska A, Zhou M, Lee K, Stagkourakis S, Broberger C, Masmanidis S,\nSilberberg G. Polysynaptic inhibition between striatal cholinergic interneurons shapes\n14\ntheir network activity patterns in a dopamine-dependent manner. Nat Commun 11, 5113\n(2020).\n15. Faull RLM, Nauta WJH, Domesick VB. The visual cortico-striato-nigral pathway in the\nrat. Neuroscience 19, 1119-1132 (1986).\n16. Hunnicutt BJ, Jongbloets BC, Birdsong WT, Gertz KJ, Zhong H, Mao T. A\ncomprehensive excitatory input map of the striatum reveals novel functional\norganization. eLife 5, (2016).\n17. Chen TW, Wardill TJ, Sun Y, Pulver SR, Renninger SL, Baohan A, Schreiter ER, Kerr\nRA, Orger MB, Jayaraman V, Looger LL, Svoboda K, Kim DS. Ultrasensitive\nfluorescent proteins for imaging neuronal activity. Nature 499, 295-300 (2013).\n18. Cohen MR, Kohn A. Measuring and interpreting neuronal correlations. Nat Neurosci 14,\n811-819 (2011).\n19. Aosaki T, Tsubokawa H, Ishida A, Watanabe K, Graybiel AM, Kimura M. Responses of\ntonically active neurons in the primate's striatum undergo systematic changes during\nbehavioral sensorimotor conditioning. J Neurosci 14, 3969-3984 (1994).\n20. Apicella P. Leading tonically active neurons of the striatum from reward detection to\ncontext recognition. Trends Neurosci 30, 299-306 (2007).\n21. Brown HD, Baker PM, Ragozzino ME. The parafascicular thalamic nucleus\nconcomitantly influences behavioral flexibility and dorsomedial striatal acetylcholine\noutput in rats. J Neurosci 30, 14390-14398 (2010).\n22. Ragozzino ME, Choi D. Dynamic changes in acetylcholine output in the medial striatum\nduring place reversal learning. Learn Mem 11, 70-77 (2004).\n23. Ragozzino ME, Mohler EG, Prior M, Palencia CA, Rozman S. Acetylcholine activity in\nselective striatal regions supports behavioral flexibility. Neurobiol Learn Mem 91, 13-22\n(2009).\n24. Lakens D. Calculating and reporting effect sizes to facilitate cumulative science: a\npractical primer for t-tests and ANOVAs. Front Psychol 4, 863 (2013).\n25. Reynolds JNJ, Avvisati R, Dodson PD, Fisher SD, Oswald MJ, Wickens JR, Zhang YF.\nCoincidence of cholinergic pauses, dopaminergic activation and depolarisation of spiny\nprojection neurons drives synaptic plasticity in the striatum. Nat Commun 13, 1296\n(2022).\n26. Bradfield LA, Bertran-Gonzalez J, Chieng B, Balleine BW. The thalamostriatal pathway\nand cholinergic control of goal-directed action: interlacing new with existing learning in\nthe striatum. Neuron 79, 153-166 (2013).\n27. Morris G, Arkadir D, Nevet A, Vaadia E, Bergman H. Coincident but distinct messages\nof midbrain dopamine and striatal tonically active neurons. Neuron 43, 133-143 (2004).\n28. Krok AC, Maltese M, Mistry P, Miao X, Li Y, Tritsch NX. Intrinsic dopamine and\nacetylcholine dynamics in the striatum of mice. Nature 621, 543–549 (2023).\n15"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have very thoroughly revised the original submission. they have added considerably more data regarding the\nbehavior of their mice during the training trials, after choice and reward exposure and, in the process, have addressed the\npoints I raised with regard to their original submission. I was particularly impressed by how thoroughly they addressed the\npoints regarding the definition of cognitive flexibility here. I do agree that CINs are likely to be centrally involved in updating\nprior learning after contingencies have changed and I think that they have now clarified where and why those changes occur\nin the current task. I was also impressed by the way they have handled alternative explanations of their data. I find my prior\ncomments have been addressed and that I have no further comments to make. From my perspective it is acceptable in its\ncurrent form.", "reviewer_2": "The manuscript describes findings from a series of experiments that demonstrates the dynamic changes in dorsal striatal\nacetylcholine efflux during learning and reversal learning. There are several innovations carried out in these studies that\nprovide significant insights of how actions of striatal cholinergic interneurons underlie learning and behavioral flexibility.\nThe original submission represented a very important set of experiments and results addressing an important issue about\nbrain-behavioral relationships. In particular, how does cholinergic signaling in the striatum contribute to behavioral flexibility.\nIn the revised version of the manuscript, the authors have clarified a lot of questions raised by reviewers by conducting\nseveral different analyses. These additional analyses clarify what conditions lead to changes in striatal ACh signaling and\nwhat conditions do not change striatal ACh signaling. Overall, the comprehensive analyses conducted provide some real\ninsights and make a strong argument for striatal ACh contributing to behavioral flexibility.", "reviewer_3": "I don't have much to add to the previous rounds of review. I appreciate that the authors have fixed some minor things here\nand there, and I appreciate that a lot of work went into collecting and analyzing this data. But my major concern is that the\nimaging just hasn't revealed much about the functional properties of striatal ACh signaling, beyond the fact of heterogeneity.