To study the PMd’s role in controlling defensive behaviors, we exposed mice to two threats: a live predatory rat or a shock grid. These two assays were used to investigate, respectively, innate and conditioned threats. For the rat assay, mice were exposed either to a safe control toy rat or to an awake rat in a long box (70 cm length, 26 cm width, 44 cm height) for 10 min. The rat was placed at one of the corners, and its movement was restricted by a harness tied to a wall, restricting its range of motion to the rat area shown in pink in Figure 1A. Rats were screened for low aggression and predatory tendencies and thus they did not attack mice. No separating barrier was used between rats and mice allowing for close naturalistic interactions. Rat and toy rat exposures were separated by 24 hr. For the shock grid assay, mice first explored a different box for three consecutive days for 10 min sessions. The shock grid was placed in one of the corners of the box, as shown in Figure 1A. On day 1, no shocks were given and mice freely explored the environment. On day two, a single 0.7 mA 2 s shock was given the first time the mouse touched the shock grid. On day 3 (fear retrieval), no shocks were given. All behavioral and neural data plotted from the shock grid is from the fear retrieval day, unless otherwise noted. The pre-shock baseline was used as the control for the fear retrieval day. All sessions were separated by 24 hr (Figure 1B). Threat exposure induced distance from the threat source, freezing, and stretch-attend postures (Figure 1C, Figure 1—figure supplement 1). (The mean freeze bout duration was 2.03 s ± 0.15.) Additionally, relative to control assays, during exposure to threat, approach velocity was lower, while escape velocity was higher (Figure 1C). These results indicate that mice slowly and cautiously approach threats and then escape in high velocity back to safer locations far from threats.

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We performed the rat exposure assay before the shock grid assay because the former is a milder experience than the latter; no actual pain is inflicted in the rat assay. We thus reasoned that the more intensely aversive assay (the shock assay) was more likely to influence behavior in the rat assay than vice-versa. Nevertheless, to determine whether there could be an effect of order, we exposed two cohorts of mice to the rat and shock grid threats in a counterbalanced manner and showed that behavior in the shock grid assay is not affected by prior experience in the rat assay (Figure 1—figure supplement 2). Taken together, these data show that both innate and conditioned threats induced defensive behaviors. Our data also support the view that escape velocity is a measure of threat-induced behavior. No sex differences were found in either behavioral assay (Figure 1—figure supplement 3) (male n = 17, female n = 15; Wilcoxon rank-sum test, p>0.05).

We next investigated the activity of PMd cells during threat exposure. To do so, we used a cck-cre line. We then injected AAV-FLEX-GCaMP6s in the PMd and implanted fiberoptic cannula above the injection site in cck-cre mice to record calcium transients in PMd-cck cells using fiber photometry (Figure 2A–C). Animals exhibited robust defensive behavior in the presence of threat (Figure 2—figure supplement 1). Examining the relationship of general locomotion and to the fiber photometry signal, we found that the signal amplitude was higher during threat exposure relative to control assays for a wide range of matched speed values (Figure 2—figure supplement 2). Averaged heat maps show PMd-cck activity was increased near the rat and the shock grid during fear retrieval (Figure 2D). Indeed, activity was increased near threats relative to control stimuli (toy rat and shock grid in pre-shock day). These comparisons were done when analyzing data at the same speed range (Figure 2E); thus, PMd-cck cells are more active near threats independently of locomotor changes. We next studied how PMd-cck cell activity changed during defensive behaviors. A representative trace suggests that these cells show high activity during escape (Figure 2F). Average data show that in both assays PMd-cck cells showed increased activation during risk-assessment stretch-attend postures and during escape, while a decrease in activity was displayed during freezing (Figure 2G–I). Furthermore, the total distance of each escape was correlated with PMd-cck activation during exposure to threats, but not control stimuli (Figure 2J). These results show that PMd-cck cells are quickly activated by proximity to threat and escape, during exposure to both innate and conditioned threats. In agreement with this view, PMd-cck cells displayed relatively high membrane input resistance (484 ± 64 MOhms) and low rheobase, which is the minimum current required to elicit an action potential (38.3 ± 6.1 pA) (Figure 2—figure supplement 3). These results indicate that fairly minor excitatory input is enough to activate these cells. These biophysical characteristics suggest that these cells may be rapidly activated in the presence of threats.

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To analyze how PMd-cck ensemble activity encodes escape, we implanted miniature head mounted fluorescent microscopes (miniscopes) above GCaMP6s-expressing PMd-cck cells (Figure 3A,B). Large ensembles of PMd-cck cells were recorded in the rat and shock grid assays (Figure 3C,D). Using a generalized linear model (GLM), we identified a large fraction of PMd-cck cells that are active during these behaviors (Figure 3E). The behavior that activated the largest and smallest number of PMd-cck cells was, respectively, escape and freezing (Figure 3E). These data agree with our fiber photometry results showing that bulk PMd-cck activity is highest during escape and lowest during freezing. Behavior-triggered averages indicate that PMd-cck cells may be significantly activated during defensive behaviors, in agreement with these results (Figure 3F). Further supporting a role for PMd-cck cells in escape, we show that ensemble activity could be used to decode ongoing escape, but not other behaviors (Figure 3G). These intriguing results raise the possibility that PMd-cck activity may be able to predict future occurrence of escape. Indeed, PMd-cck activity could predict escape from innate and conditioned threats several seconds prior to escape onset. However, ensemble activity could not predict movement away from control stimuli (toy rat and shock grid in pre-shock day) (Figure 3H). These data show that PMd-cck activity can specifically predict future escape from threats, but not moving away from objects in general. Additionally, we found that PMd-cck cells represent not only future escape onset (Figure 3H) but also escape speed. Using the correlation of single cell activity and escape speed, we classified escape speed cells (see Materials and methods) in the control and threat assays. A higher fraction of PMd cells showed activity significantly correlated with escape speed for threat than control stimuli (Figure 3I). Additionally, for these escape speed-correlated cells, the mutual information between escape speed and calcium signal is significantly greater during threat than control (Figure 3K). These data indicate that PMd-cck activity is related to defensive escape and speed in the presence of threat, rather than general locomotion.

