Glutamatergic projection neurons in the basal forebrain underlie learned olfactory associational valence assignments

Domain: physics
Published: 2026-01-16
Editorial outcome: major_revision

Abstract
Sensory perception is shaped by experience, giving stimuli behavioral significance. Basal forebrain (BF) cholinergic neurons in mice, which are crucial for arousal and motivation, also regulate sensory processing. Within BF nuclei, glutamatergic (vGlut2BF) neurons receive cholinergic input and modulate behaviors, but their roles in encoding sensory significance remain unclear. Using in vivo calcium imaging, we found that vGlut2BF neurons initially poorly encoded odor identity. However, their response to conditioned odors increased following associative learning, and their population activity more distinctly encoded paired stimuli, reflecting emergent value representation. Furthermore, pairing stimulation or inhibition of vGlut2BF neurons with specific odors altered odor preferences, suggesting that appropriately timed vGlut2BF neuronal activity is sufficient to influence valence assignment. Our findings reveal that vGlut2BF neurons transform sensory input into motivationally significant stimuli, positioning the BF as a key hub for linking sensory processing with motivational states and experience-driven plasticity.

Introduction
The behavior of organisms is intricately shaped by the neural representations of the external world1,2, where salient sensory stimuli are transformed into value-driven representations that guide action. The basal forebrain (BF) cholinergic system plays a critical role in feedback and feedforward sensory processing3–5. The projections from basal forebrain cholinergic neurons (BFCNs) to the sensory cortex send feedback information to modulate sensory processing, including amplifying signal-to-noise ratios of cortical activity to enhance the detection of sensory input6–12. Receiving inputs from multiple sensory-related areas and cortical regions involved in regulating cognition13–16, the BF is an ideal region for investigating how sensory information is represented naturally and shaped through associative learning. BFCNs are activated by both positive and negative valence stimuli17–20 and play a significant role in associative learning by exhibiting distinct neuronal responses to cues associated with reward and/or punishment17,21–23. While BFCNs are the most extensively studied neuronal population in the BF3, the BF comprises diverse neuronal types, including both GABAergic and glutamatergic projection neuron populations24,25. These different types of projection neurons extend to diverse brain areas, form reciprocal connections, and receive direct fast cholinergic neurotransmission from BFCNs14,26–30. As such, these non-cholinergic populations have been shown to participate in adaptive circuits that transform novel sensory stimuli into meaningful representations. For example, it has been previously shown that non-cholinergic populations within the BF encode motivational salience, predict stimuli associated with behavioral performance, and contribute to olfactory associative learning17,31–33. However, the roles of specific non-cholinergic subtypes in encoding motivationally significant sensory stimuli and whether/how their neural activity correlates with motivation-driven behaviors is not well understood.

Glutamatergic BF neurons (vGlut2BF) are of particular interest because they are excited via fast cholinergic transmission and project out of their respective nuclei to numerous downstream targets involved in diverse behaviors34–36. Notably, we and others have found that BFCN-targeted vGlut2BF neurons within the Diagonal Band of Broca are responsive to both appetitive and aversive external stimuli34–37, and over-activation of these neurons induces an overriding hypophagic state, altering the perception of rewarding food34,38. vGlut2BF neurons make functional connections with regions that include the lateral hypothalamus (LH), lateral habenula (LHb), and the ventral tegmental area (VTA), which are crucial for guiding feeding, avoidance, and other reward/punishment-related behaviors34–37,39–41. Thus, whether in conjunction or directly downstream of cholinergic transmission, this subcortical processing via vGlut2BF neurons may act as a neural shortcut, bypassing more complex cortical centers and rapidly eliciting physiologically important behaviors. These data suggest that vGlut2BF neurons are critical for processing different sensory information and driving subsequent motivational behaviors. However, how vGlut2BF projection neurons represent sensory information with different values, and how their activity is involved in motivational behaviors such as associative learning and valence assignment, remains unclear.

To address this, we focused on olfaction, as the basal forebrain receives strong inputs from all major olfactory processing regions13,42. Although often regarded as a monolithic structure, the basal forebrain is composed of discrete regions with distinct patterns of afferent and efferent connectivity15,43,44. Basal forebrain cholinergic neurons exhibit region-specific response patterns; for instance, neurons in caudal regions such as the globus pallidus/substantia innominata (GP/SI) and in rostral regions such as the horizontal limb of the diagonal band (HDB) differentially process sensory, reward, and aversion-related stimuli19. Here, we targeted the horizontal limb of the diagonal band (HDB) in the basal forebrain, where it not only receives olfactory information but also sends projections to the olfactory bulb and modulates olfactory perception45–47. Also, lesioning, external stimulation of the HDB, or silencing specific neuronal populations within it has been shown to affect olfactory-mediated behaviors5,33,48,49. In this study, we used in vivo calcium imaging to ask how specific olfactory stimulus features—such as identity and/or associated values—are represented by vGlut2BF neurons. We found that while individual vGlut2BF neurons do not initially distinctly represent specific odor identities, they can decode odors with associated values. Moreover, vGlut2BF neurons show differential response properties with associative learning that involve reward or punishment, suggesting a role in representing motivational salience. Further, we found that direct modulation of vGlut2BF neuronal activity impacts olfactory learning and valence assignment. That is, pairing vGlut2BF neuronal activation with given odors induces avoidance, whereas inhibition induces preference. Taken together, these studies reveal the glutamatergic projection neurons within the basal forebrain as key players in linking sensory stimuli with motivational states and experience-driven plasticity.

Results
vGlut2BF neurons exhibit broad odor responses but do not encode odor identity or innate valence
Since vGlut2BF neurons have previously been shown to be activated by both positive and negative olfactory stimuli34–37, we first examined their baseline response to different types of volatilized odorants. Using GRIN lens miniscope imaging in head-fixed vGlut2-Cre mice expressing GCaMP8s, we recorded the neuronal activity in the horizontal limb of the diagonal band of Broca (HDB) of the basal forebrain at single-cell resolution during passive odor delivery (Fig. 1A and Supplementary Fig. 1A, B). For this, mice were exposed to four different neutral odors that were not associated with any valence: rose oil, eugenol, methyl salicylate, and anisole. All odors were diluted in mineral oil at a 2% concentration. Each odor was presented for 2 s 10 times in a randomized order with a modified odor delivery system50 (Fig. 1B). Using pre-packaged detection algorithms based on principal and independent component analyses (PCA/ICA) and fluorescence response tracking via Inscopix software, individual neurons were first spatially defined, and their calcium dynamics were extracted over time (Fig. 1C, D, Supplementary Video 1).

The responses of individual vGlut2BF neurons before and after odor onset (Fig. 1E; Eugenol and Methyl Salicylate as examples, and Supplementary Fig. 1C) were grouped into categories: excited, inhibited, or non-responsive. More specifically, we defined excited neurons as those that showed elevated activity levels and reached at least three standard deviations higher than their baselines during a 2-second odor presentation period, whereas inhibited neurons were those with activity three standard deviations lower than their respective baselines. On average, each odor recruited 28.5% of all identified neurons to be responsive, with similar distributions of excited, inhibited, and non-responsive cells across different odors (Fig. 1F, Supplementary Fig. 1E, and Supplementary Table 1). Notably, 55.7% of neurons were not excited by any odors (Fig. 1G), and 61.5% of all excited cells showed increased activity to more than one odor, suggesting a lack of specificity for individual odorants (Fig. 1G).

Although individual cell responses appeared to lack odor specificity, we next directly tested if the observed populations were capable of reporting or encoding odor identity at baseline. To this end, we trained logistic regression binary models (see methods) to decode single-trial odor responses using population responses. The model could not decode odors significantly above chance (Fig. 1H), indicating a lack of ability to differentiate odors based on the recorded neuronal response patterns. These data suggest that, at baseline, vGlut2BF neurons do not distinctly represent specific odorants.

As vGlut2BF neurons have previously been identified to drive aversion-like behaviors34–36, we tested whether vGlut2BF neurons preferentially encode innately aversive odors. Using the same imaging approach, we recorded neuronal responses of vGlut2BF neurons to four natural odorants considered to be innately aversive to mice: cadaverine, isopentylamine, butyric acid, and 2-methylbutylamine51–53 (Supplementary Fig. 1D). Using the same quantitative approach, we found that the number of responsive neurons and the overall magnitude of responses (AUC during odor presentation) during the 2-second odor presentation period for each of the aversive odors were not different from those observed with neutral odorants (Supplementary Fig. 1F, G). Moreover, the model failed to decode the tested aversive odors above chance (Supplementary Fig. 1H), suggesting that vGlut2BF neurons do not selectively encode odor identity regardless of innate odor valence. Finally, we asked if vGlut2BF neurons could directly encode the assumed inherent valences of the odors. Toward this, we trained the decoding model to distinguish population responses to neutral odors versus aversive odors, and the accuracy remained near chance (Supplementary Fig. 1I). Together, these results suggest that during initial exposure to neutral or naturally aversive odors, which lack any learned associations, odor identity and/or innate valence are not represented by vGlut2BF neurons.

vGlut2BF neurons alter odor responses following associations
Although vGlut2BF neurons did not encode odor identity or valence at baseline, the basal forebrain is able to integrate sensory information to modulate cortical activity and guide behavior13,54. We therefore next asked if odor representations change when odors are associated with a salient outcome. Toward this, we paired innocuous/neutral monomolecular odorants with positive or negative reinforcement, and recorded odor-evoked vGlut2BF neuron activity before and after this association. Specifically, we paired Pentanol with a positive stimulus (60% fat, high-fat diet) and Hexanol with a negative stimulus (foot shock), while Octanol served as an unpaired control (Fig. 2A). Since olfactory responses are also prone to habituation, we also included an additional control odor (Heptanol) with three days of habituation (Supplementary Fig. 2B). To validate the behavioral association of the paired odors, we conducted an odor preference test in a three-chamber spatial choice arena in which the positively or negatively paired odors were tested against mineral oil controls. Consistent with association pairing, mice spent significantly more time in regions with high-fat diet-associated odors than the control odor, and less time in regions with foot shock-paired odors (Fig. 2B). These behavioral data confirm that the association paradigm was capable of effectively establishing and/or assigning valence to the associated odors, providing an experimental framework to investigate how the basal forebrain circuitry is involved in this process.

Following association, vGlut2BF neurons exhibited increased responses to both positive (high-fat diet) and negative associations (foot shock), while responses to the unpaired control and the habituated odor remained unchanged (Fig. 2C–E; Supplementary Fig. 2B-E). Both the newly recruited neurons and the neurons that were excited before and after association (stable neurons) increased their activity after association (Supplementary Fig. 2A). These data show that both the stable and newly recruited neurons contribute to activity changes following association. To quantify patterns of change in the response properties of individual cells, we examined how previously non-responsive neurons were recruited. Among the recorded cells that were excitable pre-association, the majority remained excited after association (66.23% for Pentanol (high-fat diet); 75.56% for Hexanol (foot shock)). However, a significant fraction of previously non-responsive neurons became excited after association in response to both positive and negative association (42.20% recruited for Pentanol; 31.65% recruited for Hexanol) (Fig. 2F, G, and Supplementary Table 2). These data indicate that vGlut2BF neurons alter their responses with conditioned and/or learned positive and negative values. A fraction of non-responsive neurons became excited after habituation (15.88%), but not as much as the percentage followed by reinforcement association (Supplementary Fig. 2F and Supplementary Table 2).

As the response properties changed following learned association, we also examined if vGlut2BF neurons were capable of encoding learned positive and/or negative associations. We found that most vGlut2BF neurons (72.72%) that were excited by positive-conditioned odors also responded to the negative-conditioned odor. However, a subset of neurons showed selectivity for either positive or negative associations. For example, 21.5% of neurons excited by Pentanol responded exclusively to Pentanol, while 23.7% neurons excited by Hexanol responded exclusively to Hexanol (Fig. 2H, I, and Supplementary Table 2). Thus, most responsive cells encode both associations, while only subgroups of neurons that encode specific valence may exist. Notably, neurons activated by both associations exhibited significantly stronger responses to the negative odor (Fig. 2J). Given that locomotor speed correlates with acetylcholine release55, an indicator of cholinergic neuronal activity and arousal state, we also analyzed their locomotion on the head-fixed wheel following odor presentations before and after association. We found that locomotion during negative odor presentations increased compared to pre-association, whereas responses to positive odors remained unchanged (Supplementary Fig. 2I, J), suggesting a difference in arousal levels to odors with distinct associations.

Together, these findings demonstrate that vGlut2BF neurons do not encode odor identity or innate valence at baseline but adaptively alter their responses following associative learning, particularly for odors with motivational significance.

Associative learning reshapes population-level odor representations
As we observed an increase in single-cell response levels and recruitment of more cells following stimulus-paired odor associations, we next examined how such associations influence population-level neural dynamics. Toward this, we applied principal components analysis (PCA) to analyze the trial-averaged trajectories of vGlut2BF population activity. We included responses from baseline (three seconds preceding odor onset) to ten seconds following odor onset. We plotted the trajectories of neural responses to the high-fat diet-associated odor (Pentanol) and the control odor (Octanol) on one PCA map, and the foot shock-associated odor (Hexanol) and the control odor (Octanol) on another PCA map (Fig. 3A–C). These visualizations revealed a clear separation between the post-association odor trajectories and both the pre-association and control odor trajectories. To quantify the proximity between odor-evoked trajectories—which reflects the similarity of neural population responses—we computed the Euclidean distance between trial-averaged activity vectors in high-dimensional cell space. We z-scored calcium responses and averaged them across trials to generate a population activity vector for each odor condition. We then calculated the Euclidean distance (see Methods for the formula) between vectors before and after conditioning to assess how population representations changed with learning. Distances were plotted from baseline (three seconds before odor onset) to 10 s after odor onset (Fig. 3D).

