To explore the circuit motifs (Tye, 2018) and computational implementation (Lockwood et al., 2020) through which the DRN^DAT system might operate, we examined whether discrete DRN^DAT projections underlie distinct features of behavior. Prominent DRN^DAT projections were identified by quantifying downstream fluorescence following Cre-dependent expression of eYFP in DAT^IREScre (B6.SJL-^{Slc6a3tm1.1(cre)Bkmn/J}) mice (Matthews et al., 2016; Bäckman et al., 2006; Cardozo Pinto et al., 2019; Lammel et al., 2015; Figure 1—figure supplement 1). We observed a distinct pattern of innervation arising from the ventral tegmental area (VTA)^DAT and DRN^DAT subpopulations (Figure 1A–D), with DRN^DAT projections most densely targeting the oval nucleus of the BNST (ovBNST) and lateral nucleus of the central amygdala (CeL). We also observed weaker, but significant, input to the posterior part of the basolateral amygdala (BLP), consistent with previous tracing studies (Lin et al., 2020; Cardozo Pinto et al., 2019; Hasue and Shammah-Lagnado, 2002; Meloni et al., 2006; Oh et al., 2014). Given that the extended amygdala and basolateral amygdala complex have been implicated in aversion- (Davis et al., 2010; Goode and Maren, 2017; Janak and Tye, 2015; Lebow and Chen, 2016) and reward-related processes (Namburi et al., 2015; Douglass et al., 2017; Jennings et al., 2013; Kim et al., 2017; Tye et al., 2008; Tye et al., 2010; Bayless et al., 2023), and connect with hindbrain motor nuclei to elicit autonomic and behavioral changes, we focused on these DRN^DAT projections (Figure 1D).

We next considered the anatomical organization of these projections to determine whether form gives rise to function. In other words, we investigated whether DRN^DAT outputs exhibit a circuit arrangement that facilitates a coordinated behavioral response. Axonal collateralization is one circuit feature that facilitates coordinated activity across broadly distributed structures (Rockland, 2018). Although VTA^DAT projections to striatal and cortical regions typically show little evidence of collateralization (Aransay et al., 2015; Beier et al., 2015; Lerner et al., 2015; Matsuda et al., 2009; Moore and Bloom, 1978), in contrast, DRN serotonergic neurons collateralize heavily to innervate the prefrontal cortex, striatum, midbrain, and amygdala (Gagnon and Parent, 2014; van der Kooy and Hattori, 1980; Waselus et al., 2011). However, it has yet to be determined whether DRN^DAT neurons are endowed with this property.

To assess whether DRN^DAT neurons exhibit axon collaterals, we performed dual retrograde tracing with fluorophore-conjugated cholera toxin subunit B (CTB; Beyeler et al., 2018). We injected each tracer into two of the three downstream sites (BNST, CeA, and/or BLP; Figure 1E, F, Figure 1—figure supplement 2A–C) and, after 7 days for retrograde transport, we examined CTB-expressing cells in the DRN that were co-labeled with tyrosine hydroxylase (TH; Figure 1G). CTB injections into the BNST and CeA resulted in numerous TH + cells labeled with both CTB-conjugated fluorophores, but fewer dual-labeled cells were observed when injections were placed in the BNST and BLP, or CeA and BLP (Figure 1H, Figure 1—figure supplement 2D, E). These data suggest significant collateralization to the extended amygdala, which includes the BNST and CeA (Lin et al., 2020; Janak and Tye, 2015). To confirm the presence of axon collaterals, we employed an intersectional viral strategy to selectively label CeA-projecting DRN^DAT neurons with cytoplasmic eYFP (Figure 1—figure supplement 2F, G). This resulted in eYFP-expressing terminals both in the CeA and in the BNST (Figure 1—figure supplement 2H, I).

We next considered whether DRN^DAT projections to the BNST, CeA, and BLP play separable or overlapping functional roles in modulating behavior. VTA dopaminergic input to the BNST and CeA has been implicated in threat discrimination (De Bundel et al., 2016; Jo et al., 2018), anxiety-related behavior (de la Mora et al., 2012), and drug-induced reward (Eiler et al., 2003; Epping-Jordan et al., 1998; Rezayof et al., 2002; Thiel et al., 2010), while in the BLA complex, dopamine signaling supports both fear (Bissière et al., 2003; Fadok et al., 2009; Guarraci et al., 1999; de Oliveira et al., 2011) and appetitive learning (Tye et al., 2010; Lutas et al., 2019). However, the question remains: do the same DRN^DAT projection neurons mediate different facets of a loneliness-like state, such as aversion, vigilance, and social motivation?

To test the hypothesis that distinct DRN^DAT projections promote sociability, vigilance, and place avoidance (Matthews et al., 2016), we performed projection-specific ChR2-mediated photostimulation. We injected an AAV enabling Cre-dependent expression of ChR2 into the DRN of DAT::Cre male mice and implanted optic fibers over the BNST, CeA, or BLP (Figure 2A, Figure 2—figure supplement 1A–F). Given that we previously observed that behavioral effects of DRN^DAT photostimulation were predicted by an animal’s social rank (Matthews et al., 2016), we also assessed relative social dominance using the tube test (Lindzey et al., 1961; Wang et al., 2011; Zhou et al., 2018b) prior to behavioral assays and photostimulation (Figure 2A, Figure 2—figure supplement 1G, H).

We first assessed whether photostimulation was sufficient to support place preference using the real-time place-preference (RTPP) assay. Here, we found that photostimulation of the DRN^DAT-BLP projection, but not the projection to the BNST or CeA, produced avoidance of the stimulation zone, relative to eYFP controls (Figure 2B–G). However, we did not find a significant correlation between social dominance and the magnitude of this effect (Figure 2H–J). Importantly, we did not detect an effect of photostimulation of DRN^DAT projections on operant intracranial self-stimulation (Figure 2—figure supplement 2).

Next, we considered whether DRN^DAT projections to the BNST, CeA, or BLP play a role in increasing vigilance, a common behavioral marker in individuals experiencing loneliness (Cacioppo et al., 2016; Cacioppo and Hawkley, 2009). To assess how projection-specific photostimulation of DRN^DAT terminals affected exploratory behavior, we used the open field test (OFT) and elevated plus maze (EPM). While we found no effect of optical stimulation of DRN^DAT terminals on locomotion or time in center in the OFT (Figure 3—figure supplement 1), we found stimulation of DRN^DAT terminals in the BNST (but not in the CeA or BLP) resulted in a weak trend toward increased time spent in the open arm of the EPM (Figure 3A–C), which can be interpreted as exploratory behavior linked with a vigilant state (Rodgers and Dalvi, 1997). However, we found no correlation between social dominance and open arm time (Figure 3D–F). Strikingly, during social interaction with a novel juvenile in the home cage, we found that photoactivation of the DRN^DAT-BNST projection increased rearing behavior (a form of nonsocial exploration Bailey and Crawley, 2009; Lever et al., 2006; Figure 3G–L), an effect that was not previously observed with cell body photostimulation (Matthews et al., 2016). However, we did not find a significant correlation between social dominance and the expression of optically induced rearing behavior (Figure 3J–L).

To assess how projection-specific photostimulation of DRN^DAT terminals affected social preference, we used the three-chamber sociability task (Moy et al., 2004), where group-housed mice freely explored a chamber containing a novel juvenile mouse and a novel object at opposite ends (Figure 4A–C). This revealed that optical stimulation of the DRN^DAT-CeA projection increased social preference, but no significant effect was observed with photostimulation of either the DRN^DAT-BNST or DRN^DAT-BLP projections (Figure 4D–F; Figure 4—figure supplement 1). Furthermore, we found that the optically induced change in social preference in DRN^DAT-CeA mice was positively correlated with social dominance, suggesting that photostimulation elicited a greater increase in sociability in dominant mice (Figure 4G–I). This emulates the previous association found with photostimulation at the cell body level and social dominance (Matthews et al., 2016).

