An intralayer microcircuit in the temporal association cortex underlies sensory-induced escape in mice

Domain: biology
Published: 2026-03-17
Editorial outcome: major_revision

Abstract
A central goal in neuroscience is to clarify how neural circuits translate sensory input into adaptive behaviours. Although unisensory evoked escape circuits in mice are well defined, it remains unclear whether a single nucleus contains specialized sensory, sensory‒motor decision, and motor command neurons for escapes driven by distinct sensory cues, and how these neurons form functional microcircuits. Using multiple sensory stimuli in mice, we identified the temporal association cortex (TeA) as a critical escape hub. Combining in vivo electrophysiology, optogenetics and chemogenetics, we characterized three distinct neuron subtypes within TeA layer 5 (L5) CaMKII neurons that correspond to these three functional classes. Intratelencephalic (IT) neurons serve as sensory‒motor decision neurons, while layer matched pyramidal tract (APT) neurons projecting to the dorsal periaqueductal grey (dPAG) act as motor command neurons. We reveal a laminar IT–PT microcircuit that converts sensory input into sensory-motor decisions and commands for escape locomotion.

Introduction
Animals’ perceptions of their external and internal environments determine and modulate their behaviours, as evidenced by studies exploring sensory modalities such as auditory1-6, visual7-11, olfactory12, and somatosensory stimuli4,5, all of which can elicit innate flight responses such as escape, which are essential for animals to survive in the face of life-threatening environmental cues13. A critical function of neural centres is to establish neuronal circuits that perceive these cues, process them as sensory signals, make decisions, transform these decisions into motor commands, and subsequently initiate appropriate defensive behaviours14. The neuronal circuits in mice that underlie the detection of and escape from threats have been extensively investigated recently. However, whether a specific neural nucleus acts as a central hub to coordinate cross-sensory escape behaviours remains unclear. Such a nucleus might integrate inputs from sensory and decision-making neurons to orchestrate motor commands, potentially within a localized microcircuit.

A widely accepted hypothesis is that sensory regions process sensory information1,3; motor regions generate motor signals (e.g., in circuits related to innate escape behaviours, which ultimately project to the deep periaqueductal grey (dPAG))1,3,15; and higher-order cortical areas (such as the association cortex), striatum, superior colliculus, and various midbrain regions generate decision-related information, facilitating the transformation of sensory signals into motor signals8,16-20. However, sensory regions also encode decision-making and reward-related information21-24, and motor-related signals have been detected in nonmotor-related nuclei25-30. These observations suggest that sensory neurons, sensory–motor decision neurons, and motor command neurons capable of directly converting sensory input into motor output may all be present within some nuclei of the mouse brain. As a region that receives sensory signals and connects to motor-related areas, the association cortex appears to be the most likely candidate for fulfilling this function.

The temporal association cortex (TeA), which is part of the association cortex, is a high-level nucleus that receives diverse sensory inputs31, processing auditory32, visual33-35, and tactile36 signals through projections from different sensory nuclei and enables the integration of multimodal sensory information37-39. Previous research has revealed the role of the TeA in processing auditory signals40 and influencing both innate41 and learned behaviours40,42. Moreover, the TeA sends projections to the dPAG, a region that controls defensive behaviours31,41. While emerging evidence has implicated the TeA in behavioural responses such as the retrieval of pups in maternal mice (an innate care behaviour)41 and auditory-cued fear conditioning (learned behaviour)40,42, its specific role in the defensive behaviour circuit remains poorly defined. This critical knowledge gap, coupled with the robust anatomical connectivity of the TeA to key command neurons, renders this region a highly compelling and potentially pivotal node worthy of in-depth investigation. We hypothesize that the TeA likely serves not only as a simple relay station but also as a critical hub coordinating diverse sensory-induced escape behaviours. It may integrate sensory information and participate in driving downstream motor command pathways, constituting a microcomputational hub that encompasses sensory integration, decision-making, and motor command functions. It plays an indispensable role in triggering adaptive escape behaviours.

In a mouse model of flight behaviours driven by sound, light, and air puffs, along with a combination of in vivo and in vitro electrophysiological recordings and chemogenetic and optogenetic interventions, our study elucidated the contribution of the TeA–dPAG pathway to sensory-induced escape behaviours and characterized the features of neurons within the TeA nucleus that are responsible for sensory response, sensorimotor decision-making, and motor command. Furthermore, information on diverse sensory stimuli is integrated into layer 5 (L5) intratelencephalic (IT) neurons as running signals; these IT neurons activate L5a critical pyramidal tract (PT) neurons, which project to the dPAG, and, together, these neurons form an intralayer IT–PT microcircuit for sensory‒motor decision-making and inducing running behaviours.

Results
The TeA is necessary for sensory-induced escape

We explored the stimulus conditions required to induce escape behaviours in mice exposed to three types of aversive sensory stimuli—sound, light, and air puffs—in an open-field escape model. The mice freely explored a custom, opaque acrylic box with side holes for the speaker, an LED, and a silicone tube, as well as central openings to prevent prolonged stillness. When the mice were located in one compartment of the experimental chamber, the random application of sound, light, or air puff stimuli at their current position invariably caused them to move away from the stimulus source to the opposite compartment, thus resulting in escape behaviour. No measurable freezing behaviour was detected either during stimulus presentation or during the execution of escape movements1 (Fig. 1a). Such escape behaviours were detected in all the experimental mice, whose running trajectories were recorded with a high-definition infrared camera positioned above the box. The data were used to create running speed heatmaps for each stimulus (Fig. 1b) and average traces for each unisensory stimulus (Fig. 1c). The sensory stimulus robustly evoked escape behaviour. The latency of running initiation following stimulation was highly temporally locked (1.84 ± 0.36 s), in stark contrast to the broadly distributed latencies of spontaneous running during the baseline period (Fig. 1d). This pronounced difference in distribution was quantified by comparing their cumulative distribution functions (CDFs) and was statistically confirmed (Fig. 1e). Based on this clear separation, we applied a conservative response window of [0, 2.4 s] poststimulus to classify evoked events (see Methods).

Among the stimuli, sound was the most effective at inducing flight behaviours, followed by air puffs and light (Fig. 1f). The latency between the onset of the sound stimulus and the flight behaviours was the shortest (Fig. 1g). The stimulation intensity of the sound, light, or air puffs was selected to evoke similar peak flight speeds across the three stimuli (Fig. 1h). The flight duration following sound stimulation was the shortest, followed by that resulting from light and air puff stimulation (Fig. 1i).

We transitioned from the open-field model to a head-fixed, turntable running model to facilitate interventions in the TeA, observe corresponding behavioural changes in the mice, and record neuronal responses. This model also allowed the running speeds of the mice to be recorded and a more detailed analysis of their movement characteristics in subsequent studies (Fig. 1j). After training, the head-fixed mice (different from those used in the open-field escape model) were able to freely walk or run on a turntable. An optical velocity sensor (blue module) placed beneath the disc recorded the rotational speed, which was used to calculate the running speed of the mice. The stimuli (sound, light, and air puffs) were delivered 10 cm in front of the mice, with the type of sensory stimulus pseudorandomly selected to minimize the variability in mouse movement induction (Fig. 1j). Example speed heatmaps of mouse flight induced by the three types of stimuli (Fig. 1k, left panel) showed significant differences after classification (Fig. 1k, right panel), which are more clearly observed in the averaged traces (Fig. 1l). The sensory stimulus elicited a highly time-locked escape response. The distribution of poststimulus running latencies differed significantly from the broad distribution of spontaneous running behaviours at baseline (Fig. 1m), confirming the evoked nature of the behaviour. The poststimulus responses were tightly concentrated, as reflected in the steep CDF (Fig. 1m, insert). Applying the objectively defined response window [0, 1.7 s], we found that the stimulus successfully evoked running in a significant majority of trials.

Among the stimuli, sound was the most effective at inducing flight behaviours, followed by air puffs and light (Fig. 1n). The latency between the onset of the sound stimulation and the flight behaviours was the shortest (Fig. 1o), whereas the peak flight speeds induced by sound, light, and air puffs were not significantly different (Fig. 1p). Moreover, the flight duration in response to sound stimulation was the shortest, followed by that in response to light and air puff stimulation (Fig. 1q). A comparative analysis of locomotor parameters, including probability, latency, peak speed, and duration, between the open-field running paradigm and the turntable-induced running model revealed convergent movement patterns in the mice, suggesting that distinct sensory modalities elicit fundamentally similar kinematic signatures during stimulus-evoked flight.

We bilaterally injected the adeno-associated virus (AAV)-hSyn-hM4D(Gi)-mCherry into the TeA, driving the expression of the inhibitory muscarinic designer receptor hM4D(Gi) in local cells to validate our hypothesis that the TeA is critical for encoding flight behaviours triggered by diverse sensory stimuli. Intraperitoneal (i.p.) injections of clozapine-N-oxide (CNO) activated hM4D(Gi) and inhibited neuronal firing43-46 (Fig. 1r), and sound-, light-, and air puff-induced flight behaviours were completely blocked (Fig. 1s). In contrast, i.p. injections of CNO had a minimal effect on control animals expressing only AAV-hSyn-mCherry without hM4D(Gi) (Fig. 1t). Second, since CNO i.p. injections still represent a chemical intervention, optogenetic interventions were performed to further confirm our findings. We performed bilateral injections of AAV-hSyn-eNpHR3.0-EYFP into the TeA to broadly express eNpHR3.0. Yellow light was then applied to suppress the firing activity of individual TeA cells (Fig. 1u). The bilateral application of yellow light effectively blocked the flight behaviours driven by the three sensory stimuli (Fig. 1v), whereas no blockade was observed in the control group injected with the hSyn-EYFP virus (Fig. 1w).

The results from chemogenetic and optogenetic interventions collectively confirm that the TeA is a critical node in the neural pathway mediating flight behaviours induced by diverse sensory stimuli.

The TeA–dPAG circuit contributes to both sensory-evoked and spontaneous running

Previous studies have established an association between the dPAG and flight behaviours15. We speculate that the inhibition of TeA neurons, which blocks the flight behaviours driven by the three sensory stimuli described above, may act through projections from the TeA to the dPAG.

Although previous studies have shown that TeA neurons project to the dPAG31,41, the details remain largely unknown. We characterized these projections further by injecting AAV-hSyn-EYFP into the TeA and observed high levels of green fluorescence in dPAG fibres (Fig. 2a). We next confirmed the projections of the TeA to the dPAG by injecting AAVretro-hSyn-mCherry into the dPAG, which retrogradely labelled TeA cells specifically in layer 5a (L5a) (Fig. 2b). These cells projected to the dPAG (TeA ) from the rostral to caudal direction (Fig. 2c), and the number of cells exhibited a Gaussian distribution (Fig. 2d). Different types of inhibitory neurons in the TeA47, such as somatostatin (SOM), vasoactive intestinal peptide (VIP), and parvalbumin (PV) neurons, are involved in processing sensory stimuli. We injected AAVretro-hSyn-EYFP into the dPAG of wild-type mice along with AAVretro-CaMKIIα (glutamatergic)-mCherry, as well as into the dPAG of SOM-, VIP-, and PV-Cre mice crossed with Ai14 mice to determine the specific neurons projecting to the dPAG (Supplementary Fig. 1a). The majority of TeA L5a neurons projecting to the dPAG were CaMKII-positive, with a colocalization rate of 72.4%. In contrast, TeA cells did not significantly overlap with inhibitory neurons (0.65% SOM-positive, 0.37% VIP-positive, and 0% PV-positive) (Supplementary Fig. 1b and c). Notably, the TeA region lacked PV neurons. As noted in previous studies48, the PV-IRES-Cre mouse line exhibits low efficiency in labelling PV-INs in the association cortex, which may be a common characteristic across regions in the association cortex.