Notably, the Euclidean distance during the odor presentation period between pre- and post-association trajectories was significantly higher for the foot shock-conditioned odor compared to the control odor octanol (Fig. 3D), indicating that aversive conditioning substantially shifted population responses. The Euclidean distance for Heptanol between before and after habituation was not significantly changed compared to Octanol (Supplementary Fig. 2G, H). Finally, we compared the Euclidean distance between Pentanol and Hexanol before and after association. Although this comparison did not reach statistical significance, the distance trended higher after conditioning (Fig. 3E).

Building upon this, we next examined whether these altered neural dynamics enhanced odor decoding accuracy. Toward this, we trained a linear decoder on population responses to the positive conditioned, negative conditioned, habituated, and unpaired odors during a 2-second presentation window (Fig. 4A). We found that decoding accuracy explicitly improved for the conditioned odors following both positive and negative associations (Fig. 4B, C). Specifically, the binary model achieved significantly higher accuracy in distinguishing between the two oppositely conditioned odors (Fig. 4D), while decoding accuracy did not improve when distinguishing between the habituated and the unpaired odor (Fig. 4E), indicating that the altered neural dynamics following association encode distinct association states. Furthermore, the binary model more reliably differentiated conditioned odors from the habituated and the unpaired odors post-association (Fig. 4F, G).

To further explore these dynamics, we plotted conditioned and unconditioned odor response trajectories on a single PCA map. Initially, all odor trajectories displayed spatial proximity (Fig. 4H, top). However, following association, we observe a distinct separation in the response trajectories for conditioned odors, revealing a significant shift in the overall neural representation for the associated odors in high-dimensional population space (Fig. 4H, bottom). We quantified this shift using the Euclidean distance from the unpaired odor (Octanol) as a reference, revealing that the conditioned odors (both Pentanol and Hexanol) deviated significantly from the Octanol post-association. In contrast, the distance between the habituated and the unpaired odor (Heptanol and Octanol) remained unchanged (Fig. 4I, J).

This increase in the Euclidean distance compared to the control odor reflects greater remodeling of the neural code for the associated odors, consistent with changes in odor-associated meaning and salience. We also reasoned that this population-level shift likely drives the model’s improved classification accuracy.

Lastly, we examined trial-to-trial response reliability, which could influence decoding accuracy. Our data showed increased trial reliability specifically for negatively associated odors, but not for the positively associated or control odors (Fig. 4K). Together, these results indicate that associative learning alters population responses, enhancing the encoding of odors with associated and/or learned salience.

vGlut2BF neurons contribute to odor-associative learning
Given that associative learning altered the response dynamics of vGlut2BF neurons, we next investigated if these neurons impact odor-associative learning. Toward this, we stereotaxically injected adult vGlut2-Cre mice with conditional AAVs encoding either the inhibitory DREADD receptor hM4Di (Gi-coupled human muscarinic M4 designer receptor exclusively activated by a designer drug) or an mRuby fluorescent control. Specifically, we used AAV-Ef1a-flex-hM4Di-mCherry and AAV-Ef1a-flex-mRuby vectors, delivering them bilaterally into the HDB of vGlut2-Cre mice for conditional expression in vGlut2BF neurons (Fig. 5A–C, Supplementary Fig. 4A). As a validation control, we performed whole-cell recordings in acute brain slices following the introduction of clozapine-N-oxide (CNO). All recorded hM4Di expressing vGlut2BF neurons (n = 7 cells) showed hyperpolarization to CNO (Fig. 5D), but variable changes in firing behavior. While 2 cells out of 7 showed cessation of spontaneous firing, 3 other cells changed the firing pattern into more burst-like patterns. The latter could possibly be due to the hyperpolarization-activated HCN channel cationic current, which was previously described in glutamatergic BF neurons56. However, this would need to be further confirmed experimentally.

To assess learning behavior while manipulating vGlut2BF neuron activity, we first trained mice expressing either DREADDs or mRuby on an olfactory Go/No-Go task57 after a 3-week post-surgery recovery period (Fig. 5E). Notably, all mice successfully passed the training stages and moved onto the testing phase. During the training phase, there was no difference in learning ability between the hM4Di and the mRuby injected mice (Supplementary Fig. 3A–C). The mice were then tested on their ability to differentiate between an S+ odor, associated with water as a reward, and an S- odor, indicating no reward availability (Fig. 5E). During this testing phase, each group received an intraperitoneal injection of either CNO (3.5 mg/kg) or saline 30 min before testing. Behavioral performance was quantified using the D prime (d’) metric, where higher d’ values indicate greater accuracy in distinguishing between the S+ and S- odors, and a zero value represents chance performance58. We found that CNO-treated DREADD-expressing mice exhibited a slower learning rate compared to their saline-treated condition or the CNO-treated mRuby-expressing control mice (Fig. 5F, Supplementary Fig. 3D). That is, when vGlut2BF neurons were silenced, although the mice were able to reach the learning criterion, defined as accuracy greater than 85%, they required significantly more blocks to learn to successfully discriminate the S+ vs. S- odors (Fig. 5G). Furthermore, inhibition of vGlut2BF neurons also led to a reduced overall performance, which is shown with the reduced D prime value for the task (Fig. 5H). These data suggest that vGlut2BF neurons are required for associative learning in this Go/No-Go discrimination task.

Given that reduced D prime measures suggested the impaired ability to discriminate associated odors, we next evaluated if vGlut2BF neurons were necessary for odor discrimination independent of learning. Toward this, we performed a habituation/dishabituation task while chemogenetically inhibiting vGlut2BF neurons (Supplementary Fig. 3E). DREADD-expressing and control mice preferred new odors after CNO or saline treatment, suggesting that vGlut2BF inhibition does not impair basic odor detection or discrimination (Supplementary Fig. 3F). Importantly, all mice tested exhibited successful habituation to the first odor (Supplementary Fig. 3G). Overall, these findings suggest that while vGlut2BF neurons are necessary for learning in an odor-discrimination task, they do not affect the fundamental ability to detect or distinguish odors.

Activation or inhibition of vGlut2BF neurons alters odor preference
Prior studies have shown that over-activating vGlut2BF neurons induced an aversive state and reduced food intake34–36. Building upon these findings, we next investigated whether targeted over activation of vGlut2BF neurons could associate an aversive state to a neutral odor stimulus and/or subsequently alter odor preference. To test this, we utilized Channelrhodopsin-2 (ChR2)-based in vivo optogenetics to selectively and acutely activate vGlut2BF neurons while presenting paired odorants. First, we evaluated effective cell type-specific targeting and activation of these cells by injecting AAV-Ef1a-flex-ChR2-EYFP into the HDB of vGlut2-Cre mice (Fig. 6A, left). After allowing 14 days for expression, we confirmed the presence of light-evoked depolarization and action potentials at 20 Hz in brain slices, consistent with our previous findings34 (Fig. 6A, top right).

After verifying ChR2 functionality, we bilaterally delivered the AAV-Ef1a-flex-ChR2-EYFP virus and implanted fiber optics in the HDB of vGlut2-Cre mice (Fig. 6B left, Supplementary Fig. 4B). After recovery, real-time place avoidance assays indicated that mice actively avoided areas associated with the photostimulation of vGlut2BF neurons (Supplementary Fig. 5A, B), suggesting that over-activating these neurons induced an aversive effect. To assess whether this cell type-specific avoidance behavior could be transferred to an odor stimulus, we paired a previously unassociated odor (5% Hexanol) with 2 s of ChR2-mediated vGlut2BF neuron activation in head-fixed mice (λ = 473 nm, 0.5 Hz, 10 ms pulses, for 10 min per session, five sessions per day for 3 days) (Fig. 6C). Notably, post-pairing, ChR2-expressing mice showed a decrease in the preference index for the zones with the photostimulation-paired odor compared to initial unpaired exposure (Fig. 6D, E, Supplementary Fig. 5C), suggesting an induced odor aversion.

We next investigated whether inhibition of vGlut2BF neurons could drive a positive effect. Toward this, we use the improved chloride-conducting Channelrhodopsin (iChloc) actuator for inhibitory in vivo optogenetics59,60. We injected AAV-Ef1a-flex-iChloc bilaterally into the HDB of vGlut2-Cre mice (Fig. 6A, left). To confirm the inhibitory effect of iChloc on vGlut2BF neurons, we again performed whole-cell current clamp electrophysiology recordings from iChloc-expressing vGlut2BF neurons. With stimulation, we observed a shift in voltage with a concomitant decrease in firing frequency and amplitude due to apparent shunting inhibition in iChloc-expressing neurons (Fig. 6A, bottom right).

Following validation, we injected AAV-Ef1a-flex-iChloc and implanted fiber optics in the HDB of vGlut2-Cre mice and performed real-time place preference (Fig. 6B right, Supplementary Fig. 4B). Mice expressing iChloc spent more time in the area associated with neuronal inhibition (Supplementary Fig. 5A, B), suggesting that vGlut2BF inhibition induced a positive effect. We then paired odor delivery (2 s of 5% Pentanol) with inhibition of vGlut2BF neurons using iChloc (λ = 473 nm, 0.5 Hz, 10 ms pulses, for 10 min per session, five sessions per day) concurrently over three days (Fig. 6C). Post-pairing, iChloc-expressing mice showed an increase in preference index in zones with the paired odor compared to periods before pairing, suggesting a light-induced odor preference (Fig. 6D, E, Supplementary Fig. 5C). Control mice injected with GFP showed no difference in preference index for the zone with the paired odor before or after photostimulation (Fig. 6D, E, Supplementary Fig. 5C).

Together, these results demonstrate that bidirectional manipulation of vGlut2BF neurons elicits opposite effects on learned/associated odor preference. Whereas paired activation of vGlut2BF neurons induces avoidance of odors, inhibition promotes odor preference.

Methods
Animals
All animal care and procedures were approved by Baylor College of Medicine and the licensed BCM IACUC protocol approval committee under protocol number AN5596. All methods have been reported in accordance with recommendations in the ARRIVE guidelines. Mice were maintained on a 12 h light/12 h dark cycle with standard chow (Harlan, 2920X) and water ad libitum. vGlut2-Cre (Slc17a6tm2(cre)Lowl/J, Stock No. 01696364) mice were purchased from Jackson Laboratories. Male and Female mice were used in all the studies, and no significant sex-specific differences were found. Mice were at least 8 weeks old for stereotaxic surgeries.

Stereotaxic viral injections
Mice were anesthetized and maintained under anesthesia using ~1–3% vaporized isoflurane with oxygen. A stereotaxic injection setup was connected to Angle Two software and was used to target the regions of the brain accurately. Viral vectors were delivered through a Nanoliter Injector (NANOJECT II, Drummond Scientific Company) mounted on a stereotaxic platform (Leica Biosystem) at a rate of 23 nL/s. Volumes and coordinates for the viral injections were as follows: Coordinates for all the basal forebrain injections were targeted to anteroposterior (AP) +0.80 mm, mediolateral (ML) ±1.00 mm, and dorsoventral (DV) −5.45 mm. For GRIN lens imaging, ~230 nl AAV-hsyn-flex-GCaMP8s (serotype DJ8) was injected on the left side, and the gradient refractive index lens (GRIN lens, diameter, 0.6 mm; length, 7.3 mm, Inscopix) was slowly advanced to 0.1 mm above the basal forebrain and cemented in place with Metabond dental cement (C&B Metabond®) following the viral injection. For the optogenetic manipulation odor-paired experiments, 90–140 nl/sides AAV-Ef1a-flex-Channlearhodopsin (serotype DJ8), AAV-Ef1a-flex-iChloc (serotype DJ8), and AAV-Ef1a-flex-GFP (serotype DJ8) were injected, and the optic cannulas [Ø1.25-mm stainless ferrule, Ø200-μm core, 0.22 numerical aperture (NA), 5.33 mm; RWD] were implanted with 5.04 angle and 0.3 mm above the targeted site. For Chemogenetic experiments, AAV-Ef1a-flex-hM4Di (serotype DJ8) and AAV-Ef1a-flex-mRuby2 (serotype DJ8) were injected. All viruses were titered to at least 10^11 viral particles/μL. For the following three days of neurosurgery, mice were treated with Meloxicam (Hospira; subcutaneous injection, 5 mg/kg) for pain relief.

Odor presentations
During GRIN lens recordings, mice were head-fixed on a running wheel with a multi-channel olfactometer positioned roughly 7–8 cm in front of their noses. The miniaturized microscope camera (nVoke2; Inscopix Inc.) was placed on the mouse’s head during odor presentations. The olfactometer provided a constant stream of room air into which experimental odors were injected. Odors were placed in individual channels, and during each presentation, the selected odor was mixed into the central air stream before being delivered to the mouse through an eductor positioned at the olfactometer output. Arduino-generated TTL pulses triggered the olfactometer and served as digital inputs during recording to precisely align Miniscope imaging data acquired with the nVista Acquisition Software (Inscopix Inc.). The order of odor delivery was controlled by a custom MATLAB program. For odor screening experiments, odorants were delivered for 2 s via injection into the central air stream with an 18-second interval between deliveries. Odors presented included Rose oil (Rainbow Abby), Eugenol (Sigma 46129), Methyl Salicylate (Sigma M6752), Anisole (Sigma 123226), Butyric acid (Sigma B103500), Cadaverine (Sigma 52063), 2-Methylbutylamine (Sigma 241407), and Isopentylamine (Sigma 126810). For the association experiments, the odors used included Pentanol (Sigma 138975), Hexanol (Sigma H13303), Heptanol (Sigma H2805), and Octanol (Sigma 29887). The odorants were dissolved in mineral oil at 2% concentration for screening and 5% concentration for association experiments by volume and presented in replicates of 10, in a randomized order.