Next, to gain further insight into the functional divergence of DRN^DAT projections in ethological behaviors, we assessed the effects of photostimulation on social interaction with a novel juvenile in the home cage. Here, photoactivation of the DRN^DAT-CeA projection modestly increased face sniffing of the juvenile mouse, consistent with a pro-social role for this projection (Figure 4J–L), although no correlation between optically induced change in face sniffing and social dominance was observed (Figure 4M–O). When we plotted the difference score (ON-OFF) for face sniffing against rearing (ON-OFF; Figure 4—figure supplement 2A–C), we observed that DRN^DAT-BNST mice tended to engage in more rearing and less face sniffing during photostimulation (i.e. located in the upper left quadrant), whereas DRN^DAT-CeA mice tended to exhibit less rearing and more face sniffing during photostimulation (i.e. located in the lower right quadrant).

To explore the relationship between social dominance and baseline behavioral profile, we applied a data-driven approach by examining behavioral measures obtained from different assays in a correlation matrix (Figure 4—figure supplement 2D). This showed a weak, negative correlation between social dominance and open arm time in the elevated plus maze (EPM) – consistent with a previous report of higher trait anxiety in dominant mice (Larrieu et al., 2017). However, social dominance did not correlate significantly with any other behavioral variable. Additionally, our analysis of baseline behavioral profile revealed a robust negative correlation between the time spent engaged in social sniffing and time spent rearing (Figure 4—figure supplement 2D). Furthermore, following dimensionality reduction on baseline behavioral variables, we did not find clearly differentiated clusters of high- and low-ranked mice (Figure 4—figure supplement 2E), suggesting that the variation governing these latent behavioral features is not related to social rank.

Finally, to determine whether DRN^DAT-CeA photostimulation affected the probability of behavioral state transition (Füzesi et al., 2016; Lee et al., 2019), we examined the sequential structure of behavior using a First-order Markov model (Lee et al., 2019; Tejada et al., 2010). Considering a two-state model consisting of ‘social’ and ‘nonsocial’ behaviors (Figure 4P), we found that photostimulation in DRN^DAT-CeA mice did not significantly change the probability of transitioning within social and nonsocial state (Figure 4Q), but did significantly change the probability of transitioning between social and nonsocial states (Figure 4R). This suggests that the DRN^DAT-CeA projection may increase engagement in social behavior by altering the overall structure of behavioral transitions.

Our data suggest that DRN^DAT projections exert divergent effects over behavior, despite substantial overlap in their upstream cells of origin. Given this overlap, we reasoned that one mechanism through which these projections might achieve distinct behavioral effects is via differential recruitment of downstream signaling pathways. We, therefore, next considered whether the pattern of receptor expression differed within the DRN^DAT terminal field of these downstream regions.

Subsets of DRN^DAT neurons co-express vasoactive intestinal peptide (VIP) and neuropeptide-W (NPW; Dougalis et al., 2012; Huang et al., 2019; Motoike et al., 2016), and so we examined both dopamine (Drd1 and Drd2) and neuropeptide (Vipr2 and Npbwr1) receptor expression within DRN^DAT terminal fields. To achieve this, we performed single molecule fluorescence in situ hybridization (smFISH) using RNAscope (Figure 5—figure supplement 1A, B). In the BNST and CeA, we observed a strikingly similar pattern of receptor expression with dense neuropeptide receptor expression in the oval BNST and ventromedial CeL, and a high degree of co-localization (Figure 5A–H, Figure 5—figure supplement 1C–H). In the BNST and CeA subregions containing the highest density of DRN^DAT terminals, dopamine receptor expression was relatively more sparse, with Drd2 more abundant than Drd1, as previously described (Kim et al., 2017; De Bundel et al., 2016; de la Mora et al., 2012; McCullough et al., 2018a; McCullough et al., 2018b; Figure 5A–H). The DRN^DAT terminal field of the BLP displayed a markedly different receptor expression pattern, dominated by Drd1 (Figure 5I–L, Figure 5—figure supplement 1I–K), consistent with previous reports (de la Mora et al., 2012; Lutas et al., 2019; McCullough et al., 2018a). Thus, in contrast to the BNST and CeA, the effects of DRN^DAT input to the BLP may be predominantly mediated via D₁-receptor signaling. Collectively, this expression pattern suggests that the dopamine- and neuropeptide-mediated effects of DRN^DAT input may be spatially segregated within downstream regions, providing the infrastructure for divergent modulation of cellular subsets.

Our data suggest that DRN^DAT projections exert divergent effects over behavior, despite substantial overlap in their upstream cells of origin. One mechanism through which these projections might achieve distinct behavioral effects is via differential modulation of activity in downstream neurons. The multi-transmitter phenotype of DRN^DAT neurons (Dougalis et al., 2012; Huang et al., 2019; Dougalis et al., 2017; Poulin et al., 2018), regionally distinct downstream receptor expression, and the observed pre- and post-synaptic actions of exogenously applied dopamine (Kash et al., 2008; Krawczyk et al., 2011; Kröner et al., 2005; Marowsky et al., 2005; Naylor et al., 2010; Rosenkranz and Grace, 1999; Rosenkranz and Grace, 2002; Silberman and Winder, 2013) provide optimal conditions for diverse modulation of neural activity. However, it remains unknown how temporally precise activation of DRN^DAT terminals influences excitability at the single-cell level.

We, therefore, next examined how DRN^DAT input affects downstream excitability. To achieve this, we expressed ChR2 in DRN^DAT neurons and used ex vivo electrophysiology to record from downstream neurons (Figure 6A–C, Figure 6—figure supplement 1A–C). Optical stimulation at the resting membrane potential evoked both excitatory and inhibitory post-synaptic potentials (EPSPs and IPSPs) in downstream cells (Figure 6D–F), which were typically monosynaptic (Figure 6—figure supplement 1D, E). During spontaneous firing, BNST cells were universally excited, whereas more diverse responses were observed with the BLP and CeA (Figure 6G–K, Figure 6—figure supplement 1F–G). The fast rise and decay kinetics of the EPSP suggest an AMPAR-mediated potential, resulting from glutamate co-release (Li et al., 2016; Matthews et al., 2016), whereas the slow IPSP kinetics are consistent with opening of GIRK channels, which can occur via D₂-receptor (Beckstead et al., 2004; Marcott et al., 2018) or GABA-_B receptor signaling (Bettler et al., 2004; Destexhe and Sejnowski, 1995; Mackay et al., 2019).

Given the diversity of responses observed in the CeA and BLP, we next examined these downstream cells in more detail. To assess whether baseline electrophysiological properties predicted the optically evoked response, we used unsupervised agglomerative hierarchical clustering to classify downstream cells (Figure 6L, M). This established approach has been successfully applied to electrophysiological datasets to reveal distinct neuronal subclasses (Cauli et al., 2000; Guthman et al., 2020; Hou et al., 2016). The resulting dendrograms yielded two major clusters in the CeA and BLP, with distinct electrophysiological characteristics (Figure 6N–Q, Figure 6—figure supplement 1H–K). CeA cells in cluster 1 represented ‘late-firing’ neurons, whereas cluster 2 was typical of ‘regular-firing’ neurons (Chieng et al., 2006; Dumont et al., 2002; Lopez de Armentia and Sah, 2004). Strikingly, these clusters exhibited dramatically different responses to DRN^DAT photostimulation, with cluster 1 ‘late-firing’ neurons excited and cluster 2 ‘regular-firing’ neurons mostly inhibited (Figure 6O). Similarly, BLP cells delineated into two major clusters, with properties characteristic of pyramidal neurons (cluster 1) and GABAergic interneurons (cluster 2; Figure 6P, Q). These clusters showed remarkably different responses to DRN^DAT input, with 93% of putative pyramidal neurons showing an inhibitory response, and 62% of putative GABAergic interneurons showing an excitatory response (Figure 6Q). In addition, clustering CeA and BLP cells together yielded a very similar result (Figure 6—figure supplement 1L–N). Thus, while photoactivation of DRN^DAT terminals elicits heterogeneous responses in downstream neurons, baseline cell properties strongly predict their response, suggesting robust synaptic organization. The opposing nature of these responses, in different neuronal subsets, suggests that – rather than inducing an overall augmentation or suppression of activity – DRN^DAT input may adjust the pattern of downstream activity, in order to exert a functional shift in behavior.

Our data thus far suggest that photostimulation of DRN^DAT projections to downstream extended amygdala targets elicits divergent behaviors that are, together, congruent with a loneliness-like state, with the DRN^DAT-CeA projection promoting sociability. Considering the diversity of responses in the CeA elicited by DRN^DAT input ex vivo, we next wondered how DRN^DAT input into the CeA in vivo during a behaviorally relevant task may modify how the CeA represents social information. Neuromodulatory input has been previously shown to alter responses to salient stimuli—for instance, stimulation of VTA dopamine terminals increases the signal-to-noise ratio to aversive stimuli in projection-specific populations of prefrontal cortex neurons (Vander Weele et al., 2018). However, how DRN^DAT input modifies the coding scheme of CeA neurons for social information remains unknown.

