Acetylcholine (ACh) is critical for cognition. Basal forebrain cholinergic neurons (BFCNs), neurons that synthesize and release ACh that are sparsely distributed throughout the base of the forebrain, provide the primary source of ACh to the cortex, hippocampus, and amygdala. Disruptions to normal cholinergic transmission are thought to contribute to several neuropsychiatric disorders (Sarter et al., 1999; Higley and Picciotto, 2014) as well as to cognition (Ananth et al., 2023) and salience-related behaviors (Jiang et al., 2016; Hersman et al., 2017; Crouse et al., 2020). BFCNs are anatomically divided into several clusters: the medial septum/diagonal band complex (MS/DB), the ventral pallidum (VP), the substantia innominata (SI), and the nucleus basalis (NBM). Between and within these anatomical groupings, BFCNs comprise heterogenous subclusters (Zaborszky and Gyengesi, 2012). How this heterogeneity contributes to the significant control that cholinergic signaling exerts over large, behaviorally relevant circuits is unclear (Zaborszky et al., 2015; Gielow and Zaborszky, 2017).

ACh plays an important role in modulating emotionally salient memories (Luchicchi et al., 2014; Ballinger et al., 2016; Knox, 2016; Ananth et al., 2023). We and others have found that cholinergic signaling in the basolateral amygdala (BLA) is important for generating defensive behaviors in response to both learned and innate threats (Power and McGaugh, 2002; Jiang et al., 2016; Wilson and Fadel, 2017). Optogenetic manipulation of endogenous ACh release in the BLA during learning modulates the expression of threat response behaviors in mice upon recall of a conditioned stimulus (Jiang et al., 2016). Stimulating release of ACh increases activity of BLA principal neurons, in part by increasing the release probability of glutamatergic inputs to these neurons, and is sufficient to induce long-term potentiation (LTP) when paired with minimal (non-LTP generating) stimulation of glutamatergic input to the BLA (Unal et al., 2015; Jiang et al., 2016). Memory formation and retrieval are associated with fast synaptic mechanisms that are modulated by ACh, that are in turn necessary for the proper learning and expression of threat response behaviors (Nonaka et al., 2014). Given the broad distribution of cholinergic input across the BLA, and the well-established role of ACh in modulating BLA plasticity, the basal forebrain cholinergic system is well positioned to serve an important role in the encoding of threat memories and generation of threat response behaviors (Ananth et al., 2023).

The BLA receives dense cholinergic input from neurons located in various regions within the basal forebrain (such as the VP, SI, and NBM). In this study, we asked how these distinct populations of BLA-projecting BFCNs contribute to threat responses. Using a genetically encoded ACh sensor, activity-dependent genetic tagging, chemogenetic manipulations, and electrophysiological recordings, we identify a population of BFCNs in the NBM/SI_p (SI_p defined as the portion of the sublenticular SI posterior to bregma −0.4 mm) that are required for learned threat responsiveness. We find that NBM/SI_p cholinergic neurons are necessary for freezing behavior following cue-conditioned threat learning while freezing behavior elicited by an innately threatening stimulus activates cholinergic neurons in the VP/SI_a (VP/SI_a; SI_a defined as the portion of the SI ventral to the anterior commissure located anterior to bregma −0.4 mm).

Animals recognize varied sensory stimuli and categorize them as either threatening or non-threatening. Recognition of threatening stimuli can be innate or acquired, for example, by association of an aversive experience with an innocuous, co-occurring sensory input. In this study, we sought to understand if the basal forebrain cholinergic system participates in the encoding of associative threat or in response to innate threat.