Calcium imaging recording using GRIN lens imaging and imaging processing
GCaMP8s signals, indicative of Ca2+ activity, were detected using a 435–460 nm excitation LED (1.2 LED power), with a frame rate of 15 FPS. Subsequent analysis of the imaging data was performed using the Inscopix data processing software (IDPS). Videos were pre-processed to correct defective pixels and 4x spatially downsampled. Background fluorescence was removed using a spatial band-pass filter, and fluorescence videos were motion-corrected using the Inscopix motion correction algorithm. We then implemented an independent and principal component analyses (PCA/ICA) algorithm to identify the region of interest, and manually reviewed by the experimenter whether to accept it as a neuron or reject.

Single-neuron analyses for the calcium imaging recordings
Accepted neurons, along with their Ca2+ activity traces, were exported to MATLAB for analysis. Cells were tracked across sessions using the longitudinal registration function available in the Inscopix Data Processing Software. Z-scored df/f was calculated for the 3 s prior to and 10 s after each odor presentation onset. In conjunction with odor delivery, baseline activity (z-score) was established 2 s before odor onset. Neurons were classified as responsive if their average neuronal activity during odor presentation (2 s) exceeded three standard deviations from the average baseline activity (excited) or their neuronal activity during odor presentation (2 s) was three standard deviations below their baselines (inhibited). The criteria for classifying neurons as excited or inhibited were applied consistently throughout the paper. For all the neurons that are responsive to mineral oil, which served as the solvent of all odors, their activity was baseline-subtracted with their own response to mineral oil. Heatmaps and average traces were generated in MATLAB (version 2024).

Olfactory association
Following GRIN lens surgery, mice were subjected to positive and negative association protocols. For negative association, mice were habituated to training chambers for 10 min daily over three days before training. The training involved one session per day for three consecutive days to establish robust odor-shock associations82. Each training session comprised five trials, with each trial involving a 10-second odor presentation co-terminating with a 1-second, 0.4-mA foot shock and a 120-second intertrial interval (ITI). The association chambers featured a grid-based floor composed of stainless rods connected to a shock delivery system (ENV-414S, Med Associates). During odor delivery, positive pressure was applied to a reservoir containing 5% Hexanol diluted with mineral oil via a microinjection dispenser (Picospritzer II, Parker).

For positive association, a high-fat diet pellet (#D12492, Research Diets) was introduced into home cages daily for one week, accompanied by 5% Pentanol dissolved in mineral oil. Mice were fed ad libitum during the association process. This procedure aimed to create a passive association between Pentanol and the high-fat pellets.

For odor habituation, 5% Heptanol was delivered through the olfactometer to the head-fixed mouse for 30 min, with each presentation consisting of 2 s of odor followed by an 18-second inter-trial interval.

Population decoding
We employed multinomial and binary logistic regression models to decode odor identity from neural activity patterns. First, a multinomial logistic regression model was used to discriminate activity patterns among K different odor presentations in one experimental setting: 

(Equation 1)

where y is the odor presented in trial i to the animal, X i is a m dimensional vector of the mean activity of m neurons during the 2 s window following odor presentation, W k is a m dimensional vector of the weights assigned to each neuron, and W 0,k is the bias term. The weights are specific to odor k and k set of weight that were estimated in total. The weights and biases of the model were estimated by minimizing the cross-entropy loss with an l-2 regularization. The confusion matrix of the multinomial logistic regression models was visualized to assess the decoding performance.

Subsequently, we implemented binary logistic regression models for pairwise odor discrimination between all combinations of the K odors:

(Equation 2)

where p(X i) the probability of one odor (versus another) being presented in the trial i, X i is a m dimensional neural activity vector as described above, w the weight vector, and w 0 the bias term. The weights and biases of the model were estimated by minimizing the log loss with an l-2 regularization. To obtain unbiased estimates of decoding performance, we implemented a 5 × 2 cross-validation procedure. For each fold, trials were randomly partitioned into equal-sized training and test sets. Models were trained on one set and evaluated on the other, and then the sets were swapped, and the process was repeated. This entire procedure was performed five times. The final decoding performance is reported as the mean accuracy across all test sets.

Odor preference tests
A custom-designed three-chamber box, created by the engineering core at Baylor College of Medicine, was used for behavioral tests. The experiments were set up as “pretest” (before odor pairing) and “test” (after odor pairing). During both pretest and test, mice were placed in the chamber for 10 min to habituate to the environment and the tea diffusers. Following habituation, tea diffusers containing 100 μL of 5% odor solution in mineral oil on gauze were placed at the center edge of each side chamber. Each mouse ran 10 min for the preference test. EthoVision XT software (XT16; https://www.noldus.com/ethovision-xt) was used to track the time the mice spent in each region.

For the odor pairing experiment, the preference index was defined as the time spent in the paired odor zone minus the time spent in the unpaired odor zone divided by the total time spent in both zones. We then compared the preference index for the paired odor before odor pairing with that after. The mice had a preference for the paired odor during pretest, defined as spending more than 75% of time in the paired odor side were excluded from the analysis.

In vitro electrophysiology
For acute basal forebrain slice preparation, animals were anesthetized with isoflurane and perfused with a cold artificial cerebrospinal fluid (ACSF) solution containing 125 mM NaCl, 2.5 mM KCl, 1.25 mM NaH2PO4, 2 mM CaCl2, 1 mM MgCl2, 10 mM glucose, and 25 mM NaHCO3. Brains were removed and transferred into an ice-cold cutting solution (pH 7.35) containing 87 mM NaCl, 2.5 mM KCl, 1.25 mM NaH2PO4, 0.5 mM CaCl2, 7 mM MgCl2-6H2O, 13 mM ascorbic acid, 75 mM sucrose, 10 mM glucose, and 25 mM NaHCO3. Both solutions were pH-equilibrated with a 5% CO2/95% O2 gas mixture for at least 10 minutes before the experiment. 300 micrometer thick coronal forebrain slices were prepared using a Leica VT1200 vibratome and placed at least 15 min at 37°C in 5% CO2/95% O2 bubbled ACSF solution. They were then gradually lowered to ambient temperature (25°C) and allowed to acclimate for at least 15 minutes before recording.

For recording, slices were transferred into a recording chamber perfused with ACSF at 1.5–2 mL/min at 25°C. Neurons were identified by an optiMOS camera (QImaging) controlled by Micro-Manager version 1.4.22 software (University of California at San Francisco) using 900 nm DIC (Olympus BX50WI) optics, and fluorescence reporter using Thorlabs DC4100 470-nm or 565-nm LED illumination with 49002-ET-EGFP (FITC/Cy2) or HcRed1 emission filters (Chroma Technology), respectively. Recordings were acquired using an Axon MultiClamp 700B amplifier digitized at 10 kHz (Axon Digidata 1440A). Recording electrodes (3–5 MΩ) were fabricated from borosilicate glass microcapillaries (outer diameter 1.5 mm) with a micropipette puller (Sutter Instruments). The internal solution contained 110 mM K-gluconate, 10 mM KCl, 4 mM ATP-Mg, 0.5 mM GTP-Na, 10 mM phosphocreatine-di-Na, 1 mM EGTA, 10 mM HEPES; pH was adjusted to 7.3 with KOH, and the osmolarity was adjusted to 305 mOsm/L with K-gluconate. The access resistance was monitored throughout the experiment and was typically in a range of 10–20 MΩ and considered acceptable up to 30 MΩ. Obtained recordings were processed offline using Clampfit 10.7.0.3 (Molecular Devices).

In total, 22 BF neurons from 8 animals were recorded. Of those, 7 didn’t show spontaneous AP activity, while others demonstrated irregular firing activity at 6.05 ± 1.78 Hz. To test the inhibitory response of the hM4Di receptor, neurons were recorded in current clamp mode to measure baseline firing and voltage changes before and after CNO bath application. To test the optogenetic tools for controlling neuronal activity, cells were recorded in either cell-attached or whole-cell current clamp mode. The ChR2-induced firing was evoked by 20 Hz, 5 ms, 470 nm light pulses. For iChloc validation, cells were monitored for changes in membrane voltage and firing frequency (spontaneous or slow current induced) for 30 s before and 30 s after 470 nm light pulses at 1 Hz, 2 s.

Go/no-go training
Freely moving mice underwent training in an olfactory-based go/no-go discrimination task utilizing olfactory association chambers manufactured by Med Associates and controlled by Med PC software. These chambers featured a designated odor delivery port and a water port, each with infrared beams to track entries. The odor delivery port maintained a consistent flow of purified room air, facilitated by a vacuum system. These chambers were situated within larger behavioral isolation boxes, also provided by Med Associates. Within these boxes, box fans operated continuously to facilitate ambient air circulation away from the association chambers, mitigating the buildup of ambient odors.

Odors were introduced into the continuous airflow by activating valves connected to one of two odor reservoirs. This enabled the airflow to pass through the headspace of the reservoir containing the volatilized odor. These reservoirs, each 20 mL in volume, contained 2.5 mL of odor diluted to a 1% concentration in mineral oil. Water rewards, dispensed in volumes of 5 μl, were provided exclusively in the distinct reward port upon successfully completing “Hit” trials.

Mice underwent five stages of training to learn to perform the Go/No-Go task as previously described64 for 10–14 days. Mice were first water-restricted to no less than 85% of their body weight. They were then trained to associate an S+ odor cue (1% Eugenol in mineral oil) with receiving a water reward. After this association was successfully formed, an S- odor cue (1% Methyl Salicylate in mineral oil) was introduced. During this phase, mice were trained to avoid seeking a reward when presented with the S- odor and instead initiate a new trial; otherwise, they would receive a 4-second time-out punishment. This training phase was considered complete once the mice achieved over 85% accuracy in at least two consecutive blocks of trials. After they learned to go for the S+ odor for reward and not to go for the S- odor, they were moved to the testing phase. In the testing sessions, the same procedure was followed, but new odor pairs were introduced in each session. For saline treatment, the odor pair was Isoamyl acetate (Sigma w205508) and 2-heptanone (Sigma 02476); for CNO treatment, the odor pair was Alpha-pinene (Sigma 147524) and 2-Methylpyrazine (Sigma M75608).

Performance in the go/no-go task was assessed using signal detection theory, specifically the d prime (d’ = Z(Hit Rate) – Z(False Alarm Rate)), which quantifies sensitivity and overall task performance. The hit rate was defined as the proportion of trials in which the mouse correctly sought a water reward within 5 s of S+ odor presentation, and the false alarm rate as the proportion of trials in which the mouse incorrectly attempted to retrieve water during S− odor presentation. Both rates were converted to z-scores using the inverse cumulative distribution function of the standard normal distribution, and d’ was computed as their difference. To evaluate changes in sensitivity over time within a session, d’ was computed using a rolling window of 40 trials. The session summary d’ values were determined as the maximum d’ within this rolling window, excluding early trials where mice were learning new S+ and S- associations and later trials where task engagement may have declined. The overall performance was calculated using the Hit rate and the False alarm rate across the whole test. Learning ability was assessed by calculating the number of blocks (each consisting of 20 trials) required to first meet the criterion of 85% correct trials. Summary d’ calculations and rolling-window plots across trials were generated using custom MATLAB scripts.

Odor discrimination
Spontaneous odor discrimination was evaluated using a habituation/dishabituation assay83,84. Mice were initially habituated to the test chamber, which contained blank tea diffusers, for 10 minutes before the experiment began. Following habituation, removable walls were placed to divide the chamber into three compartments, restricting the mouse to the central compartment. Tea diffusers containing 50 μL of 2% odor in mineral oil on gauze were positioned at the center of each side chamber’s edge. At the start of the experiment, the dividers were removed, and the time the mice spent near the tea diffusers was recorded.

Every 2 minutes, the removable walls were reinserted, and the gauzes with the odors in the tea diffusers were renewed. After renewing the odors, the walls were removed again, initiating the subsequent 2-minute trial. During the first three trials (Habituation phase), both tea diffusers contained the same odor: Acetophenone (Sigma A10701) for the CNO group and Isoamyl acetate (Sigma W205508) for the Saline group. During the fourth trial (Dishabituation phase), one tea diffuser contained the initial odor (odor 1), and the other diffuser contained a new odor (odor 2): Anisole (Sigma 123226) for the CNO group and isoamyl butyrate (W206008 Sigma) for the saline group. Behavioral data were captured using a Noldus behavior tracking camera, and EthoVision XT software (XT16; https://www.noldus.com/ethovision-xt) was used to track the time spent by the mice in each compartment.

Real-time place preference/avoidance (RTPP/RTPA)
We used a Noldus Phenotyper divided into two halves for these experiments: one paired with photostimulation and the other without. Mice were placed in the chamber for three days to habituate, during which baseline tracking data were collected using EthoVision XT software (XT 16; https://www.noldus.com/ethovision-xt). After the habituation period, mice were tracked in the chamber for 30 minutes, with a light pulse applied whenever they entered the photostimulation-paired side. We compared the time spent on the photostimulation-paired side during the experiment to the baseline time spent on the same side during habituation.