\nI'm very interested in this subfield, but despite that I don't find the concrete results here especially novel or interesting and I\ndo not think they will have broad impact.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts."}, "author_response": "RESPONSE TO REVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThe authors have very thoroughly revised the original submission. they have added considerably\nmore data regarding the behavior of their mice during the training trials, after choice and reward\nexposure and, in the process, have addressed the points I raised with regard to their original\nsubmission. I was particularly impressed by how thoroughly they addressed the points regarding\nthe definition of cognitive flexibility here. I do agree that CINs are likely to be centrally involved\nin updating prior learning after contingencies have changed and I think that they have now\nclarified where and why those changes occur in the current task. I was also impressed by the way\nthey have handled alternative explanations of their data. I find my prior comments have been\naddressed and that I have no further comments to make. From my perspective it is acceptable in\nits current form.\nAuthor response\nWe thank the Reviewer for their thoughtful assessment and positive response to our work. We\nare glad that our revisions and additional analyses have addressed all concerns raised.\nReviewer #2 (Remarks to the Author):\nThe manuscript describes findings from a series of experiments that demonstrates the dynamic\nchanges in dorsal striatal acetylcholine efflux during learning and reversal learning. There are\nseveral innovations carried out in these studies that provide significant insights of how actions of\nstriatal cholinergic interneurons underlie learning and behavioral flexibility.\nThe original submission represented a very important set of experiments and results addressing\nan important issue about brain-behavioral relationships. In particular, how does cholinergic\nsignaling in the striatum contribute to behavioral flexibility. In the revised version of the\nmanuscript, the authors have clarified a lot of questions raised by reviewers by conducting\nseveral different analyses. These additional analyses clarify what conditions lead to changes in\nstriatal ACh signaling and what conditions do not change striatal ACh signaling. Overall, the\ncomprehensive analyses conducted provide some real insights and make a strong argument for\nstriatal ACh contributing to behavioral flexibility.\nAuthor response\nThank you for reviewing our manuscript and for the valuable feedback. We are pleased that the\nreviewer found the revised manuscript to be improved and insightful.\nReviewer #3 (Remarks to the Author):\nRe-review of Sarpong et al.\nAs noted in my initial review, this manuscript includes some interesting observations, and I\napplaud the authors’ efforts to use 2p imaging to probe the spatiotemporal organization of ACh\nsignals. At the same time, these efforts, as currently presented, do not seem to have been\nparticularly successful. To see this, look at the title and abstract: essentially none of the results\nmentioned there rely on the imaging - they could more easily have been obtained with simple\nfiber photometry. This feels like a missed opportunity. It’s also a mismatch to how the\nmanuscript is presented, which repeatedly emphasizes the novelty of and “new insight” gained\nfrom the imaging method. When the image is divided into 50um x 50um zones the authors\nobserve some heterogeneity between zones. But beyond the apparent fact of heterogeneity,\nthere’s not much learned about the spatiotemporal scales or patterns of ACh release, and how\nthese relate to anatomy or physiology. Overall the results are somewhat interesting to specialists\nbut represent a modest advance over our prior knowledge of striatal ACh and behavioral\nflexibility.\nAuthor response\nWe thank the reviewer for acknowledging the contribution of our 2-photon approach beyond\nwhat could be obtained with fiber photometry. We have modified the title and abstract to better\nmatch the findings, and hopefully make their significance clearer to the non-specialist, as\nfollows:\nTitle: “Spatially heterogeneous acetylcholine dynamics in the striatum promote behavioral\nflexibility”\nAbstract: (line 28): “Analysis of 2-photon images by tiling the field of view into small quadrats\nrevealed spatially inhomogeneous and temporally distinct acetylcholine signals within the dorsal\nstriatum. Distinct patterns of outcome-related responses were differentially distributed across the\nstriatum, revealing functionally diverse acetylcholine microdomains.”