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Our fiber photometry results indicate that PMd-cck cells were more active during close proximity to threat (Figure 2D). These data suggest that PMd-cck ensemble activity may represent position in threat assays. We thus decoded position in both control and threat assays using PMd-cck ensemble activity. Strikingly, the error of position decoding was both smaller in threat than in control assays and significantly less than chance error (Figure 4A–B). These results show that PMd-cck cells represent distance to threat more prominently than distance to control objects.

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Having observed that a greater proportion of PMd cells correlate with speed (Figure 3I,J), we then studied if ensemble activity could predict movement vigor, measured by velocity. Indeed, PMd-cck activity could be used to decode velocity during threat exposure with higher accuracy than during exposure to control stimuli (Figure 4—figure supplement 1). Furthermore, decoding of velocity in control assays was less accurate than in threat assays, for both the rat and the shock assays (Figure 4—figure supplement 1). Since PMd ensemble activity can predict future escape, but not approach, we hypothesized that PMd activity could be used to decode velocity away from threats more accurately than velocity toward threats. Representative traces showing predicted and observed velocity support this hypothesis (Figure 4). Indeed, averaged data across mice show that the error for predicted velocity is lower for decoding velocity away from threat compared to velocity toward threat. Moreover, only velocity away from threat can be predicted with an error significantly less than chance (Figure 4C,D). These data show that PMd-cck cells represent key kinematic variables related to rapid escape from threats.

Recordings of PMd-cck ensemble activity revealed that these cells are highly active during escape and that their activity can be used to decode escape (but not approach) velocity. Moreover, neural activity could only decode escape, but not other behaviors. We thus hypothesized that inhibition of PMd-cck cells would decrease escape velocity without affecting other defensive behaviors. To test this view, we expressed the inhibitory receptor hM4Di in PMd-cck cells (Figure 5A). We confirmed that the hM4Di receptor ligand clozapine-N-oxide (CNO) produced hyperpolarization (Figure 5B). We then exposed mice to the assays described in Figure 1A. Mice were exposed to each threat and control assay twice, following treatment with either saline or the hM4Di ligand CNO (Figure 5C). Inhibition of PMd-cck cells in CNO-treated mice decreased escape velocity from both threats, in line with our prediction (Figure 5D). Importantly, inhibiting these cells did not change velocity, while mice moved away from control safe stimuli (toy rat and shock grid prior to fear conditioning) (Figure 5—figure supplement 1). This manipulation did not change freezing or stretch-attend postures (Figure 5D), showing PMd-cck activity is selectively required for escape, rather than defensive behaviors in general.

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As PMd-cck inhibition decreases escape velocity, but not other behaviors, we predicted that activating these cells would specifically induce running and escape-related motion. Indeed, optogenetic activation of ChR2-expressing PMd-cck cells caused an increase in speed, but not in the amount of freezing or stretch-attend postures (Figure 6A,B).

Figure 6

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We next investigated which downstream regions are recruited following activation of PMd-cck cells. Prior studies showed that the PMd projects to several structures involved in defense, such as the dlPAG and the anterior hypothalamus. Interestingly, it also projects to the anteromedial ventral thalamus (amv) (Canteras and Swanson, 1992). The amv has head direction cells (Bassett et al., 2007) and is a region critical for spatial navigation (Jankowski et al., 2013) and threat-conditioned contextual memory (Carvalho-Netto et al., 2010).

We hypothesized that activation of PMd-cck cells would recruit not only these known direct downstream areas, but also other structures involved in mounting a defensive behavioral state and regions involved in escape-related motor actions. To test this hypothesis, we optogenetically activated ChR2-expressing PMd-cck cells with blue light for 10 min (20 Hz, 5 ms pulses). Following perfusion, we performed an antibody stain against the immediate early gene cfos. PMd activation increased fos expression in regions that it projects to, such as the amv and the dlPAG. Interestingly, other nuclei critical for defensive behaviors, such as the basolateral amygdala, lateral septum, and the bed nucleus of the stria terminalis, were also activated (Figure 6C), even though they are not innervated by the PMd (Canteras and Swanson, 1992). These results show that the PMd recruits not only its direct downstream outputs, but also other regions involved in threat-related defense. Striatal regions were also activated, such as the caudate nucleus, possibly due to the hyperlocomotion and escape-related actions observed during optogenetic stimulation. Importantly, not all regions were engaged, showing functional specificity. For example, the dentate gyrus and the PMd-adjacent ventral PMd did not show increases in fos expression following PMd stimulation (Figure 6D). These data show that PMd-cck cells can recruit a broad network of threat-activated regions, which may contribute to a transition to a defensive state. Despite these intriguing data, it is possible that endogenous natural PMd activation does not result in recruitment of the same nuclei seen following optogenetic PMd-cck activation.

To identify which PMd downstream targets control escape, we studied its two main outputs, the amv and the dlPAG (Canteras and Swanson, 1992). The amv has head direction cells (Bassett et al., 2007) and is a region critical for threat-conditioned contextual memory (Carvalho-Netto et al., 2010) and spatial navigation (Jankowski et al., 2013).

The amv is also necessary for the acquisition of contextual fear elicited by predators (Carvalho-Netto et al., 2010), demonstrating this region has a role in defensive behaviors. In contrast, the dlPAG is a critical node in the escape network (Del-Ben and Graeff, 2009; Tovote et al., 2016).