Odor-light pairing
Before pairing, mice were placed in a three-chamber setting for 10 minutes to evaluate their preference for two odors (5% Pentanol and 5% Hexanol v/v in mineral oil). The odors were contained within tea filters, with one positioned on the left side and the other on the right side of the chamber. During pairing, the mice were first head-fixed and habituated on the running wheel each pairing day for 15 minutes. Pentanol and Hexanol were randomly delivered to the mice using an olfactometer50 with a delivery duration of 2 s and a 30-second inter-stimulus interval. Each session consisted of 20 trials, repeated for 5 sessions. During pairing, only Hexanol was simultaneously delivered with 2-second, 20 Hz trains of light pulses (10 ms, 473 nm) for targeted neuronal activation in CHR2-expressing mice. The same pairing paradigm was used in GFP-expressing mice as controls. Conversely, only Pentanol was simultaneously delivered with 2-second, 0.5 Hz trains of light pulses (10 ms, 473 nm) for neuronal inhibition in iChloc-expressing mice. Following three days of odor pairing, mice were reassessed in the same three-chamber setting to evaluate their preference for the odors.

Post-hoc analysis
Perfusion and tissue dissection. Animals were transcardially perfused with PBS, followed by 4% paraformaldehyde (PFA) perfusion. Brains were then post-fixed in 4% PFA solution at 4°C for at least one day, followed by overnight cryoprotection in 30% sucrose. Cryoprotected brains were subsequently embedded and frozen in O.C.T. (Fisher HealthCare No. 4585) and stored at −80°C until sectioning. Coronal sections measuring 40-50 μm thick were obtained using a cryostat (Leica CM1860), with every third section collected. The slices for checking the targeting for optogenetics, chemogenetics, and GRIN lens experiments were then mounted onto microscope slides with DAPI Fluoromount-G (Southern Biotech, 0100–20), and images were acquired using a Leica SP8X confocal microscope.

Quantification and Statistical Analysis. GraphPad Prism 10.2.0 (GraphPad Software, Inc., La Jolla, CA, USA) was used for all statistical analyses and figure generation. Figures illustrating mean response, heatmaps, and PCA trajectories for the calcium imaging data were generated using MATLAB scripts.

The Euclidean distance measure shows different the activity patterns of the neuronal population are when responding to different stimuli. Specifically, we created a vector representing the mean population response to each odor, with each element corresponding to the activity of an individual neuron. We then calculated the Euclidean distance between these vectors, which quantifies the overall dissimilarity between the two response patterns. Larger distances indicate more distinct representations of the odors across the recorded neurons. To calculate the Euclidean distance between neural activity vectors derived from calcium imaging data, the neural activity matrix was first reduced to mean activity vectors by averaging across trials. The Euclidean distance between these vectors was computed using the formula

(Equation 3)

where n represents the number of neurons, A exp and A control represent the average neuronal activity under the experimental and the control odors.

The trial reliability of neural responses to odor presentations was assessed by calculating correlations between subsets of trials. Specifically, all trials were randomly divided into two halves, and the correlation between the average neural activity of each half was computed. This procedure was repeated 10 times, and the final trial reliability value was obtained by averaging the correlations across all 10 iterations.

Statistical analyses were performed to assess differences across conditions. Paired two-tailed Student’s t-tests were used to compare the time spent in the conditioned odor zone versus the mineral oil zone. They were also applied to evaluate changes in mean neuronal responses before and after association across odor groups, the accuracy of the binary classifier for different odors, and the Euclidean distance both before and after association, as well as between odors. Finally, paired two-tailed Student’s t-tests were used to compare the preference index for the paired odor and the time spent in each zone before and after odor pairing across manipulations.

To assess reliability across different odors and associations and the number of trials required for learning the Go/No-Go task, we conducted a two-way repeated measures ANOVA followed by Sidak’s multiple comparisons. The same analysis was used to compare d-prime values for Go/No-Go task performance across treatments and mouse groups, and the time spent in the region with paired odors across different mouse groups and time points.