We determined where the dPAG neurons receiving projections from the TeA ( dPAG neurons) are distributed by injecting AAV-Flp into the TeA and AAV-fDIO-EYFP into the dPAG to label dPAG neurons (Fig. 2e). The dPAG neurons were distributed in all regions of the dPAG from the rostral end to the caudal end, and their numbers similarly followed a Gaussian distribution (Fig. 2e and f). As an approach to further clarify their types, we used the Flp + fDIO virus to label the dPAG neurons of wild-type mice and three transgenic mouse strains in which different inhibitory neurons (SOM, VIP, and PV) are labelled (Supplementary Fig. 1d). The dPAG neurons were mainly CaMKII-positive (colocalization rate with CaMKII of 58%). A small percentage (5%) were SOM cells, whereas no VIP (0%) or PV (0%) neurons were observed (Supplementary Fig. 1e and f). The low proportion of inhibitory neurons aligns with the sparse distribution of these neurons in the dPAG. SOM inhibitory neurons were the most abundant among the three inhibitory neurons and were distributed primarily in the dorsomedial and intermediate parts of the dPAG (Supplementary Fig. 1g), whereas fewer VIP inhibitory neurons were scattered throughout the entire dPAG (Supplementary Fig. 1h), and PV inhibitory neurons were not present (Supplementary Fig. 1i).

As described above, the dPAG cells were primarily CaMKII-positive, while a small percentage were inhibitory cells (Supplementary Fig. 1f). We locally injected Cre (AAV-Cre-EYFP, TeA) + DIO (AAV-DIO-CaMKIIα-hM4D(Gi)-mCherry, dPAG) to specifically inhibit CaMKII-positive dPAG neurons (Fig. 3a). Subsequent i.p. injections of CNO specifically activated hM4D expression in CaMKII-positive dPAG cells (Fig. 3b) and blocked the flight behaviours of the mice. Similarly, no blockade was observed in the control group injected with the virus lacking hM4D (Fig. 3c and d).

Unsurprisingly, blocking neurons in the dPAG inhibited escape behaviours, as the PAG is a central hub for controlling defensive behaviours8; specifically, the dPAG contains “flight” neurons that directly control defensive running behaviours49,50. However, the cerebral cortex, particularly layer 5, is capable of monitoring the activity of neurons associated with running26. Direct activation of cortical layer 5 neurons can drive running behaviours in mice1, and these neurons likely project to the PAG3. We hypothesize not only that dPAG neurons directly influence behaviour but also that TeA neurons may be involved in the direct control of running behaviours. The CaMKII-positive TeA neurons that projected to the dPAG were mostly from L5a (Fig. 2b and c). We verified whether TeA neurons directly influence running behaviours by injecting AAVretro-CaMKIIα-eNpHR3.0-EYFP into the dPAG (Fig. 3e) to express eNpHR3.0 in CaMKII TeA cells (Fig. 3f). Bilateral stimulation with yellow light specifically inhibited glutamatergic TeA neurons, which also blocked flight behaviours, whereas no blockade was observed in the control group injected with the eNpHR-free virus (Fig. 3g and h).

The above results indicated that TeA neurons are necessary to induce escape from diverse sensory threats, such as sound, light, and air puff stimuli. Contextual escape at least includes contextual signalling and locomotion (running). We conducted additional experiments in an open-field environment to further validate the ability of TeA neurons to control running behaviours. We observed that the blockade of both TeA (Fig. 3i and j) and dPAG (Fig. 3m and n) neurons separately affected the ability of the mice to freely explore the open field (Fig. 3k and o). Moreover, the inhibition of TeA or dPAG neurons induced bradykinesia, as indicated by significant decreases in total distance travelled and movement speed (Fig. 3l and p, upper panels), and akinesia, as indicated by a substantial increase in immobility (Fig. 3l and p, lower left panels). However, the inhibition of these neurons did not affect the time mice spent exploring the central zone (Fig. 3l and p, lower right panels), indicating that anxiety-like behaviours were not affected. Similarly, no blockade was observed in the control group injected with the hM4D-free virus. These results indicate that the TeA–dPAG neural circuit is a key node that positively modulates general locomotor activity.

Taken together, the findings of these experiments indicate that CaMKII-positive projections from the TeA L5a to the dPAG regulate running behaviours.

Identification and characterization of three functional neuron types in the TeA region

The TeA, a region that both receives and integrates diverse types of sensory information, modulates behavioural output. Thus, it is likely to play a pivotal role in the transformation of sensory inputs into behaviours. We conducted in vivo, single-cell, loose-patch recordings in running animals to simultaneously monitor the firing activity of TeA neurons and the running speed of the animals (Fig. 4a, top panel). The recorded neurons were labelled with biocytin for localization (Fig. 4a, bottom panel). The stimulation paradigms employed for this analysis involved sound, light, and air puff stimuli, which differed from the parameters used in the behavioural experiments (sound: 50 dB, 12 kHz, and 1 s or 5 s; light: 2 lux and 1 s or 5 s; air puff: 0.8 L/min and 1 s or 5 s). The stimulus intensities were reduced because, despite the use of head fixation in the turntable model, high-speed locomotion compromised the stability of the in vivo electrophysiological recordings, and thus maintaining consistent single-unit isolation over extended periods was challenging. Reducing the stimulus intensity decreased both the efficacy and temporal precision of the evoked running. The latency distribution was broader, and the defined response window was consequently wider [0, 3.4 s] (Fig. 4b). At a reduced stimulus intensity, running was still evoked with a probability (51.58%) significantly exceeding spontaneous levels, and the behaviour occurred with a defined latency (2.574 ± 0.259 s), further confirming a time-locked relationship.

We recorded action potentials (APs) from a total of 104 single neurons and recorded the generation of flight behaviours in the mice during neuronal firing. These neurons were classified into two categories based on their responses to sensory stimuli and the relationship of their firing to running speed.

We identified running-related neurons (R-neurons) whose firing appeared to be associated with running rather than with sensory stimuli and whose firing preceded running (Fig. 4c). We begun by confirmed that these neurons did not respond to sensory stimuli. Based on the sensory stimulus–running time window of mice running on the turntable, we selected instances with a failure to elicit running behaviour. Alignment of the timing of each sensory stimulus revealed no response in the example neuron to any stimulus (Fig. 4d). We further examined the relationship between the firing rate and running by analysing spontaneous running events without sensory stimuli, aligning them to run onset and inspecting activity before running began. As shown in Fig. 4e, a consistent increase in the firing rate was observed before the onset of the spontaneous running, and the firing–running time window was [-1.995–0 s]. Based on this criterion, we analyzed all neurons recorded from the TeA region to identify R-neurons. (Supplementary Fig. 2a and 2b). The firing–running time window [-3.034–0 s] was determined by analyzing the peri-event time histogram (PETH) (see Methods).

We performed a cross-correlation function (CCF) analysis on the firing rate and running speed of the example R-neuron (Fig. 4c) to explore the potential correlation between neuronal firing and running (Fig. 4f). The CCF indicated that the strongest correlation between neuronal firing and running speed was observed when the speed trace was shifted forwards in time. The maximum correlation coefficient (r_max = 0.778) was observed to form a stable plateau for a time lag ranging from τ = 2.1 s to τ = 2.5 s, indicating a positive correlation between the two variables. The peak exhibited a broad temporal width, with a full width at half maximum (FWHM) of 10.588 s. The distribution of the optimal correlation time lag, which extended to 0.4 s, suggests a sustained relationship between neural activity and running speed parameters, as opposed to one precisely time-locked to a single instant. This result suggests that spiking may encode running speed, with the firing rate being continuously modulated during the running preparation and execution phases to represent speed information. The CCF results for all recorded R-neurons (Supplementary Fig. 2d) also revealed a significant and relatively stable positive correlation (r_max = 0.824 ± 0.08) between firing rate and running speed (Fig. 4g, left panel). The optimal time lag was centred at approximately 2.066 ± 0.222 s (Fig. 4g, right panel), and this value remained within the firing–running time window (-3.034–0 s). These results indicate that changes in firing activity occur prior to behavioural changes, supporting the functional role of spiking in encoding behaviour and suggesting that their relationship is characterized by a relatively stable temporal order.

The second category of neurons was defined as sensory-related neurons (S-neurons) (Fig. 4h). Similarly, based on the sensory stimulus–running time window defined using the turntable model, we selected instances where the sensory stimulus failed to elicit a running event. Aligning the time points of each sensory stimulus revealed that the example neuron (Fig. 4h) consistently responded to the sound stimuli (Fig. 4i). Analysis of the population peri-stimulus time histograms (PSTHs) for all recorded S-neurons under different sensory stimuli revealed that the response latency to sound stimuli was with 10-48.38 ms, to light stimuli was within 375.602–648.251 ms, and to air puff stimuli was within 29.65–71.763 ms (Supplementary Fig. 2c).

After confirming that these neurons responded to sensory stimuli, we further investigated the relationship between neuronal firing and running. We performed a CCF analysis of the correlations between the firing rates and running speeds for all the S-neurons. The results showed that among these neurons, a subset exhibited similarities to R-neurons, such that advancing the running speed curve resulted in the highest level of alignment between neuronal firing and running speed (Supplementary Fig. 2e). Fig. 4h presents an example from this neuronal subset, where the highest correlation coefficient (r_max = 0.540) showed a stable peak within a time delay ranging from τ = 2.5 s to τ = 2.8 s (Fig. 4k), indicating a positive correlation between the two variables.

The CCF analysis of this neuronal subset (Supplementary Fig. 2e) further demonstrated a significant and relatively stable positive correlation (r_max = 0.588 ± 0.081) between firing and running speed (Fig. 4l, left panel), with optimal time lags concentrated within the range of 2.455 ± 0.324 s (Fig. 4l, right panel). We defined this group of neurons, which respond to sensory stimuli while also exhibiting firing rates correlated with running speed, as SR-neurons. In contrast to SR-neurons, another group of neurons, which were capable of responding to sensory stimuli (Fig. 4m, j), showed no correlation or a significant but weak correlation (r_max < 0.3) between the firing rate and running speed based on CCF results (Fig. 4n, o and Supplementary Fig. 2f). This group was defined as unSR-neurons. We recorded neuronal responses to sound, light, and air puff stimuli in both SR-neurons and unSR-neurons (Supplementary Fig. 3). Comparison across groups revealed differences in the r_max between firing rate and running speed among the three neuron types (Fig. 4p). R-neurons exhibited the highest mean (r_max = 0.824 ± 0.08), followed by SR neurons (r_max = 0.588 ± 0.081), while unSR neurons had the lowest mean r_max (r_max = 0.152 ± 0.132).