ACh is released in the basal lateral amygdala in response to threat

The BLA plays a central role in associative threat learning and in the generation of threat responses. We have previously demonstrated that silencing cholinergic input to the BLA during cue-conditioned threat learning (pairing a naive tone with a foot shock) blunts learned freezing in response to the conditioned stimulus (tone) (Jiang et al., 2016). Given this, the first question we asked was whether ACh was released in the BLA during associative threat learning (Figure 1, Figure 1—figure supplements 1–4). To monitor acute changes in extracellular ACh levels during the cue conditioned threat-learning task, we expressed a genetically encoded ACh sensor, GRAB_ACh3.0 (Jing et al., 2018; Jing et al., 2020) in BLA neurons and visualized fluorescence using fiber photometry (Figure 1A). Our associative threat-learning protocol involved placing mice in a novel chamber and exposing them to an 80 dB tone for 30 s. During the final 2 s of the tone the mice received a foot shock (0.7 mA). The tone–shock pairing was repeated twice (for a total of three pairings). Twenty-four hours later, mice were placed in a different chamber (with different tactile, visual, and olfactory cues to the training chamber) and exposed to tone alone.

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Figure 1—source data 1

Processed ACh release fiber photometry data for Figure 1.

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Foot shock, either alone or paired with tone, increased ACh release in the BLA whereas the naive tone that is the first tone before shock presentation (Tone 1), did not (Figure 1—figure supplement 1C, Figure 1C, D, left; baseline (BL) vs. Tone 1, p = 0.8311). In contrast to Tone 1, the recall tone, presented 24 hr after the three tone–shock pairings, resulted in significant increase in ACh release in the BLA (Figure 1C, D, right; p = 0.0039). The change in tone-associated ACh release required pairing with foot shock: naive tone (Figure 1—figure supplement 2C left, p = 0.8437), three consecutive tones alone (without shock), or a subsequent repeat tone presentation after 24 hr (not previously paired with shock) (Figure 1—figure supplement 2C, right; p = 0.3152), did not induce significant changes in ACh release in the BLA (Figure 1—figure supplement 2).

To verify that the increases in ACh release were indeed specific to the tone–shock association and not due to generalization from prior shock exposure, we also subjected mice to three shocks (day 1) followed by a tone presentation 24 hr later (day 2) (Figure 1—figure supplement 3A). While mice demonstrated freezing behavior during the session on day 2, there was no significant increase in freezing behavior to the 24 hr tone presentation (Figure 1—figure supplement 3C, p = 0.2418). There was no increase in ACh in response to the tone when it was not explicitly paired with a shock, confirming that the changes in ACh release were indeed associative (Figure 1—figure supplement 3D; baseline (pre-tone, day 2)–24 hr tone (tone presentation, day 2): p = 0.7272). Therefore, after repeated tone–shock pairings, BLA-projecting cholinergic neurons acquire enhanced tone responsiveness.

NBM/SI_p cholinergic neurons are activated by threat learning and reactivated during threat memory recall

Following associative threat-learning, cholinergic neurons exhibited increased ACh release in the BLA in response to a previously innocuous auditory stimulus; this increase occurred exclusively following pairing of the tone with a shock. Using a two-color labeling system, we asked whether NBM/SI_p cholinergic neurons were activated during the training session and reactivated during the recall session. To do this, we injected the offspring of a cross of Chat-IRES-Cre x Fos-tTA:Fos-shGFP with a viral vector, AAV₉-TRE-DIO-mCherry-P2A-tTA^H100Y, resulting in activity (tTA) dependent, Cre dependent (aka ADCD) mCherry expression (see methods and Figure 2—figure supplement 1). These mice carry three transgenes: one encoding Cre recombinase in cholinergic neurons, a second doxycycline (Dox) repressible, tetracycline transactivator (tTA) expressed following activation of the fos promoter, and a third destabilized green fluorescent protein (short half-life GFP) also under transcriptional regulation of the fos promoter. tTA and shGFP are transiently expressed in activated neurons. In the absence of Dox (delivered via chow diet), activation of Cre-expressing cholinergic neurons leads to tTA expression and expression of the virally transduced mCherry along with a mutant tTA, which is insensitive to Dox. Thus, after closure of the labeling window by re-administration of Dox, cholinergic neurons activated during the Dox off period maintain mCherry expression permanently driven by the mutant tTA. When ADCD labeling is coupled with the transient expression of Fos−shGFP, we can label and visualize participation of cholinergic neurons in two separate behavioral sessions (mCherry+ = session 1 activated cells and GFP+ = session 2 activated cells) (Figure 2—figure supplement 1B).