\nReviewer comment\nOther points:\nline 37: “more formally…” - this doesn’t seem especially more “formal” than the prior\nsentences.\nAuthor response\nWe have deleted “more formally” from the text (line 39).\nReviewer comment\nline 65: why was the reversal performed between, rather than within, sessions? Wouldn’t within-\nsession provide more power to detect neural changes (although the low number of trials may\n2\ncancel this out).\nAuthor response\nIn principle, a within-session reversal design might provide more statistical power for detecting\nneural changes. However, in the present study we opted for between-session reversals due to\npractical constraints of two-photon imaging. Specifically, the duration and number of trials per\nsession are limited by exposure (laser heating) and photobleaching of the ACh sensor and by the\nneed to maintain stable optical access and imaging quality across the session. These factors\nreduce the feasibility of including a sufficient number of pre- and post-reversal trials within a\nsingle imaging session. As the reviewer notes, the limited trial count would likely offset the\npotential power gains of a within-session design.\nWe agree that exploring within-session reversals would be valuable, and we view our current\napproach as a first step toward characterizing striatal ACh dynamics during behavioral\nflexibility. We hope that future studies, potentially using complementary methods with higher\nthroughput (e.g., fiber photometry or miniature endoscopes), will build on our findings and test\nwithin-session reversal designs.\nReviewer comment\nline 69: “average percent correct was 82.1%…” when? during the sessions defined as happening\nafter an 80% correct session?\nAuthor response\nWe appreciate the opportunity to clarify this point. The reported value of 82.1% correct\ncorresponds to the average percent correct choices in the late learning phase across mice,\nspecifically during the last session preceding reversal. We have revised the text to explicitly\nstate this to avoid confusion. We now state that as follows (line 74):\n“In the late learning phase, defined as the final session preceding reversal, mice performed with\nan average correct choices across subjects of 82.1% ± 0.9% (mean ± s.e.m, P < 0.0001, n = 11\nmice)”\nReviewer comment\nline 116: “…time to dip (0.48s) is comparable to the timecourse of physiological action of\nACh…” why is the response time of striatal neurons a reasonable comparison, given time taken\nfor GPCR-mediated action of ACh?\nAuthor response\nWe agree this text may cause confusion and we have deleted it.\nline 126: “(confirmed by comparison with ACh insensitive signals…”. The tdTomato signal is\nbarely used in the manuscript - all the main results should include comparison to tdTomato to\nhelp rule out contributions of movement artifacts etc.\nAuthor response\nThe tdTomato experiment was added in response to this reviewer’s previous comment “how do\nthe authors distinguish real biological variability from trial to trial, from measurement noise?”\nTo demonstrate the actual measurement noise for the reviewer we showed the signal from an ACh\ninsensitive fluorophore (tdTomato) expressed in the same region and observed under similar\nconditions. We did not record tdTomato and iAChSnFR simultaneously in all trials because\nexcitation of each fluorophore requires different laser wavelengths and applying both lasers at\nthe same time increases the power and potential for overexposure, bleaching, and heating of the\ntissue. Therefore, tdTomato was imaged separately in a subset of trials. It should be noted that unlike\nisosbestic emission commonly used in fiber photometry the tdTomato signal is not emitted from\nthe same sensor molecules: tdTomato is expressed in different subcellular locations and therefore\nis not suitable for subtraction from signal. As noted in the Methods, all images were processed for\nx-y motion correction using a standard normalized cross-correlation method to eliminate any\nspatially uniform motion and movement artifact. Being able to see the detailed field of view is one\nof the advantages of two-photon imaging over fiber photometry, and our approach is consistent\nwith prior two-photon imaging studies (e.g., Patriarchi et al., Science, 2018; Sun et al., Nat.\nMethods, 2020). We have clarified this in the revised text as follows (line 131):\n“…(confirmed by comparison with ACh-insensitive tdTomato signals; Extended Data Fig. 2).\ntdTomato was imaged in a subset of trials and did not display the large positive-going transients\nobserved with iAChSnFR, supporting the conclusion that our reported ACh signals reflect\ngenuine cholinergic activity rather than measurement noise1,2.”\nReviewer comment\nline 151: “…indicating weak separation between clusters lying along a continuum (Extended\nData Fig. 4d). This is consistent with the fact that the concentration measures in the analysis are\nfrom physically adjacent quadrats along continuous concentration gradients“\n- if true, this seems like an acknowledgement that clustering doesn’t really make sense here.