The distribution of all 104 neurons in the TeA relative to the cortical surface is shown in Fig. 4q. TeA R-neurons and SR-neurons were predominantly concentrated in L5, whereas unSR-neurons, which were recorded less frequently, were located primarily in layers 2 to 5. Notable differences in the firing rates were observed among the three types of neurons (Fig. 4r); specifically, R-neurons had the highest firing rates, followed by SR-neurons and unSR-neurons. The firing latency of S-neurons to each individual sensory stimulus (sound, light, and air puff) was calculated. The sensory response latencies of SR-neurons were significantly longer than those of unSR-neurons (Fig. 4s). The behavioural latency in response to stimulus presentation was also calculated and named the running latency. This latency did not differ between SR- and unSR-neurons under identical stimulation conditions (Fig. 4t). Since R-neurons did not produce APs in response to stimulation with the three stimuli, the difference in time between the onset of behaviour-related firing and the onset of running was calculated (see the Methods) and referred to as the difference in the firing–running time. Similarly, the difference in firing–running time for S-neurons was calculated as the difference in time between the onset of related firing and the onset of running (Fig. 4u). The differences in the firing–running time between R-neurons (2.117 ± 0.24 s), and SR-neurons (2.449 ± 0.18 s) or unSR-neurons (2.5 ± 0.15 s) were significant.

Based on these results, we hypothesized that at least three types of neurons are involved in information processing from sensation to running behaviour. unSR-neurons respond to sensory stimuli and are localized upstream of SR-neurons. The SR-neurons translate sensory information into a running signal and relay this signal to R-neurons, which then are responsible for driving running behaviours. R-neurons might be TeA neurons, whereas SR-neurons might be those that receive projections from input sources, hereafter referred to as TeA neurons. Both types of neurons are located in L5, but they play different roles in the sensory-to-motor transformation process.

Contextual signals delivered to the TeA modulate sensory-induced escape

The TeA receives extensive input from sensory projections31,37,41,51-54. We investigated the characteristics of TeA neurons, which we proposed receive contextual signals from sensory sources and modulate sensory-induced escape, to test the hypothesis described in the previous subsection.

We injected AAVretro-hSyn-mCherry into the TeA to determine the sources and distribution of projections to this region (Fig. 5a, left panel). Abundant cells expressing mCherry were observed in the auditory cortex (AC: Au1, AuD, and AuV), somatosensory cortex (S1), visual cortex (V1 and V2L), dorsal lateral geniculate nucleus (DLG), and medial geniculate body (MGB) (Fig. 5a, middle and right panel). Neurons originating from the cortex were located mainly in L2/3 and L5, consistent with the findings of a previous report31.

We focused on five areas—the AC, S1, V2L, MGB, and DLG—and locally injected AAV-hSyn-Cre into each area to further elucidate the specific locations of the projections from different sources to the TeA (Supplementary Fig. 4a). Projections from AC and S1 were prominent and concentrated mainly in layers 2–5 of the TeA. Although the V2L, MGB, and DLG also projected to the TeA, these projections were not as robust (Supplementary Fig. 4b). For more precise visualization, we injected AAV-hSyn-Cre into the AC, S1, V2L, MGB, and DLG of different animals individually and Cre-dependent AAV-hSyn-DIO-mCherry into the TeA to label TeA neurons. Thus, TeA neurons receiving projections from different sensory areas ( TeA; e.g., TeA, TeA, TeA, TeA, and TeA) were labelled separately (Fig. 5b). Projections to the TeA neurons were located primarily in L5, with a small number distributed in other layers, whereas projections from the MGB were located mainly in L4 (Fig. 5c). A statistical analysis of the fluorescence intensity curves from different projection areas to the TeA matched these results (Fig. 5c).

We also investigated the neuronal types of TeA neurons receiving sensory projections ( TeA neurons). AAV-Cre was injected into different sensory cortices, and AAV-hSyn-DIO-mCherry was injected into the TeA to label neurons receiving distinct sensory inputs in the TeA (Supplementary Fig. 4c). Direct identification of these neurons was performed via GAD67 (a marker for GABAergic neurons) immunostaining (Supplementary Fig. 4d). The results revealed a low proportion of GABAergic neurons among these TeA neurons. The colocalization rates were 0% for the AC, 0% for S1, 0% for V2L, 0.40% for the MGB, and 0.90% for the DLG projections (Supplementary Fig. 4e). Since the cerebral cortex is primarily composed of glutamatergic and GABAergic neurons, these findings suggest that the TeA neurons receiving projections from different sensory areas, are mostly glutamatergic neurons.

We also attempted parallel labeling using a dual-virus strategy (CaMKIIα-DIO-mCherry and hSyn-fDIO-EYFP) (Supplementary Fig. 4f, g). We observed that the colocalization ratio of EYFP (green, broad spectrum) and mCherry (expression in magenta, glutamatergic) signals in labeled TeA neurons varied depending on the input region, with rates of AC: 50%, S1: 60%, V2L: 66%, MGB: 48%, and DLG: 51% (Supplementary Fig. 4e). The low overlap rate is likely attributable to differences in the expression efficiency of the two viral vectors. Therefore, based on the conclusive evidence from GAD67 staining (showing a near absence of GABAergic neurons) and to circumvent the technical limitations of dual-virus co-labeling efficiency, we selected the broadly expressed AAV-hSyn promoter for all subsequent labeling and manipulations. This approach allows us to confidently and comprehensively target the TeA neuron population, which we have established as predominantly glutamatergic.

Next, we sought to determine whether different sensory inputs converge to the same cells in the TeA. We injected viruses (AAV-hSyn-Cre and AAV-hSyn-Flp) into pairs of sensory areas (AC and S1, AC and V2L, and S1 and V2L) and a mixture of viruses (AAV-hSyn-DIO-mCherry and AAV-hSyn-fDIO-EYFP) into the TeA (Fig. 5d). Subsequently, Cre-expressing TeA neurons exhibited red fluorescence (expression in magenta), whereas Flp-expressing neurons exhibited green fluorescence. Although TeA neurons receiving projections from different pairs of sensory areas presented different rates of overlap (Fig. 5e, f), the differences between projections within each pair of sensory areas (magenta vs. green) were not significant, indicating that TeA neurons may receive multiple sensory inputs indistinguishably.

Although TeA neurons seemed to receive multiple types of sensory input, including auditory (Au1, AuD, AuV, and MGB), visual (V1, V2L, and DLG), and somatosensory air puff (S1) inputs, we still believe that different types of sensory information in the TeA exert varying degrees of influence on driving running behaviours. Therefore, we locally injected anterograde transsynaptic AAV-hSyn-Cre into the AC, S1, V2L, MGB, and DLG areas of the animals and AAV-hSyn-hM4D(Gi)-DIO-mCherry into the TeA. CNO was injected i.p. to selectively inhibit the activity of these targeted TeA neurons (Fig. 5g). As an example, we show hM4D-mCherry expression in TeA neurons (Fig. 5h). When activated by CNO, TeA neurons were inhibited, and both the running speed and the probability of running behaviours driven by the three sensory stimuli decreased (Fig. 5i). We systematically assessed the behavioural effects of inhibiting TeA neurons defined by inputs from the AC, MGB, S1, V2L, and DLG on the peak running speed and the probability of inducing running behaviours. Chemogenetic inhibition of these neuronal populations significantly reduced the probability of sound-induced flight behaviours. This effect was more pronounced when the TeA neurons targeted by AC and MGB projections were inhibited (Fig. 5j). In addition, inhibition of TeA neurons receiving inputs from V2L, DLG, or AC significantly affected light-induced flight behaviours (Fig. 5k), while inhibition of those receiving S1 input significantly affected air puff-induced flight behaviours (Fig. 5l). These results demonstrate that TeA neurons exhibit modality-specific functional roles in driving flight behaviours. While TeA neurons receiving AC or MGB inputs are crucial for sound-induced flight, those receiving V2L/DLG inputs are crucial for light-induced flight, and those receiving S1 inputs are crucial for air puff - induced flight. This input-defined functional specificity ensures appropriate behavioural responses to distinct threats. Despite this specificity, all three sensory inputs contributed to the overall regulation of flight behaviours. The presence of bisensory responsive neurons partly explains the influence of different stimuli on movement speed (Fig. 5e and f), although the impact of multisensory integration cannot be excluded.

Theoretically, motor behaviours induced by multisensory stimuli should be more robust than those caused by unisensory stimuli. The mice were presented with one, two, or three different types of sensory stimuli simultaneously after training to visually observe the behavioural responses induced by different types of stimuli (Supplementary Fig. 5a). We calibrated the intensity of the sounds, lights, and air puffs through multiple tests to ensure that each stimulus alone did not significantly drive running behaviours in the mice, which was confirmed by the low running speeds and low probabilities of successfully inducing running behaviours (Supplementary Fig. 5b). Then, we combined different types of sensory stimuli to produce multisensory stimuli. Compared with the unisensory stimuli, the multisensory stimuli significantly drove running behaviours (Supplementary Fig. 5c, d), with a significantly higher probability (Supplementary Fig. 5e) and peak speed (Supplementary Fig. 5f). Nevertheless, although the speed of the running behaviours induced by multisensory stimuli was notably faster than that of the running behaviours induced by the unisensory stimuli, the difference between combinations of two and three sensory stimuli was not significant (Supplementary Fig. 5f). These results indicate that multisensory inputs contribute to the overall modulation of running behaviours.

The integration of multisensory sensory information likely relies on the weights assigned to different sensations. The related stimuli can also vary in terms of characteristics such as the type, intensity, and duration of a single sensation. In this context, understanding which factors influence the corresponding weights in multimodal sensory integration is important. For auditory stimuli, these factors include frequency, intensity, and duration. Mouse escape behaviour is related to the perceived level of danger6,8, and in this context, higher-intensity auditory stimuli drive faster animal running behaviours1. Therefore, we focused on auditory stimuli with different parameters to further explore the mechanisms underlying unisensory stimulus integration.

We systematically altered the sound stimulus frequency, intensity, and duration to observe the corresponding effects on running latency, duration, and peak speed. We found that changes in frequency had minimal effects on the latency, duration, and peak speed of the induced running behaviours (Supplementary Fig. 5g, j–l, left panel). In addition, increasing the duration of the sound increased only the duration of running (Supplementary Fig. 5h, j–l, middle panel). The sound intensity, however, significantly impacted running latency, duration, and peak speed (Supplementary Fig. 5i, j–l, right panel). The results of these experiments indicated that auditory stimulus integration depends more on the intensity and duration of the stimulus than on the specific (frequency) characteristics.

TeA SR-neurons to R-neurons microcircuits

Based on the abovementioned hypothesis, we next checked whether SR-neurons translate sensory information into a running signal and relay it to R-neurons to initiate flight behaviours (running).