To identify which of these PMd outputs control escape speed, we injected AAV9-syn-GCaMP6s in wild-type mice in either the amv or the dlPAG and obtained calcium transient recordings in the rat and shock grid assays (Figure 7A). DLPAG activity increased during escape from the rat (Figure 7C), in agreement with prior work showing this region is active during escape from innate threats (Deng et al., 2016; Evans et al., 2018). However, the dlPAG also showed increased activity during exposure to the fear conditioned shock grid during fear retrieval (Figure 7D,E). To our knowledge, there are no prior reports showing the dlPAG is active during escape from conditioned threats. Surprisingly, like the dlPAG, the amv was also active during escape from both threat modalities (Figure 7G–I), even though there are no prior reports implicating the amv in escape.

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Our fiber photometry results show that both major outputs of the PMd to the dlPAG and the amv are active during escape from threats, indicating the PMd-cck projections to these regions may control escape vigor. To identify which projection controls escape vigor, we expressed the inhibitory opsin Arch in PMd-cck cells and implanted fiberoptic cannulae bilaterally over either the amv or the dlPAG (Figure 8A–C). Inhibition of the PMd-cck projection to the dlPAG with green light decreased escape velocity in both assays (Figure 8D). This manipulation did not alter other defensive behaviors such as freezing or stretch-attend postures (Figure 8D). In contrast, inhibition of the PMd-cck projection to the amv did not change any defensive behavioral measure in either assay (Figure 8E). These data show that the activity in the PMd-cck projection to the dlPAG, but not to the amv, is necessary for normal escape vigor during exposure to both innate and conditioned threats.

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Having shown that inhibition of the PMd-dlPAG projection impairs escape from threat, we hypothesized these regions show increased functional connectivity during threat exposure. To test this view, using cck-cre mice, we injected AAV-dio-GCaMP6s in the PMd and AAV-syn-GCaMP6s in the dlPAG contralaterally and implanted fiber optic cannula above each injection site to monitor the simultaneous calcium activity of these regions during threat and control assays (Figure 9A–C). Using the mutual information metric – an information-theoretic quantity that reflects the amount of information obtained for one variable by observing another variable – we found that the mutual information between the PMd and dlPAG is higher during exposure to threat than control assays (see Materials and methods for details). This was also true when escapes were removed, indicating that the mutual information change seen is related to threat exposure, rather than specific defensive behaviors (Figure 9D).

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We opted to use mutual information instead of correlation because the former, but not the latter, can quantify both linear and non-linear relationships between two variables. Importantly, these dual-site recordings were done in PMd-cck cells and dlPAG-syn cells contralaterally.

As the PMd-cck projection to the dlPAG is unilateral (Figure 9—figure supplement 1; Canteras and Swanson, 1992), performing contralateral recordings ensures that dlPAG-syn cell body signals are not contaminated by signals from GCaMP-expressing PMd-cck axons terminating in the dlPAG. The dlPAG does not project to the PMd (Comoli et al., 2000); thus, there is no risk of recording signals from GCaMP-expressing dlPAG axons in the PMd.

To investigate whether a functionally similar pathway exists in humans, we examined functional connectivity (i.e., covariation of BOLD signal in the hypothalamus and PAG) as participants received aversive stimulation during fMRI scanning (N = 48). We developed a predictive model to identify a pathway between the hypothalamus (HTH) and the PAG, which consisted of a multi-voxel pattern across brain voxels in each region optimized for maximal HTH–PAG covariation (Figure 10, see Materials and methods). We then tested activation in this pathway in held-out participants. This HTH–PAG pathway responded more strongly to aversive images than non-aversive images and its activation also scaled monotonically with aversiveness (Figure 10C). Examination of the multi-voxel patterns contributing to the HTH–PAG pathway revealed that portions of medial posterior hypothalamus (neighboring the mammillary bodies) were most consistently associated with PAG activation. We also show that activation of the HTH–PAG pathway is selective and does not correlate with activation of a different major subcortical input pathway to the PAG, such as the amygdala–PAG pathway (Figure 10—figure supplement 1).

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Thus, these data show that functional connectivity in a hypothalamic-PAG pathway is increased in humans during aversive situations, in agreement with our results in mice showing that the hypothalamic to brainstem PMd–dlPAG pathway is engaged during exposure to aversive threats (Figure 9).

Cck-IRES-Cre mice (Jackson Laboratory stock No. 012706) and wild-type C57BL/6 J mice (Jackson Laboratory stock No. 000664) were used for all experiments. Male and female mice between 2 and 6 months of age were used in all experiments. Mice were maintained on a 12 hr reverse light–dark cycle with food and water ad libitum. Sample sizes were chosen based on previous behavioral optogenetics studies on defensive behaviors, which typically use 6–15 mice per group. All mice were handled for a minimum of 5 days prior to any behavioral task.

Male Long-Evans rats (250–400 g) were obtained from Charles River Laboratories and were individually housed on a standard 12 hr light–dark cycle and given food and water ad libitum. Rats were only used as a predatory stimulus. Rats were handled for several weeks prior to being used and were screened for low aggression to avoid attacks on mice. No attacks on mice were observed in this experiment.

All vectors were purchased from Addgene.

AAV9.EF1a.DIO.hChR2(H134R)-eYFP.WPRE.hGH, AAV9-EF1a-DIO-eYFP and AAV9-Ef1a-DIO-Arch-GFP.