\n- although, the quadrats within the clusters don’t seem to generally appear physically contiguous,\nat least in the examples shown. Is this because the real spatial scale of ACh signals is comparable\n(or smaller) to 50µm? Or because the statistical criterion - an arbitrarily chosen 2SD - is not\nrigorous enough? Not enough is done to address such concerns.\nAuthor response\nrather than sharply discrete groups. This result does not undermine the validity of the approach;\nrather, it reveals that the ACh signal is inhomogeneous and graded in the dorsal striatum, like the\ntopography of many natural landscapes. Hierarchical clustering is an unsupervised, unbiased\nmethod well-suited for exploring datasets where the true underlying structure is unknown. In\nmany biological systems, activity patterns exist on a continuum rather than as discrete\ncategories, and hierarchical clustering allows the visualization of such graded relationships\nwhile still grouping local similarities (Xu & Wunsch, IEEE Trans Neural Netw., 2005). Thus, the\nfinding of weak separation is an informative result that indicates that ACh signals across\nquadrats are better conceptualized as varying along a gradient, rather than forming categorical\n“types.”\nIt is also important to note that structurally the cell bodies of CINs in the dorsal striatum are not\nclustered but have an inhomogeneous random spatial distribution, as we3,4 and others5 have\nshown. Each CIN produces an estimated 55 mm of axon6 that arborises profusely to fill a\nspheroidal volume with long axis of 500-700 micrometers7,8,9. Therefore, the axonal\narborizations individual CINs extend over multiple 50 µm x 50 µm quadrats. Given that with 2-\nphoton imaging we can reliably detect ACh efflux from single action potentials in individual\nCINs using similar-sized regions of interest10, the sampling spatial frequency obtained by using\n50 x 50 µm quadrats is appropriate 11,12,13.\nAs we have previously suggested, macroscopic features such as travelling and stationary waves\ncan arise from the dynamical interactions of multiple CIN axon arborizations overlapping in the\nsame volumes 10. While we agree that it would be of interesting to characterize the spatial spread,\ncontours and dimensions of these features, our study was not designed to do this but rather\nfocused on the dynamics associated with behavioral flexibility.\nRegarding the reviewer’s concern about spatial contiguity: it is not surprising that quadrats from\nthe same cluster are not always physically adjacent given that interactions among CINs can occur\nover distances greater than 50 µm 14 and corticostriatal afferents typically project to multiple,\nwidely separated striatal locations 15,16.\nRegarding 2SD threshold we applied – this was not “arbitrarily chosen”. It is a standard criterion\nwidely used to assess signal selectivity and confidence in calcium and neurotransmitter imaging\nstudies (e.g. Chen et al., Nature, 2013)17. This threshold ensures that identified responses are\nreliably distinct from baseline fluctuations while avoiding overfitting or false positives.\nIn summary, the clustering objectively reveals local similarities in response morphology and\nshows that ACh release patterns in the striatum exist along a continuum rather than in discrete\ncategories. This aligns with the broader literature, where clustering is often used not only to\n5\ndefine strong categorical differences, but also to explore and describe heterogeneous and\ngraded neural response patterns (Cohen & Kohn, Nat. Neurosci., 2011)18.\nWe have added this clarification as follows:\nLine 156:\n“Hierarchical clustering provides an unsupervised and unbiased method, heuristic framework to\ncapture local similarities and to visualize graded relationships, even in the absence of sharply\ndiscrete groups.”\nLine 1459:\n“The 2SD threshold applied here is a standard criterion for defining reliable responses in neural\nimaging17.”\nLine 481:\nStructurally, the cell bodies of CINs in the dorsal striatum are not clustered but have a random\nspatial distribution3,4,5. The axon of individual CINs arborises profusely to fill a spheroidal\nvolume with long axis of 500-700 micrometers6,7,8,9. Therefore, the axonal arborization of each\nCIN would extend over multiple 50 µm x 50 µm quadrats. This might explain why the clustering\nresults indicate gradations rather than sharply discrete groups. On the other hand, the clusters are\nnot necessarily due to anatomical structures but may be dynamically generated. Macroscopic\nfeatures such as travelling and stationary waves can arise from the dynamical interactions of\nmultiple CIN axon arborizations overlapping in the same volumes 10. Finally, given that\ninteractions among CIN axons can occur over distances greater than 50 µm 14 and corticostriatal\nafferents typically project to multiple striatal locations 15,16 it is not surprising that quadrats from\nthe same cluster are not always physically adjacent.