Two to three weeks following injection with the ADCD virus, mice were either (1) kept in home cage throughout, (2) exposed to tone without foot shock (tone alone), or (3) put through the standard threat-learning paradigm (tone + shock). Twenty-four hours prior to the training session (session 1) mice were switched from Dox-containing to Dox-free chow to allow function of tTA. Immediately following tone–shock pairings, mice were placed back on Dox-containing chow (Figure 2A). This switch from Dox on → Dox off → Dox on was also performed for mice that remained in their home cages and for those that were exposed to tones without shock. Recall was performed 72 hr later (tone alone in a new context), and mice were sacrificed ~2.5 hr following recall (the peak of the Fos−shGFP expression). We quantified the number of mCherry+/GFP+ (double positive) neurons following session 2 (e.g. white arrow, Figure 2B). Significantly more double positive cholinergic neurons were seen following the complete associative threat-learning paradigm (tone + shock followed by tone recall) compared to mice that underwent session 1 without shocks (Figure 2C, p = 0.0249). To further ensure that the reactivation of these cholinergic neurons was not due to a generalized increase in responsiveness of these neurons following shock exposure, we quantified reactivated neurons in mice exposed to shock alone during session 1 followed by tone alone during session 2 (shock alone (session 1) → tone alone (session 2)) along with shock alone (session 1) → home cage (session 2), and home cage controls (Figure 2D). All three conditions showed few reactivated neurons and no differences between groups (p = 0.9471). Thus, associative threat-learning results in activation of NBM/SI_p cholinergic neurons which are reactivated during subsequent cue-induced memory recall.

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Reactivation of cholinergic neurons activated by training is required for learned behavioral responses

BLA-projecting cholinergic neurons acquire tone responsiveness following associative threat learning (Figure 1) and a population of NBM/SI_p cholinergic neurons are activated during tone–shock pairing and reactivated during the recall session (Figure 2). If these cholinergic neurons are indeed part of a threat memory engram, then their reactivation would be required for generation of learned threat responses. To block reactivation of cholinergic neurons in response to tone, we expressed the inhibitory, designer receptor hM4Di, in an activity dependent, Cre-dependent manner in NBM/SI_p cholinergic neurons (ADCD-hM4Di; Figure 3A) and subjected these mice to the threat-learning paradigm (Figure 3A). Mice were taken off Dox-chow 24 hr prior to the training session, immediately placed back on Dox-chow after training, and then tested for tone recall after 72 hr. ADCD-hM4Di and sham operated control mice were injected with clozapine (CLZ; 0.1 mg/kg; injected intraperitoneally (i.p.)) 10 min prior to the recall session to selectively silence the population of NBM/SI_p cholinergic neurons that were previously activated during training (Figure 3A). Freezing behavior was quantified during both the training and recall sessions. Freezing was compared between the ‘Pre-Tone’ period and ‘Recall Tone Response’ (defined as freezing occurring from the onset of the recall tone through the end of the recall session) (Figure 3—figure supplement 1B). Both groups of mice showed the same freezing behavior during the training session (Figure 3C, p = 0.6482, Figure 3—figure supplement 1A). In the recall session, sham mice displayed typical freezing behavior in response to tone (Figure 3D, gray boxes; Pre-Tone vs. Recall Tone Response, p = 0.0001). In contrast, ADCD-hM4Di mice did not show increased freezing in response to the tone (Figure 3D, red boxes; BL vs. tone response, p = 0.8451). Overall ADCD-hM4Di mice showed lower freezing behavior compared to sham controls (Figure 3D; sham – gray, hM4Di – red: p = 0.0052), indicating that reactivation of training-activated NBM/SI_p cholinergic neurons during the recall session was required for the expression of learned threat response behavior.