Chemogenetics: pAAV8-hSyn-DIO-hM4D(Gi)-mCherry and AAV8.Syn.DIO. mCherry

Fiber Photometry AAV9.Syn.GCaMP6s.WPRE.SV40 and AAV9.Syn.FLEX.GCaMP6s.WPRE.SV40

Surgeries were performed as described previously (Adhikari et al., 2015). Eight-week-old mice were anaesthetized with 1.5–3.0% isoflurane and placed in a stereotaxic apparatus (Kopf Instruments). A scalpel was used to open an incision along the midline to expose the skull. After performing a craniotomy, 40 nl of one of the viral vectors listed above at a titer of 2 × 10¹² particles/ml was injected per site (PMd, amv, dlPAG) using a 10 μl nanofil syringe (World Precision Instruments) at 0.08 μl/min. AAV8-hSyn-DIO-hM4D(Gi)-mCherry and AAV8-hSyn-DIO-mCherry were injected at a titer of 2 × 10¹² particles/ml. The syringe was coupled to a 33-gauge beveled needle, and the bevel was placed to face the anterior side of the animal. The syringe was slowly retracted 20 min after the start of the infusion. Mice received unilateral viral infusion and fiber optic cannula implantation. Infusion locations measured as anterior-posterior, medial-lateral, and dorso-ventral coordinates from bregma were as follows: dlPAG (−4.75,–0.45, –1.9), dorsal PMd (−2.46,–0.5, –5.35), and amv (−0.85,–0.5, –3.9). For optogenetic experiments, fiber optic cannula (0.22 NA, 200 μm diameter; Newdoon) were implanted bilaterally 0.15 mm above the viral infusion sites. Only mice with viral expression restricted to the intended targets were used for behavioral assays.

For photometry experiments, mice were injected with 0.16 µl at a titer of 3 × 10¹² of AAV9.Syn.Flex.GCaMP6s.WPRE.SV40 in the PMd of cck-cre mice. The same volume and titer of AAV9.Syn.GCaMP6s.WPRE.SV40 was injected into the dlPAG or amv. Mice were implanted unilaterally with fiberoptic cannulae in the PMd, amv, dlPAG. A 400 μm diameter, 0.48 NA optical fiber (Neurophotometrics) was used for photometry experiments. Adhesive cement (C&B metabond; Parkell, Edgewood, NY) and dental cement (Stoelting, Wood Dale, IL) were used to securely attach the fiber optic cannula to the skull. For miniaturized microscope experiments, 40 nl of AAV9-DIO-GCaMP6s was injected in the PMd of cck-cre mice and a 7 mm GRIN lens was implanted 200 µM above the infusion site. Three weeks following surgery, animals were base-plated. For dual photometry recordings, injections and fiber implants were done in the same mouse contralaterally to record PMd-cck and dlPAG-syn cell bodies simultaneously.

Mice were accustomed to handling prior to any behavioral assay. On day 1, mice were habituated to a rectangular box (70 cm length, 26 cm width, 44 cm height) for 20 min. This environment consisted of a large aquarium made of glass. Sheets of paper lined the outside glass surface. The box was cleaned with ethanol between mice. Twenty-four hours later, mice were exposed to the same environment but in the presence of a toy rat for 20 min. Mice were then exposed to an adult rat or a toy rat in this environment on the two following days. The rat was secured by a harness tied to one of the walls and could freely ambulate only within a short radius of approximately 20 cm. The mouse was placed near the wall opposite to the rat and freely explored the context for 20 min. No separating barrier was placed between the mouse and the rat, allowing for close naturalistic encounters that can induce a variety of robust defensive behaviors.

To better evaluate a broader species-specific defense repertoire in face of a conditioned stimulus, we used a modified version of the standard contextual fear conditioning method (Schuette et al., 2020). Pre-shock, fear conditioning, and retrieval sessions were performed in a context (70 cm length × 17 cm width × 40 cm height) with an evenly distributed light intensity of 40 lux and a Coulbourn shock grid (19.5 cm × 17 cm) set at the extreme end of the enclosure. The fear conditioning environment is made of laminated white foam board. The box was cleaned with ethanol between mice. Forty-eight hours after rat exposure, mice were habituated to this context and could freely explore the whole environment for 20 min. On the following day, the grid was activated, such that a single 0.7 mA foot shock was delivered for 2 s only on the first time the mouse fully entered the grid zone. Twenty-four hours later, retrieval sessions were performed in the same enclosure but without shock. Mice could freely explore the context for 20 min during pre-shock habituation, fear conditioning, and retrieval sessions.

To extract the pose of freely behaving mice in the described assays, we implemented DeepLabCut (Nath et al., 2019), an open-source convolutional neural network-based toolbox, to identify mouse nose, ear, and tailbase xy-coordinates in each recorded video frame. These coordinates were then used to calculate velocity and position at each timepoint, as well as classify behaviors such as escape runs and freezes in an automated manner using custom Matlab scripts. Specifically:

‘Escapes’ were defined as epochs for which (1) the mouse speed away from the threat or control threat exceeded 2 cm/s (as there was little room for acceleration between the threat zone and opposite wall, the speed threshold was set to this relatively low value), (2) movement away from the threat was initiated at a minimum distance-from-threat of 30 cm, and (3) the distance traversed from escape onset to offset was greater than 10 cm. Thus, escapes were required to begin near the threat and lead to a substantial increase in distance from the threat.

'Escape speed' was defined as the average speed from escape onset to offset.

'Escape angle' was defined as the cosine of the mouse head direction in radians, such that the values ranged from –1 (facing toward the threat) to 1 (facing away from the threat). Mouse head direction was determined by the angle of the line connecting a point midway between the ears and the nose.

'Approaches' were defined as epochs for which (1) the mouse speed toward the threat or control threat exceeded 2 cm/s and (2) the distance traversed from approach onset to offset was greater than 10 cm.

'Stretch-attend postures' were defined as epochs for which (1) the distance between mouse nose and tailbase exceeded a distance of approximately 1.2 mouse body lengths and (2) mouse tailbase speed fell below 1 cm/s.

'Freezes' were defined as periods for which mouse nose and tailbase speed fell below 0.25 cm/s for at least 0.33 s (Schuette et al., 2020). ‘Freeze bout duration’ was defined as the amount of time that elapsed from freeze onset to offset.

'Walks' were defined as epochs for which (1) movements along the safe wall of the enclosure, perpendicular to the threat, exceeded 2 cm/s and (2) the distance traversed from walk onset to offset was greater than 5 cm.