Therefore, to test the hypothesis that DRN^DAT input alters the responses of CeA neurons to functionally relevant stimuli, we examined the dynamics of CeA neurons while simultaneously stimulating DRN^DAT terminals during a three-chamber sociability task. To achieve this, we expressed the calcium indicator GCaMP7f nonspecifically in the CeA and either the red-shifted opsin ChrimsonR or a control fluorophore (TdTomato) in the DRN of DAT::Cre mice, and additionally implanted a gradient index (GRIN) lens over the CeA (Figure 7A, Figure 7—figure supplement 1A). This allowed us to stimulate DRN^DAT terminals in the CeA while resolving single-cell calcium dynamics in the CeA in vivo (Figure 7B). We confirmed ex vivo that blue light delivery alone onto DRN^DAT terminals did not elicit a ChrimsonR-mediated postsynaptic potential in CeA neurons (Figure 7—figure supplement 1B–F), and that red light delivery was still capable of eliciting ChrimsonR-mediated EPSPs and IPSPs during continuous delivery of blue light (Figure 7—figure supplement 1G–I).

We then performed microendoscopic epifluorescent calcium imaging during the three-chamber sociability task where mice freely explored a chamber containing a novel juvenile mouse and a novel object at opposite ends. Given that social isolation produces changes in long-term potentiation of synapses onto DRN^DAT neurons (Matthews et al., 2016), we were limited to a single manipulation of social isolation for each mouse. We hypothesized that stimulation of DRN^DAT inputs to CeA would mimic a loneliness-like state, consistent with our ChR2 manipulations with group-housed mice. Thus, we compared three conditions —group-housed without DRN^DAT stimulation (GH off), group-housed with DRN^DAT stimulation (GH on), and 24 hr socially isolated without DRN^DAT stimulation (SI off; Figure 7C, D) to allow for within-subjects comparisons.

In contrast to the photostimulation experiments in Figure 4, here we aimed to investigate the impact of DRN^DAT neuron stimulation on neural dynamics within the CeA without inducing robust behavioral changes that could introduce sensorimotor confounds to changes in neural activity due to stimulation. We successfully optimized viral expression and illumination parameters to minimize changes in social preference with DRN^DAT-CeA with ChrimsonR to prioritize comparison of the neural dynamics (Figure 7E) and also did not observe any behavioral effects of illumination in TdTomato-expressing mice (Figure 7—figure supplement 1J, K).

We then aligned the recorded CeA calcium traces with social cup and object cup interactions and found a striking diversity of neuronal responses to these stimuli under the three experimental conditions (Figure 7—figure supplement 2A, B). We next determined the response strength of individual CeA neurons to either stimulus under the three conditions (Figure 7F, G) using an area under ROC curve-based approach (Kingsbury et al., 2019; Li et al., 2017) to determine responsiveness of CeA neurons to social and object stimuli (Figure 7—figure supplement 1M). At a single-cell level, we did not observe significant changes in CeA response strength or proportion of neurons significantly responding to social or object stimuli across the three conditions (Figure 7G, H). However, we did find a trend indicating stronger responses toward social stimuli compared to object stimuli in the GH on condition compared to the GH off condition in mice expressing ChrimsonR (Figure 7J), but not TdTomato (Figure 7—figure supplement 1L) in DRN^DAT neurons. Importantly, in co-registered neurons, we found little overlap between CeA neurons excited by the social stimulus in both GH on and GH off conditions (Figure 7I), suggesting that DRN^DAT terminal stimulation may recruit separate ensembles of CeA neurons to represent social stimuli. Considering the variability in social preference behavior across mice and the diverse effects of photostimulation depending on the mouse’s social history, we next considered the responses of CeA neurons to social and object stimuli on an animal-by-animal basis. While we did not observe significant changes in the proportion of excitatory or inhibitory responses to social or object stimuli across the three conditions (Figure 7K, L), we did find a significant positive correlation between the proportion of socially excited CeA neurons and social preference in the GH on condition, but not in the GH off or SI off conditions (Figure 7M). Importantly, we do not observe a correlation between social preference and object-excited CeA neurons (Figure 7—figure supplement 1N) or socially inhibited CeA neurons (Figure 7—figure supplement 1O). This result may suggest that DRN^DAT input in the CeA in a behaviorally relevant task allows for a functional shift in its dynamics that enables it to predict the amount of social preference the mouse exhibits.

Finally, considering that the DRN^DAT-CeA projection is sufficient in promoting sociability, we next assessed whether activity in the DRN^DAT-CeA projection is necessary for the rebound in sociability that occurs following acute social isolation (Matthews et al., 2016). We injected an AAV enabling Cre-dependent expression of NpHR into the DRN of DAT::Cre male mice and implanted optic fibers over the BNST, CeA, or BLP (Figure 8A). We allowed 7 weeks for adequate terminal expression, after which we inhibited DRN^DAT terminals in the BNST, CeA, and BLP while mice performed the three-chamber sociability task (Figure 8B). Inhibition of DRN^DAT terminals in downstream regions while mice were group-housed did not change social preference (Figure 8—figure supplement 1A, B). However, inhibition of DRN^DAT terminals in CeA, but not BNST or BLP, blocked the rebound in sociability associated with acute social isolation (Figure 8C). Additionally, we found that optically inhibited changes in social preference in DRN^DAT-CeA mice were negatively correlated with social dominance (Figure 8D), suggesting that the DRN^DAT-CeA projection is necessary for the expression of isolation-induced social rebound in a rank-dependent manner.

Our findings revealed several features of the DRN^DAT circuit which might facilitate a concerted response to novel social and non-social situations. Firstly, we observed dissociable roles for discrete downstream projections – a common motif of valence-encoding neural circuits (Tye, 2018). Biased recruitment of these ‘divergent paths’ Tye, 2018 to the BNST, CeA, and BLP by upstream inputs may serve to fine-tune the balance between social investigation and environmental exploration: facilitating behavioral flexibility with changing environmental conditions or internal state. Secondly, we demonstrate extensive collateralization of DRN^DAT neurons. In other populations, collateralization is proposed to aid temporal coordination of a multifaceted response: enabling synchronous activation of distributed regions (Rockland, 2018; Waselus et al., 2011). This feature may, therefore, facilitate coordinated recruitment of the BNST and CeA, allowing these regions to work in concert to promote social approach while also maintaining vigilance to salient environmental stimuli. Thirdly, we find precise synaptic organization in the DRN^DAT modulation of downstream neuronal activity that allows for qualitatively distinct response profiles of downstream targets. Combined with the spatially segregated downstream receptor expression pattern, this organization may allow DRN^DAT neurons to elicit broad, yet finely tuned, control over the pattern of neuronal activity, on multiple timescales—perhaps explaining the diverse behavior effects between the BNST- and CeA- projecting DRN^DAT populations, despite heavy collateralization.

Although we hypothesized that stimulating DRN^DAT inputs to the CeA in group-housed mice would mimic a state similar to that of isolation, we did not observe that the isolation OFF condition produced neural responses more similar to the group-housed ON condition. This suggests two possibilities: (1) that the photostimulation impacted neural activity beyond the endogenous dopamine innervation that may occur with social isolation because it is more potent of a change or (2) that the timing of endogenous dopamine innervation is different and is partially quenched upon exposure to a social agent. To completely understand the temporal dynamics of dopamine signaling with isolation and the firing of DRN^DAT neurons upon isolation, further experiments will require exploration of DRN^DAT stimulation parameter space, endogenous neural activity in the DRN^DAT-CeA circuit during social isolation, and the effects of DRN^DAT stimulation timing.