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BLA-projecting NBM/SI_p cholinergic neurons are reactivated during threat memory recall

To investigate whether NBM/SI_p cholinergic neurons that are reactivated during recall are BLA projecting, we injected Chat-IRES-Cre × Fos-tTA:Fos-shGFP mice with ADCD-mCherry in the NBM/SI_p, and simultaneously delivered the retrograde tracer Fast Blue (FB) into the BLA (Figure 4A). The mice were taken off doxycycline containing chow during the training period, returned to dox-chow for 72 hr and then exposed to the tone alone. We then quantified BLA-projecting cholinergic neurons that were reactivated by tone (ChAT immunoreactive, FB labeled and ADCD-mCherry+/Fos-shGFP+; Figure 4C). We found that ~20% of NBM/SI_p cholinergic neurons in both the home cage and the threat-learning + recall paradigm group (at Bregma −0.8 mm) were labeled with FB, with no significant differences in the percentage of cholinergic neurons with retrograde label between groups (Figure 4D; p = 0.5192). Next, we quantified the percentage of BLA-projecting NBM/SI_p cholinergic neurons that were active during session 1 and reactivated during session 2. We found that, on average, ~21% of BLA-projecting cholinergic neurons were reactivated during recall (Figure 4E). This reactivation of BLA-projecting BFCNs was significantly higher in mice that underwent training + recall compared to mice that remained in their home cage but still underwent the Dox on → Dox off → Dox on protocol (Figure 4E; p = 0.0183). Based on these data we conclude that BLA-projecting BFCNs are activated by associative threat learning and reactivated by threat recall.

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Silencing BLA-projecting BFCNs during training or recall prevents activation of BLA neurons and conditioned freezing behavior

To determine whether chemogenetic silencing of BLA-projecting cholinergic neurons during training or during recall interfered with the activation of BLA neurons, we injected the BLA of Chat-IRES-Cre mice with CAV₂-DIO-hM4Di.mCherry and AAV₉-camk2a-GCaMP (cav.hM4Di^BLA mice) or AAV₉-camk2a-GCaMP alone (sham mice) (Figure 5A, Figure 5—figure supplement 1A; GFP fluorescence from GCaMP was used to mark the injection sites). We found mCherry was expressed in cholinergic neurons predominantly in the NBM/SI_p, followed by the VP/SI_a, with a small contribution from the horizontal limb of the diagonal band of Broca (hDB) (Figure 5A, right). These data support previous findings (Zaborszky and Gyengesi, 2012) that NBM/SI_p cholinergic neurons provide a major input to the BLA.

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We injected cav.hM4Di^BLA or sham control mice with CLZ 10 min prior to initiating cue-conditioned threat learning (Figure 5B) or 10 min prior to the memory recall session (Figure 5C). In both experiments, mice were sacrificed 45–60 min following recall and assessed for Fos immunoreactivity (IR) in the BLA. We found that DREADD-mediated silencing of BLA-projecting cholinergic neurons during training alone blunted recall-induced freezing behavior and activation of BLA neurons (Figure 5B: freezing behavior, sham vs. cav.hM4Di^BLA (Recall Tone Response), p < 0.0001, Figure 5b’, b’’: Fos density, sham vs. cav.hM4Di^BLA p = 0.0286). Similarly, DREADD-mediated silencing of BLA-projecting cholinergic neurons during recall alone also reduced recall-induced freezing and activation of BLA neurons (Figure 5C: freezing behavior, sham vs. cav.hM4Di^BLA (Recall Tone Response) p = 0.0279, Figure 5c’, c’’: Fos density, sham vs. cav.hM4Di^BLA p = 0.0317). Mice in both sham groups showed equivalent freezing behavior (Figure 5B, C, gray boxes; comparing sham groups, p = 0.8155) and density of Fos-IR cells (Figure 5b’, b’’, black circles; comparing sham groups, p = 0.5273) indicating that 0.1 mg/kg CLZ alone (in the absence of DREADD expression) did not alter Fos expression or expression of the learned threat response behavior. Thus, activity of BLA-projecting cholinergic neurons is required during both training and recall for recall induction of Fos expression in BLA neurons and freezing behavior. Preventing cholinergic neuron activity during either training or recall significantly reduced the density of Fos+ BLA neurons and tone-induced freezing.