All behaviors were manually checked by the experimenters for error.

The order of assays was identical for behavioral characterization (Figure 1), fiber photometry (Figures 2, 7 and 9) and miniscope (Figure 3) experiments, as detailed in Figures 1 and 2C. Specifically, mice were habituated to the rat enclosure for days 1–3. The toy rat and live rat were introduced on days 4 and 5. This was followed by habituation to the fear conditioning enclosure on days 6–8. Day nine was the pre-shock control session. Fear acquisition and retrieval were performed, respectively, on days 10 and 11.

The DREADD experiments were performed as diagrammed in Figure 5C. Saline or CNO were administered on contiguous days. Habituation to the rat enclosure occurred on days 1–3. The toy rat was introduced on days 4 (saline) and 5 (CNO) and the live rat on days 8 (saline) and 9 (CNO). This was followed by habituation to the fear conditioning enclosure on days 11–13. Days 14 (saline) and 15 (CNO) were considered the pre-shock sessions. Fear acquisition occurred on day 16, followed by fear retrieval on days 17 (saline) and 18 (CNO).

Photometry was performed as described in detail previously (Kim et al., 2016). Briefly, we used a 405 nm LED and a 470 nm LED (Thorlabs, M405F1 and M470F1) for the Ca²⁺-dependent and Ca²⁺independent isosbestic control measurements. The two LEDs were bandpass filtered (Thorlabs, FB410-10 and FB470-10) and then combined with a 425 nm longpass dichroic mirror (Thorlabs, DMLP425R) and coupled into the microscope using a 495 nm longpass dichroic mirror (Semrock, FF495-Di02−25 × 36). Mice were connected with a branched patch cord (400 μm, Doric Lenses, Quebec, Canada) using a zirconia sleeve to the optical system. The signal was captured at 20 Hz (alternating 405 nm LED and 470 nm LED). To correct for signal artifacts of a nonbiological origin (i.e., photobleaching and movement artifacts), custom Matlab scripts leveraged the reference signal (405 nm), unaffected by calcium saturation, to isolate and remove these effects from the calcium signal (470 nm).

To plot the behavior-triggered averages, only mice that displayed a minimum of three behavioral instances were included in the corresponding behavioral figure. Moreover, event-triggered averages were only calculated from behavioral instances that were separated from other classified behavioral instances by a minimum of 5 s.

All videos were recorded at 30 frames/s using a Logitech HD C310 webcam and custom-built head-mounted UCLA miniscope (Cai et al., 2016). Open-source UCLA Miniscope software and hardware (http://miniscope.org/) were used to capture and synchronize neural and behavioral video (Cai et al., 2016).

The open-source UCLA miniscope analysis package (https://github.com/daharoni/Miniscope_Analysis, Daniel, 2021, Aharoni and Hoogland, 2019) was used to motion correct miniscope videos. They were then temporally downsampled by a factor of four and spatially downsampled by a factor of two. The cell activity and footprints were extracted using the open-source package Constrained Nonnegative Matrix Factorization for microEndoscopic data (CNMF-E; https://github.com/zhoupc/CNMF_E, Pengcheng, 2021, Schuette et al., 2020; Zhou et al., 2018). Only cells whose variance was greater than or equal to 25% of the maximum variance among non-outliers were used in the analysis.

Discrete classification of escape behavior was performed using multinomial logistic regression. Timepoints following escape by 2 s were labeled 'escape', and a matched number of non-escape timepoints were randomly selected for training and validation. Each time point was treated as an individual data point. Training and validation were performed using fivefold cross-validation, with a minimum of 10 s between training and validation sets. As equal numbers of escape and non-escape samples were used to build the training and validation sets, chance accuracy was 50%. Sessions with less than five escapes were excluded from the analysis. The same analysis was performed for approach, stretch-attend postures, and freeze. To predict escape at negative time lags from behavior onset, the same analysis procedure was implemented, using 2 s epochs preceding escape by 2, 4, 6, 8, and 10 s.

We used a GLM to identify cells that showed increased calcium activity during approach, stretch-attend, escape, and freeze behaviors. We fit this model to each cell’s activity, with behavior indices as the predictor variable and behavior coefficients as the measure of fit. Behavior onset times were then randomized 100 times and a bootstrap distribution built from the resulting GLM coefficients. A cell was considered a behavior-categorized cell if its coefficient exceeded 95% of the bootstrap coefficient values.

Escape speed cells were classified using the method described in Iwase et al., 2020. Briefly, we calculated the Pearson product-moment correlation coefficient between each cell’s firing rate and the animal’s running speed during escape. The chance distribution was determined using a shuffling procedure whereby the calcium data was time-shifted in a circular manner relative to speed by a random duration between 30 s and the total duration of the assay minus 30 s. This was repeated 100 times for each cell. Thus, a cell was categorized as a 'escape speed-correlated cell' if the absolute value of its Pearson product-moment correlation exceeded the 95th percentile of distribution of speed scores from the chance distribution of all cells recorded in the PMd.

To predict position and speed from neural data, the data dimensionality was reduced by principal component analysis, such that the top principal components, representing at least 80% of the total variance, were used in the following decoding analysis. This output and the related position/speed data were then separated into alternating 60 s training and testing blocks, with 10 s of separation between blocks. Odd blocks were used to train a generalized linear regression model (GLM; Matlab function ‘glmfit’) and withheld even blocks were used to test the resulting model. Accuracies of this withheld testing block were reported as mean squared error. The level of chance error was calculated as the mean testing error of the GLM on circularly permuted data (100 iterations per session) across animals.