\nReviewer comment\nline 202 “…increase in spatially averaged ACh levels that occurred gradually over seconds…”.\nThis is certainly an interesting observation, although given that the size of the increase is very\nmodest (<1 Z) and ACh can change very fast, it’s harder to have confidence this is a discrete,\nspecific signal being used by the brain.\nAuthor response\nIn addition to the observed signal, the evidence suggesting this signal is used by the brain is that\nit is predictive of the lose-shift behavior, and blocking CIN activity reduces lose-shift behavior.\nWhile we also agree that ACh concentration can change rapidly, that does not preclude slow\nchange and operation on multiple time scales, as we have shown here (Extended Data Fig. 6a).\nReviewer comment\nline 232: “…confirming that this signal is not only present but more spatially widespread during\n6\nbehavioral switching (Extended Data Fig. 6d).”\n- this figure panel is important enough to the authors’ argument to be in the main figures.\nAuthor response\nWe thank the reviewer for this helpful suggestion. We agree that this panel provides important\nsupport for our central argument. A related version of this analysis has been included in the main\nfigures (Fig. 4g,h), which compares ACh dynamics before and after reversal and thus directly\naddresses changes across training phases. The Extended Data panel (Extended Data Fig. 6d) was\nadded in response to Reviewer 1’s comment to illustrate phase-specific differences in greater\ndetail. To avoid redundancy while still highlighting the dynamic changes during, before and after\ntask switching, we have clarified in the text such that the Extended Data Fig. 6d panel\nspecifically emphasizes the differences (line 238). This way, both figures highlight\ncomplementary aspects of the data without duplication.\n“Comparison of ACh responses to non-reward before and after reversal within the same animals,\nusing per-mouse averages, revealed a significant increase in the spatially averaged ACh response\nto non-reward during reversal compared to non-reward during task acquisition (training phase\ni.e., early, and pre-reversal periods i.e., late), confirming that this signal is not only present but\nmore spatially widespread during behavioral switching (Fig. 4g,h and Extended Data Fig. 6d).”\nReviewer comment\nline 233: “ …the ACh increases observed in response to non-reward during acquisition, present\nin a subset of individual quadrats (cluster 4 in Fig. 4b-d), may reflect a preexisting substrate for\noutcome-related ACh modulation”. If this is correct, would it not imply that the specific quadrats\nwith increases during acquisition would also show (bigger) increases during reversal? Is this\ntrue?\nAuthor response\nThe quadrats with increases during acquisition may be the forerunners. We do not imply they\nincrease their activity further during reversal, rather that they show pre-existing increases. As\nshown in Fig. 5b–d, there is a clear population-level shift in quadrat responses from few\nincreases and predominantly decreases during acquisition to predominantly increases during\nreversal, indicating that other quadrats become active later.\nReviewer comment\nMore generally, how consistent are the quadrat-level signals from one imaging day to the next?\nShowing consistency could boost confidence in the quadrat-level results. Determining the spatial\nscale of such consistency might allow the authors to pick a quadrat size in a principled manner.\nAuthor response\ndifferent stages of learning. During early acquisition, only two main response motifs emerged\n(short-latency pause, ~79%; long-latency pause, ~21%), whereas during late learning four\ndistinct patterns were observed (pause, burst-pause, pause-burst, and increase). Comparison on\nthe 2D score plot revealed that the emergence of these additional motifs appears to arise from\nwithin the same space and boundaries of the early-phase responses, where reward-decreased\ncells reduce in proportion to give rise to more differentiated motifs. We speculate that early-\nphase motifs reflect heightened sensitivity to outcome novelty or reward prediction error,\nconsistent with prior reports of strong pause responses to unexpected rewards or salient cues in\nCINs19,20. With learning, the additional motifs likely represent more specialized roles in\nexpectation tracking and behavioral updating. Although we did not explicitly quantify spatial\nconsistency of individual quadrats across imaging days, the reproducibility of these motifs across\nsessions and mice supports the reliability of our quadrat-based approach.\nReviewer comment\nline 239: “The change in ACh response after unexpected non-reward could, in theory, be related\nto motor activity due to orienting responses. To address this, we analyzed the trial-by-trial\nvelocities ...”\n- velocities are not the same as orienting.