Differences in recall-induced Fos expression between sham and cav.hM4Di^BLA mice were maximal in rostral portions of the BLA (between bregma −0.8 and −1.4 mm) (Figure 5—figure supplement 1B). This region of the rostral BLA has been shown to contain genetically distinguishable neurons that are activated by aversive stimuli and preferentially project to the capsular portion of the central amygdala (CeC), a region known to drive freezing behavior (Kim et al., 2016; Kim et al., 2017). We examined the CeC of mice in which BLA-projecting BFCNs were silenced during recall and found significantly reduced Fos+ cell density in cav.hM4Di^BLA mice compared to control mice (Figure 5—figure supplement 1C sham vs. cav.hM4Di^BLA p = 0.0091). Thus, silencing cholinergic input to the BLA altered activation of BLA circuits involved in the execution of defensive behaviors.

Mapping BLA-projecting BFCNs infected by CAV₂-DIO-hM4Di revealed that the majority of the cholinergic input to the BLA originates in the NBM/SI_p (Figure 5A). As such, we delivered AAV₉-DIO-hM4Di.mCherry or AAV₉-DIO-eCFP (sham mice) into the NBM/SI_p of Chat-IRES-Cre mice (Figure 5—figure supplement 2). Both hM4Di and eCFP animals were injected with CLZ 10 min prior to the recall session. Animals in which NBM/SI_p cholinergic neurons were silenced during the recall session did not show increased freezing in response to tone (Figure 5—figure supplement 2A, sham, gray boxes: Pre-Tone to Recall Tone Response, p = 0.0004; cav.hM4Di^NBM, red boxes: Pre-Tone to Recall Tone Response, p > 0.9999). Thus, silencing NBM/SI_p BFCNs was sufficient to block expression of the learned threat response behavior.

Recall-induced activation of NBM/SI_p cholinergic neurons correlates with the degree of threat response behavior

During recall, we observed variability in individual freezing responses to the conditioned tone. Based on their responsiveness, we stratified the mice into two groups – high and low responders. ‘High Responders’ were defined as mice who showed a >10 percentage points increase in time spent freezing in response to the tone compared to the pre-tone period (see methods for further details). Mice with <10 percentage points increase in time spent freezing in response to the tone compared to the pre-tone period were defined as ‘Low Responders.’ When stratified as high or low responders according to this criterion, only High Responders showed a statistically significant increase in freezing during the recall tone compared to the pre-tone period (Figure 6A; Pre-tone vs. tone: High Responders, p = 0.0016; Low Responders, p > 0.9999). High Responders showed more freezing compared to Low Responders specifically during the recall tone presentation (High vs. Low responders: recall tone blue shading, p = 0. 0454). ‘High Responders’ spent more time freezing in response to the tone compared to the pre-tone period (Figure 6B).

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We next examined whether there was a relationship between the extent of freezing and the engagement of the cholinergic neurons. Since the majority of training-activated cholinergic neurons were reactivated during recall (in high responding mice −~82%, Figure 6—figure supplement 1A), we labeled cholinergic neurons activated during the recall session with ADCD-mCherry (on dox during training, off dox during recall; Figure 6C). Next, we quantified the fold change in the number of mCherry+ neurons in each group relative to corresponding home cage control mice (Figure 6D). While there was no difference in mCherry expression in Low Responders compared to the home cage group (fold change ~1, p > 0.9999), High Responders displayed a threefold increase (p = 0.0121) in mCherry+ cells (High Responders vs. Low Responders, p = 0.0121, Figure 6D).