Mutual information is an information theory-derived metric reflecting the amount of information obtained for one variable by observing another variable. In the case of the fiber photometry analysis, the related variables were the simultaneously recorded PMd and dlPAG signals. Mutual information was calculated using custom Matlab code (Delpiano, 2021) for all samples where the speed was greater than 1 cm/s. Calculating mutual information requires computing the joint distribution over the PMd and dlPAG fiber photometry signals. This distribution was calculated using a histogram count after discretizing PMd and dlPAG fiber photometry signals each into 20 bins. The same approach was used for the miniscope mutual information analysis, for which this metric was computed for all escape samples between the calcium signal of individual PMd-cck cells and speed.

Mice used for chemogenetic experiments were exposed to each threat and control stimuli twice, once following treatment with saline and once following treatment with CNO (5 mg/kg, injected intraperitoneally) 40 min prior to the experiment. Only one control or threat-exposure assay was performed per day with each mouse.

All behavior videos were captured at 30 frames/s in standard definition (640 × 480) using a Logitech HD C310 webcam. To capture fiber-photometry synchronized videos, both the calcium signal and behavior were recorded by the same computer using custom Matlab scripts that also collected timestamp values for each calcium sample/behavioral frame. These timestamps were used to precisely align neural activity and behavior.

For PMd-cck ChR2 mice, blue light was generated by a 473 nm laser (Dragon Lasers, Changchun Jilin, China) at 4.5 mW unless otherwise indicated. Green light was generated by a 532 nm laser (Dragon Lasers), and bilaterally delivered to mice at 10 mW. A Master-8 pulse generator (A.M.P.I., Jerusalem, Israel) was used to drive the blue laser at 20 Hz. This stimulation pattern was used for all ChR2 experiments. The laser output was delivered to the animal via an optical fiber (200 μm core, 0.22 numerical aperture, Doric Lenses, Canada) coupled with the fiberoptic implanted on the animals through a zirconia sleeve.

Fixed brains were kept in 30% sucrose at 4°C overnight and then sectioned on a cryostat (40 µm) slices. Sections were washed in PBS and incubated in a blocking solution (3% normal donkey serum and 0.3% triton-x in PBS) for 1 hr at room temperature. Sections were then incubated at 4°C for 12 hr with polyclonal anti-fos antibody made in rabbit (1/500 dilution) (c-Fos (9F6) Rabbit mAb CAT#2250, Cell Signalling Technology) in blocking solution. Following primary antibody incubation, sections were washed in PBS three times for 10 min and then incubated with anti-rabbit IgG (H + L) antibody (1/500 dilution) conjugated to Alexa Fluor 594 (red) (CAT# 8,889 S, cellsignal.com) for 1 hr at room temperature. Sections were washed in PBS three times for 10 min, incubated with DAPI (1/50,000 dilution in PBS), washed again in PBS, and mounted in glass slides using PVA-DABCO (Sigma).

Mice were anesthetized with Fatal-Plus and transcardially perfused with phosphate buffered saline followed by a solution of 4% paraformaldehyde. Extracted brains were stored for 12 hr at 4°C in 4% paraformaldehyde. Brains were then placed in sucrose for a minimum of 24 hr. Brains were sectioned in the coronal plane in a cryostat, washed in phosphate buffered saline, and mounted on glass slides using PVA-DABCO. Images were acquired using a Keyence BZ-X fluorescence microscope with a 10 or 20× air objective.

Cck-cre driver line mice were injected with AAV9-FLEX-ChR2-YFP in the PMd. Acute slices were prepared from these mice. For electrophysiological measurements, slices were transferred as needed to the recording chamber, where they were perfused with oxygenated aCSF at 32°C. The slices were held in place using a nylon net stretched within a U-shaped platinum wire. Visually guided whole-cell patch clamp recordings were made using infrared differential interference contrast optics. We also verified the identity of PMD neurons by only recording from YFP-positive neurons. All recordings were obtained using a MultiClamp 700B amplifier system (Molecular Devices, Union City, CA). Experiments were controlled by PClamp 10 software running on a PC, and the data were acquired using the Digidata 1,440A acquisition system. All recording electrodes (3–8 MΩ) were pulled from thin-walled capillary glass (A-M Systems, Carlsborg, WA) using a Sutter Instruments P97 puller. The patch pipettes were filled with internal solution containing (in mM) 100 K- gluconate, 20 KCl, 4 ATP-Mg, 10 phospho-creatine, 0.3 GTP-Na, and 10 HEPES (in mM) with a pH of 7.3 and osmolarity of 300 mOsm. Only cells with a stable, uncorrected resting membrane potential (RMP) between –50 and –80 mV, overshooting action potentials, and an input resistance (RN) >100 MW were used. To minimize the influence of voltage-dependent changes on membrane conductances, all cells were studied at a membrane potential near –60 mV (using constant current injection under current clamp mode). To study intrinsic firing properties of PMD neurons, WCRs were conducted under current clamp using the following protocol: (1) Voltage–current (V-I) relationships were obtained using 400 ms current steps (range –50 pA to rheobase) and by plotting the plateau voltage deflection against current amplitude. Neuronal input resistance (RN) was determined from the slope of the linear fit of that portion of the V-I plot where the voltage sweeps did not exhibit sags or active conductance. (2) Intrinsic excitability measurements were obtained using 1 s current steps (range 0–500 pA) and by plotting the number of action potentials fired against current amplitude. (3) RMP was calculated as the difference between mean membrane potential during the first minute immediately after obtaining whole cell configuration and after withdrawing the electrode from the neuron.

For validating hM4Di in PMd-cck cells, acute brain slices preparation and electrophysiological recordings were performed using standard methods as previously described (Nagai et al., 2019). Briefly, Cck-Cre+ mice that had received AAV microinjections into PMd were deeply anesthetized with isoflurane and decapitated with sharp shears. The brains were placed and sliced in ice-cold modified artificial CSF (aCSF) containing the following (in mM): 194 sucrose, 30 NaCl, 4.5 KCl, 1 MgCl₂, 26 NaHCO₃, 1.2 NaH2PO₄, and 10 D-glucose, saturated with 95% O₂ and 5% CO₂. A vibratome (DSK-Zero1) was used to cut 300 μm brain sections. The slices were allowed to equilibrate for 30 min at 32°C–34°C in normal aCSF containing (in mM); 124 NaCl, 4.5 KCl, 2 CaCl₂, 1 MgCl₂, 26 NaHCO₃, 1.2 NaH₂PO₄, and 10 D-glucose continuously bubbled with 95% O₂ and 5% CO₂. Slices were then stored at 21°C–23°C in the same buffer until use. All slices were used within 2–6 hr of slicing.