\nAuthor response\nWhile we agree that locomotor velocity is not equivalent to orienting per se, we included this\ndata in response to a comment from Reviewer 1 who wondered “…if the change in ACh\nresponse after reversal in the presence of non-reward is related to motor activity/ orienting/\nattentional changes?” Because mice were head-fixed in our setup, they could not make postural\nor head- movements typical of orienting responses. Locomotor velocity on the ball was therefore\nthe only measurable motor activity. Reviewer 1 was happy that we report that. However, given\nthe concern raised, we have modified the text as follows:\nLine 248:\n“The change in ACh activity after unexpected non-reward could, in theory, be related to motor\nactivity due to orienting responses. In our setup, mice were head-fixed and therefore unable to\nperform the postural or head movements typical of orienting responses. However, they could\nexpress some degree of orienting through changes in locomotor activity. We analyzed locomotor\nvelocity on the ball on a trial-by-trial basis during the outcome period and found no significant\ncorrelation between mean ACh signals and velocity following non-reward (Fig. 4k).”\nReviewer comment\nline 288-294: The new results about subregions are interesting and relevant and as the authors\nnote they are consistent with prior results, though it’s surprising that these findings barely, or do\n8\nnot, reach significance (and are not visible in the “3D map”) What if “quadrats” are dropped and\nthe authors analyze much broader fields-of-view?\nAuthor response\nOur analysis along the medial–lateral axis did reveal a significant bias for quadrats with\nincreased ACh responses to unexpected non-reward toward the medial striatum (Pearson’s r = -\n0.55, P = 0.03, simple linear regression). These findings are consistent with prior reports linking\nmedial striatum ACh signaling to reversal learning. We therefore do not interpret the results as\n“barely” reaching significance but rather as reflecting a reproducible trend aligned with existing\nliterature. Broader averaging across the entire field of view would obscure the regional\ndifferences we observed. Finally, the GRIN lens positioning provided coverage of the dorsal\nstriatum sufficient to capture the medial–lateral and anteroposterior distributions, giving us\nconfidence in the spatial patterns reported (Fig. 5d).\nReviewer comment\nline 348: “…hM4D(Gi)-mCherry bilaterally into the DS of ChAT-Cre mice...”\nthe “DS” here is actually “DMS”, which makes sense given prior results but the imaging field is\nnot consistently DMS - why?\nAuthor response\nWe thank the reviewer for noting this. Our imaging experiments via the GRIN lens covered\ndorsal striatal regions that included both medial and lateral portions of the DS, whereas the\nchemogenetic manipulations targeted primarily the dorsomedial striatum (DMS). This focus was\nguided by two considerations: first, our imaging analyses revealed that increases in ACh\nresponses to unexpected non-reward were most prominently distributed medially; second, prior\npharamcological and lesion studies have consistently implicated the medial striatum in reversal\nlearning and flexible behavior21,22,23.However, we do not view the term “DS” or the injection\nsites as a discrepancy, but rather as a targeted experimental design choice that complements our\nimaging findings and leverages prior literature to test the functional role of medial striatal ACh\nsignaling.\nWe have added this to the Methods section as follows (line 1347):\n“Although injections were performed bilaterally in the dorsal striatum, they were centered\nprimarily in the dorsomedial striatum (DMS), guided by both our imaging findings of enhanced\nmedial ACh responses to non-reward and prior reports implicating the medial striatum in\nreversal learning and behavioral flexibility.”\nReviewer comment\nline 360: “Cumulative errors over the first 80 trials post-reversal were higher in CIN-inhibited\nmice, reflecting impaired overall task adaptation beyond specific error types (Fig. 6f).” How\n9\ndoes this observation support this conclusion?\nAuthor response\nOur conclusion that cumulative errors over the first 80 trials post-reversal reflect impaired\noverall task adaptation is supported by two complementary observations. First, CIN-inhibited\nmice showed a significant reduction in correct choices specifically during the early reversal\nphase (Fig. 6d,e), when flexible updating is most critical, whereas performance in later sessions\nwas unaffected. This indicates that CIN activity is particularly important for the initial adaptive\nresponse to changing contingencies rather than for late, steady-state performance. Second, our\nimaging results further support this interpretation: unexpected non-reward after reversal elicited\nwidespread increases in acetylcholine signals, especially in lose-shift trials, where animals\nadapted by switching to the opposite arm. Thus, higher ACh levels were predictive of adaptive\nswitching behavior. By contrast, chemogenetic inhibition of CINs reduced this adaptive shift,\nresulting in greater cumulative errors that could not be explained by specific error types alone.