For odor discrimination tasks, a one-sample t-test was used to determine whether preference for a novel odor significantly differed from zero. Throughout all experiments, statistical significance was defined as p<0.05. Detailed statistical information, including exact sample sizes and representations for each experiment, is provided in the text and figure legend.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Summary of Main Findings:\nThe authors investigate the role of BF (basal forebrain) vGlut2-expressing neurons in odor processing and associative learning. They report the following key findings:\n1. Odor responsiveness at baseline: Nearly half of BF vGlut2 neurons exhibit either excitation or inhibition in response to odor presentation. However, at baseline, these neurons do not encode odor identity or valence at either the single-neuron or population level. Their molecular markers and circuit-level connectivity remain unclear.\n2. Changes following associative learning: After odor-outcome associative learning, the population-level decoding accuracy of BF vGlut2 neurons for odors increases, suggesting substantial changes in population coding. Notably, a subset of previously non-responsive neurons becomes activated or inhibited by the odors following learning. However, the extent to which these newly recruited neurons contribute to the improved decoding performance is not clearly addressed.\n3. Chemogenetic and optogenetic manipulation: Chemogenetic inhibition of BF vGlut2 neurons impairs associative learning performance in mice. Furthermore, optogenetic activation or inhibition of these neurons induces place avoidance or preference, respectively, and this valence can be transferred to odors paired with the stimulation.\nTogether, these findings lead the authors to conclude that BF vGlut2 neurons are involved in the encoding of odor valence and contribute to associative learning.\nMajor Concerns:\n1. Disentangling associative learning from repeated exposure:\nThe authors report that after associative learning with pentanol and hexanol, previously non-responsive BF vGlut2 neurons become activated or inhibited. However, it remains unclear whether these changes are specific to associative learning or simply due to repeated odor exposure. It would be important to analyze the neural representations of octanol and heptanol—odors that were not associated with reinforcement—between the first and last exposure to address this concern.\n2. Contribution of newly recruited neurons to odor decoding:\nWhile the study shows that associative learning recruits new BF vGlut2 neurons responsive to odors, it is not clear whether these newly recruited neurons significantly contribute to the improved decoding performance. More detailed analyses are needed to assess their specific role.\n3. Single-cell vs. population-level analysis in Fig. 2:\nLine 213 mentions “an increase in single-cell response levels following stimulus-paired odor associations,” but Fig. 2 shows average traces across all recorded neurons, which could be inflated simply by including more responsive cells after learning. This conflates increased recruitment with increased response magnitude at the single-neuron level. The authors should provide direct comparisons of individual neuron responses before and after learning.\n4. Concerns about the associative learning paradigm:\n○ The number of exposures to each odor is not balanced across conditions, and appropriate controls are lacking.\n○ The positive and negative reinforcers differ substantially (high-fat food vs. footshock) in both sensory modality and intensity, which may introduce bias in associative learning strength and neural responses. These differences should be more carefully controlled or discussed.\n5. Decoding improvement without increased Euclidean distance:\nThe authors argue that improved odor decoding after associative learning is due to altered neural representations. However, pentanol does not show significantly greater Euclidean distance from octanol, despite improved decoding. How can decoding accuracy increase without a corresponding increase in representation separability?\n6. Effect of repeated odor exposure on decoding and trajectories:\nDoes repeated odor exposure alone improve decoding accuracy? Are there changes in neural trajectories between first and last exposure to non-reinforced odors? In Fig. 4I, after just two exposures to heptanol, BF vGlut2 responses already show a trend toward divergence, similar to that seen for pentanol. Pentanol’s more pronounced changes may simply reflect the higher number of exposures (seven days), whereas heptanol was only presented twice. Data from octanol—an odor repeatedly presented but not paired with reinforcement—should be shown to clarify this point.\n7. Causal role of BF vGlut2 neurons remains uncertain:\nThe authors show that optogenetic activation or inhibition of BF vGlut2 neurons induces place avoidance or preference, suggesting these neurons broadly influence behavioral state or valence. Therefore, manipulation of BF vGlut2 neurons during learning may impair performance via altered motivational state, not necessarily due to specific disruption of odor-value association. To strengthen causal claims, targeted manipulation of odor-responsive BF vGlut2 subpopulations would be more appropriate. As it stands, the data support a correlation between BF vGlut2 representations and associative learning, but do not provide strong evidence of a causal role.\nMinor Points:\n1. Odor response plots in Fig. 1:\nAlthough eight odors were tested, only response data for four odors are shown. Please clarify or include the remaining odors for completeness.\n2. Clarification of Fig. S1E:\nIt is unclear which neurons and which time windows are represented in Fig. S1E. What does each data point signify?\n3. Missing values in pie charts:\nMany pie charts lack absolute numbers or percentage values. Please indicate the total number of recorded neurons and the proportions in each category.\n4. Clarification of cell inclusion in summary plots:\nIn Fig. 1E and Fig. S1C, are all recorded neurons included, or only those that were responsive to odors?\n5. Scale bar visibility:\nThe scale bars in Fig. 1C and 1D are hard to read. Please improve the visibility and labeling.\n6. Data presentation in Fig. 1H–J and Fig. 3:\nThe meaning of the plots in these panels is not clear without referring to the figure legend. Consider improving figure annotations and axis labels for better standalone clarity. Similar issues apply to Fig. 3 and should be addressed throughout the manuscript.", "reviewer_2": "Summary\nIn this study, the authors investigate the role of glutamatergic basal forebrain neurons (vGlut2BF) in encoding and learning associative odor valence. Based on calcium responses of vGlut2BF neurons, the authors show that while these neurons do not encode innate valence or odor identity, associative learning alters their response dynamics and the associative odor valence can be decoded from population responses. Further experiments coupling chemogenetic manipulation with a go/no-go task demonstrate that vGlut2BF neurons are necessary for learning a valence-dependent odor discrimination task. The study closes with results suggesting a potential role of the polarity of vGlut2BF activity for switching associated odor valence.\nComments\nThe results are very interesting and the data on the involvement of vGlut2BF during associative learning will be highly relevant for a broad readership. The experiments are generally well-designed and executed with appropriate controls, and the results are presented and discussed comprehensively.\nA notable limitation, however, is the lack of counterbalancing of odors between positive and negative valence associations. This is particularly relevant for the last result, where the authors conclude that bidirectional manipulation of vGlut2BF neurons can induce opposite associative odor valence. The observed effects are modest, and the current experimental design, which uses three different odors assigned exclusively to the three groups, is insufficient to rule out innate odor preferences or biases.\nIf the authors can address this major concern and implement the minor issues below, this manuscript will be a strong candidate for publication in Nature Communications.\nPlease find below a list of specific comments:\nFigure 1\nDefine MCPO\nHow many neurons were recorded per animal?\nWhat is the distribution of responsive cells across animals and experiments?\nLabel for heatmap in E missing\nFigure 2\nInvestigation for Octanol missing\nFigure 2D: Does this include only excitatory neurons or all?\nPlease discuss what happened to the cells that became non-responsive after association? Is this a physiological effect or rather an experimental constraint?\nDid you split the assignment of rewarded and punished odor for both animal groups? I.e., was Pentanol ever associated with punishment and Hexanol with reward?\nLegend for Figure 2J: top and bottom, instead of left/right\nWhy does the number of experimental animals change between 2B and 2E?\nFigure 3\nSince the effect is quite weak, please be explicit about how many neurons (excited and inhibited) from how many animals have been included in the analysis?\nFigure 4\nA strong effect of odor association can be seen. Please state the number of neurons and experimental animals included in the analysis.\nFigure 2K: Reliability measure is indeed increasing during negative association but was much lower specifically for Hexanol before association. Why did the authors not split valence of odor association into two groups? Would you expect the same effect if the odor used for negative association was a different one?\nFigure 5\nDid all hM4Di expressing cells stop firing upon CNO bath application? How many cells did the authors test in vitro?\nPlease show the data on the training stages of all animals as part of the supplement\nFigure 6\nFigure 6A and the left part of C are quite redundant\nWhy was the effect of optogenetic activation and inhibition measured under different conditions (cell-attached vs whole cell)? Please show data or at least comment on the large variability in spontaneous activity between both examples shown.\nFigure 6D: Please include heat maps of the preference test before pairing?\nLegend for Figure 6D: should state arranged from top to bottom\nWhy did the authors use different odors to test place avoidance and preference? If the tested odors had the same valence, should the preference not be the same during pre-pairing for all 3 cohorts?\nThe preference index produces rather variable results with a modest effect. In addition, the group sizes were relatively small. Would a different readout, such as investigation time of the odor source give clearer results?\nMinor:\nSuppl. Fig 2D: Data points missing\nSuppl. Fig 2F: x-axis label typo (Pentanol)\nSuppl. Fig 3A: Legend states saline instead of CNO; x-labels are misplaced\nLine 1187: should state left half of the chamber, not the right half\nLine 158: valence\nLine 421: foot shock\nLine 51: Channelrhodopsin\nBe consistent with the nomenclature of AAVs (DIO vs FLEX)", "reviewer_3": "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.", "reviewer_4": "In this paper by Chin et al, the authors use single-cell calcium imaging of vGlut2 expressing neurons in the horizontal limb of the diagonal band (hDB) to describe how calcium activity of these neurons change with exposure to odors. The authors show initially, vGlut2 neurons in the hDB exhibit small responses to novel odors. This calcium response is potentiated following associative learning, when odors were paired with either a positive or negative outcome. The authors then use chemo- and optogenetic tools to show vGlut2 hDB neurons are necessary for associative learning in a Go/No-Go task, and the activity of vGlut2 hDB neurons can alter behavior in an odor-preference test.\nOverall, this paper fills a gap in the literature by highlighting the functional significance of glutamatergic neurons in the basal forebrain (BF). However, this manuscript can be improved with clarifications to the introduction, figures and results, as well as additions to the discussion. Furthermore, the inclusion of additional experiments will better support the authors conclusions of a role for vGlut2 BF neurons in odor associated learning.\nMajor Points:\n1. Is there a rationale for using distinct odors during the associative learning task? If vGlut2 BF neurons do not initially encode valence of olfactory stimuli, a reasonable hypothesis would be that pairing an odor with either a positive or negative stimulus would result in identical changes in the calcium activity of vGlut2 BF neurons. This would further support the authors hypothesis that learning itself (and not the stimulus the odor is paired with) leads to the potentiated calcium response of vGlut2 BF neurons. I.e., what do the authors expect if pentanol was paired with a negative stimulus, or if hexanol was paired with a positive stimulus?\n2. In the Go/No-Go task described in Figure 5, it appears the task was only done with an appetitive stimulus (i.e., the water reward). Do the authors expect the same or different results if the task was completed with a footshock?\n3. The results for the DREADD inhibition experiments are a bit confusing. It appears even at trial 0, there are differences between hM4Di and mRuby groups. Can the authors expand on these interesting findings in the discussion? Could vGlut2 hDB neurons have a role in direct sensory processing of olfactory stimuli. Also in the results, lines 275-276, the authors state the hM4Di mice required more trials to learn the Go/No-Go task. How many more trials did it take before the hM4Di mice eventually learned the task? In the results, lines 277-278 is confusing. The authors state “inhibition of vGlut2 BF neurons also led to reduced final D prime value for the task, which shows an impaired ability to discriminate..” Does this mean the hM4Di mice never learned the Go/No-Go task? This would seem to contradict the previous point in which the authors state hM4Di mice required more trials to reach 85% accuracy. Can the authors clarify these statements?\n4. Throughout the paper (for example: abstract lines 37-39, introduction lines 75-77, introduction line 85, etc.) it is unclear what the authors mean by “cholinergic basal forebrain”. Previous studies have demonstrated the presence of non-cholinergic neurons within the basal forebrain, so it is not necessary to repeatedly state “cholinergic basal forebrain”. Furthermore, it is unclear what the authors mean when describing the relationship between BF glutamatergic and cholinergic neurons. For example, in the abstract lines 37-39 “Within the BF nuclei, glutamatergic (vGlut2BF) neurons relay cholinergic input to modulate behavior”- does this mean BF glutamatergic neurons also release acetylcholine? If so, can references be provided. In the introduction lines 64-66, “These different types of projection neurons extend to diverse brain areas, form reciprocal connections, and receive direct fast cholinergic neurotransmission from BFCNs”- do the authors mean BF GABA and glutamate neurons receive cholinergic input from BFCNs? Can the authors provide clarification and provide references.\nThe introduction should be expanded to mention differences between BF regions (if any). There are multiple distinct brain regions that comprise the BF and describing any functional differences between neurons in the different BF regions would be helpful to the reader. Moreover, it the focus of this current paper was in the hDB. The introduction should be expanded to include studies that have focused specifically within hDB.\n5. The discussion section of the paper would be enhanced if authors first focused the discussion more specifically on the hDB- i.e., what are the inputs and outputs of the hDB? What is the neuronal composition of the hDB? Can any of these factors influence the authors findings? Although a discussion of the BF in general is appreciated, the authors should first focus on the brain region of interest described in the current study (hDB).\nMinor Points:\n1. In the introduction, additional references describing the role of BFCNs in valence encoding would help the reader: Stephenson-Jones et al, 2020; Kim et al, 2024; Sun et al, 2025.\n2. Results line 116- it would be helpful to describe each odor in laymen’s terms (if possible). For example, are they associated with rewarding or aversive behaviors? Or are they neutral odors that don’t elicit any behavioral responses? Likewise, if the odor was diluted, the concentration of the odor and the diluent should be mentioned.\n3. In Figure 1, is a heatmap for rose oil and anisole odors?\n4. For the heatmap in Figure 1E, what is the scale? The authors mention the criteria for “excited” or “inhibited” neurons is 3 standard deviations above or below baseline. However, the scale for the heatmap ranges from -1 to 2. Therefore, is this scale for delta F/F? Moreover, what is the x-axis for the heatmap (i.e., what is the timescale)?\n5. In the circle plots in Figure 1F, having the actual numbers within the circles would be helpful to the reader.\n6. For reinforcement pairing of odors- were mice food restricted when the odor was paired with a high fat diet (pentanol with high fat diet)? Food restricting mice beforehand (or satiety of mice) will change motivation.\n7. The schematics for Figures 3A/4A are unclear.\n8. Can the authors provide an explanation for Euclidean Distance (line 218)?\n9. In the Euclidean Distance plots in Figure 3, can the authors show a plot for the Euclidean Distance for Pentanol vs. hexanol? The bar graph shows there could be difference between the odors and it would be interesting to see how the two odors look on the Euclidean Distance graph.\n10. In the results line 180-182, do the authors use the same criteria as earlier in the paper (i.e., 3 SD above baseline for “excited”, 3 SD below baseline for “inhibited” ) to describe changes in vGlut2 BF neurons following association?\n11. In Figure 4H, can the authors provide an explanation of what spatial proximity and directionality means? Since differences in these measures are observed, what does this mean in terms of odor discrimination or associative learning?\n12. In distinct parts of the BF, cholinergic afferents co-express vGlut3 (Barabas et al, 2024). Can the authors expand on vGlut2 vs. vGlut3 BFCNs in the discussion and what the potential implications are?\n13. In the discussion, the authors mention multiple subtypes of vGlut2 BF. What are these subtypes and what factors distinguish the different subtypes of vGlut2 BF neurons? How would this impact the authors findings?", "reviewer_5": "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": "Response to reviewers:\nManuscript: \"Glutamatergic projection neurons in the cholinergic basal forebrain underlie learned olfactory associational valence assignments\"\nCorresponding author: Benjamin Arenkiel, arenkiel@bcm.edu\nWe would like to first thank the reviewers for the thoughtful and detailed feedback, and for providing great suggestions to improve our manuscript titled: Glutamatergic projection neurons in the cholinergic basal forebrain underlie learned olfactory associational valence assignments. In this study we show that basal forebrain glutamatergic projection neurons play a role in assigning valence-related behavioral actions through paired stimulus association, revealing that glutamatergic projection neurons within the cholinergic basal forebrain act as key players in linking sensory stimuli with motivational states and experience-driven plasticity.