We investigated the properties of TeA and TeA neurons. Dual injections with AAV-hSyn-Cre in individual sensory regions (AC, S1, VC, MGB, or DLG) and AAV-hSyn-DIO-EYFP in the TeA (Fig. 6a, left panel) were performed to label TeA neurons, namely, TeA, TeA, TeA, TeA, and TeA neurons, in green. Concurrently, AAVretro-hSyn-mCherry was injected into the dPAG to label TeA neurons, visualized in magenta (Fig. 6a). The TeA neurons were located in L5a of the TeA, whereas the TeA neurons were located in L2–5b, with differences reflecting different input sources. We characterized the morphology of these two types of neurons. Performing three-dimensional reconstructions in regions with high viral expression intensity (Fig. 6a) posed certain challenges. For single-cell 3D reconstruction, we preferentially selected images containing only one isolated neuron (Supplementary Fig. 6a and c). A secondary strategy was to choose regions with relatively sparse viral labeling (Supplementary Fig. 6b and d, the white dashed boxes in the left panels). When two or more neurons were located in close proximity, it often became difficult to trace dendritic arbors accurately in two-dimensional planes. In such cases, we prioritized 3D reconstruction, rotating the model to broadly assess neuronal morphology, and made efforts to select neurons whose structures remained discernible after rotation (as indicated by the white arrows in the right panels of Supplementary Fig. 6b and d). Following these selection criteria, five cells from each group were ultimately chosen for reconstruction.

The reconstruction results revealed that both neuronal types exhibited a pyramidal morphology characterized by an apical dendritic tuft and basal dendrites around the soma. Nevertheless, TeA neurons receiving sensory inputs (Fig. 6a, green; 6b) had significantly more basal dendrites around the soma and broader apical tufts (thick-tufted) than TeA neurons, which displayed a relatively slender apical tuft (slender-tufted) (Fig. 6c). A 3D reconstruction of dendritic branches via Sholl analysis revealed significant differences in dendritic branching between the two types of neurons, particularly at the base of the soma and the distal dendritic branches of the terminal cluster. TeA neurons were thick-tufted, whereas TeA neurons were slender-tufted (Fig. 6d, e). Additionally, the soma diameter of TeA neurons was slightly larger than that of TeA neurons (Fig. 6f). These differences suggest that the two types of neurons may serve as distinct information channels, mediating different functions in sensation‒behaviour transformation. We postulate that TeA neurons in L5 integrate sensory information to drive the activity of L5a TeA neurons.

Do TeA neurons synapse with TeA neurons? We separately injected anterograde AAV-hSyn-Flp into the sensory regions (AC, S1, and V2L) and AAV-hSyn-fDIO-mCherry + AAV-hSyn-DIO-EYFP into the TeA. Concurrently, we injected AAVretro-CMV-WGA (wheat germ agglutinin)-Cre into the dPAG. Fig. 6g illustrates the principle of virus labelling, wherein WGA-infected neurons retrogradely and transsynaptically infect upstream neurons. The results revealed the presence of merged neurons (white) in TeA L5, with colocalization rates of 23.54% for AC, 15.07% for S1, and 12.99% for V2L projections (Fig. 6h and Supplementary Movie 1; see the Methods). These results indicate that TeA neurons directly project to TeA neurons.

We determined the function of this direct projection by injecting AAVretro-hSyn-mCherry in the dPAG to retrogradely label TeA neurons while injecting AAV-hSyn-Cre in the AC or S1 and AAV-hSyn-DIO-hChR2-EYFP in the TeA, which enabled TeA neurons to express ChR2. We subsequently performed in vitro whole-cell recordings of TeA neurons (Fig. 6i). After successful patch clamp sealing, we checked the firing characteristics of the cells by holding them at different currents. In response to a current pulse, TeA neurons displayed a brief burst (often observed as an AP doublet) followed by repetitive single nonadapting APs (Fig. 6j, top panel). In contrast, TeA neurons behaved as regular spiking neurons and showed pronounced changes in the firing rate in response to a depolarizing current pulse (Fig. 6j, bottom panel). Additionally, compared with the TeA neurons, the TeA neurons had a smaller AP half-peak width (Fig. 6k, top panel). When we compared the repeated firing characteristics of the two types of neurons, we found that the F/I (firing rate/current) slope of the TeA neurons was greater (Fig. 6k, bottom panel). Then, we optogenetically activated the TeA neurons with blue light (5 ms pulses delivered by a 5 mW LED with a frequency of 0.05 Hz)40, and excitatory postsynaptic currents (EPSCs) and inhibitory postsynaptic currents (IPSCs) were recorded from the TeA neurons (Fig. 6l). The IPSCs completely disappeared with the addition of tetrodotoxin (TTX) + 4-aminopyridine (4AP), whereas the EPSCs were not blocked but were weakened (Fig. 6l). These findings suggest that TeA neurons form direct monosynaptic excitatory connections onto TeA neurons.

Activating TeA neurons drives TeA neurons, whereas activating TeA neurons triggers running.

Thus far, we have identified two types of neurons, TeA and TeA, based on their projections, morphology and electrophysiology, as well as SR-neurons and R-neurons, based on their function, firing and relationship to running behaviours. We hypothesize that SR-neurons and R-neurons may correspond to TeA and TeA neurons, respectively, and that SR-neurons may activate R-neurons. One group of mice received injections of AAV-hSyn-Cre into the AC and AAV-hSyn-DIO-hChR2-EYFP into the TeA (Fig. 7a), whereas another group received injections of AAVretro-CaMKIIα-hChR2-EYFP into the dPAG (Fig. 7b) to selectively label and express ChR2 in TeA and TeA neurons. The control group received injections of AAV-hSyn-DIO-EYFP into the TeA to rule out direct effects of the virus and confirm that the effects were due to ChR2. Upon 20 Hz blue light stimulation (15 mW, 5 s), TeA-ChR2+ neurons responded consistently to each stimulus that induced running behaviours, in which the speed increased from baseline values to a plateau with repeated light stimulation (Fig. 7c). We then performed in vivo recordings in the TeA to identify neurons that were activated by this pathway but were not directly expressing ChR2. We identified the locally recorded, light-responsive, ChR2-negative neurons as AC-TeA-ChR2- responsive neurons (Thereafter referred to as AC-TeA-ChR2- neurons). These cells represent the functional postsynaptic targets of the AC to TeA pathway in our experimental paradigm. These AC-TeA-ChR2- neurons also responded to photostimulation. Their firing rates gradually increased across successive light pulses until reaching a plateau (Fig. 7d). This pattern was mirrored in the animal's behavioural output: the evoked running speed also increased progressively across trials before stabilizing (Fig. 7d). In contrast, the firing of the presynaptic TeA-ChR2+ neurons remained stable throughout the stimulation train (Fig. 7c), indicating that the observed facilitation originated within the local TeA circuit. The observed facilitation of both postsynaptic firing and behaviour suggests that the AC to TeA pathway engages a local microcircuit that exhibits short-term plasticity. Crucially, this microcircuit is positioned to directly influence running behaviour. Stimulation of TeA-ChR2+ neurons with each stimulus resulted in a firing response, and the firing rate was correlated with the speed of the induced running behaviour (Fig. 7e), whereas no response was recorded from TeA-ChR2- neurons (neurons upstream of TeA neurons, equivalent to TeA neurons) in TeA-ChR2+ mice. The firing rates of TeA-ChR2+ and TeA-ChR2+ neurons were significantly greater than those of AC-TeA-ChR2- neurons (Fig. 7f). Moreover, at the same stimulation frequency, the speed of the running behaviours induced by TeA neuronal activation was significantly faster than that induced by the activation of TeA neurons (Fig. 7g).

The firing patterns of the three neuronal types after exposure to blue light were strongly correlated with the running speed (Fig. 7c–e, correlation coefficient r in Fig. 7h). The average latency of the firing of AC TeA-ChR2+ and TeA-ChR2+ neurons was 4.36 ± 0.574 ms and 4.157 ± 0.476 ms, respectively, indicating direct effects of blue light stimulation on ChR2+ neurons (Fig. 7i). In contrast, the latency of the firing of AC-TeA-ChR2- neurons was 407.8 ± 60 ms, indicating that this firing was a result of synaptic activation from TeA-ChR2+ neurons following blue light stimulation (Fig. 7i). The difference in latency between these two types of neurons was similar to the difference in firing–running time between R-neurons and SR-neurons (332 ms; Fig. 4u). Additionally, the average difference in time between the firing of TeA-ChR2+ neurons and the initiation of animal running was 2.419 ± 0.184 s, which was longer than that for AC-TeA-ChR2- neurons (2.075 ± 0.202 s) and TeA-ChR2+ neurons (2.078 ± 0.148 s) (Fig. 7j). On the other hand, SR-neurons and R-neurons also exhibited a difference in time between firing and running (Fig. 4u), and the difference in time between these two neuronal types (332 ms) closely matched that between the AC TeA-ChR2+ neurons and the TeA-ChR2+ neurons (341 ms) (Fig. 7j). Together, these results suggest that TeA neurons represent a subset of SR-neurons, whereas TeA neurons represent a subset of R-neurons, although the possibility of multisynaptic neural circuits involving SR-neurons cannot be excluded. Notably, we observed a highly similar time difference between firing and the initiation of running in AC-TeA-ChR2- neurons (2.075 ± 0.202 s) and TeA-ChR2+ neurons (2.078 ± 0.148 s) (Fig. 7j). Combined with the finding that the facilitatory profile of AC-TeA-ChR2- neuron firing precisely matched the facilitation of running speed evoked by TeA-ChR2+ stimulation positions the activity of AC-TeA-ChR2- neurons as a direct predictor of behavioural plasticity. The convergence of these congruent temporal and dynamic response properties provides evidence that AC-TeA-ChR2- neurons are functionally equivalent to, and likely encompass, the TeA neuron population that directly governs running behaviour.

Since the activation of both TeA ( TeA) and TeA neurons can induce running behaviours in mice, the inferred delay between the two suggests that TeA neurons are downstream of TeA ( TeA) neurons. The synaptic relationship between these two types of neurons is likely the neural circuit mediating the transformation of sensory input into behaviour. We injected AAV-hSyn-Flp into the sensory cortex (AC or S1), AAV-hSyn-fDIO-ChR2-EYFP + AAV-CaMKII-DIO-hM4D(Gi)-mCherry into the TeA, and AAVretro-hSyn-Cre into the dPAG to further investigate this hypothesis. This labelling strategy allowed us to label TeA neurons and induce the expression of ChR2 while hM4D(Gi) TeA cells were selectively blocked after an i.p. injection of CNO (Fig. 7k). Subsequently, 20 Hz blue light stimulation of the TeA drove running behaviours in the mice. However, when we specifically blocked TeA cells, the ability to induce running in mice was abolished (Fig. 7l, m). Further analysis of the data across multiple animals further confirmed that after the TeA–dPAG circuit was blocked, the activation of TeA neurons, which are responsible for sensory input, could no longer drive running behaviours in mice (Fig. 7n). This observation suggests that the connection between TeA and TeA neurons is monosynaptic and supports the hypothesis that TeA neurons (namely, SR-neurons) transform sensory inputs into running signals and activate TeA neurons (namely, R-neurons) through weak monosynaptic connections. We surmise that this weak connection requires an extended period of temporal summation to sufficiently excite the downstream neuron, which in turn drives running behaviours. These results further confirm that the activation of the TeA–dPAG neural circuit is sufficient to elicit running behaviour. Given its capacity to integrate multisensory inputs, this circuit is proposed to function as a critical pathway orchestrating adaptive escape behaviour (Fig. 7b, e, k–n).