Mapping of recall-activated NBM/SI_p cholinergic neurons revealed that activated BFCNs in ‘High Responder’ mice were in anatomically distinct regions from those in ‘Low Responder’ mice (Figure 6E). In a different cohort of ‘wild-type’ mice, we assessed Fos and ChAT expression following recall and found that in the Low Responders, few ChAT and Fos co-labeled neurons were found. These co-labeled cells were located in caudal regions of the NBM/SI_p (~Bregma −1.3; Figure 6—figure supplement 2A – bottom row). In High Responders an additional population of activated cholinergic neurons in more rostral portions of the NBM/SI_p was found (~Bregma −0.8; Figure 6—figure supplement 2A – top row). Thus, a discrete population of activated cholinergic neurons in the rostral NBM/SI_p is present in mice that respond to the learned threat. When comparing retrograde mapping of BLA-projecting cholinergic neurons using CAV₂-DIO-hM4Di.mCherry (Figure 5) to the distribution of ADCD-mCherry-labeled activated neurons (Figure 6), we find a similar distribution along the rostro-caudal axis of the NBM/SI_p (Figure 6—figure supplement 2B, C).

Finally, we examined the proportion of high and low responding mice in our experiments where we silenced BLA-projecting cholinergic neurons either during training or during recall (Figure 5B, C). We found that under sham conditions (no cholinergic silencing), 80–90% of the mice were ‘High Responders’. Silencing BLA-projecting cholinergic neurons during training shifted the proportion such that 100% of the mice were ‘Low Responders’ (Figure 6—figure supplement 1B sham vs. cav.hM4Di^BLA inhibition during training). Silencing BLA-projecting cholinergic neurons during recall resulted in ~50% of the mice being ‘Low Responders’ (Figure 6—figure supplement 1B, sham vs. cav.hM4Di^BLA inhibition during recall). Thus, silencing BLA-projecting cholinergic neurons only during recall resulted in an all-or-none behavioral phenotype (50:50 chance of becoming a High or Low Responder).

Cholinergic neurons activated during threat memory recall have altered intrinsic excitability

Changes in excitability of neurons have been consistently associated with the threat memory engram (Zhang and Linden, 2003; Zhou et al., 2009; Cai et al., 2016; Rashid et al., 2016; Pignatelli et al., 2019). We asked whether cholinergic neurons activated during memory recall differed in their intrinsic excitability compared to non-activated cholinergic neurons. To do this, we prepared acute brain slices from Fos-tTA/shGFP mice for electrophysiological recording of activated (Fos−GFP+) and non-activated (Fos−GFP−) NBM/SI_p neurons two and a half hours after the recall session or from mice that remained in their home cage. Cholinergic identity was verified post-recording by single-cell reverse transcriptase polymerase chain reaction (scRT-PCR) of each recorded cell (Figure 7A).

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Cholinergic neurons that were Fos+ following the recall session differed significantly from Fos− cholinergic neurons (Figure 7B, C) and from cholinergic neurons from home cage mice. Properties that showed significant differences included: action potential (AP) half-width, rheobase, and maximum firing rate (Figure 7D; half-width: HC vs. Fos−shGFP+ p = 0.0006, Fos−shGFP− vs. Fos−shGFP+ p = 0.021; Figure 7E; rheobase: Fos−shGFP− vs. Fos−shGFP+ p = 0.023; Figure 7F; max firing rate: HC vs. Fos−shGFP+ p = 0.003, Fos−shGFP− vs. Fos−shGFP+ p = 0.0034) as well as latency to fire (Figure 7—figure supplement 1E; latency: HC vs. Fos−shGFP+ p = 0.0062) and afterhyperpolarization (AHP) amplitude (Figure 7—figure supplement 1F, HC vs. Fos−shGFP+ p = 0.0041). Resting membrane potential, AP amplitude, AP threshold, and AHP half-width did not differ (Figure 7—figure supplement 1A–D).