\nWe have fully considered the reviewer responses and provide extensive new experimentation, analyses, and interpretation of these data to both complement and supplement the revised manuscript. Major changes are referenced here, with a point-by-point account listed below.\nMajor highlights:\n We have performed additional suggested experiments by reviewers and include extensive new data.\nSome data is shown here for reviewers, but not included in the revision, whereas other new data are now included in the revised manuscript.\n We have included new control recording data, alongside additional quantitative analyses and data representation.\n We have fully described methodology, training, and behavioral analyses.\n We have modified language for clarification and expanded noted discussion points per reviewer suggestions.\nREVIEWER COMMENTS\nReviewer #1\nSummary of Main Findings:\nThe authors investigate the role of BF (basal forebrain) vGlut2-expressing neurons in odor processing and associative learning. They report the following key findings:\n1. Odor responsiveness at baseline: Nearly half of BF vGlut2 neurons exhibit either excitation or inhibition in response to odor presentation. However, at baseline, these neurons do not encode odor identity or valence at either the single-neuron or population level. Their molecular markers and circuit-level connectivity remain unclear.\n2. Changes following associative learning: After odor-outcome associative learning, the population-level decoding accuracy of BF vGlut2 neurons for odors increases, suggesting substantial changes in population coding. Notably, a subset of previously non-responsive neurons becomes activated or inhibited by the odors following learning. However, the extent to which these newly recruited neurons contribute to the improved decoding performance is not clearly addressed.\n3. Chemogenetic and optogenetic manipulation: Chemogenetic inhibition of BF vGlut2 neurons impairs associative learning performance in mice. Furthermore, optogenetic activation or inhibition of these neurons induces place avoidance or preference, respectively,\n1\nand this valence can be transferred to odors paired with the stimulation.\nTogether, these findings lead the authors to conclude that BF vGlut2 neurons are involved in the encoding of odor valence and contribute to associative learning.\nMajor Concerns:\nR1: 1. Disentangling associative learning from repeated exposure:\nThe authors report that after associative learning with pentanol and hexanol, previously non-responsive BF vGlut2 neurons become activated or inhibited. However, it remains unclear whether these changes are specific to associative learning or simply due to repeated odor exposure. It would be important to analyze the neural representations of octanol and heptanol—odors that were not associated with reinforcement—between the first and last exposure to address this concern.\nWe appreciate the reviewer’s important point. To further address whether the observed recruitment of vGlut2BF neurons is specific to associative learning rather than just repeated odor exposure, we have now analyzed both cellular composition and population-level representations of non-reinforced odors over time.\nAs an example, we examined the odor Heptanol, which was repeatedly presented over three days without reinforcement. We found that only a small percentage of previously non-responsive neurons became active after habituation (15.88%) (Extended Figure 2F, P7 line 211), and that 22.75% (38 neurons out of 167 neurons) were recruited from the pool of non-responsive neurons for the non-reinforced odor Octanol, but this proportion was notably lower than the recruitment observed with reinforced odors and was not significant (Figure 2B). This suggests that repeated exposure alone to any odorant has a limited effect on vGlut2BF recruitment compared to the associated odors.\nTo further assess changes in neural responses/representation, we also compared Euclidean distances between population activity before and after habituation. The trajectory shift for Heptanol was not significantly greater than that of Octanol, an odor only presented during pre- and post-tests, indicating that repeated exposure does not substantially alter the population-level representation. (See Extended Data 2G-H, line 266-271).\nLastly, as shown in Figure 4E, decoding accuracy for Heptanol did not significantly improve after repeated exposure when compared to Octanol. Together, these results suggest that repeated non-reinforced exposure leads to minimal changes in both cell recruitment and population-level coding, supporting the interpretation that the observed changes specifically to reinforcement stimuli are linked to associative learning.\nWe have added the analysis of the habituated odor Heptanol in our revised new figure (Extended Data 2G-H).\nR1: 2. Contribution of newly recruited neurons to odor decoding:\nWhile the study shows that associative learning recruits new BF vGlut2 neurons responsive to odors, it is not clear whether these newly recruited neurons significantly contribute to the improved decoding performance. More detailed analyses are needed to assess their specific role.\nWe thank the reviewer for the comments and considering this notion. We have further analyzed the decoding accuracy using only the newly recruited neurons from both the positive and negative associations, and noted that both the newly recruited neurons and the stable neurons contribute the increased accuracy for the decoding.\nWe clarified this point but did not include this analysis in the revised manuscript. We are open to considering the inclusion if this reviewer thinks it is critical, but certainly wanted to provide the data here. (0: The stable cells/1: the newly recruited cells/all: The stable cells + the\n2\nnewly recruited cells)\nR1: 3. Single-cell vs. population-level analysis in Fig. 2:\nLine 213 mentions “an increase in single-cell response levels following stimulus-paired odor associations,” but Fig. 2 shows average traces across all recorded neurons, which could be inflated simply by including more responsive cells after learning. This conflates increased recruitment with increased response magnitude at the single-neuron level. The authors should provide direct comparisons of individual neuron responses before and after learning.\nWe appreciate the reviewer’s insightful comment and agree that the apparent increase in population response could indeed reflect both the recruitment of additional responsive neurons and increased response magnitude in individual cells. To address this, we separately analyzed the responses of newly recruited neurons (cells that became responsive only after learning) and stable neurons (cells that were active both before and after the associations). Our analyses show that response amplitudes increased in both the newly recruited neurons and the stable neurons following associative learning. However, when comparing responses to the control odor, stable neurons did not exhibit significant changes in activity across sessions. These results indicate that the enhanced responses observed after learning are not solely due to increased recruitment, but also reflect potentiation at the single-neuron level. We have included this result in a new Extended Data Fig. 2A, and show here.\nR1: 4. Concerns about the associative learning paradigm:\nThe number of exposures to each odor is not balanced across conditions, and appropriate controls are lacking.\nTo address concerns about odor exposure imbalance, we have now conducted additional passive exposure controls. In the original manuscript, mice were exposed to Heptanol for three days, and repeated exposure did not alter the average response, the Euclidean distance from the control odor Octanol, or the decoding accuracy relative to Octanol. In the revised manuscript, we have now added a separate experiment in which another odor (Octanol in this experiment) was presented for seven days in the home cage, matching the duration of the food-paired odor exposure. We did not observe any behavioral preference for this odor (Figure C). The Euclidean distance between pre- and post-exposure conditions was smaller than that of the food-associated odor, though not significant (Figure D). From the confusion matrix, the percentage of correctly classified trials for Octanol increased modestly (39.17 to 55), but this increase was substantially lower than that seen with either the food-associated odor (46.66 to 80.83) or the high-fat-diet associated odor (40.83 to 78.33) (Figure E). These results suggest that passive odor exposure in the home cage has only limited effects on odor representation and decoding compared to reinforced odors. We acknowledge, however, that this conclusion is based on three animals, and additional animals would be needed to reach statistical significance. See response to reviewer data figure below; not included in the revised manuscript.\nThank you for raising this important point. We fully agree that the positive and negative reinforcers differ in both modality and salience, which could contribute to differences in the strength of associative learning and the magnitude of neural responses. In the revised manuscript, we discuss this limitation explicitly.\nSpecifically, we note that foot shock is often considered a more salient aversive stimulus and is not directly comparable to high-fat food in terms of motivational impact, as reflected in differences in locomotion following odor presentation (see Extended Data Fig. 2D–E). While this discrepancy likely influences the magnitudes of behavioral and neural measures, our findings nonetheless demonstrate that vGlut2BF neurons can flexibly encode odor-value associations of both positive and negative valence. We have clarified this point further in the Discussion and emphasized that future studies using reinforcers with more closely matched reinforcement strength and sensory modality will be important to more precisely isolate valence-specific encoding.\nR1: 5. Decoding improvement without increased Euclidean distance:\nThe authors argue that improved odor decoding after associative learning is due to altered neural representations. However, pentanol does not show significantly greater Euclidean distance from octanol, despite improved decoding. How can decoding accuracy increase without a corresponding increase in representation separability?\nWe appreciate the reviewer’s thoughtful observation. The linear decoding model identifies and weights the neurons that most reliably discriminate between odors, effectively emphasizing selective subsets of the population. In contrast, the Euclidean distance is computed across the averaged activity of all recorded neurons, including those that are less selective or uninformative. As a result, decoding accuracy can improve through re-weighting of informative neurons without necessarily producing a large change in the average Euclidean distance between conditions. We add this point in hopes of providing clarification.\nR1: 6. Effect of repeated odor exposure on decoding and trajectories:\nIn Fig. 4I, after just two exposures to heptanol, BF vGlut2 responses already show a trend toward divergence, similar to that seen for pentanol. Pentanol’s more pronounced changes may simply reflect the higher number of exposures (seven days), whereas heptanol was only presented twice. Data from octanol—an odor repeatedly presented but not paired with reinforcement—should be shown to clarify this point.\nIn terms of decoding accuracy, we further assessed whether repeated exposure improved classification of non-reinforced odors across trials. We found no significant increase in decoding accuracy for Heptanol (See figure 4E), suggesting that repeated exposure has limited effect on enhanced discriminability of non-reinforced odors.\nIn the revised manuscript, we performed an additional experiment where Octanol was presented for seven days in the home cage, matching the duration of the food-paired odor exposure. We did not observe behavioral preference for this odor (Figure C). The Euclidean distance between pre- and post-exposure conditions was smaller than that of the food-associated odor, Hexanol, though not significant (Figure D). From the confusion matrix, the percentage of correctly classified trials for Octanol increased modestly (39.17 to 55), but this increase was substantially lower than that seen with either the food-associated odor (46.66 to 80.83) or high-fat-diet associated odor (40.83 to 78.33) (Figure E). These results suggest that passive odor exposure in the home cage has only limited effects on odor representation and decoding. We acknowledge, however, that this conclusion is based on three animals, and that additional animals would likely be needed to reach statistical significance. We show the data here in this response (see above), but do not include in the revised manuscript.\nR1: 7. Causal role of BF vGlut2 neurons remains uncertain:\nThe authors show that optogenetic activation or inhibition of BF vGlut2 neurons induces place avoidance or preference, suggesting these neurons broadly influence behavioral state or valence. Therefore, manipulation of BF vGlut2 neurons during learning may impair performance via altered motivational state, not necessarily due to specific disruption of odor-value association. To strengthen causal claims, targeted manipulation of odor-responsive BF vGlut2 subpopulations would be more appropriate. As it stands, the data support a correlation between BF vGlut2 representations and associative learning, but do not provide strong evidence of a causal role.\nWe appreciate the reviewer’s thoughtful comments regarding the specificity of the causal manipulations.\nWe fully agree that this would be a fantastic experiment. In fact we attempted this (multiple times) via targeted recombination in the active population to TRAP odor-responsive BF vGlut2 neurons. However, these trapping experiments were not sufficiently reliable to demonstrate that the captured ensembles were consistently and selectively activated by the presented odor. It was unclear if the TRAP system doesn’t effectively work in this region, or if the TRAPPED cells were not able to provide sufficient drive to influence this biology. Additionally, our calcium imaging data indicate that associative learning recruits new neurons to become responsive, suggesting that some BF vGlut2 neurons may primarily encode learned associations rather than odor identity. Consequently, the TRAP system may not be effective here, or restricting manipulations only to neurons classified as “odor responsive” prior to learning may not effectively confer value to the odor, as the relevant population likely includes neurons recruited dynamically by associative processes but not sensory processing. However, we appreciate this suggestion, since we spent significant effort towards this goal.\n1. Odor response plots in Fig. 1:\nAlthough eight odors were tested, only response data for four odors are shown. Please clarify or include the remaining odors for completeness.\nWe thank the reviewer for pointing this out. Indeed, eight odors were tested in total: four neutral and four aversive odors. The responses to the aversive odors are presented in Figure S1. We have now clarified this in the revised main text. (See line 129, 163).\n2. Clarification of Fig. S1E:\nIt is unclear which neurons and which time windows are represented in Fig. S1E. What does each data point signify?\nWe appreciate this opportunity to clarify. The time window used for calculating the area under the curve corresponds to the 2-second odor presentation period. Each data point represents the area under the curve for an individual animal during this time window. We have now updated the revised figure legend to specify this explicitly (see Fig S1G legend).\n3. Missing values in pie charts:\nMany pie charts lack absolute numbers or percentage values. Please indicate the total number of recorded neurons and the proportions in each category.\nThank you for this helpful suggestion. We have now added the percentage values directly to the revised figure panels to improve clarity. Additionally, the total number of recorded neurons and the proportions in each category are now specified in the corresponding figure legends (see the updates in figure 1F, 1G, 2H, S1E-F, and the corresponding legends).\n4. Clarification of cell inclusion in summary plots:\nIn Fig. 1E and Fig. S1C, are all recorded neurons included, or only those that were responsive to odors?\nWe would like to thank the reviewer for this inquiry. To clarify, all recorded neurons were included in these analyses, not only the odor-responsive cells. We have added this point in the figure legends to avoid confusion.\n5. Scale bar visibility:\nThe scale bars in Fig. 1C and 1D are hard to read. Please improve the visibility and labeling.\nWe thank the reviewer for bringing this to our attention. We have modified the scale bars to improve their visibility and updated the labels for greater clarity.\n6. Data presentation in Fig. 1H–J and Fig. 3:\nThe meaning of the plots in these panels is not clear without referring to the figure legend. Consider improving figure annotations and axis labels for better standalone clarity. Similar issues apply to Fig. 3 and should be addressed throughout the manuscript.\nWe appreciate the thoughtful comment. We have now modified figure 3A which shows the experimental design for the whole figure which is similar to figure 2A. Also, we modified the figure legend of figure 1H for improved clarity.\nReviewer #2 (Remarks to the Author):\nSummary\nIn this study, the authors investigate the role of glutamatergic basal forebrain neurons\n6\n(vGlut2BF) in encoding and learning associative odor valence. Based on calcium responses of vGlut2BF neurons, the authors show that while these neurons do not encode innate valence or odor identity, associative learning alters their response dynamics and the associative odor valence can be decoded from population responses. Further experiments coupling chemogenetic manipulation with a go/no-go task demonstrate that vGlut2BF neurons are necessary for learning a valence-dependent odor discrimination task. The study closes with results suggesting a potential role of the polarity of vGlut2BF activity for switching associated odor valence.\nComments\nThe results are very interesting and the data on the involvement of vGlut2BF during associative learning will be highly relevant for a broad readership. The experiments are generally well-designed and executed with appropriate controls, and the results are presented and discussed comprehensively.