Taken together, the above results reveal a microcircuit within TeA L5 wherein different types of sensory information (such as auditory, visual, and somatosensory information) encoded by neurons from the cortex (e.g., A1, V1, and S1) and subcortex (e.g., MGB and DLG) serve as inputs and are potentially integrated and converted into running signals, which then activate R-neurons to produce running behaviours in the animal (Fig. 7o). The TeA CaMKII neurons in L5, which can receive different kinds of sensory inputs and have a broad apical tuft and relatively large number of basal dendrites, respond to sensory stimuli, and their firing rate is correlated with running speed. In contrast, the firing rate of L5a TeA neurons, which have a narrow apical dendrite tuft and few basal dendrites, is correlated with running speed, and these neurons can be activated by L5 TeA neurons. The activation of TeA neurons drives dPAG neurons and triggers running in the mice. Therefore, dPAG neurons serve as executors of running commands from TeA neurons, functioning as neurons controlling running (behaviour or motor activity). Alternatively, TeA neurons can be considered running (behaviour or motor) command neurons, whereas TeA neurons in L5 process diverse types of sensory information into running signals, acting as sensory running (behaviour or motor) decision neurons.

Methods
Animals

Male and female C57BL/6J (Laboratory Animal Center of Southern Medical University, Guangzhou, China), SOM-Cre (RRID: IMSR_JAX: 013044), Vip-Cre (RRID: IMSR_JAX: 031628), PV-Cre (RRID: IMSR_JAX: 008069), and Ai14 (Cre-dependent tdTomato reporter line RRID: IMSR_JAX: 007914) (Model Organisms Center, Shanghai) mice aged 4–12 weeks were used in this study. All the Cre driver lines were crossed with Ai14 mice. The mice were housed in a vivarium with the temperature controlled at 21–25°C, humidity controlled at 50–60%, and a 12-hour day/night cycle (lights on at 8 am). Food and water were available ad libitum. The experiments were conducted during the day phase. Implanted animals were habituated to handling by the researcher cupping the mouse in his or her hand for 5 min per day for 2 days before the start of the experiment. All animal procedures were conducted in accordance with the Regulations on the Management of Laboratory Animals (China) and were approved by the Animal Care and Use Committee of Southern Medical University (L2017207).

Viral vectors

For retrograde monosynaptic tracing, we used AAV2/retro-hSyn-mCherry (5.53×1012 vg/mL, BrainVTA), AAV2/retro-hSyn-Cre (2.2×1012 vg/mL, BrainVTA), AAV2/retro-hSyn-EYFP (5.18 × 1012 vg/mL, BrainVTA), AAV2/retro-CaMKII-mCherry (5.17× 1012 vg/mL, BrainVTA), and AAV2/retro-CMV-WGA-NLS-Cre (2.6×1013 vg/mL, OBiO).

For anterograde tracing, we used AAV2/9-hSyn-EYFP (4.6×1012 vg/mL, BrainVTA), AAV2/1-Cre-EYFP (4.6×1012 vg/mL, BrainVTA), AAV2/9-hSyn-mCherry (5.84×1012 vg/mL, BrainVTA), AAV2/1-hSyn-Flp (1.02× 1013 vg/mL, BrainVTA), AAV2/9-hSyn-fDIO-EYFP (5.27× 1012 vg/mL, BrainVTA), AAV2/1-hSyn-Cre (1.16×1013 vg/mL, BrainVTA), AAV 2/9-hSyn-DIO-mCherry (5.82×1012 vg/mL, BrainVTA), AAV2/9-CaMKIIα-DIO-mCherry (5.72 × 1012 vg/mL, BrainVTA), AAV2/9-hSyn-fDIO-EYFP (5.27×1012 vg/mL, BrainVTA), and AAV2/9-hSyn-DIO-EYFP (5.05×1012 vg/mL, BrainVTA). AAV2/1 was used for anterograde monosynaptic tracing86-88.

For optogenetic activation and inhibition, we used AAV2/9-hSyn-eNpHR3.0-EYFP (4.47×1012 vg/mL, OBiO), AAV2/retro-CaMKIIα-eNpHR3.0-EYFP (4 × 1012 vg/mL, OBiO), AAV2/9-Ef1α-DIO-hChR2(H134R)-EYFP (2.15 × 1013 vg/mL, OBiO), AAV2/retro-CaMKIIα-hChR2(H134R)-EYFP (8.07×1012 vg/mL, BrainVTA), and AAV2/9-hSyn-fDIO-hChR2-EYFP (5.25×1012 vg/mL, BrainVTA).

For the empty vector control group, we used AAV2/9-hSyn-EYFP (4.6× 1012 vg/mL, BrainVTA) and AAV2/retro-CaMKIIα-EYFP (3.8×1012 vg/mL, BrainVTA).

For chemogenetic activation, we used AAV2/9-hSyn-hM4D(Gi)-mCherry (2.5×1012 vg/mL, OBiO), AAV2/9-Ef1α-DIO-hM4D(Gi)-mCherry (5.18×1012 vg/mL, BrainVTA), and AAV2/9-CaMKIIα-DIO-hM4D(Gi)-mCherry (5.5×1012 vg/mL, BrainVTA). For the empty vector control group, we used AAV2/9-hSyn-mCherry (1×1012 vg/mL, OBiO), AAV2/9-Ef1α-DIO-mCherry (5.13 × 10¹² vg/ml, BrainVTA), and AAV2/9-CaMKIIα-DIO-mCherry (5.1 × 10¹² vg/ml, BrainVTA).

Animal preparation

Animal preparation was performed in a relatively sterile room at a temperature of 23–26°C.

Virus injection. Four- to five-week-old mice were anaesthetized with sodium pentobarbital (60–70 mg/kg, 15 mg/ml, i.p., Sigma, USA) and positioned in a stereotaxic frame (RWD Life Science, China). Body temperature was maintained at 37°C with a heating pad. After a median incision was made, small skull openings (≤ 0.5 mm2) were generated over the target sites with a miniature handheld cranial drill (RWD Life Science, China) to allow virus injection through a glass micropipette (tip diameter: 20–30 µm; Drummond Scientific, USA) driven by a microinjector (KD Scientific, USA). A 40–100 nl volume of the virus (depending on the desired expression strength and viral titre) was injected at a rate of 30 nl/minute. The stereotaxic coordinates (posterior to the bregma, lateral to the midline, and below the brain surface, in mm) for the different target sites were as follows: TeA: -2 to -3, 4.7, 0.5; A1: -3.0, 4.2, 0.5; S1: -1.7, 3.0, 0.6; V2L: -4.0, 3.4, 0.5; MGB: -2.8, 2.0, 3.0; DLG: -2.3, 2.2, 2.3; and dPAG: -4.0, 0.2, 2.2. The head was laterally rotated for precise injection at 80° into the TeA and A1. The glass micropipette was laterally rotated 15° for injection into the V2L. For injection into S1 and the MGB, DLG, and dPAG, the animal’s head was fixed as described by The Allen Institute (2011). After the injection, the virus needle was retracted to a depth of approximately 100 μm at the injection point and left in place for 5–10 minutes to allow the virus to fully spread. Medical sutures were used to close the wounds, and erythromycin ointment was applied to prevent infection. After the mice woke, they were returned to the animal facility for feeding. The mice were collected for the corresponding experiments 4 weeks after the virus injection.

Head fixation. Except for the animals designated for the open-field experiment, all the mice underwent head-fixation surgery. At least three days before the experiments, the mice were deeply anaesthetized with pentobarbital sodium (60–70 mg/kg, i.p., Sigma, USA) and fixed onto a stereotaxic instrument. Lidocaine was injected locally under the scalp, the skull was exposed, and the exudate was removed. A tripod-shaped head nail was attached to the skull with dental cement.

Electrophysiological recording window. For in vivo electrophysiological recordings in the TeA, some of the head-fixed mice simultaneously underwent craniotomy over the TeA with preservation of the dura. Vaseline was applied to cover the surface of the dura.

Cannula implantation. For optical stimulation of the TeA, one or two optic fibre cannulas (0.2 mm, 0.37 NA core diameter; Newdoon, China) with inner cores were embedded into the skull above the unilateral or bilateral TeA with dental cement during head-fixation surgery. The cannulas were anchored in a horizontal position within the TeA at a depth of 300 μm from the dura. After the operation, the mice were allowed to recover in their cages, and antibiotic ointment was applied to the wounds to prevent infection. Cannulas were not implanted in mice in which in vivo electrophysiological recordings were to be obtained at the same time as optogenetic activation during the experiment. Instead, an internal injection tube connected to the silicone hose or optical fibre jumpers connected to the optical fibre core pin was directly fixed to a micropropulsor (NARISHIGE, MN-151), which was slowly advanced to the anchor point in a horizontal position within the TeA at depths of 400–500 μm.

Running training. After three days of recovery (but prior to the electrophysiological recordings or behavioural experiments), the head-fixed mice were placed on a circular plate (diameter, 20 cm) to adapt to free running under head fixation conditions, in which the mice were head fixed by screwing their tripod pins to a metal pole. The height of the disc was adjusted so that the mice could adapt to free running on the disc while the head remain stable. Following three consecutive days of daily training (1–2 hours/day), 95% of the animals successfully adapted to the head-fixed experimental paradigm, demonstrating stable, self-initiated locomotion on the rotary disc platform with coordinated limb movements. Animals that failed to maintain such locomotor stability (5%), characterized by limb coordination deficits or complete immobility, were excluded from subsequent recordings and analysis to ensure experimental consistency.

Preparation for the recording sessions. The electrophysiological recordings and behavioural tests were all performed in a soundproof room at a temperature of 23–26°C. Individual recording sessions lasted for no more than 2 h. The animal was administered 5% sucrose drops through a pipette every hour. Some animals were subjected to more than one session of recording, separated by at least 1 day.

Sensory stimulation

Sound (pure tone, white noise), light (white light flash), and air puff stimuli were used, each of which was controlled by a Tucker–Davis Technologies System 3 (TDT3). The control programs were written with RPvdsEx software and loaded into the multifunction processor (RX6) of the TDT3 system. The three types of sensory stimuli were presented in a pseudorandom order. Each stimulus was repeated only 10 times to prevent habituation in mice, which could diminish behavioural responses, while also ensuring the validity of our results.

Sound stimuli were generated with the TDT3 system. The synthesized signals produced by RX6 (characterized as sine waves starting at the zero phase and a 5 ms rise/fall time) were amplified by an electrostatic speaker driver (ED1) and delivered through a loudspeaker (ES1, frequency range 2–110 kHz) placed 10 cm in front of the animal. The intensities of the sounds were controlled by a programmable attenuator (PA5). Before the experiments were performed, the loudspeaker was calibrated with an amplifier (Bruker and Kjaer 2610) connected to 1/4- and 1/8-inch microphones (Bruker and Kjaer 4135). All tone amplitudes are reported as the sound pressure level (SPL; 0 dB = 20 µPa). The sound parameters, such as the duration, frequency, and intensity, were controlled with an RP2.1 real-time processor controlled by BrainWare software. Pure tones were used with durations of 1, 5, and 8 s; frequencies of 6.5, 12, and 18.5 kHz; and intensities of 50, 80, and 110 dB SPL were used to drive running behaviours in the animals. White noise stimuli (2–50 kHz, 50 ms duration, and a 5 ms rise/fall time) were generated to examine the effects of drugs and viruses on AP recordings. Noise bursts varying in amplitude (0–90 dB SPL, 10 dB steps) were produced during the amplitude scan (A-scan). The sound parameters of the A-scans were controlled by BrainWare software through a computer and were presented pseudorandomly at a rate of 1/2 s. The A-scans were repeated 20–30 times to obtain a set of peri-stimulus time histograms (PSTHs).