\nCollectively, these findings suggest that CIN-dependent ACh release supports early task\nadaptation following contingency change, and its inhibition impairs this process.\nReviewer comment\nline 364: “While CIN inhibition reduced lose-shift behavior (Fig. 6g, left; unpaired t-test, P =\n0.015), it did not alter win-stay probability following the reversal (Fig. 6g, right; P = 0.14).\n“ Judging by Fig. 6g, this is not a convincing result. The analysis appears to lack statistical power\ndue to few, scattered data points.\nAuthor response\nLose-shift behavior showed a robust and statistically significant reduction following CIN\ninhibition. We agree that we cannot exclude an effect on win-stay probability. The sample size (n\n= 5 per group) used in our chemogenetic experiments is within the range (5-9 animals/group)\ncommonly used in CIN manipulation studies (Okada et al., Eur J Neurosci., 2015). To address\nthe reviewer’s concern, we (i) now report exact p-values and included effect sizes (Cohen’s d to\nquantify the magnitude of effect) for all primary comparisons, (ii) have shown individual animal\ndata to display the underlying distribution. These additional analyses indicated that the primary\neffect (e.g., reduced lose-shift probability after CIN inhibition) is consistent across subjects and\nof large magnitude, increasing confidence in the biological effect. For the comparisons reported\nin Fig. 6d,g, the effect sizes were very large (e.g., d = (–)1.54, (–)1.01, (–)1.77, and (–)0.94),\nrespectively, indicating robust group differences despite modest power. Even in the case of a\nsmaller effect (d = 0.43 and 0.21; Ext. Data Fig. 9b,d), the observed direction was consistent with\nour hypothesis. Thus, although the small sample number may limit statistical power, the effect\nsize analysis suggests that the observed differences are biologically meaningful and reproducible.\nThe Methods section has been revised as follows (line 1554):\n“Effect size analysis\nobserved group differences, we calculated Cohen’s d for all pairwise comparisons. For unpaired\nt-tests, d was computed as the standardized difference in means between treatment groups, using\nthe pooled standard deviation as the denominator. 95% confidence intervals were also estimated\nto provide an index of precision. Effect sizes were interpreted according to conventional\nbenchmarks (d ≈ 0.2: small; d ≈ 0.5: medium; d ≥ 0.8: large; negative values in figures indicate\ndirection of effect)24. Reporting effect sizes is particularly important in studies with modest\nsample sizes, as it provides information about the strength and potential biological relevance of\neffects beyond p values alone.”\nThe figure legend has been updated as follows (line 1094):\n“…Cohen’s d = (–)1.54, 95% CI [-2.98, -0.09]) but comparable in Test 5 (P = 0.11, Cohen’s d =\n(–)1.01, 95% CI [-2.35, 0.32]). Data shown as mean ± SEM. Effect sizes (Cohen’s d) was\ncalculated to quantify the magnitude of differences (see Methods for details). These values\nhighlight that, despite the modest sample size, the observed differences represent substantial\nbiological effects in most comparisons.”\nResults section has been revised as follows (line 374):\nLose-shift behavior was significantly reduced by CIN inhibition (Fig. 6g, left; unpaired t-test, P\n= 0.015, Cohen’s d = (–)1.77, 95% CI [-3.27, -0.26]). However, CIN inhibition effects on win-\nstay probability following the reversal were not significant (Fig. 6g, right; P = 0.14, Cohen’s d =\n(–)0.94, 95% CI [-2.27, 0.38]).\nReviewer comment\nline 497: “we propose that neural pathways exposed to decreased ACh on reward delivery –\ncausing a pause after successfully shifting – would experience synaptic potentiation”. It’s not\nfully clear from the figures, but it looks like decrease ACh on reward delivery is strong even at\nthe end of behavioral acquisition - would it really be helpful to keep potentiating synapses at that\npoint?\nAuthor response\nAs noted in the preceding sentence of the discussion, long-term potentiation of cortical inputs to\nstriatal projection neurons requires coincidence of pauses in CINs with phasic dopamine\nactivation. Phasic dopamine release generally occurs in response to unexpected reward but not in\nresponse to reward that is expected. At the end of behavioral acquisition, it is likely that reward\nwill be expected, and dopamine will not be released, so there will be no potentiation. To make\nthis clearer we have revised the text as follows (line 516):\n“Recent work shows that long-term potentiation of cortical inputs to striatal projection neurons\nrequires coincidence of pauses in CINs with phasic dopamine activation25, which occurs in\n11\nresponse to unexpected reward. Thus, we propose that neural pathways exposed to decreased\nACh on unexpected reward delivery – causing a CIN pause and dopamine activation after\nsuccessfully shifting – would experience synaptic potentiation. At the end of behavioral\nacquisition it is likely that reward will be expected, and dopamine will not be released, so there\nwill be no potentiation. Pathways exposed to increased ACh on unexpected non-reward would\nnot be potentiated, preventing reinforcement of incorrect responses. This would ultimately lead\nto a reduction in interference from pre-existing memory26.”