\nA notable limitation, however, is the lack of counterbalancing of odors between positive and negative valence associations. This is particularly relevant for the last result, where the authors conclude that bidirectional manipulation of vGlut2BF neurons can induce opposite associative odor valence. The observed effects are modest, and the current experimental design, which uses three different odors assigned exclusively to the three groups, is insufficient to rule out innate odor preferences or biases.\nIf the authors can address this major concern and implement the minor issues below, this manuscript will be a strong candidate for publication in Nature Communications.\nR2: Figure 1\nDefine MCPO\nWe thank the reviewer for this pointing this out. MCPO refers to the Magnocellular Preoptic Nucleus, a subregion of the basal forebrain. We have added this definition to the figure legend and the first mention in the main text for clarity. (See Fig.1A legend).\nWhat is the distribution of responsive cells across animals and experiments?\nHow many neurons were recorded per animal?\nWe appreciate the reviewer for this inquiry, so that we can more clearly present the data. We have now listed the number of neurons recorded and the distribution of responsive cells across animals and experiments.\nWe have added an extended table to provide this information (see Extended Table 1).\nLabel for heatmap in E missing\nWe appreciate the reviewer for this pointing this out. We have now added a label indicating “z-score” next to the heatmap to clarify that the color scale represents the normalized fluorescence signal (z-scored). This has been updated in the revised figure.\nR2: Figure 2\nInvestigation for Octanol missing\nRespectfully, we did not perform this here.\nFigure 2D: Does this include only excitatory neurons or all?\nWe thank the reviewer for the inquiry. Figure 2D includes all recorded neurons, not only excitatory neurons. We clarified this explicitly in the figure legend (see Fig.2D legend).\nWe thank the reviewer for raising this point. In our analysis, we only included cells that were consistently tracked across both pre- and post-association sessions. These cells were selected based on their presence in the imaging field and detectable calcium activity at some point during the experiment.\nRegarding cells that appeared responsive before association but not afterward, there are two possible explanations. First, some cells may have undergone physiological changes due to associative learning, such as synaptic depression or shifts in network dynamics, which could reduce their odor-evoked responses below the responsiveness threshold. Alternatively, some of these changes could be due to technical or experimental constraints—for instance, fluctuations in signal-to-noise ratio or slight shifts in imaging quality might make it more difficult to detect reliable responses post-association.\nThus, while some loss of responsiveness may reflect real physiological changes, we cannot completely rule out experimental factors such as decreased response amplitude or subtle changes in calcium indicator expression that may influence our ability to detect responses. We appreciate this important inquiry.\nDid you split the assignment of rewarded and punished odor for both animal groups? I.e., was Pentanol ever associated with punishment and Hexanol with reward?\nFor clarification, Pentanol was consistently paired with reward, and Hexanol with punishment. In the revised manuscript, we now also performed the association paradigm where we flip the odors for reward and punishment to address this point (see the response for Figure 4-2).\nLegend for Figure 2J: top and bottom, instead of left/right\nWe thank the reviewer for this pointing this out. We have now corrected the figure legend in the revised manuscript.\nWhy does the number of experimental animals change between 2B and 2E?\nWe appreciate the reviewer’s inquiry. After verifying GCaMP expression and beginning the association experiments, we found that some animals did not maintain consistent signal quality across sessions. Specifically, in certain cases the GCaMP expression became unstable or the imaging field shifted (e.g., due to lens movement or deterioration of cell health), which prevented reliable longitudinal tracking of neuronal activity before and after association. For this reason, these animals were excluded from the analyses shown in Figure 2E. We now present the data to include only the reproducible/reliable responding animals that are used in Figure 2E (see figure 2B).\nR2: Figure 3\nSince the effect is quite weak, please be explicit about how many neurons (excited and inhibited) from how many animals have been included in the analysis?\nWe thank the reviewer for raising this point. We used 8 animals and 154 neurons for recording and the inclusion of the habituated odor. For better clarification, we have now listed the exact numbers of excited and inhibited neurons, as well as the number of animals used for analysis, in Extended Table 2 and the figure legend of Figure 3.\nR2: Figure 4\nA strong effect of odor association can be seen. Please state the number of neurons and experimental animals included in the analysis.\nWe want to thank the reviewer for acknowledging these data. We have now provided the exact numbers of neurons and experimental animals in the revised figure legend for clarity (see Figure 4 legend).\nWe appreciate this important point. Our primary goal was to compare decoding performance and reliability within the same neuronal population recorded from the same animals across different stimuli and timepoints. This approach allows us to control for variability in GCaMP expression, cell sampling, and overall imaging quality. Splitting the data by valence across different animal groups would introduce differences in the total number of neurons and features included in the analysis, potentially confounding interpretation of decoding performance. For this reason, we kept the analyses within the same cohort and did not separate the data by valence across animals.\nWould you expect the same effect if the odor used for negative association was a different one?\nTo test whether the effects depend on odor identity, we reversed the assignments: Pentanol, previously paired with high-fat diet, was conditioned with\nfoot shock, while Hexanol, previously paired with foot shock, was conditioned with high-fat diet.\nConsistent with our original results, and also\ndescribed above, mice developed a preference for\nthe positive odor and avoided the negative odor\n(Figure A-C). The newly conditioned negative odor\nproduced a larger increase in Euclidean distance\nbetween pre- and post-conditioning compared to\nthe positive odor (Figure D), similar as Figure 3D in\nthe manuscript. Both reinforced odors also\nshowed a greater increase in the percentage of\ncorrectly predicted trials compared to the passive\nexposed and habituated control. Decoding\naccuracy for distinguishing the two reinforced\nodors and distinguishing between the reinforced\nodors and the passive exposed odor improved\nafter conditioning (Figure F). These results, based\non three animals, suggest that the effect is not\nspecific to odor identity, though additional\nanimals are needed to achieve statistical\nsignificance.\nR2: Figure 5\nDid all hM4Di expressing cells stop firing upon CNO bath application? How many cells did the authors test in vitro? List the number of cells.\nTo address this we performed additional whole cell recordings in vitro and found out that although all hM4Di\nexpressing cells show some degree of\nhyperpolarization to CNO, they do not\nuniformly show decrease or cessation of\nfiring to the injected current. Some cells (3\nout of 7) didn’t stop firing spontaneously but\nrather changed their firing pattern to more\nburst-like. As a result, we decided to replace\nthe original figure and show two examples of\n9\nthe voltage and firing response to CNO on a larger time scale. We also provide the stats showing that after\nCNO, the membrane potential significantly decreases. (p=0.0028, n=7 cells) (see Figure 5D and legend, Line\n324-329).\nPlease show the data on the training stages of all animals as part of the supplement\nWe appreciate the reviewer’s concern regarding potential differences in task proficiency. For clarification,\nall animals included in the study were required to reach a performance criterion of at least 85% correct\nresponses in the Go/No-Go task using a familiar odor pair (eugenol vs. methyl salicylate) before any\nmanipulations or introduction of novel odor associations. This ensured that all animals were fully capable of\nperforming the task reliably. We also analyze the data from the odor pair used for the last stage of training:\nEugenol and Methyl Salicylate. There was no difference in d prime and/or number of blocks to learn between\nthe hM4Di injected mice vs. the mRuby control injected mice. We provided the data in Extended Fig. 3A-C,\nline 332.\nR2: Figure 6\nFigure 6A and the left part of C are quite redundant\nWhy was the effect of optogenetic activation and inhibition measured under different conditions (cell-attached vs whole cell)? Please show data or at least comment on the large variability in spontaneous activity between both examples shown.\nIn the original version of the manuscript, we presented the expression validation data for the optogenetic\nreporters. In both cases (ChR2 and iCloch) the recordings were performed in both cell-attached and whole\ncell mode. In case of the ChR2 we were evaluating light evoked firing, which can be easily demonstrated in\ncell-attached mode. For iCloch recordings we also wanted to evaluate membrane voltage changes to the\nchloride conductance, where whole cell recordings were more informative.\nFor the revised manuscript we further validate the optogenetic tools by performing additional recordings\nwith a revised figure with new data. Both ChR2 and iCloch evoked responses shown were both acquired in\nthe whole cell mode. We also now provide further quantitative data and additional description to the revised\nfigure. We also now provide the overall statistics of baseline firing rate across all recordings in the methods\nsection (see figure 6A and legend).\nFigure 6D: Please include heat maps of the preference test before pairing?\nWe thank the reviewer for this suggestion. We have now added heat maps of the pre-pairing preference test\nto a revised Figure 6D to provide a clearer comparison.\nLegend for Figure 6D: should state arranged from top to bottom\nWe appreciate the suggested corrections and now provide an updated legend.\nWhy did the authors use different odors to test place avoidance and preference? If the tested odors had the same valence, should the preference not be the same during pre-pairing for all 3 cohorts?\nDuring the pretest for all three groups, the mice did not show preference for either odor. In the GFP and\nChR2 groups, the preference index was calculated for Hexanol, and for iChloc the preference index was\ncalculated for Pentanol (GFP, one-sample t-test, p=0.9492; ChR2, one-sample t-test,p=0.0759, iChloc\ngroup, one-sample t-test,p=0.3155). Although there was no preference for the odors, our rationale for\nchoosing different odors for the iChloc was that in the pretest mice showed a slightly lower (though not\nsignificant) preference for Hexanol compared to Pentanol. We therefore reasoned that pairing Pentanol with\niChloc would provide a dynamic range to detect whether inhibitory manipulations would enhance the\n10\nrelative preference for a less-preferred odor. In all cases however, comparisons were made within each odor\npair and across pre- and post-conditioning sessions, which controls for any baseline differences. To further\ntest whether odor identity itself constrained the effect, we also applied the same inhibitory paradigm to\nHexanol. This manipulation produced a trend toward increased preference, although the effect did not\nreach statistical significance (mean +- SEM: -0.01198 +- 0.086; 0.0762+-0.08171, paired t-test 0.219, N=6).\nThe preference index produces rather variable results with a modest effect. In addition, the group\nsizes were relatively small. Would a different readout, such as investigation time of the odor source\ngive clearer results?\nWe thank the reviewer for this helpful suggestion. In response, we have reanalyzed the data using\ninvestigation time as the primary behavioral readout\ninstead of the preference index. This alternative measure\nprovides a more direct and potentially less variable\nassessment of odor approach behavior. However, we\nbelieve the preference index remains a valuable metric in\nthis context. It normalizes for total exploration time,\nwhich can vary between animals or change during the\ntest. This normalization allows us to detect shifts in odor\npreference even when total investigation time is\nreduced. To address both perspectives, we have\nincluded both analyses in the revised manuscript.\nMinor:\nSuppl. Fig 2D: Data points missing\nSuppl. Fig 2F: x-axis label typo (Pentanol)\nSuppl. Fig 3A: Legend states saline instead of CNO; x-labels are misplaced\nLine 1187: should state left half of the chamber, not the right half (See Line 1627)\nLine 158: valence\nLine 421: foot shock\nLine 51: Channelrhodopsin\nBe consistent with the nomenclature of AAVs (DIO vs FLEX)\nWe want to thank the reviewer for catching these oversights, and we have made the corrections in the\nrevised manuscript.\nReviewer #3 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nReviewer #4\nIn this paper by Chin et al, the authors use single-cell calcium imaging of vGlut2 expressing\nneurons in the horizontal limb of the diagonal band (hDB) to describe how calcium activity of\nthese neurons change with exposure to odors. The authors show initially, vGlut2 neurons in\nthe hDB exhibit small responses to novel odors. This calcium response is potentiated\nfollowing associative learning, when odors were paired with either a positive or negative\noutcome. The authors then use chemo- and optogenetic tools to show vGlut2 hDB neurons are\n11\nnecessary for associative learning in a Go/No-Go task, and the activity of vGlut2 hDB neurons\ncan alter behavior in an odor-preference test.\nOverall, this paper fills a gap in the literature by highlighting the functional significance of\nglutamatergic neurons in the basal forebrain (BF). However, this manuscript can be improved\nwith clarifications to the introduction, figures and results, as well as additions to the\ndiscussion. Furthermore, the inclusion of additional experiments will better support the\nauthors conclusions of a role for vGlut2 BF neurons in odor associated learning.\nR4: 1. Is there a rationale for using distinct odors during the associative learning task?If vGlut2 BF\nneurons do not initially encode valence of olfactory stimuli, a reasonable hypothesis would be that\npairing an odor with either a positive or negative stimulus would result in identical changes in the\ncalcium activity of vGlut2 BF neurons. This would further support the authors hypothesis that learning\nitself (and not the stimulus the odor is paired with) leads to the potentiated calcium response of vGlut2\nBF neurons. I.e., what do the authors expect if pentanol was paired with a negative stimulus, or if\nhexanol was paired with a positive stimulus?\nWe appreciate this thoughtful and reasonable inquiry, and this concern was also raised by other reviewers\n(also addressed above). Towards this, our primary goal was to compare decoding performance and\nreliability within the same neuronal population across different stimuli and timepoints. Using distinct odors\nfor the positive and negative associations allowed us to track the same neurons throughout training while\navoiding confounds from repeated pairing of a single odor with different outcomes. This approach also\nhelped ensure that any observed changes in neural representation reflected learned associations rather\nthan repeated stimulus exposure.\nTo test whether the effects depended on odor identity, we have now added experimentation where\nwe reversed the assignments: Pentanol, previously paired with high-fat diet, was conditioned with foot\nshock, while Hexanol, previously paired with foot shock, was conditioned with high-fat diet. Consistent with\nour original results, mice developed a preference for the positive odor and avoided the negative odor\n(Figure A-C) below. The newly conditioned negative odor produced a larger increase in Euclidean distance\nbetween pre- and post-conditioning compared to the positive odor (Figure D), similar as Figure 3D in\nthe manuscript. Both reinforced odors also showed a greater increase in the percentage of correctly predicted trials\ncompared to the passive exposed and habituated control.\nDecoding accuracy for distinguishing the two reinforced odors and distinguishing between the reinforced\nodors and the passive exposed odor improved after conditioning (Figure F). These results, based on three\nanimals, suggest that the effect is not specific to odor identity, though additional animals are needed to\nachieve statistical significance.\n12\n***Figure Legend\nTime spent by GRIN lens implanted mice in regions\nwith foot shock associated odor (Pentanol), (B) the\nhigh-fat-diet associated odor (Hexanol), and (C) the\npassive exposed odor Octanol. The\nassociated/passive exposed odor is shown in red\nand the odorless control is shown in gray. (N=3 mice,\nPaired Student’s t-test: (A)p=0.0307 , (B) p=0.0256,\n(C) p=0.7104\n(D) Quantification of Euclidean distances during the\n2-second odor presentation period (N = 3) between\nbefore and after association. Mean+-SEM for\nPentanol=3.64+-1.159 ; , Hexanol=2.09+-0.1458, and\nOctanol=1.565+-0.094).\n(E) Confusion matrices are used for decoding odor\nidentities before (left) and after (right) association.\nFor better decoding purposes, we need to include\none odor. this includes another odor Heptanol which\nis used for another kind of habituation, which is given\nto the mouse on the wheel for seven days. However,\nthis does not match the condition we use in the\npaper so it is not further analyzed. (F) Accuracy of\ndecoding Pentanol vs Hexanol, Pentanol vs Octanol,\nand Hexanol vs Octanol before and after association.\n(Mean+- SEM, Paired Student’s t-test: Pentanol vs Hexanol:0.5633+-0.053 (Before), 0.7600+-0.035 (After);\nPentanol vs Octanol:0.593+-0.026 (Before), 0.753+-0.075 (After); Hexanol vs Octanol:0.58+-0.075 (Before),\n0.707+-0.056 (After))\nR4: 2. In the Go/No-Go task described in Figure 5, it appears the task was only done with an appetitive\nstimulus (i.e., the water reward). Do the authors expect the same or different results if the task was\ncompleted with a footshock?