Light stimuli were delivered through LED lamp beads driven by the RP2.1 real-time processor of the TDT3 system. The parameters of the white light flash, including the duration (5 s) and intensity (2 or 5 lux; calibration with KOMAX, Germany), were regulated through the RP2.1 real-time processor controlled by BrainWare software.

Air puff stimuli were generated through a microvalve (Kamoer-KVP04, China) controlled by an RP2.1 real-time processor. The air puff was presented through a tube connected to the microvalve placed 10 cm away from the animal. The air puff stimulation parameters included a duration of 5 s and an air intensity of 0.8 L/min or 1.2 L/min (calibrated with Darhor-LZB-2, USA).

For the multimodal sensory integration experiments, the intensities of the sound (30 dB SPL), light (1 lux), and air puff (0.4 L/min) stimuli were selected through multiple tests to ensure that none of them could significantly drive mouse running behaviours, while the stimulus duration was 5 s. We used the following stimulus parameters to record the firing of a TeA neuron: sound, 50 dB, 12 kHz, for 1 s or 5 s; light, 2 lux for 1 s or 5 s; and air, 0.8 L/min for 1 s or 5 s.

Manipulation

Chemogenetic manipulation. Mice that expressed hM4D(Gi) were intraperitoneally injected with CNO (0.33 mg/ml, 1 mg/kg). Prior to the formal experiments, a high-concentration stock solution of CNO (10 mg/ml) was prepared by dissolving CNO powder (BrainVTA, China) in sterile dimethyl sulfoxide (DMSO) (HY-Y0320; Med-Chem Express, USA). Briefly, sterile DMSO was added to the tube containing the CNO powder. The tube was tightly capped and vortexed vigorously for 30–60 seconds to achieve initial dispersion of the powder. The tube was then subjected to sonication in an ultrasonicator for 5–10 minutes to facilitate complete dissolution. The tube was inspected visually to confirm complete dissolution, indicated by a clear and transparent appearance without any undissolved particles at the bottom or on the walls of the tube. If the particles remained, the cycle of vortexing and sonication was repeated until the solution was entirely clear. Once fully dissolved, the stock solution was immediately aliquoted into single-use portions to avoid repeated freeze‒thaw cycles and stored at -20°C until use. The stock solution was thawed and diluted in sterile physiological saline (0.9% NaCl) to the desired working concentration (0.33 mg/ml) immediately prior to intraperitoneal (i.p.) injection into the animals.

Optogenetic manipulation. For optogenetic manipulation, an optical fibre jumper connected to an LED source (blue light 473 nm, yellow light 593.5 nm; THINKERTECH, China) was inserted at the target site at the proper depth for hChR2- or eNpHR3.0-expressing mice through the optic fibre cannula. The optic fibre tip was covered with black tape to prevent light leakage. No hChR2- or eNpHR3.0-expressing neural fibres or structures other than the target structure were present in the light pathway within 800 μm from the end of the optic fibre to ensure the specificity of the optogenetic stimulation. The axis of the light path was the same as the central axis of the optic fibre, and the NA of the fibre affected the illumination angle40. After each experiment, the brains were removed, sectioned and imaged under a confocal microscope to confirm the expression of hChR2-mCherry or eNpHR3.0-EYFP. Data from animals for which the injections were unsuccessful were excluded from further analysis.

Running behaviour test

Open-field escape model. A custom-made rectangular chamber was constructed from opaque acrylic material with dimensions of 50 cm (length) × 6 cm (width) × 15 cm (height). Two circular openings (4 cm in diameter) were made in the lower portions of both sidewalls to place high-frequency speakers for auditory stimulation, LED bulbs for light stimulation, and silicone tubing for air puff delivery. Additional 5 cm-wide apertures were placed at the mid-section of both sidewalls to prevent the mice from remaining stationary in a specific area of the chamber for extended periods. For the auditory stimulation experiments, high-frequency speakers were installed on both sides of the chamber. A high-definition infrared camera (LRCP10620, 20 FPS, China) mounted above the chamber allowed real-time monitoring of the locomotor patterns and positional changes of the mice from outside the shielded testing environment. The two speakers were independently controlled, allowing the experimenters to deliver auditory stimuli along specific directions through camera-guided position tracking using specialized imaging software. The chamber was elevated 30 cm above ground level to prevent animals from escaping through the lateral openings.

During the experimental procedures, the mice were gently grasped by the tail with forceps and positioned at the central opening as video recording commenced. Following a 10-minute acclimatization period, during which the mice typically occupied the end of either the left or right chamber, auditory stimulation was delivered through the speaker adjacent to the animal's position to elicit contralateral movement. The light and air puff stimulation protocols involved similar operational principles, with LED bulbs and silicone tubing replacing the speakers at the corresponding positions. The stimulation timing was randomized according to the real-time locomotor status of the animal, with each mouse performing 10 trials per stimulation type. Notably, no freezing behaviours were observed in the mice either prior to movement initiation or during sensory stimulation.

Turntable running model. After adapting to the head-fixing running paradigm, the mice could run freely on a plastic circular plate (diameter, 20 cm), under which a rotatory encoder (E2-US Digital, 20 kHz, USA) was mounted on the stubble to record the turning speed. An external high-definition infrared camera (LRCP10620, 20 FPS, China) positioned approximately 15 cm above the plate was used to monitor the behaviour of the animals; both the behaviour and the turning speed were monitored in real time. All the data were digitized and stored on a computer for online and offline analyses. Unless specified otherwise, all in vivo electrophysiology, optogenetic, and chemogenetic behavioural experiments were performed with a head-fixed turntable model.

The running behaviour tests and in vivo cellular recordings were performed in a soundproof room at 23–26°C. Sound, light, or air puff stimuli were delivered from a loudspeaker, an LED lamp, or a silicone hose placed 10 cm in front of the animal, respectively. During the experiments, all the equipment (the stimulation software BrainWare, the speed recording software US Digital, and the camera recording software used to collect the data) was triggered by the RP2.1 real-time processor with a custom-made program. The three sensory stimuli were presented in a pseudorandom manner with a 25-s interstimulus spacing, and this process was repeated 10 times. Each recording session lasted 15 minutes.

The open-field test was conducted during the night cycle, beginning 90 min after the lights were turned off and after an acclimation period of at least 2 h to the testing room. A 3-day window was maintained between the open-field tests to avoid intertest effects. The testing room was dimly lit by a lamp with a luminosity between 5 and 20 lux. The open-field test serves as a standard procedure for evaluating spontaneous movement and curiosity-driven actions in rodents. Experiments were conducted in a rectangular enclosure (50 cm length × 50 cm width × 40 cm height) partitioned into core and edge zones through the VisuTrack behavioural analysis platform (Shanghai XinRuan Information Technology Co., Ltd., China). The animals were carefully picked up by the tail from their housing units and centrally positioned within the testing area. Data acquisition commenced automatically upon the detection of the first motion-triggered sensor interruption and then continued continuously over a 5-minute period. The subsequent analysis of rodent navigation paths was conducted using the VisuTrack software suite.

Electrophysiological recordings

In vivo cellular recordings40,89. The running-trained mice were head fixed by screwing their tripod pins into a metal pole so that they could run freely on the running turntable, and the Vaseline and dura were removed. A patch pipette (tip diameter: 1.5 μm, resistance: 5–8 MΩ) filled with artificial cerebrospinal fluid (ACSF, containing (in mM) 126 NaCl, 2.5 KCl, 1.25 NaH2PO4, 26 NaHCO3, 1 MgCl2.6H2O, 2 CaCl2, 2 sodium pyruvate, and 10 glucose; pH 7.35–7.45; osmolarity 290–310 mOsm/kg) and controlled by a micromanipulator (Siskiyou, USA) was embedded into the TeA horizontally for in vivo cellular recordings. A slight positive pressure (0.5–1 psi) was applied before the pipette was embedded to prevent the tip from clogging. The pipette was quickly advanced to a depth of 100 μm and then further advanced in 1 μm steps. When the impedance changed to 5–10 MΩ, the pressure was switched to negative (-0.3 psi) until a loose seal was achieved (30–100 MΩ), indicating that the cellular attachment was suitable for recording spikes.

During the experiment, all the equipment (the stimulation software BrainWare, the speed recording software US Digital, and the camera recording software used to collect the data) was triggered by the RP2.1 real-time processor with a custom-made program to record the neuronal activity and the running speed and running state of the mouse while it ran on the turntable. Cellular recordings were performed in voltage-clamp mode (Vcmd = 0 mV) using a MultiClamp 700B amplifier (Axon, USA). The signals were bandpass filtered at 300–3000 Hz and sampled at 20 kHz. Clampex software was used to detect and record the original signal, and Clampfit and BrainWare software were used to observe and record neuronal firing (action potentials). Following the establishment of a seal resistance ranging from 0.2 to 1 GΩ90, the amplifier was switched from voltage-clamp to current-clamp mode. A positive direct current (3–10 nA, 1 Hz) was applied for 20 minutes to iontophoretically deliver biocytin for an assessment of neuronal morphology. The biocytin tracer was prepared as a 1% (wt/vol) solution to enable the subsequent visualization of neuronal morphology. A total of 20 mice were used for the intracellular recordings, with an average of 6–10 neurons recorded per mouse.

We primarily employed biocytin labelling to localize the recorded neurons and obtain their coordinates during recording; however, the labelling efficiency was suboptimal. Accordingly, for a subset of neurons, an alternative approach was adopted. Following the electrophysiological recordings, glass micropipettes with larger tip diameters than those of standard recording electrodes were filled with an eosin dye solution. This dye was then pressure-ejected (~2 psi) at the recording site. Subsequent frozen sectioning and histological examinations allowed us to confirm whether the recording site was located within the TeA.

Brain slice electrophysiology. The mice were anaesthetized with urethane40,91. After the mouse brains were rapidly dissected, 300-µm-thick coronal slices containing the TeA from the virus-infected hemisphere were prepared with a vibratome (Leica, VT1200S) in ice-cold cutting solution (60 mM NaCl, 3 mM KCl, 1.25 mM NaH2PO4, 25 mM NaHCO3, 115 mM sucrose, 10 mM glucose, 7 mM MgCl2, 0.5 mM CaCl2; 300–305 mOsm/L; pH 7.4). Slices were transferred to the holding chamber and incubated in fresh ACSF at 34°C to recover for 30 min. The slices were incubated at room temperature before recording. The cutting solution and ACSF were continuously bubbled with 95% O2 and 5% CO2. Slices were submerged in the recording chamber and perfused with ACSF at 3–4 ml/min using a pump (longer pump). Whole-cell patch-clamp recordings were performed under an upright microscope (Eclipse FN1, Nikon). Patch pipettes were filled with a K+-based internal solution (containing 140 mM K+-gluconate, 9 mM HEPES, 5 mM EGTA, 4 mM Mg-ATP, 0.3 mM GTP, 4.5 mM MgCl2, and 4.4 mM phosphocreatine sodium; pH 7.3; 295 mOsm). The spatial expression patterns of ChR2-EYFP and hSyn-mCherry were examined in each slice under a fluorescence microscope before recording; only slices in which the expression patterns were observed in the correct locations were used for further recording. In whole-cell current-clamp mode, the voltage response was recorded from the cells by injecting a step current (amplitude range -100 pA to 620 pA, step duration 300 ms, and interval 20 pA). EPSCs and IPSCs were recorded by clamping the cell membrane potential at -70 and 0 mV, respectively. Light-evoked EPSCs and IPSCs were recorded by delivering 5 ms of 5 mW blue LED light (473 nm, Thorlabs) through the objective to the TeA. Light-evoked PSCs were recorded before and after perfusion with 1 µM of the sodium channel blocker TTX and 1 mM of the potassium channel blocker 4-AP to measure the responses of different synapses.