Our data support the hypothesis that separable DRN^DAT projections mediate distinct functional roles: a feature which has been previously observed in other neuronal circuits (e.g. Han et al., 2017; Kim et al., 2013; Kohl et al., 2018; Lammel et al., 2011; Namburi et al., 2015). The DRN^DAT circuit attributes we describe above may further enable this system to modulate other diverse forms of behavior (e.g. arousal Cho et al., 2017, fear/reward associations Lin et al., 2020; Groessl et al., 2018, and antinociception Li et al., 2016; Meyer et al., 2009; Yu et al., 2021). These could be mediated via other downstream projections and/or via these same projections under different environmental contexts, testing conditions, social histories, and/or internal states. Further work is required to determine how this system is able to exert a broad influence over multiple forms of behavior. Indeed, a recent study examined DRN^DAT projection to the nucleus accumbens and its role in promoting sociability (Choi et al., 2022), suggesting a parallel circuit to that described in the current study. Collectively, however, our data and others support a role for the DRN^DAT system in exerting a coordinated behavioral response to novel situations – both social and non-social.

The CeA has been implicated in mediating the response to threats – orchestrating defensive behavioral responses and autonomic changes via efferents to subcortical (Davis et al., 2010; Fadok et al., 2018; Gungor and Paré, 2016) and brainstem nuclei (Tovote et al., 2016). One possible interpretation, therefore, is that DRN^DAT input to the CeA suppresses fear-promoting neuronal ensembles in order to facilitate social approach. In the maintenance of social homeostasis, suppression of fear in the presence of social stimuli may represent an adaptive response – preventing salient social stimuli from being interpreted as a threat. Indeed, other need states, such as hunger, are associated with fear suppression and higher-risk behavior (Padilla et al., 2016), suggesting a conserved response to homeostatic imbalance (Matthews and Tye, 2019). However, the motivation to attend to social stimuli may also be driven by territorial defense (interacting with social rank), highlighting a need to further understand how internal states can play into the output of this system. A more comprehensive knowledge of the functional cell types modulated by DRN^DAT activity will facilitate our understanding of how this input can shape the downstream neuronal representation of social and non-social stimuli.

In contrast to the CeA, photoactivation of the DRN^DAT-BLP projection produced avoidance of the stimulation zone, suggesting an aversive state. This differs from the valence-independent role of VTA dopamine input to the greater BLA complex, wherein dopamine signaling gates synaptic plasticity for associative learning of both positive and negative valence (Tye et al., 2010) and responds to salient stimuli predicting both positive and negative outcomes (Lutas et al., 2019). However, DRN and VTA axonal fields differ within the BLA complex, with DRN^DAT terminals being more concentrated within the BLP, and VTA^DAT inputs traversing the LA, BLA, and intercalated cells more densely.

While there have been seemingly contradictory reports on the effect of dopamine on excitability in the BLA (Tye et al., 2010; Bissière et al., 2003; Lutas et al., 2019), our observations using photostimulation of DRN^DAT terminals (in short phasic bursts) are consistent with in vivo extracellular recordings combined with electrical stimulation of the midbrain (Rosenkranz and Grace, 1999). One unifying hypothesis is that dopamine induces an indirect GABA-mediated suppression of pyramidal neurons, which may attenuate their response to weak inputs, while directly exciting pyramidal neurons to augment their response to large inputs (Kröner et al., 2005; Rosenkranz and Grace, 1999). In this way, amygdala dopamine may underlie a similar role to cortical dopamine (Vander Weele et al., 2018; Gulledge and Jaffe, 2001) – enhancing signal-to-noise ratio to facilitate behavioral responses to salient stimuli (Vander Weele et al., 2018).

A similar complexity surrounds dopamine’s effects in the BNST. Photostimulation of DRN^DA-BNST projections has been shown to have antinociceptive effects on male mice upon formalin injection into the paw, whereas females do not show this antinociceptive effect and instead display contextual hyperlocomotion (Yu et al., 2021). Given that our study does not include females, there are likely behavioral and physiological sex differences that beg further investigation. Additionally, prior studies using optogenetic stimulation (Li et al., 2016; Yu et al., 2021) or exogenously applied dopamine (Krawczyk et al., 2011; Maracle et al., 2019) report a wide range of outcomes on BNST excitability, including a higher frequency of inhibitory responses that we observed. Notably, one study found that higher doses of exogenously applied dopamine decreased the amplitude of GABA_A-IPSC in ovBNST neurons (Krawczyk et al., 2011), potentially consistent with our results. Interestingly, animals given intermittent access to sucrose displayed a significant increase in GABA_A-IPSCs in ovBNST neurons following dopamine application (Maracle et al., 2019), raising the possibility that experience-dependent factors shape dopamine sensitivity in the BNST, paralleling how dopaminergic modulation in the cortex may be state-dependent (Vander Weele et al., 2018). Considering that our ex vivo recordings were performed in group-housed mice, future work should test whether social isolation alters ovBNST responses to dopamine, providing a framework for understanding how environmental states influence dopaminergic neuromodulatory tone.

DRN^DAT neurons possess an impressive repertoire of signaling molecules: alongside dopamine and glutamate subsets of DRN^DAT neuron express VIP and NPW (Dougalis et al., 2012; Huang et al., 2019; Motoike et al., 2016). While there is some partial segregation of VIP- and NPW-expressing neurons (Huang et al., 2019), our receptor expression analyses suggest that these neuropeptides converge on the same neurons in the BNST and CeA. This co-localization is intriguing, given that Vipr2 is typically coupled to the excitatory G_s-protein (White et al., 2010), while Npbwr1 is coupled to the inhibitory G_i-protein (Nagata-Kuroiwa et al., 2011; Tanaka et al., 2003). Therefore, signaling through these receptors may exert opposing actions on downstream cells. Recruitment of neuropeptidergic signaling pathways may support slower, sustained downstream modulation, for example, in hunger-mediating hypothalamic Agouti-Related Peptide (AgRP) neurons, neuropeptide co-release is essential for sustaining feeding behavior (Chen et al., 2019). Therefore, a delayed, persistent neuropeptide-mediated signal might enable downstream modulation to outlive phasic DRN^DAT activity: promoting behavioral adjustments over longer timescales.

While the functional role of these neuropeptides remains to be elucidated, studies on knockout mice suggest a role for NPW in social behavior and stress responding (Nagata-Kuroiwa et al., 2011; Motoike et al., 2016). Furthermore, in humans with a single-nucleotide polymorphism (SNP) of the NPBWR1 gene (which impairs receptor function), the perception of fearful/angry faces was more positive and less submissive (Watanabe et al., 2012), suggesting a possible role for NPW signaling in interpreting social signals. Similarly, the function of DRN VIP +neurons has received little attention in rodent models, but there has been more focus on the role of VIP in avian social behavior (Kingsbury and Wilson, 2016). Of particular interest, in the rostral arcopallium (a homolog of mammalian amygdala Reiner et al., 2004), VIP binding density is elevated in birds during seasonal flocking (Wilson et al., 2016). This suggests that elevated VIP receptor expression may encourage affiliative social grouping behavior in birds (Wilson et al., 2016). Thus, NPW and VIP may act in concert with fast glutamate-mediated and slow dopamine-mediated neurotransmission in the central extended amygdala to modulate behavior on different timescales.

Together, these findings suggest that NPW and VIP may act in concert with fast glutamate and slower dopamine signaling to shape social behavior across multiple timescales within the extended amygdala. Although we observed relatively low expression of Drd1 in the BNST and CeA compared to Vipr2 and Npbwr1, it remains possible that dopamine’s effects are mediated by other receptors, such as Drd3, Drd4, and Drd5—warranting further investigation.

Together, these findings reveal that DRN^DAT projections exhibit substantial functional specialization, with anatomically distinct pathways modulating different facets of behavior. The DRN^DAT-CeA projection promotes sociability, DRN^DAT-BLP input drives avoidance, and DRN^DAT-BNST enhances vigilant exploration, highlighting the diverse roles of this neural circuit in coordinating adaptive responses to social and environmental contexts. These findings uncover a circuit mechanism through which DRN^DAT projections orchestrate distinct behavioral features of a loneliness-like state, providing a framework for understanding how neuromodulatory systems guide complex social and emotional behaviors and suggesting potential targets for therapeutic intervention in affective disorders.