We also compared the firing rate of cholinergic neurons in home cage mice with those expressing Fos two and a half hours after training or at longer intervals following recall (measured 2.5 hr (Fos−shGFP) and at 3 and 5 days (ADCD labeling during recall) after the recall session, Figure 7—figure supplement 1G). We found no differences in firing rate between home cage cholinergic neurons and cholinergic neurons that expressed Fos after training: that is, the change in firing rate was only seen in cholinergic neurons activated during recall. This increase in maximal firing rate seen after recall returned to baseline within 3–5 days (compared to recall D0, p < 0.05 for all).

Distinct subsets of BLA-projecting cholinergic neurons differentially contribute to learned vs. innate threat processing

Given the importance of BFCNs in a learned threat paradigm, we next asked whether these cells participate in innate threat responses as well. We stimulated an innate threat response by exposing Fos-tTA/shGFP mice to predator odor (mountain lion urine; Figure 8A; Blanchard and Blanchard, 1990). Exposed mice increased active and passive defensive behaviors compared to mice exposed to a saline wetted pad, including freezing (Figure 8A, p = 0.028), avoidance (Figure 8—figure supplement 1B, left, p = 0.0012), and defensive digging (Figure 8—figure supplement 1B, right, p = 0.023).

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We quantified the number of cholinergic neurons expressing Fos (Fos−shGFP+) after saline or predator odor exposure (Figure 8B/Figure 8—figure supplement 1A; Fos−shGFP+/ChAT+). The number of Fos−shGFP expressing cholinergic neurons was significantly elevated in the predator odor exposed group in the VP/SI_a (Figure 8B, Figure 8—figure supplement 1A – middle row, p = 0.0023), but not NBM/SI_p (Figure 8—figure supplement 1A – bottom row, p = 0.4441), or the hDB (Figure 8—figure supplement 1A – top row, p = 0.2465).

VP/SI_a cholinergic neurons formed the second largest source of cholinergic input to the BLA in our retrograde mapping experiments (Figure 5A). Since VP/SI_a cholinergic neurons were found to be activated during predator odor exposure, rather than NBM/SI_p or hDB cholinergic neurons, we asked if the BLA-projecting pool of VP/SI_a cholinergic neurons was activated by predator odor exposure. We injected the retrograde tracer FB into the BLA of Fos-tTA/shGFP mice and then exposed them to either saline (control) or predator odor (Figure 8C, left). FB labeled approximately 30% of ChAT-IR neurons located in the VP/SI_a (data not shown). Nearly, the entire subset of BLA-projecting VP/SI_a cholinergic neurons (median 94% ± Std.dev 12.5) were also GFP+ (Figure 8C, right).

To determine whether activity of these BLA-projecting cholinergic neurons was necessary for mice to freeze in response to predator odor, we used CAV₂-DIO-hM4Di to silence BLA-projecting cholinergic neurons. Silencing during predator odor exposure resulted in significantly less freezing compared to sham mice (Figure 8D, sham vs. cav.hM4Di^BLA p = 0.019). Other measures of active avoidance of the predator odor were not significantly altered by silencing BLA-projecting cholinergic neurons (Figure 8—figure supplement 1C; avoidance p = 0.8485; defensive digging p = 0.0714). These data support the conclusion that activity of BLA-projecting cholinergic neurons is critical for normal freezing behavior in response to innate threat. Taken together, we find that distinct populations of BLA-projecting BFCNs are involved in associative threat learning and the response to innately threatening stimuli.

A small number of sparsely distributed cholinergic neurons in the basal forebrain provide extensive innervation to most of the brain. These cholinergic neurons and their network of axonal terminal fields play a critical role in modulating cognitive processes (Ballinger et al., 2016; Záborszky et al., 2018; Ananth et al., 2023).