Brain slices were prepared similarly, and whole-cell current-clamp recordings were obtained from neurons expressing hM4D(Gi) to test its efficacy. Spontaneous spikes were recorded from neurons expressing hM4D(Gi) before and after perfusion with CNO (10 μM) and after CNO wash out.

Immunohistochemistry

After the behavioural experiments were performed, the mice were deeply anaesthetized with pentobarbital sodium (60–70 mg/kg, i.p.) and sequentially perfused with saline and 4% (wt/vol) PFA (pH 7.4). The brains were subsequently removed and postfixed with 4% PFA at 4°C overnight. After the brains were cryoprotected with 30% (wt/vol) sucrose, coronal sections (40 or 100 μm) were cut on a cryostat (Leica CM1860, Germany).

For immunofluorescence staining, the brain sections were then washed with PBS (15 min, 3 times), incubated with 0.3% (v/v) Triton X-100 for 1 h, blocked with 5% normal goat serum (NGS, Boster) for 1 h at room temperature, and then incubated with primary antibodies, including an anti-CaMKII monoclonal rabbit antibody (1:200; ab52476; Abcam, USA), anti-GAD67 monoclonal mouse antibody (1:200; MAB5406; Millipore, USA) or streptavidin-Cy3 (1:200; 438315; Thermo Fisher Scientific, USA), in 5% NGS overnight at 4°C. For anti-CaMKII immunofluorescence staining, sections were incubated with the corresponding fluorophore-conjugated secondary antibody, goat anti-rabbit IgG Alexa Fluor 647 (1:500; A21244; Invitrogen™, USA), in PBS for 2 h at room temperature. Sections stained with the GAD67 antibody were incubated with goat anti-mouse IgG (H+L) cross-adsorbed secondary antibody [Alexa Fluor 488 (1:500; A32723, Invitrogen™, USA) ], followed by washes with PBS (10 min, 3 times).

Finally, all the sections were mounted onto microscope slides and covered with coverslips along with an anti-fade reagent containing DAPI (S2110; Solarbio, Beijing).

Image acquisition and quantification

The fluorescence signals were visualized with a laser scanning confocal microscope (A1R, Nikon) and analysed using ImageJ 1.4 (NIH) or NIS-Elements software. All images of each brain were captured with the same settings. Confocal images were acquired, and cells were counted using ImageJ. The imaging settings were kept constant for all groups of sections. The fluorescence intensity of the presynaptic terminals was quantified with ImageJ. All fluorescence images were obtained by capturing serial z-stack images through the 10X, 20X, or 40X objectives of the confocal microscope, and the cell morphology was reconstructed in volume view (A1R, Nikon). Confocal image stacks were imported into FIJI software. Background noise was reduced using the “Subtract Background” function, and contrast was appropriately enhanced to improve neurite visibility. The soma was identified either in the maximum intensity projection or in the optical slice where it appeared largest and most clearly defined. With the “Freehand selection” tool, the somatic contour was carefully traced, and its cross-sectional area (in μm²) was measured using the “Measure” command. The “Simple Neurite Tracer” plugin was used for semiautomated 3D reconstruction of dendritic and axonal processes. Tracing began at the soma, and paths were manually followed through the z-stack by placing nodes along the neurites. The plugin interpolates between nodes to create a 3D structure. Tracing continued until all visible neuronal processes were fully reconstructed. Sholl analysis was performed using the “Sholl Analysis” plugin. The plugin calculates the number of intersections between the neurites and each concentric circle, generating a profile of the dendritic density as a function of radial distance from the soma. All measured data, including the somal area and number of Sholl intersections, were exported for further statistical analysis and graphing using the appropriate software (e.g., GraphPad Prism, SPSS).

In accordance with the Allen Brain Atlas, each brain slice was converted to an 8-bit image by manually drawing selection contours that contained the brain region of interest. The fluorescence density was subsequently calculated by summing the grey value of each pixel using a blind method and dividing the sum by the number of pixels. The axonal density of each brain structure was normalized to the mean fluorescence density of TeA neurons and TeA neurons. Using standard histological methods and confocal microscopy imaging, we also validated the location of the optical fibre tips for all optogenetic behavioural experiments in this study.

Data collection and statistical analysis

BrainWare software for the TDT3 system and Clampfit10.2 software for the Axon amplifier were used to export and analyse the recorded electrophysiological data. The custom software Animal.exe (Shanghai Jiliang Company) was used to analyse and export the recorded video data. In addition to these data, all experimental data, including the number of APs per unit time (firing rate), firing duration, firing latency, and AP waveforms and the speed, latency, and duration of the mouse running behaviours, were extracted and analysed offline with MATLAB 2016b (MathWorks) software. Microsoft Excel 2016, Origin 8.0, and MATLAB 2016b were used for data processing and further analysis. We analysed the data with custom software (MATLAB, MathWorks). The recorded running speeds were aggregated and processed for each animal individually using custom MATLAB code. The 30 stimulus–movement trials per animal were categorized by stimulus type, resulting in three groups: 10 sound–speed curves, 10 light–speed curves, and 10 air puff–speed curves. Each set of curves was then aligned to the stimulus onset and averaged, producing a mean movement speed curve for each animal in response to each type of sensory stimulus. The average time-dependent speed profile and neuronal firing rate curve were smoothed with a smoothing function (smooth) in MATLAB. Specifically, we used MATLAB’s built-in function yy = smooth(y), which applies a moving average filter (window width = 0.5 s) to the column vector y and returns a smoothed column vector yy of the same length. we applied a compensatory delay to the latency measurements to correct for the effects of data smoothing on the calculated motion onset latency (i.e., advanced by approximately 0.2 s).

During the behavioural tests, the rotation speed of the turntable was measured with a rotatory encoder and recorded in real time89. We defined “running behaviour” as movements in which the running speed of the mouse exceeded 0.5 cm/s, and the onset of the running behaviour was defined as the point at which this speed threshold was first exceeded. The onset of sensory-evoked flight was defined as the time point when the speed exceeded the baseline level (mean speed during the 5 s prestimulus period) by 2 standard deviations (SDs). Neural data were analysed with custom MATLAB (MathWorks) scripts. A mean spike density function was constructed for each neuron by applying a Gaussian kernel (σ = 10 ms) to each spike.

Definitions of the Response Windows for Evoked Behaviours. We defined a response window for each experimental paradigm based on a comparative analysis of pre- and poststimulus running latencies to objectively distinguish sensory-evoked escape behaviour from spontaneous running. We confirmed that the distribution of running latencies following sensory stimulation significantly differed from the distribution of spontaneous running intervals during the pre-stimulus baseline period using a two-sample Kolmogorov‒Smirnov test. This significant difference confirmed that the poststimulus running events constituted a distinct, time-locked population. Given this clear separation, we defined the response window to capture the specific temporal characteristics of the evoked response. The upper bound of the window was set to the 99th percentile of the poststimulus-evoked latency distribution. This approach ensures that the window is tailored to the actual temporal profile of the stimulus-locked behaviour, providing a sensitive measure of elicitation efficacy. The spontaneous latency distribution was used as a null model to statistically confirm the specificity of the evoked behaviour within the defined window. We performed a binomial test to confirm that these responses were specifically evoked by the stimulus rather than occurring by chance. For all three experimental models, binomial tests were conducted against the null hypothesis that poststimulus running occurred at the spontaneous rate. The tests revealed that the number of runs within the defined response windows significantly exceeded the number expected by chance (P < 10-4 in each model), providing strong statistical evidence that running was specifically evoked by the sensory stimulus.

Neuronal Classification. Recorded neurons were classified based on their responses to sensory stimuli and the correlation of their spiking activity with running speed. To dissociate sensory responses from running-related activity, analyses of sensory responses were performed exclusively using trials in which sensory stimulation failed to elicit running behavior.

1. Assessment of Sensory Responses.

Trial Selection: To isolate pure sensory responses, we analyzed only trials where sensory stimulation did not successfully evoke running. A trial was defined as a "non-running" trial if the running speed remained below a threshold of 0.5 cm/s throughout the posts-timulus-running window [0, 3.4 s].

Response Definition: For each neuron and each sensory modality (sound, light, air puff), a stimulus-aligned peri-stimulus time histogram (PSTH; bin size = 10 ms) was constructed. A neuron was considered responsive to a given stimulus if the mean firing rate within the post-stimulus response window [0, 1] s showed a significant increase compared to the mean firing rate during a 2-s pre-stimulus baseline window (Wilcoxon signed-rank test, P < 0.05). The response threshold was defined as the mean baseline rate plus two times the standard deviation (baseline + 2 × SD). The mean baseline rate and its standard deviation (SD) were calculated from a 2 s pre-stimulus baseline window.

Definition of S-neurons: Neurons that exhibited a significant response to at least one type of sensory stimulus were classified as sensory-related neurons (S-neurons). Neurons not meeting this criterion proceeded to the assessment for running-related activity.

2. Assessment of Running Correlation.

Trial Selection: Running-related activity was assessed using all spontaneous running events (i.e., running initiated in the absence of any sensory stimulus ). Run onset was defined as the time point when running speed first exceeded a threshold of 0.5 cm/s.

Response Definition: Running onset times were aligned to time zero for each neuron. Peri-event time histograms (PETHs) were constructed (bin size = 10 ms) centered on running onset. A neuron was considered to exhibit a significant running-related change in activity if its mean firing rate during the defined pre-running window showed a significant increase compared to the mean firing rate during a quiescent baseline period (Wilcoxon signed-rank test, P < 0.05). The activity threshold for defining the onset of this change was set as the mean baseline firing rate plus two times its standard deviation (baseline + 2 × SD). The mean baseline rate and standard deviation were calculated from periods when the mouse was not running.

3. Final Classification

Cross-Correlation Function (CCF) Analysis: For each neuron, the CCF between its firing rate and running speed was computed over a time lag range of [-10, 10] s with 100 ms bins. The maximum correlation coefficient (r_max) and its corresponding time lag (τ) were recorded.

Significance Testing: The statistical significance of the observed correlation was assessed using a permutation test. A null distribution was generated by performing F = 1000 random circular shuffles of the running speed trace relative to the firing rate. The correlation was deemed statistically significant if the absolute value of the observed |r_max| exceeded the 95th percentile of this null distribution (P < 0.05).