All procedures involving animals were conducted in accordance with NIH guidelines and approved by the MIT Committee on Animal Care or the Salk Institute Institutional Animal Care and Use Committee (IACUC protocol #18–00060). DAT^IREScre (B6.SJL-Slc6a3^{tm1.1(cre)Bkmn}/J) (Bäckman et al., 2006) mice were purchased from the Jackson Laboratory (stock no. 006660; the Jackson Laboratory, ME, USA) and bred in-house to generate heterozygous male offspring for experiments. Wild-type C57BL/6 J male mice were purchased from Charles River Laboratories (MA, USA). Mice were housed on a 12 hr:12 hr reverse light-dark cycle (MIT: lights off 9am-9pm; Salk Institute: lights off 9:30am-9:30pm) with food and water available ad libitum. Mice were housed in groups of 2–4 with same-sex siblings. For photoinhibition and CeA calcium imaging experiments, mice were additionally tested following 24 hr of social isolation. Only mice with acceptable histological placements were included in final datasets.

Mice (>7 weeks of age) were anesthetized with isoflurane (inhalation: 4% for induction,~2% for maintenance, oxygen flow rate 1 L/min) before being placed in a digital small animal stereotax (David Kopf Instruments, CA, USA). Surgeries were performed under aseptic conditions with body temperature maintained by a heating pad throughout. Injections of recombinant adeno-associated viral (AAV) vectors, herpes simplex virus (HSV), or cholera toxin subunit-B (CTB) were performed using a beveled 33-gauge microinjection needle with a 10 μL microsyringe (Nanofil; WPI, FL, USA). Virus or CTB was delivered at a rate of 0.1 μL/min using a microsyringe pump (UMP3; WPI, FL, USA) connected to a Micro4 controller (WPI, FL, USA). Following injection, the needle was maintained in place for ~2 min, then raised up by 0.05 mm and held for ~10 min (to permit diffusion from the injection site) before being slowly withdrawn. Skull measurements were made relative to Bregma for all injections and implants. Implants were secured to the skull by a layer of adhesive cement (C&B Metabond; Parkell Inc, NY, USA) followed by a layer of black cranioplastic cement (Ortho-Jet; Lang, IL, USA). Mice were given preemptive analgesia (1 mg/kg buprenorphine slow-release; subcutaneous; delivered concurrent with warmed Ringer’s solution to prevent dehydration), supplemented with meloxicam (1.5 mg/kg; subcutaneous) where necessary, and were monitored on a heating pad until recovery from anesthesia.

AAV₅-EF1α-DIO-ChR2-eYFP, AAV₅-EF1α-DIO-eYFP, AAV₅-EF1α-fDIO-eYFP, and AAV₉-Syn-ChrimsonR-tdTomato were packaged by the University of North Carolina Vector Core (NC, USA) and received the AAV₅-EF1a-fDIO-eYFP construct from Karl Deisseroth and Charu Ramakrishnan. HSV-LS1L-mCherry-IRES-flpo was packaged by Dr. Rachael Neve at the Viral Gene Transfer Core Facility at MIT (now located at Massachusetts General Hospital). AAV₅-EF1α-DIO-eNpHR3.0-eYFP and AAV₁-syn-jGCaMP7f were packaged by Addgene (MA, USA), and AAV₁-CAG-TdTomato was packaged by the UPenn vector core (PA, USA).

Mice were deeply anesthetized with sodium pentobarbital (200 mg/kg, intraperitoneal; IP) or euthasol (150 mg/kg; IP) followed by transcardial perfusion with 10 mL ice-cold Ringer’s solution and 15 mL ice-cold 4% paraformaldehyde (PFA). The brain was carefully dissected from the cranial cavity and immersed in 4% PFA for ~6–18 hr before transfer to 30% sucrose solution in phosphate-buffered saline (PBS) at 4 °C. After at least 48 hr, brains were sectioned at 40 µm on a freezing sliding microtome (HM430; Thermo Fisher Scientific, MA, USA) and sections stored at 4 °C in 1 X PBS. For immunohistochemistry, sections were blocked in PBS containing 0.3% Triton X-100 (PBS-T; Sigma-Aldrich, MO, USA) with 3% normal donkey serum (NDS; Jackson Immunoresearch, PA, USA) for 30–60 min at room temperature. This was followed by incubation in primary antibody solution chicken anti-TH (1:1000; AB9702; EMD Millipore, MA, USA) in 0.3% PBS-T with 3% NDS overnight at 4 °C. Sections were then washed in 1 X PBS four times (10 min each) before incubation in secondary antibody solution containing donkey anti-chicken 488 or 647 (1:1000; Jackson Immunoresearch, PA, USA) and a DNA-specific fluorescent probe (DAPI; 1:50,000; Invitrogen, Thermo Fisher Scientific, MA, USA) in 0.2% PBS-T with 3% NDS for 1.5–2 hr at room temperature. Sections were again washed four times in 1 X PBS (10 min each) before being mounted on glass slides and coverslipped using warmed PVA-DABCO (Sigma-Aldrich, MO, USA).

Images were captured on a laser scanning confocal microscope (Olympus FV1000, Olympus, PA, USA) using Fluoview software version 4.0 (Olympus, PA, USA). Images were collected through a 10 X/0.40 NA objective for injection site and optic fiber placement verification, a 20 X/0.75 objective for terminal fluorescence quantification, and an oil-immersion 40 X/1.30 NA objective for neurobiotin-filled neurons and RNAscope analysis (see individual Methods sections for more detail). FIJI Schindelin et al., 2012, CellProfiler 3.1 (Broad Institute, MA, USA) McQuin et al., 2018, and Adobe Photoshop CC (Adobe Systems Incorporated, CA, USA) were used for subsequent image processing and analysis.

In DAT::Cre mice, AAV₅-EF1α-DIO-ChR2-eYFP (300 nL) was injected into the DRN (ML:1.20, AP:–4.10, DV:–2.90; needle at a 20° angle from the midline, bevel facing medial) or VTA (ML:0.85, AP:–2.70, DV:–4.50), and after 8 weeks mice underwent perfusion-fixation. Brains were subsequently sectioned at 40 µm, processed with immunohistochemistry for TH and DAPI, and serial z-stack images (3 µm optical thickness) collected at 20 X on a confocal microscope. (See ‘Immunohistochemistry and confocal microscopy’ section above for details). A maximum projection was generated in FIJI and background subtraction based on the ‘rolling ball’ algorithm (radius = 50 pixels) was applied to correct for uneven illumination. The appropriate brain atlas slice Paxinos and Franklin, 2004; Paxinos and Franklin, 2019 was overlaid onto the fluorescent image using the BigWarp plugin (https://imagej.net/BigWarp) Bogovic et al., 2016 in FIJI, by designating major anatomical landmarks based on DAPI staining and TH expression. Regions of interest (ROIs) were then annotated from the overlaid atlas, and mean fluorescence within each ROI was quantified using FIJI. The PFC was examined from AP: 2.22–1.34, the striatum from AP 1.70–0.74, the BNST from AP 0.37 to –0.11, the CeA from AP –0.82 to –1.94, and the amygdala from AP –0.82 to –2.92. Average images in Figures 1D, 5A, E and I were created by aligning individual images (from the middle AP region of the BNST, CeA, or BLP), using the line ROI registration plugin (https://imagej.net/Align_Image_by_line_ROI) in FIJI. An average projection was then performed across all images and the ‘royal’ LUT applied to visualize relative fluorescence intensity.

C57BL/6 mice were injected with 150–250 nL CTB conjugated to Alexa Fluor-555 (CTB-555) or Alexa Fluor-647 (CTB-647; Molecular Probes, OR, USA Conte et al., 2009) in two of three locations: the BNST (ML:1.10, AP:0.50, DV: –4.30; needle bevel facing back), CeA (ML:2.85, AP: –1.20; DV:–4.75; needle bevel facing back), or BLP (ML:3.35, AP:–2.20, DV:–5.25; needle bevel facing back). To assess retrograde CTB co-expression following injection of both fluorophore-conjugates of CTB into the same region (Figure 1—figure supplement 2A–C), injections were either performed sequentially, or CTB-555 and CTB-647 were mixed prior to a single injection. After 7 days to allow for retrograde transport, mice were deeply anesthetized with sodium pentobarbital (200 mg/kg) and perfused-fixed for subsequent histology. Brain sections containing injection sites and the DRN were prepared at 40 µm and processed with immunohistochemistry for TH and DAPI. (See ‘Immunohistochemistry and confocal microscopy’ section above for details). CTB injection sites were verified with images acquired on a confocal microscope through a 10 X objective (serial z-stack with 5 µm optical thickness) and images of the DRN were acquired through a 40 X objective (serial z-stack with 3 µm optical thickness). DRN cells co-expressing CTB and TH were counted manually using the ROI ‘point’ tool in Fluoview software version 4.0 (Olympus, PA, USA). Counted files were imported into FIJI, and images overlaid onto the appropriate brain atlas image of the DRN using the BigWarp plugin (https://imagej.net/BigWarp; Bogovic et al., 2016). The x-y coordinates of counted/marked CTB+/TH + cells were extracted using the ‘Measure’ function in FIJI. These coordinates were then used to generate heatmaps of cell location (Figure 1—figure supplement 2D, E) by creating a 2D histogram using the Matplotlib package Hunter, 2007 in Python.