Slices were placed in the recording chamber and continuously perfused with 95% O₂ and 5% CO₂ bubbled normal aCSF. pCLAMP10.4 software and a Multi-Clamp 700B amplifier was used for electrophysiology (Molecular Devices). Whole-cell patch-clamp recordings were made from neurons in the PMd or dorsolateral PAG (dlPAG) using patch pipettes with a typical resistance of 4–5 MΩ. Neurons were selected based on reporter fluorescence (mCherry for hM4Di-mCherry). The intracellular solution for recordings comprised the following (in mM): 135 potassium gluconate, 5 KCl, 0.5 CaCl₂, 5 HEPES, 5 EGTA, 2 Mg-ATP, and 0.3 Na-GTP, pH 7.3 adjusted with KOH. The initial access resistance values were <20 MΩ for all cells; if this changed by >20%, the cell was discarded. Light flashes (0.2 mW/mm²) from a blue LED light source (Sutter Instruments) were delivered via the microscope optics and a 40× water immersion objective lens and controlled remotely using TTL pulses from Clampex. Cell responses were recorded in whole-cell mode and recorded using an Axopatch 700B amplifier connected via a digitizer to a computer with pCLAMP10 software. To stimulate ChR2 expressed in PMd neurons or axons, 5 ms pulses were delivered at inter-pulse intervals of 200 ms, 50 ms, or 25 ms for 5, 20, or 40 Hz optical stimulations, respectively.

Participants received five different types of aversive stimulation (mechanical pain, thermal pain, aversive auditory, aversive visual, and pleasant visual), each at four stimulus intensities. Twenty-four stimuli of each type (six per intensity) were presented over six fMRI runs in random order. Following stimulation on each trial, participants made behavioral ratings of their subjective experience. Participants were instructed to answer the question ‘How much do you want to avoid this experience in the future?’. Ratings were made with a non-linear visual analog rating scale, with anchors ‘Not at all’ and ‘Most’ displayed at the ends of the scale.

Visual stimulation was administered on the MRI screen and included normed images from the International Affective Picture System (IAPS) database (Lang et al., 2008). To induce four ‘stimulus intensity levels’ we selected four groups of seven images based on their normed aversiveness ratings (averaged across male and female raters) available in the IAPS database and confirmed by N = 10 lab members (five male, five female) in response to ‘How aversive is this image? 1–100’. Selected images included photographs of animals (n = 7), bodily illness and injury (n = 12), and industrial and human waste (n = 9). Four stimulus levels were delivered to participants for 10 s each.

Whole-brain fMRI data were acquired on a 3T Siemens MAGNETOM Prisma Fit MRI scanner at the Intermountain Neuroimaging Consortium facility at the University of Colorado, Boulder. Structural images were acquired using high-resolution T1 spoiled gradient recall images (SPGR) for anatomical localization and warping to standard MNI space. Functional images were acquired with a multiband EPI sequence (TR = 460 ms, TE = 27.2 ms, field of view = 220 mm, multiband acceleration factor = 8, flip angle = 44°, 64 × 64 image matrix, 2.7 mm isotropic voxels, 56 interleaved slices, phase encoding posterior >> anterior). Six runs of 7.17 min duration (934 total measurements) were acquired. Stimulus presentation and behavioral data acquisition were controlled using Psychtoolbox.

fMRI data were preprocessed using an automated pipeline implemented by the Mind Research Network, Albuquerque, NM. Briefly, the preprocessing steps included distortion correction using FSL’s top-up tool (https://fsl.fmrib.ox.ac.uk/fsl/), motion correction (affine alignment of first EPI volume [reference image]) to T1, followed by affine alignment of all EPI volumes to the reference image and estimation of the motion parameter file (sepi_vr_motion.1D, AFNI, https://afni.nimh.nih.gov/), spatial normalization via subject’s T1 image (T1 normalization to MNI space [nonlinear transform]), normalization of EPI image to MNI space (3dNWarpApply, AFNI, https://afni.nimh.nih.gov/), interpolation to 2 mm isotropic voxels, and smoothing with a 6 mm FWHM kernel (SPM 8, https://www.fil.ion.ucl.ac.uk/spm/software/spm8/).

Prior to first-level (within-subject) analysis, we removed the first four volumes to allow for image intensity stabilization. We also identified image-wise outliers by computing both the mean and the standard deviation (across voxels) of intensity values for each image for all slices to remove intermittent gradient and severe motion-related artifacts (spikes) that are present to some degree in all fMRI data.

Data were analyzed using SPM12 (http://www.fil.ion.ucl.ac.uk/spm) and custom MATLAB (The MathWorks, Inc, Natick, MA) code available from the authors’ website (http://github.com/canlab/CanlabCore, Wager, 2021). First-level GLM analyses were conducted in SPM12. The six runs were concatenated for each subject. Boxcar regressors, convolved with the canonical hemodynamic response function, were constructed to model periods for the 10 s stimulation and 4–7 s rating periods. The fixation cross epoch was used as an implicit baseline. A high-pass filter of 0.008 Hz was applied. Nuisance variables included (1) ‘dummy’ regressors coding for each run (intercept for each run); (2) linear drift across time within each run; (3) the six estimated head movement parameters (x, y, z, roll, pitch, and yaw), their mean-centered squares, their derivatives, and squared derivative for each run (total 24 columns); and (4) motion outliers (spikes) identified in the previous step. A ‘single-trial model’ was used to uniquely estimate the response to every stimulus in order to assess functional connectivity.