\nWe thank the reviewers for this observation and question. To address whether basal forebrain glutamatergic\nneurons play a similar role in aversive learning, we conducted an additional experiment using an odor-cued\naversive conditioning paradigm. Specifically, we chemogenetically inhibited vGlut2BF neurons during the\nacquisition phase of odor-shock associative learning across two training days. On the following test day, we\nassessed freezing behavior in response to the conditioned odor. We found no significant difference in\nfreezing time between control animals and those that received CNO injections to inhibit vGlut2BF neurons\nduring training. These results suggest, that unlike in the appetitive Go/No-Go task, inhibition of vGlut2BF\nneurons does not disrupt the formation of aversive odor associations under these conditions.\n13\n(A) Schematic of the aversive conditioning\nparadigm. During each training day, mice\nreceived five odor presentations (10 s each),\neach immediately followed by a 1.5 s foot\nshock (0.5 mA). To inhibit vGlut2 BF neurons,\nclozapine-N-oxide (CNO) was injected\nintraperitoneally 30 minutes before training.\nOn the test day, mice were placed in a novel\ncontext and exposed to five odor\npresentations without shock. (B) Percentage\nfreezing time during odor presentations on\nthe second training day in control (mRuby,\nblue, N=7) and hM4Di (red, N=7) groups. Two-\nway ANOVA, F(1,12)=0.02574, p=0.8752. (C)\nPercentage freezing time during odor\npresentations on the test day in control\n(mRuby) and hM4Di groups. Two-way\nANOVA, F(1,12)=0.05907, p=0.8121.\nR4: 3. The results for the DREADD inhibition experiments are a bit confusing. It appears even at trial 0,\nthere are differences between hM4Di and mRuby groups. Can the authors expand on these interesting\nfindings in the discussion? Could vGlut2 hDB neurons have a role in direct sensory processing of\nolfactory stimuli.\nThank you for noticing this point. We agree that this aspect of the figure could be clearer, thus we have\nmodified it for the revision. To quantify d-prime performance across training, we used a rolling window of 40\ntrials in which each data point reflects the average d-prime over a set of consecutive trials (e.g., trials 1–40,\n2–41, etc.). As a result, even the first point plotted on the original graph (“trial 1”) incorporated performance\nacross the initial several trials. Because mice often acquire discrimination rapidly within the first few trials,\nthis can create apparent differences early in the curve. We have updated the figure and figure legend so that\nthe axis starts with trial 20, and each point represents a rolling window that includes 20 trials before and 20\ntrials after (see revised Figure 5F).\nAlso in the results, lines 275-276, the authors state the hM4Di mice required more trials to learn the\nGo/No-Go task. How many more trials did it take before the hM4Di mice eventually learned the task?\nThank you for this important question. First, and importantly, all mice ultimately learned to discriminate\nbetween the odor pairs. On average, the hM4Di experimental group required an average of 3.15 more blocks\n(20 trials/ block) compared to controls to reach the performance criterion. Now we include the average\nnumber of blocks and SEM in the figure legend to clarify the extent of the delay (Figure 5G). This represents\nan impact with this manipulation post activation.\nIn the results, lines 277-278 is confusing. The authors state “inhibition of vGlut2 BF neurons also led to\nreduced final D prime value for the task, which shows an impaired ability to discriminate..” Does this\nmean the hM4Di mice never learned the Go/No-Go task? This would seem to contradict the previous\npoint in which the authors state hM4Di mice required more trials to reach 85% accuracy. Can the\nauthors clarify these statements?\nThank you for raising this important point. All hM4Di mice did eventually learn the Go/No-Go task and\nreached the 85% accuracy criterion, demonstrating that they were able to acquire the discrimination\ncapacity. However, their d-prime values reported the difference between the Hit rate and the False alarm\n14\nrate over the whole learning task. Since the Control and the hM4Di animals showed a similar number of\ntrials (there is a limit number of trials for the task to prevent the mice drinking too much water), the results\nsupported that the learning rate affected the d prime and overall performance. To better clarify this point we\nhave revised the text in the Results section to describe that the inhibition of vGlut2 BF neurons resulted in\ndelayed learning and reduced the overall performance, but did not prevent task acquisition (see line 362-\n366, 845-858).\nR4: 4. Throughout the paper (for example: abstract lines 37-39, introduction lines 75-77, introduction\nline 85, etc.) it is unclear what the authors mean by “cholinergic basal forebrain”. Previous studies\nhave demonstrated the presence of non-cholinergic neurons within the basal forebrain, so it is not\nnecessary to repeatedly state “cholinergic basal forebrain”.\nFurthermore, it is unclear what the authors mean when describing the relationship between BF\nglutamatergic and cholinergic neurons. For example, in the abstract lines 37-39 “Within the BF nuclei,\nglutamatergic (vGlut2BF) neurons relay cholinergic input to modulate behavior”- does this mean BF\nglutamatergic neurons also release acetylcholine? If so, can references be provided?\nWe appreciate the reviewer’s careful reading and thoughtful comments. To avoid redundancy and improve\nclarity, we have removed the repeated the reference to “cholinergic basal forebrain” throughout the\nmanuscript when not specifically relevant. Regarding the statement that vGlut2BF neurons “relay\ncholinergic input,” our intention was not to imply that these glutamatergic neurons themselves release\nacetylcholine. Rather, we meant that their activity is modulated by cholinergic signaling within the basal\nforebrain microcircuit. We agree that the term “relay” could be misleading in this context, so we have\nrevised the text to clarify that vGlut2BF neurons integrate cholinergic modulation to influence behavior. The\nrevised phrasing makes it explicit that these neurons are glutamatergic and do not co-release acetylcholine\n(see line 38).\nIn the introduction lines 64-66, “These different types of projection neurons extend to diverse brain\nareas, form reciprocal connections, and receive direct fast cholinergic neurotransmission from\nBFCNs”- do the authors mean BF GABA and glutamate neurons receive cholinergic input from BFCNs?\nCan the authors provide clarification and provide references?\nWe appreciate to reviewer for this helpful request for clarification. Yes, we are referring to the basal\nforebrain GABAergic and glutamatergic neurons receiving cholinergic input from BFCNs. To make this\nclearer, we have revised the text to specify that these populations receive fast cholinergic synaptic input\nfrom cholinergic neurons within the basal forebrain. We have also added references to support this point\n(Patel et al. 2019; Yang et al. 2014; McKenna et al. 2021).\nThe introduction should be expanded to mention differences between BF regions (if any). There are\nmultiple distinct brain regions that comprise the BF and describing any functional differences between\nneurons in the different BF regions would be helpful to the reader. Moreover, the focus of this current\npaper was in the hDB. The introduction should be expanded to include studies that have focused\nspecifically within hDB.\nWe appreciate the reviewer’s suggestion to better contextualize the anatomical and functional\nheterogeneity of the basal forebrain (BF). In response, we have expanded the introduction to describe the\ndistinct roles of major BF subregions, including the globus pallidus/substantia innominata (GP/SI) and the\nhorizontal limb of the diagonal band (HDB), emphasizing their differential involvement in sensory, reward,\nand aversion-related processing. We now highlight the rationale for focusing on the HDB in this study, as it is\nuniquely positioned to modulate olfactory perception through its reciprocal connectivity with the olfactory\nbulb. Additionally, we cite studies showing that lesioning or silencing neuronal populations in the HDB\n15\nimpairs olfactory-guided behavior, supporting its functional relevance in odor-driven associative learning.\nThese additions help establish the HDB as a distinct and appropriate target for investigating basal forebrain\ncontributions to valence encoding and sensory modulation (see line 96-103).\nR4: 5. The discussion section of the paper would be enhanced if authors first focused the discussion\nmore specifically on the hDB- i.e., what are the inputs and outputs of the hDB? What is the neuronal\ncomposition of the hDB? Can any of these factors influence the authors findings? Although a\ndiscussion of the BF in general is appreciated, the authors should first focus on the brain region of\ninterest described in the current study (hDB).\nWe appreciate this thoughtful suggestion to improve the focus of the discussion. In the revised manuscript,\nwe have now expanded the section discussing the horizontal limb of the diagonal band (HDB). Specifically,\nwe describe that the HDB is composed of cholinergic, GABAergic, and glutamatergic neurons (e.g., Gracia-\nLlanes et al., 2010; Záborszky et al., 1986), and that it receives inputs from regions such as the olfactory\nbulb, piriform cortex, hypothalamus, and brainstem modulatory centers. The HDB projects prominently to\nthe olfactory bulb, where it modulates olfactory processing and perception (Niedworok et al., 2012; Gracia-\nLlanes et al., 2010). Additionally, lesion or silencing studies have demonstrated that disrupting HDB activity\nalters olfactory-mediated behaviors (Nunez-Parra et al., 2013; Moss et al., 2025; Smith et al., 2015;\nPaolini and McKenzie, 1993). We have also added a more thorough introduction describing the anatomical and\nfunctional properties of the HDB to provide better context for our findings.\nMinor Points:\n1. In the introduction, additional references describing the role of BFCNs in valence encoding would\nhelp the reader.\nWe thank the reviewer for suggesting these relevant references. We have incorporated them into the\nintroduction to better contextualize the role of basal forebrain cholinergic neurons in valence encoding.\n2. Results line 116- it would be helpful to describe each odor in laymen’s terms (if possible). For\nexample, are they associated with rewarding or aversive behaviors? Or are they neutral odors that\ndon’t elicit any behavioral responses? Likewise, if the odor was diluted, the concentration of the odor\nand the diluent should be mentioned.\nWe thank the reviewer for the suggestion. We have now clarified in the revised text whether each odor was\nconsidered neutral and/or if they are associated with value in the odor screening experiment. Additionally,\nwe have included detailed information about the odor concentrations, dilutions, and diluents to improve\ntransparency and reproducibility.\n3. In Figure 1, is a heatmap for rose oil and anisole odors?\nThank you for pointing this out. We have now included heatmaps for rose oil and anisole in a supplemental\nfigure to provide a complete overview of the neural responses to all tested odors (see Figure S1).\n4. For the heatmap in Figure 1E, what is the scale? The authors mention the criteria for “excited” or\n“inhibited” neurons is 3 standard deviations above or below baseline. However, the scale for the\nheatmap ranges from -1 to 2. Therefore, is this scale for delta F/F? Moreover, what is the x-axis for the\nheatmap (i.e., what is the timescale)?\nWe thank the reviewer for bringing this to our attention and for the opportunity to clarify. The scale of the\nheatmap represents the z-scored fluorescence signal, normalized to the average response during the\nentire odor presentation period for each neuron. The axis ranging from –1 to 2 reflects these z scores rather\nthan raw ΔF/F values. Regarding the timescale, the x-axis represents time, and the distance between the two\nblack lines corresponds to a 2-second interval. The timescale is the same as the timescale for the average\n16\ntraces. We have now updated the figure to clearly label the z-score scale and add explanation for the\ntimescale in the figure legend in the revised manuscript.\n5. In the circle plots in Figure 1F, having the actual numbers within the circles would be helpful to the\nreader.\nWe appreciate this suggestion. We have now added the actual numbers inside the circles in Figure 1F to\nimprove clarity in the revised manuscript.\n6. For reinforcement pairing of odors- were mice food restricted when the odor was paired with a high\nfat diet (pentanol with high fat diet)? Food restricting mice beforehand (or satiety of mice) will change\nmotivation.\nAgain, we appreciate the reviewer raising this important question. The mice were not food restricted during\nthe reinforcement pairing procedures. We have clarified this detail in the Methods section of the revised\nmanuscript.\n7. The schematics for Figures 3A/4A are unclear.\nWe have now revised the schematics in Figures 3A and 4A to more clearly illustrate the experimental\nprocedures and to improve the clarity of the color scheme.\n8. Can the authors provide an explanation for Euclidean Distance (line 218)?\nThank you for this request for clarification. The Euclidean distance measures how different the activity\npatterns of the neuronal population are when responding to different stimuli. Specifically, we create a vector\nrepresenting the mean population response to each odor, with each element corresponding to the activity of\nan individual neuron. We then calculate the Euclidean distance between these vectors, which quantifies the\noverall dissimilarity between the two response patterns. Larger distances indicate more distinct\nrepresentations of the odors across the recorded neurons. We have added this explanation to both the\nResult and Method section to improve clarity. (See line 253-262, Method line 933-939).\n9. In the Euclidean Distance plots in Figure 3, can the\nauthors show a plot for the Euclidean Distance for\nPentanol vs. hexanol? The bar graph shows there\ncould be a difference between the odors and it would\nbe interesting to see how the two odors look on the\nEuclidean Distance graph.\nWe appreciate the reviewer for this helpful suggestion.\nWe have now included a plot showing the Euclidean\ndistance between pentanol and hexanol before and after association. Although there was an increase in the\naverage distance across the eight animals following association, this difference did not reach statistical\nsignificance (p = 0.053). The new plot has now been added to Figure 3 and referenced in the Results section\n(see line 268-271 and revised Figure 3E)\n10. In the results line 180-182, do the authors use the same criteria as earlier in the paper (i.e., 3 SD\nabove baseline for “excited”, 3 SD below baseline for “inhibited” ) to describe changes in vGlut2 BF\nneurons following association?\nFor clarification, yes, the criteria for classifying neurons were defined as excited or inhibited (3 standard\ndeviations above or below baseline activity) were applied consistently throughout the paper. We have now\nclarified this in the revised Methods section.\n11. In Figure 4H, can the authors provide an explanation of what spatial proximity and directionality\nmeans? Since differences in these measures are observed, what does this mean in terms of odor\ndiscrimination or associative learning?\nbetween population activity vectors evoked the odors. We compared the distance between the same odor\nbefore and after conditioning, or between different odors in the same session. This metric captures how\nmuch the overall neural representation shifts in high-dimensional population space as a result of learning or\nstimulus identity. A larger Euclidean distance reflects greater remodeling of the neural code, consistent with\nchanges in odor-associated meaning or salience (see revised text).\nWe further use the term directionality to refer to the angle between population vectors, indicating how the\nrelative orientation of odor representations changes across conditions. However, we did not quantify the\nabsolute change in vector angle across sessions in this analysis. Since we did not quantify the vector angle\nin the result session, we did not further discuss the changes in the directionality. We have clarified these\ndefinitions and their implications for odor discrimination and associative learning in the revised figure\nlegend and Results section.\n12. In distinct parts of the BF, cholinergic afferents co-express vGlut3 (Barabas et al, 2024). Can the\nauthors expand on vGlut2 vs. vGlut3 BFCNs in the discussion and what the potential implications are?\nWe appreciate the reviewer raising this point, and agree it is important to clarify the differences between\nvGlut2 and vGlut3 expression in the basal forebrain. vGlut2 and vGlut3 mark distinct glutamatergic\npopulations. Specifically, vGlut2 does not overlap with ChAT (McKenna et al., 2021), indicating that vGlut2+\nneurons are non-cholinergic glutamatergic projection neurons. In contrast, vGlut3 is often co-expressed\nwith ChAT (Barabas et al., 2024), reflecting a unique subset of cholinergic neurons capable of glutamate co-\nrelease. We expected that vGlut3 expressing neurons will act the same as the cholinergic neurons in the\nbasal forebrain, however we feel this would require experimentation beyond the scope of this study.\n13. In the discussion, the authors mention multiple subtypes of vGlut2 BF. What are these subtypes\nand what factors distinguish the different subtypes of vGlut2 BF neurons? How would this impact the\nauthor's findings?\nWe appreciate this inquiry, given it’s an outstanding question we also have. Our data show that some vGlut2\nBF neurons are odor responsive while others are recruited specifically after associative learning, suggesting\nthis population is functionally heterogeneous. However, molecular markers that clearly define distinct\nsubpopulations of vGlut2 BF neurons are still being established. For example, McKenna et al. (2021)\nidentified that subsets of BF vGlut2-tdTomato neurons co-express either the calcium-binding protein\ncalbindin (Calb) or calretinin (Calr). Whether there are neurons that co-express both proteins remains\nunclear, and the functional roles of these subpopulations have not yet been tested.\nFuture experiments using these molecular markers could help determine whether specific subtypes are\npreferentially involved in encoding odor identity versus associative or emotionally salient reinforcement.\nSuch studies could clarify whether the observed heterogeneity in our recordings reflects distinct functional\nspecializations within the vGlut2 BF population. We have added topic area and future directions to the\ndiscussion in the revised manuscript.\nReviewer #5 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\n18"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I have reviewed the authors' responses and the revised manuscript. They have addressed all of my concerns satisfactorily.", "reviewer_2": "The authors have done an excellent and thorough job revising the manuscript and have sufficiently addressed all my concerns.", "reviewer_3": "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.", "reviewer_5": "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": ""}]

Decision letter