Intersectional labeling of the dopaminergic projection from the DRN to the CeA was achieved by injecting HSV-LS1L-mCherry-IRES-flpo (300 nL) into the CeA (ML:2.85, AP:–1.45, DV:–4.55; needle bevel facing medial) and AAV₅-fDIO-eYFP (300 nL) into the DRN (ML:1.20, AP:–4.10, DV:–2.90; needle at a 20° angle from the midline, bevel facing medial) of a DAT::Cre mouse. After 8 weeks, mice were perfused-fixed with 4% PFA, and the brain sectioned on a freezing microtome at 40 µm before immunohistochemical processing with TH and DAPI. Images of eYFP-expressing cells in the DRN and terminals in the CeA and BNST were captured on a confocal microscope through a 20 X objective with a serial z-section thickness of 3 µm.

DAT::Cre mice were injected with 300 nL AAV₅- EF1α-DIO-ChR2-eYFP or AAV₅-EF1α-DIO-eYFP in the DRN (ML:1.20; AP:–4.10; DV:–2.90; needle at a 20° angle from the right side, bevel facing medial) and optic fibers (300 µm core, NA = 0.37; Thorlabs, NJ, USA), held within a stainless steel ferrule (Precision Fiber Products, CA, USA), were implanted unilaterally or bilaterally over the BNST (unilateral: ML:1.10, AP:0.40, DV:–3.50; bilateral: ML:1.65, AP:0.40, DV:–3.35; 10° angle from midline), CeA (ML:2.85, AP:–1.35, DV:–4.00), or BLP (ML:3.30, AP:–2.20, DV:–4.30). Behavioral experiments commenced 7–8 weeks following surgery for each cohort of mice (18 in total). Mice were handled and habituated to patch cable connection once per day for at least 3 days before beginning optical manipulations. Behavioral testing was performed in a dimly-lit soundproofed room during the mice’s active dark phase (~10am-5pm). On each testing day, mice were given at least 1 hr to acclimate to the testing room before experiments began. For optical manipulations, optic fiber implants were connected to a patch cable via a ceramic sleeve (Precision Fiber Products, CA, USA), which itself was connected to a commutator (rotary joint; Doric, Québec, Canada) using an FC/PC adapter, to permit uninhibited movement. The commutator, in turn, was connected via a second patch cable (with FC/PC connectors) to a 473 nm diode-pumped solid-state (DPSS) laser (OEM Laser Systems, UT, USA). To control the output of the laser, a Master-8 pulse stimulator (AMPI, Israel) was used, and the light power set to 10 mW.

DAT::Cre mice received an injection of 300 nL AAV₅-DIO-ChR2-eYFP or AAV₉-FLEX-ChrimsonR-TdTomato in the DRN (ML:1.20, AP:–4.10, DV:–2.90; needle at a 20° angle from the midline, bevel facing medial), and after at least 8 weeks for transgene expression, mice were deeply anesthetized with sodium pentobarbital (200 mg/kg) or euthasol (150 mg/kg; IP). They were then transcardially perfused with ice-cold (~4 °C) modified artificial cerebrospinal fluid (ACSF; composition in mM: NaCl 87, KCl 2.5, NaH2PO4*H20 1.3, MgCl2*6H2O 7, NaHCO3 25, sucrose 75, ascorbate 5, CaCl2*2H2O 0.5, in ddH20; osmolarity 320–330 mOsm, pH 7.30–7.40), saturated with carbogen gas (95% oxygen, 5% carbon dioxide) before the brain was rapidly and carefully extracted from the cranial cavity. Thick coronal (300 µm) slices containing the BNST, CeA, BLP, and DRN were prepared on a vibrating blade vibratome (VT1200; Leica Biosystems, Germany), in ice-cold modified ACSF saturated with carbogen gas. Brain slices were hemisected with a scalpel blade before transfer to a holding chamber containing ACSF (composition in mM: NaCl 126, KCl 2.5, NaH2PO4*H20 1.25, MgCl2*6H2O 1, NaHCO3 26, glucose 10, CaCl2*H2O 2.4; osmolarity 298–302 mOsm, pH 7.30–7.40) saturated with carbogen, in a warm water bath (~30 °C).

Electrophysiological recordings were commenced after the slices had rested for at least 45 min. During recording, the brain slice was maintained in a bath with continuously perfused ACSF, saturated with carbogen, at 31 ± 1°C using a peristaltic pump (Minipuls3; Gilson, WI, USA). Slices were visualized through an upright microscope (Scientifica, UK) equipped with infrared-differential interference contrast (IR-DIC) optics and a Q-imaging Retiga Exi camera (Q Imaging, Canada). In the BNST, CeA, and BLP, recordings were performed in the region containing fluorescent DRN^DAT terminals (expressing ChR2-eYFP or Chrimson-TdTomato) with neurons visualized through a 40 X/0.80 NA water immersion objective. Terminal expression was confirmed by brief illumination from a 470 nm LED light source (pE-100; CoolLED, NY, USA) for ChR2-eYFP, or a metal halide lamp (Lumen 200; Prior Scientific Inc, UK), for ChrimsonR-TdTomato, combined with the appropriate filter set. Borosilicate glass capillaries were shaped on a P-97 puller (Sutter Instrument, CA, USA) to produce pipettes for recording that had resistance values of 3.5–5 MOhm when filled with internal solution composition in mM: potassium gluconate 125, NaCl 10, HEPES 20, MgATP 3, and 0.1% neurobiotin, in ddH20 (osmolarity 287 mOsm; pH 7.3). Whole-cell patch-clamp recordings were made using pClamp 10.4 software (Molecular Devices, CA, USA), with analog signals amplified using a Multiclamp 700B amplifier, filtered at 3 kHz, and digitized at 10 kHz using a Digidata 1550 (Molecular Devices, CA, USA). A 5 mV, 250ms hyperpolarizing step was used to monitor cell health throughout the experiment, and recordings were terminated if significant changes (>20%) occurred to series resistance (R_s), input resistance (R_in), or holding current.

Passive cell properties (capacitance, membrane resistance) were estimated from the current response to hyperpolarizing 5 mV, 250ms steps, delivered in voltage-clamp from a holding potential of –70 mV, using custom MATLAB code written by Praneeth Namburi, based on MATLAB implementation of the Q-Method (Novák and Zahradník, 2006). To examine the membrane potential response to current injection, cells were recorded in current-clamp mode, and a series of 1 s steps were delivered, in 20 pA increments, from –120 pA to 260 pA. The voltage sag amplitude (attributable to the hyperpolarization-activated cation current; I_h) was measured as the difference between the peak instantaneous and steady-state membrane potential elicited during a –120 pA step (see Figure 6L). The ramp ratio was calculated by dividing the average membrane potential between 900–1000ms by the membrane potential between 100–200ms following step onset, using the largest current step that elicited a subthreshold response (i.e. did not evoke action potentials). The firing delay was taken as the time between current step onset and the first elicited action potential, on delivery of the first current step that was elicited a suprathreshold response (i.e. rheobase current). The max instantaneous firing frequency (max freq._inst) was taken as the maximum firing frequency attained during the first 100ms of the depolarizing current steps.