To begin addressing whether the cholinergic system encodes stimulus-specific information, or whether it is generally recruited with salient experiences we monitored ACh release in the BLA during threat learning and retrieval. We anatomically mapped and electrophysiologically characterized behaviorally relevant BFCNs, and then investigated the contribution of different subsets of BFCNs to threat response behaviors. Taken together, our results demonstrate distinct populations of cholinergic neurons that are an integral part of encoding a learned threat memory or contribute to innate threat responses.

Source data for the fiber photometry experiments presented in Figure 1 and supplements are provided as individual source data files. Code for fiber photometry data was previously published in Crouse et al., 2020.

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Author details

1. Prithviraj Rajebhosale

   National Institute of Neurological Disorders and Stroke, NIH, Bethesda, United States

   Contribution

   Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing – review and editing

   Contributed equally with

   Mala R Ananth

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9893-3025

2. Mala R Ananth

   National Institute of Neurological Disorders and Stroke, NIH, Bethesda, United States

   Contribution

   Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing – review and editing

   Contributed equally with

   Prithviraj Rajebhosale

   Competing interests

   No competing interests declared

3. Ronald Kim

   National Institute of Neurological Disorders and Stroke, NIH, Bethesda, United States

   Contribution

   Data curation, Investigation, Methodology

   Competing interests

   No competing interests declared

4. Richard Crouse

   Yale Interdepartmental Neuroscience Program, Yale University, New Haven, United States

   Present address

   Office of New Haven Affairs, Yale University, New Haven, United States

   Contribution

   Data curation, Software, Formal analysis, Validation, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9509-9263

5. Li Jiang

   National Institute of Neurological Disorders and Stroke, NIH, Bethesda, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

6. Gretchen López-Hernández

   Kansas City University of Medicine and Biosciences, Kansas City, United States

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

7. Chongbo Zhong

   National Institute of Neurological Disorders and Stroke, NIH, Bethesda, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

8. Christian Arty

   Stony Brook University, Stony Brook, United States

   Present address

   LinkedIn Corporation, Sunnyvale, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

9. Shaohua Wang

   National Institute of Environmental Health Sciences, Durham, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

10. Alice Jone

    Program in Neuroscience, Stony Brook University, Stony Brook, United States

    Present address

    Regulatory Affairs Division, STERIS Corporation, Mentor, United States

    Contribution

    Investigation

    Competing interests

    No competing interests declared

11. Niraj S Desai

    National Institute of Neurological Disorders and Stroke, NIH, Bethesda, United States

    Contribution

    Investigation

    Competing interests

    No competing interests declared

12. Yulong Li

    1. State Key Laboratory of Membrane Biology, Peking University School of Life Sciences, Beijing, China
    2. PKU-IDG/McGovern Institute for Brain Research, Beijing, China
    3. Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China

    Contribution

    Resources, Methodology

    Competing interests

    No competing interests declared

13. Marina R Picciotto

    1. Yale Interdepartmental Neuroscience Program, Yale University, New Haven, United States
    2. Department of Psychiatry, Yale University, New Haven, United States

    Contribution

    Supervision, Funding acquisition, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-4404-1280

14. Lorna W Role

    National Institute of Neurological Disorders and Stroke, NIH, Bethesda, United States

    Contribution

    Conceptualization, Supervision, Funding acquisition, Methodology, Project administration, Writing – review and editing

    Contributed equally with

    David A Talmage

    For correspondence

    Lorna.Role@nih.gov

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-5851-212X

15. David A Talmage

    National Institute of Neurological Disorders and Stroke, NIH, Bethesda, United States

    Contribution

    Conceptualization, Supervision, Funding acquisition, Methodology, Project administration, Writing – review and editing

    Contributed equally with

    Lorna W Role

    For correspondence

    david.talmage@nih.gov

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-4627-3007

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