\nReviewer comment\nFig. 1c: please show the data divided into the individual sessions.\nAuthor response\nWe have added information about the demarcation of individual sessions, in the figure legend\n(line 842):\n“c, Sample choice behavior in representative trials (200 trials across 5 sessions; 40 trials per\nsession) from an example mouse…”\nReviewer comment\nFig. 1f/g (and later figures): since the point is to compare “early” and “late”, the statistical test\nshould incorporate both these conditions (e.g. as a factor in ANOVA) rather than running\nseparate tests for each condition.\nAuthor response\nResponse:\nWe thank the reviewer for this suggestion. Our original paired t-tests within “early” and “late”\nwere intended to provide simple, within-animal contrasts for each phase. However, we agree that\na model that includes Session (Early vs. Late) and Outcome (Reward vs. No reward) in the same\nanalysis is statistically stronger. Accordingly, we have re-analyzed velocity and licking rate\nusing a within-subject repeated-measures framework with factors Session (2 levels) and\nOutcome (2 levels) in ANOVA.\nFor anticipatory licking, this analysis revealed a significant main effect of Outcome (F(1,20) =\n5.14, P = 0.03), indicating that mice exhibited higher anticipatory licking when expecting reward\nrelative to non-reward outcomes. The main effect of Session did not reach significance (F(1,20)\n= 3.51, P = 0.076), and the Session × Outcome interaction also did not reach significance\n(F(1,20) = 3.78, P = 0.066). Combined, these findings suggest that anticipatory licking is\nrobustly driven by reward outcome across learning, with a trend toward higher overall levels in\nlate sessions. Importantly, this confirms our conclusion that anticipatory behavior reflects\nsensitivity to reward expectation.\n11.69, P = 0.0027), indicating that overall velocities increased from early to late learning. The\nmain effect of outcome did not reach significance (F(1,20) = 0.17, P = 0.68), though the trend\nwas consistent with higher velocities in rewarded trials. The Session × Outcome interaction was\nalso not significant (F(1,20) = 0.28, P = 0.60). Combined, these results indicate that the clearest\neffect was a robust increase in approach velocity across learning, supporting our original\nconclusion that motivational drive strengthens with task experience. Thus, the direction of the\neffect is consistent with our within-phase t-tests, which suggested higher velocity in rewarded\ntrials during late learning. The respective figures (Fig. 1f,g and 4d,e) have been updated.\nReviewer comment\nFig. 4b : why show the orange and blue ticks twice each?\nAuthor response\nThe lower raster plot shows trial-by-trial choices, where blue ticks denote correct trials and\norange ticks denote incorrect trials. Above this, a heatmap depicts the same trial outcomes in\nbinary format (1 = correct, 0 = incorrect), aligned to the raster plot for visual comparison. Thus,\nthe raster + binary heatmap give complementary information: the raster conveys variability\nacross trials, while the heatmap makes the correct/incorrect sequence categorical and easy to\nread.\nReviewer comment\nEx Fig 6b: why are these particular three “representative” mice shown, rather than all of them?\nThe right part of this panel is hard to follow and the argument presented from these individual\nmice is unconvincing.\nAuthor response\nWe appreciate the reviewer’s concern. We note, however, that the individual activity patterns\nand mean responses of individual mice are already presented in Fig. 4g (heatmaps) and Fig. 4j\n(scatter plot of lose-shift correlation). The representative traces in Extended Data Fig. 6b were\nincluded primarily as a visual illustration of how cholinergic responses to unexpected non-\nreward can influence choice behavior across reversal, complementing the full dataset shown in\nFig. 4g,j. This approach, using representative animals to illustrate trial dynamics alongside\ngroup-level statistics, has been employed in previous studies of striatal acetylcholine and TAN\nactivity for example19,27,28. We therefore believe these examples are appropriate and serve as a\nheuristic aid, while the statistical conclusions are drawn from the complete dataset (n = 11).\nReviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. 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