To photostimulate ChR2-expressing DRN^DAT terminals in the BNST, CeA, and BLP, 470 nm light was delivered through the 40 X/0.8 NA objective from an LED light source (pE-100; CoolLED, NY, USA). Neurons were recorded at their resting membrane potential in current-clamp mode, and 470 nm light (8 pulses at 30 Hz, 5ms pulse width) was delivered every 30 s. In a minority of cells that showed spontaneous activity at the resting potential, negative current was injected to hold the cell at a subthreshold potential (typically ~–60 mV). The peak amplitude of the optically evoked excitatory post-synaptic potential (EPSP) or trough amplitude of the inhibitory post-synaptic potential (IPSP) was measured from the average trace using Clampfit 10.7 (Molecular Devices, CA, USA), using the 5 s prior to stimulation as baseline. Tau for the decay phase of the IPSP was estimated by fitting the IPSP with a single exponential, from the IPSP trough until return to baseline. Total voltage area was calculated from 0 to 5.5 s following the onset of the first light pulse. In cells where optical stimulation evoked only an EPSP, the response was classed as an ‘excitation’; only an IPSP was classed as an ‘inhibition’, and a combined optically evoked EPSP and IPSP was classed as ‘mixed’. To assess the effect of photostimulation on firing activity, constant positive current was injected to elicit consistent spontaneous action potentials, and 470 nm light (8 pulses at 30 Hz, 5ms pulse width) was delivered every 30 s. The interevent interval (IEI) between action potentials was calculated for 5 s before and 5 s after the first pulse of blue light using Clampfit 10.7 (Molecular Devices, CA, USA). A decrease in IEI (indicating an increase in firing rate) was classed as an ‘excitation’ and an increase in IEI (indicating a decrease in firing rate) was classed as an ‘inhibition’.

Following the recording, images showing the location of the recording pipette within the slice were captured through a 4 X/0.10 NA objective. Images were subsequently overlaid onto the appropriate brain atlas image (Paxinos and Franklin, 2004; Paxinos and Franklin, 2019), recorded cell locations were annotated, and then converted into x-y coordinates in FIJI. Python was used to generate a scatter plot of cell location, with points color-coded by the overall membrane potential response to photostimulation (Figure 6—figure supplement 1A–C).

Unsupervised agglomerative hierarchical clustering was used to classify cells according to their baseline electrophysiological properties. This approach organizes objects (in this case cells) into clusters, based on their similarity. The electrophysiological properties used as input features for clustering CeA cells were ramp ratio, max firing frequency, firing delay, and voltage sag, which are characteristics that have been previously shown to distinguish between subtypes of CeA neuron (Hou et al., 2016). For clustering BLP cells, we replaced ramp ratio with capacitance, as this measure is often used to distinguish between pyramidal neurons and GABAergic interneurons, which are the two main cell types in this region. Data for each cell property was max-min normalized to produce a 4 x n matrix of input features (where n=total number of cells). Clustering was performed using the ‘linkage’ function of SciPy Virtanen et al., 2020 in Python, using Ward’s linkage method (Ward, 1963) and Euclidean distance. Briefly, this approach begins with each cell assigned to a single cluster. Cells that are in closest proximity (i.e. have highest similarity) are then linked to form a new cluster. Then the next closest clusters are linked, and so on. This process is repeated until all cells are included in a single cluster. The output of this analysis is plotted as a hierarchical tree (dendrogram), in which each cell is a ‘leaf’ and the Euclidean distance on the y-axis indicates the linkage between cells (larger distance indicates greater dissimilarity). To annotate the photostimulation response of cells on the dendrogram (Figure 6N, P, Figure 6—figure supplement 1M), the response was designated as ‘excitation’ if action potential IEI decreased with optical stimulation and ‘inhibition’ if action potential IEI increased on stimulation. If firing data was not available, cells were designated as showing an ‘excitation’ if only an EPSP was evoked on optical stimulation, and ‘inhibition’ if only an IPSP was evoked. In cells where a mixed EPSP/IPSP was elicited, the response was designated as an ‘excitation’ if the overall voltage area (0–5.5 s following light onset) was positive, and an ‘inhibition’ if the overall voltage area was negative.

At the end of recording, brain slices were fixed in 4% PFA overnight and then washed in 1 X PBS (4x10 min each). Slices were blocked in 0.3% PBS-T (Sigma-Aldrich, MO, USA) with 3% NDS (Jackson Immunoresearch, PA, USA) for 30–60 min at room temperature. They were then incubated in PBS-T 0.3% with 3% NDS and CF405- or CF633-conjugated streptavidin (1:1000; Biotium, CA, USA) for 90 min at room temperature to reveal neurobiotin labeling. Slices were finally washed four times in 1 X PBS (10 min each) before being mounted on glass slides and coverslipped using warmed PVA-DABCO (Sigma-Aldrich, MO, USA).

C57BL/6 mice were deeply anesthetized with 5% isoflurane, and brains were rapidly extracted and covered with powdered dry ice for ~2 min. Frozen brains were stored in glass vials at –80 °C before sectioning at 20 µm using a cryostat (CM3050 S; Leica Biosystems, Germany) at –16 °C. Coronal sections were thaw-mounted onto a glass slide, by gentle heating from the underside using the tip of a finger to encourage adhesion of the section to the slide. They were then stored at –80 °C until processing.

Fluorescent in situ hybridization (FISH) was performed using the RNAscope Multiplex Fluorescent assay v2 (Advanced Cell Diagnostics, CA, USA). The following products were used: RNAscope Multiplex Fluorescent Reagent Kit V2 (Catalog #323110), Fluorescent Multiplex Detection Reagents (#323110), target probes for Mus musculus genes – Drd1a (#406491-C1), Drd2 (#406501-C3), Npbwr1 (#547181-C1), and Vipr2 (465391-C2) – and the Tyramide Signal Amplification (TSA) Plus Fluorescence Palette Kit (NEL760001KT; PerkinElmer Inc, MA, USA) with fluorophores diluted to 1:1000-1:5000. The protocol was performed as recommended by the manufacturer, with some modifications to prevent tissue degradation and optimize labeling specificity in our ROI. Fresh frozen slices were fixed in 4% PFA for 1 hr at 4 °C. Slices were dehydrated in an ethanol series (50%, 70%, 100%, and 100% ethanol, 5 min each) and then incubated in hydrogen peroxide for 8 min at room temperature. Protease treatment was omitted in order to prevent tissue degradation. Slides were then incubated with the desired probes (pre-warmed to 40 °C and cooled to room temperature) for 2 hr at 40 °C in a humidified oven. Following washing (2x30 s in 1 X RNAscope wash buffer), signal amplification molecules (Amp 1, 2, and 3) were hybridized to the target probes in sequential steps, with 30 min incubation for Amp 1 and 2 and 15 min incubation for Amp 3 at 40 °C, all in a 40 °C humidified oven followed by washing (2x30 s in wash buffer). For fluorescent labeling of each amplified probe, slides were incubated in channel-specific HRP for 10 min, followed by incubation with TSA fluorophore (PerkinElmer, MA, USA) for 20 min, and then incubation in HRP-blocker for 10 minutes (with 2x30 s washes between each step). Probes for Drd1a, Drd2, Npbwr1, and Vipr2 were each labeled with green (TSA Plus Fluorescein), red (TSA Plus Cyanine 3), or far red (TSA Plus Cyanine 5) fluorophores in counterbalanced combinations. Slides were then incubated in DAPI (Advanced Cell Diagnostics, CA, USA) for 10 min, washed in 1 X RNAscope wash buffer, dried for 20 min, coverslipped with warmed PVA-DABCO (Sigma-Aldrich, St. Louis, MO), and left to dry overnight before imaging.

Images were captured on a confocal laser scanning microscope (Olympus FV1000; Olympus, PA, USA) using a 40 X/1.30NA oil immersion objective. Serial Z-stack images were acquired using FluoView software version 4.0 (Olympus, PA, USA) at an optical thickness of 1.5 µm. All images were acquired with identical settings for laser power, detector gain, and amplifier offset. A maximum Z-projection was performed in FIJI followed by rolling ball background subtraction to correct for uneven illumination. Image brightness and contrast were moderately adjusted using FIJI, with consistent adjustments made across images for each probe-fluorophore combination. ROIs were annotated on each image by overlaying the appropriate brain atlas image Paxinos and Franklin, 2019; Bogovic et al., 2016 with guidance from DAPI staining and using the BigWarp plugin (https://imagej.net/BigWarp) in FIJI. These ROI outlines were used to generate binary masks in order to regionally restrict subsequent image analysis. Automated cell identification and analysis of fluorescent mRNA labeling was performed in CellProfiler (McQuin et al., 2018) using a modified version of the ‘Colocalization’ template pipeline (https://cellprofiler.org/examples). The pipeline was optimized to identify DAPI labeling (20–40 pixels in diameter), in order to define cell outlines. This was followed by identification of fluorescent mRNA puncta (2–10 pixels in diameter) for each probe. Puncta that were localized within DAPI-identified cells (classified using the ‘relate objects’ module) were assigned to that cell for subsequent analysis. Quantification and further analysis/data visualization was performed using a custom-written Python code. Violin plots were made using the violin plot function in the Seaborn library Waskom et al., 2020 of Python (with smoothing set to 0.2), and colocalization matrices were generated using the Seaborn heatmap function.