Functional connectivity between the hypothalamus and PAG was estimated using PLS (Wold et al., 2001) regression, which identifies latent multivariate patterns that maximize the covariance between two blocks of data (i.e., BOLD activity in hypothalamus and PAG voxels). Here, data comprised single trial estimates of brain activation in response to aversive thermal, mechanical, auditory, and visual stimuli, in addition to a set of pleasant visual stimuli that were used as a control. For the PLS model, the predictor block of variables included all voxels in an anatomically defined mask of the hypothalamus (Pauli et al., 2018) (337 voxels) and the outcome block included all voxels in the PAG (Kragel et al., 2019) (42 voxels). Localization of the hypothalamus signal that covaries with the PAG responses was performed by bootstrapping the PLS regression and examining the distribution of PLS regression coefficients and their deviation from zero (using normal approximation for inference). Hyperalignment of fMRI data (Haxby et al., 2011) was conducted separately for each region as a preprocessing step, and leave-one-subject-out cross-validation was performed to estimate the strength of functional connections (i.e., the Pearson correlation between the first ‘X score’ and ‘Y score’ estimated by PLS), similar to the canonical correlation (Hardoon et al., 2004).

A benefit of the pathway-identification model we employed is that it can, in principle, identify HTH and PAG patterns that distinctly participate in the HTH–PAG pathway. For example, the central nucleus of the amygdala (CeA) projects to both the hypothalamus and the PAG (Kim et al., 2013) and could indirectly explain variation in BOLD signals in the PAG. To test pathway specificity, we separately modeled a pathway between the CeA and the PAG using the approach described above. This allowed us to evaluate how much variation in PAG activity the HTH–PAG pathway explained above and beyond the CeA–PAG pathway. To evaluate this, we computed the partial correlation between latent sources in the hypothalamus and PAG, controlling for the latent source in the CeA.

Nonparametric Wilcoxon signed-rank or rank-sum tests were used, unless otherwise stated. Two-tailed tests were used throughout with α = 0.05. Non-parametric tests were used because normality tests are severely underpowered for n < 100, indicating that, with small n, normality tests will often fail to detect non-normal distributions (Razali and Wah, 2011). However, by necessity, rodent cohorts are much smaller than n = 100. Thus, to avoid unwarranted normality assumptions about our data, we used non-parametric tests. Asterisks in the figures indicate the p values. Standard error of the mean was plotted in each figure as an estimate of variation. Multiple comparisons were adjusted with the false discovery rate (FDR) method.

Initial behavioral characterization of the assays (Figure 1) was replicated three times, with cohorts containing 10, 10, and 12 mice (32 in total). PMd cell body fiber photometry experiments (Figure 2) were replicated twice with cohorts containing 7 and 8 mice (15 in total). Miniscope experiments (Figures 3 and 4) were replicated twice, with cohorts of four and five mice (nine in total). Chemogenetics experiments (Figure 5) were replicated twice (cohort 1 with 10 controls and 6 hM4Di mice and cohort 2 with 9 controls and 5 hM4Di mice). ChR2 experiments (Figure 6) were done once, with five YFP and four ChR2 mice. dlPAG fiber photometry experiments (Figure 7) were replicated twice, with cohorts of four and five mice (nine in total). Amv body fiber photometry experiments (Figure 7) were replicated once with six mice. PMd–dlPAG optogenetic projection inhibition experiments (Figure 8) were replicated twice. Both cohorts had 12 controls and six arch mice. PMd–amv optogenetic projection inhibition experiments (Figure 8) were replicated twice. Both cohorts had six controls and nine arch mice. Appropriate fluorophore-only expressing mice were used as controls for chemogenetic and optogenetic experiments. For fMRI data (Figure 9), a cohort of 48 human subjects was used only once. Each mouse was only exposed to each assay once, as defensive behavior assays cannot be repeated. Thus, there are no technical replicates. No outliers were found or excluded. All mice and humans were used. Sample sizes for human and mouse experiments were determined based on comparisons to similar published papers.

For chemogenetic and optogenetic experiments, mice in each cage were randomly allocated to control (mcherry or YFP -expressing mice) or experimental conditions (hM4Di-, ChR2-, or Arch-expressing mice). Data collection was done blinded to treatment group in mice. For human fMRI data and mouse neural activity recordings, all data were obtained from subjects in identical conditions, and thus they were all allocated to the same experimental group. There were no experimentally controlled differences across these subjects and, thus, there were no ‘treatment groups’.

Custom analysis scripts are available at https://github.com/schuettepeter/PMd_escape_vigor (copy archived at swh:1:rev:5a9232a5dee602fa57cb1e959f63c10da91cd1db, Schuette, 2021). Data is available at https://datadryad.org/stash/share/dYuSl2nnXsyi0nTDjCDeHR08gwW7paFL4Eo3TmF_aH4.

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

We were supported by the NIMH (R00 MH106649 and R01 MH119089) (AA), the Brain and Behavior Research Foundation (Grants # 22663, 27654, 27780, and 29204, respectively, to AA, FMCVR, WW, and JCK), the NSF (NSF-GRFP DGE-1650604, PJS), the UCLA Affiliates fellowship (PJS), the Achievements Rewards for College Scientists Foundation, NIMH (F31 MH121050-01A1) (MQL), the Hellman Foundation (AA), and FAPESP (Research Grant #2014/05432–9) (NSC). FMCVR was supported with FAPESP grants #2015/23092–3 and #2017/08668–1. We thank Profs. BSK and JN for performing patch-clamping experiments.

All procedures have been approved by the University of California, Los Angeles Institutional Animal Care and Use Committee, protocols 2017-011 and 2017-075.

© 2021, Wang et al.

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