DAT::Cre mice received an injection of 300 nL AAV₉-Syn-FLEX-ChrimsonR-TdTomato in the DRN (ML:1.20, AP:–4.10, DV:–2.90; needle at a 20° angle from the midline, bevel facing medial), and 250 nL of AAV₁-Syn-GCaMP7f in the CeA (ML:2.85, AP:–1.20, DV:–4.75, needle bevel facing posterior). After ~4 weeks, mice underwent a second surgery to implant an integrated 0.6 mm diameter, 7.3 mm long gradient refractive index (GRIN) lens with attached baseplate (Inscopix, CA, USA) over the CeA (ML: 2.85, AP:–1.50, DV:–4.60). The lens was lowered slowly into the cleaned craniotomy by hand. The GRIN lens was adhered to the skull by a layer of adhesive cement (C&B Metabond; Parkell Inc, NY, USA) followed by a layer of black cranioplastic cement (Ortho-Jet; Lang, IL, USA), and protected by a small PCR tube cap, held in place by cement. The nVoke miniaturized microscope (Inscopix, CA, USA) consists of a 455±8 nm blue LED for GCaMP excitation, and a 620±30 nm red LED for simultaneous optogenetic manipulation (Stamatakis et al., 2018).

Behavioral experimentation commenced at least 1 week after baseplate surgery. Mice were first habituated to handling and connection of the microscope for a minimum of 3 consecutive days. For recording, mice were connected to the nVoke miniature microscope by tightening a small set screw on the baseplate. The microscope data cable was connected to a commutator (Inscopix, CA, USA), to allow unrestricted movement, and the commutator was itself connected to a data acquisition (DAQ) box. Grayscale images were acquired at a rate of 20 frames/s (fps; ~50ms exposure time) with the blue LED delivering 0.2–0.3 mW light power and analog gain on the image sensor set to 2. For the social approach task, mice were placed in the three-chamber apparatus (57.5lx22.5 w x 16.5h chamber with clear walls and grey floors). Following microscope connection, mice freely explored the chamber for 5 min. They were then confined to the center portion of the chamber, by the insertion of clear Plexiglas panels, during which a novel juvenile mouse was placed under one cup and a novel object was placed under the other cup. The panels were removed, and the test mouse was allowed to freely explore for a further 10 min. One ‘group-housed’ session was conducted without red-light delivery, and another ‘group-housed’ session was conducted with red 620 nm light delivery (8 pulses with 5ms pulse width, at 30 Hz, every 1 s; 10 mW) through the objective lens of the microscope, to activate ChrimsonR-expressing DRN^DAT terminals. The order of ‘group-housed’ sessions was counterbalanced. 2–3 weeks later, mice were isolated for 24 hours, and another session (‘isolated’) commenced without red-light delivery. A top-down SLEAP (Pereira et al., 2022) (v1.3.1) model was trained using 774 labeled frames, annotating a skeleton composed of 15 keypoints (comprised of (1) nose, (2) head, (3) left ear, (4) right ear, (5) neck, (6) left forelimb, (7) right forelimb, (8) trunk, (9) left hindlimb, (10) right hindlimb, (11) tail base, (12-14) points along the length of the tail, and (15) tail tip). Pose estimation using the trained model was then performed on the behavior videos to determine if there was a social or object cup interaction. To determine if there was a social or object cup interaction, the nose of the mouse must be within 1.3 x the diameter of the cup, and the cup must be within a 90° cone in front of the mouse’s head.

Raw videos of GCaMP fluorescence were first pre-processed in Inscopix Data Processing Software 1.3.0 (Inscopix, CA, USA) by cropping the region outside the GRIN lens, applying 2 x spatial downsampling, and a 3x3 median filter to fix defective pixels. A spatial band-pass filter was applied (0.005–0.5 oscillations/pixel) to remove high and low spatial frequency content, and rigid motion correction was performed (to account for small lateral displacements) by registering to a stable reference frame with a prominent landmark (e.g. blood vessel). Processed recordings were then exported as TIFF stacks for additional piecewise non-rigid motion correction using the NoRMCorre algorithm Pnevmatikakis and Giovannucci, 2017 in overlapping 64x64 pixel grids using a MATLAB implementation. Constrained non-negative matrix factorization for endoscopic recordings (CNMF-E) was then used to extract the spatial shapes and calcium signals from individual cells in the imaging field of view (Zhou et al., 2018a) using a MATLAB implementation (key parameters: minimum local correlation for seeding pixels = 0.9, minimum peak-to-noise ratio for seeding pixels = 12). The extracted calcium signals were inspected, and non-neuronal objects were manually excluded. All following downstream analyses used raw CNMF-E traces.

Calcium traces were aligned to detected behavioral events (interaction to the social and object cups, as determined by feature thresholds extracted from SLEAP keypoints). Single cell responses to social and object cup interaction were determined using an ROC (receiver operating characteristic) analysis, which has been previously described to determine neural responses to social behavior (Kingsbury et al., 2019; Li et al., 2017). A binary behavior vector of social or object cup interaction (calculated in 40ms time bins) was compared to a binary neural activity vectors generated by applying thresholds that span 100 steps from the minimum to maximum z-score value of each calcium trace to determine a true positive rate (TPR) and false positive rate (FPR) at each step. From these values, we yielded an ROC curve for each neuron that corresponded to the performance of that single neuron in predicting social or object cup interactions. The area under the ROC curve (auROC) was used to determine how strongly modulated each neuron was to the social and object stimuli. To determine the significance of single-cell responses, a null distribution of 1,000 auROC values was generated by randomly circularly shifting the binary behavior vectors and again comparing it to the binary neural activity signal. A neuron was considered to have a significant excitatory response to the social or object stimulus if the auROC value exceeded the 97.5th percentile of the 1000 shuffled auROC values and was considered to have a significant inhibitory response if the auROC value was less than the 2.5th percentile of the 1000 shuffled auROC values.

Co-registration of active neurons during imaging sessions was performed using CellReg (Sheintuch et al., 2017). In short, the spatial footprint matrices from each imaging session (as determined by CNMF-E) were used to align different imaging sessions within each animal to a reference session through translational and rotational shifts. Spatial correlation and centroid distance between cells were used to probabilistically register active cells across sessions.

Agglomerative hierarchical clustering was performed by averaging each neuron’s response to the onset of social or object cup interaction throughout the trial. A social or object cup interaction was classified as a trial if it (a) lasted a minimum of 1 s, (b) if there had been at least 5 s that elapsed since the last interaction, and (c) if there was less than 1.5 s pause in interaction with the social or object cups. The z-scored averaged traces (5 s before and after the onset of social or object cup interaction) were concatenated, such that each row corresponds to one neuronal unit. Agglomerative hierarchical clustering was performed using MATLAB’s ‘cluster’ function. Each neuron was initially designated as an individual cluster. Those that were in closest proximity were merged to form a new cluster, then the next closest were merged, etc. until a hierarchical tree was formed with all neurons contained within a single cluster. A threshold at 0.770×max(linkage) was set to prune branches from the hierarchical tree, so that all neurons below each cut were assigned to a single cluster. After the dendrogram was constructed, the average traces were displayed as a heatmap alongside their corresponding leaf. The traces of all neurons belonging to a single cluster were then averaged, and the number of neurons that corresponded to each behavior group was calculated for each cluster.

Statistical tests were performed using GraphPad Prism 8 (GraphPad Software, CA, USA). Normality was evaluated using the D’Agostino-Pearson test, and data are expressed as mean ± standard error of the mean (SEM), unless otherwise noted. Data that followed a Gaussian distribution were compared using a paired or unpaired t-test (non-directional) for two experimental groups, and a one-way or two-way ANOVA with repeated measures for three or more experimental groups. Data for two experimental groups that did not follow a Gaussian distribution were compared using a Mann-Whitney U test. Correlation between two variables was assessed using the Pearson’s product-moment correlation coefficient. Threshold for significance was set at *p<0.05, **p<0.01, and ***p<0.001.