Neurons were categorically assigned as follows: R-neurons (Running-related neurons): Neurons that were not classified as S-neurons (i.e., no significant sensory response) but showed a significant positive correlation (P < 0.05) between their firing rate and running speed. SR-neurons: Neurons that were classified as S-neurons (significant sensory response) and also exhibited a significant positive correlation (P < 0.05) between their firing rate and running speed. unSR-neurons: Neurons that were classified as S-neurons either show no significant correlation between firing rate and running speed (P ≥ 0.05), or exhibit a correlation (P < 0.05) with a maximum correlation coefficient r_max < 0.3.

Heatmap analysis of R-neurons and S-neurons in response to different sensory stimuli.

To clearly visualize the response patterns of different neuronal populations to sensory stimuli, we converted raw raster plot data into firing rate heatmaps. Each row represents the average activity of a single neuron across all trials. All recorded neurons in the TeA region were presented with three types of sensory stimuli: auditory (sound), visual (light), and tactile (air puff), and corresponding heatmaps were generated for each stimulus modality. Only trials without running were included in the analysis. Non-running trials were defined based on a stimulus‑running window: trials with no running events within 3.4 s after stimulus onset were considered unrelated to the stimulus, as determined by prior analysis of stimulus‑running latency. The time bin size was set to 0.1 s to balance temporal resolution and statistical stability. Stimulus parameters were as follows: stimulus onset at 2.0 s, stimulus offset at 7.0 s, stimulus duration of 5.0 s, and the analysis time window spanned 0 to 10 s (aligned to stimulus onset at 2.0 s).

For each neuron i, the average firing rate in time bin j was calculated as:

Frequency_ij = SpikeCount_ij /(N_i ⋅Δt)

where SpikeCount_ij is the total number of spikes of neuron i in time bin j across all non-running trials, N_i is the number of non-running trials for neuron i, and Δt is the bin width (0.1 s).

Heatmaps were generated using the hot colourmap, ranging from black (no firing) to white (highest firing rate). Each heatmap was internally normalized by min‑max scaling to preserve relative firing patterns and enhance visualization. This method was applied to analyse the response properties of different neuronal types in TeA: 29 R‑neurons showed no clear response to any of the three sensory stimuli, whereas 66 SR‑neurons and 9 unSR‑neurons exhibited clear stimulus‑locked increases in activity for all three sensory modalities, demonstrating distinct sensory‑evoked responses in the sensory neuron populations.

To examine the relationship between spontaneous running and neural activity in R-neurons, we generated heatmaps of peri-event time histograms (PETHs) aligned to the onset of spontaneous running events. Each row represents the average activity of a single neuron across all trials. For each R-neuron, spontaneous running onsets were detected throughout the recording session using a speed threshold method (running onset defined as the time point when speed first exceeded 0.5 cm/s. Running-aligned neural activity was analyzed as follows: For each spontaneous running event, we extracted neural firing data from 10 seconds before to 10 seconds after running onset (time zero). Only running events that met the following criteria were included: no other running events occurred within 10 seconds before the onset, and running speed reached at least 0.5 cm/s during the event.

The average firing rate for each neuron i in time bin j was calculated across all qualified spontaneous‑running trials using the same method as described for the sensory‑stimulus analysis (see above). Heatmaps were generated using the “hot” colourmap (black to white), with each row representing one R‑neuron’s average running‑aligned firing pattern across trials. Each heatmap was internally normalized using min–max scaling to preserve relative firing patterns while enabling visual comparison across neurons. This analysis was performed separately for the 29 R‑neurons that showed no sensory‑evoked responses, allowing us to examine whether these neurons exhibited running‑related activity during spontaneous locomotion.

Quantification of Response Latency. The onset of each sensory stimulus was aligned to time 2.0 s. Spike times from all trials for a given stimulus condition were pooled. Peri-stimulus time histograms (PSTHs) were constructed with a bin width of 10 ms to visualize the temporal dynamics of neuronal firing rates in response to each stimulus. The neuronal response latency was determined for each effective stimulus using a threshold-based method. The mean firing rate (baseline rate) and its standard deviation (SD) were calculated from a 2 s pre-stimulus epoch. The response threshold was defined as the baseline rate plus two times the standard deviation (baseline + 2 × SD). The response latency was identified as the first bin in the poststimulus period (starting from time zero) where the firing rate in the PSTH exceeded this threshold. Only neurons that showed a significant excitatory response to at least one stimulus type were included in the latency analysis.

Quantification of Running-Related Firing Activity. We defined a running epoch to identify neurons whose firing was modulated by spontaneous running. Spontaneous running onsets were aligned to time zero. Peri-event time histograms (PETHs) were constructed for each neuron using a 10-ms bin width centred on the running onset. The baseline firing rate was defined as the average firing rate when the mouse was not running. The standard deviation (SD) of the firing rate during this baseline period was also calculated. The onset of running-related neuronal activity was determined using a threshold-crossing method. The threshold was set as the baseline mean firing rate plus two times its standard deviation (Baseline + 2 × SD). The start of the running-related activity window was identified as the first time bin where the firing rate in the PETH exceeded this threshold. The temporal window of running-related activity was then defined as the period from this identified start time until movement onset (time zero). This window represents the period of a significant increase in the firing rate preceding observable running.

Statistical analysis and reproducibility

All key experiments were independently repeated at least three times with similar results. For representative micrographs shown in the figures, each image is representative of at least three independent experiments. The sample size (n) for all quantitative data is defined in the figure legends and represents biological replicates. This refers either to the number of individual animals or to the number of cells sampled from distinct animals, as explicitly stated in each legend. The number of biological replicates and statistical details for each figure panel are provided in the figure legends and/or Supplementary Data 1. OriginPro 2017 (OriginLab Corporation) or GraphPad Prism 10 (GraphPad Software) was used for statistical analysis and graphing. Shapiro–Wilk test was first applied to examine whether samples had a normal distribution. Student’s t tests and one-way repeated-measures or two-way ANOVA were used to evaluate statistical significance, unless stated otherwise. The Mann‒Whitney U, Kolmogorov‒Smirnov or Kruskal‒Wallis test was used if the data were not normally distributed. In the figures, significance levels are indicated as *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001, and n.s. indicates a nonsignificant difference. The results are presented as the means ± SDs, unless specified otherwise.

Data Availability: All relevant data are included within the paper and its Supplementary Information files. Source Data are provided with this paper.

Code Availability: We used MATLAB 2016b to write custom code for subsequent data processing. The MATLAB code for processing the neural, behavioural, and electrophysiology data is available at https://github.com/LiHe0606/Animal-speed-and-Neuron-firing-1-XiaoLab.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Review on \"Cells and their coding for running, backing away, rebound 1 running, stopping” by Chen et al. (NCOMMS-24-78436-T).\nGeneral comments on the two companion papers:\nThis paper has been submitted as a companion paper (NCOMMS-24-78434-T) by Zhongju Xiao and Wen Zhong. The two papers investigate neuronal circuits driving different defensive behaviors (running away, stopping, backing away and rebound running) in the dorsal periaqueductal grey (dPAG), temporal association areas (TeA) and superior colliculus (SC). The authors use a combination of viral tracing, projection selective neuronal manipulation and single-unit extracellular recordings in head-fixed behaving mice to define the neuronal circuits driving defensive behaviors.\nThe two papers cover two distinct aspects of the circuitry with only little overlap. The first paper (Chen et al., NCOMMS-24-78436-T) describes the role of two pathways, TeA-to-dPAG and SC-to-dPACG in driving running and backing away, involving two distinct cell populations in the dPAG. The second paper (Li et al., NCOMMS-24-78434-T) investigates in more details the TeA neuronal circuits involved in integrating sensory cues from different modality into motor action (running) showing that L5 IT cells in TeA integrate sensory information from sensory cortical areas and thalamic nuclei and drive L5PT cells projecting to dPAG to initiate running.\nBoth papers present exciting and new findings that are largely supported by an impressive set of experimental data using state of the art methodologies. I find particularly attractive the combination of cell-type- and projection- specific manipulations combined with single-cell electrophysiology that provides a straightforward functional characterization of the neuronal circuits involved in simple innate behaviors.\nOn a less positive note, I found both papers not easy to fully grasp due to lack of clarity. This comes both from a purely formal aspect with the writing of the paper – some sentences are just difficult to understand (starting with the title!) - and from some lack of explanation about the experimental procedures and data analysis.\nBoth papers would also require a better and more throughout analysis of the neuronal spiking data using more standard approaches to disentangle what part of the activity is related to sensory cues and movements (not only running speed). It would be good to homogenize as much as possible the analyses across the two papers, in particular for the neuronal activity (see my comments below).\nIn addition, I have a series of more specific concerns and questions for each paper that I detail below for this paper (Chen et al. NCOMMS-24-78436-T):\nMajor comments:\n- It is really not clear in this paper whether we are dealing with spontaneous or sensory-evoked running behavior. Line 98 seems to indicate that sensory stimuli are presented but there is no indication of sensory stimuli in the figures (1a-b and 2a-b) and no further mention of sensory stimuli in the rest of the paper. If running is largely evoked by sensory stimuli, that should be taken into account in the analyses: the sensory-evoked responses might become a confounding factor for the correlation between neuronal activity and running, and a more careful analysis of sensory-evoked and running related activity need to be carried out – in line with what is done in the second paper.\n- Overall, I am not convinced by the analysis of the neuronal activity and the correlation of firing rate with running speed. My understanding is that the correlation is computed after realigning the neuronal activity and the running speed curves for each event (running bout), which will likely artificially maximize the correlation and does not seem right to me. If the increase in neuronal activity does not reliably correlate with running at short and fixed latencies across trials, one cannot conclude that this neuron encodes running speed. The time-lag between the neuronal activity and the running speed could be measured in a more unbiased and simpler way by computing the cross-correlogram and defining the time-lag at the peak of the cross-correlogram. Then this specific time-lag should be used to fit the one-phase function only for the neurons, if any, with strong significant correlation. My impression when looking at the data presented is that the activity of TeA neurons at single cell level might be a good predictor of running onset time (initiation) but is a poor predictor running speed (due to important trial-to-trial variability). A better approach would be to use a more standard Generalized Linear Model (GLM) approach (fitting neuronal activity using running speed as regressor) or decoding approach (predict running speed from neuronal activity).\nAltogether I believe that a simpler and more standard analysis using event triggered PSTHs aligned to different event times (running onset, stimulus onset) and ROC analysis would be just as good. It would also help to clarify how running-responsive cells are defined. In any case, the authors should show single-neuron examples and grand-average PSTHs to be show overall time course of the neuronal activity relative to different events (onset of running, sensory cues…)\n- I have also a general conceptual problem with the time delays between neuronal activity and movement onset time reported in this (and the companion) paper. In a similar study, Evans et al. (Nature 2018 https://doi.org/10.1038/s41586-018-0244-6 see figure 3) report running latencies in the range of 0.2 s when stimulating dPAG neurons and 0.5 s when stimulating SC. In the current paper, stimulation of TeA or dPAG neurons also seems to evoke running with short latencies (based on the example sessions displayed in the figures). However, the time differences between running and firing rate reported (figure 2c) are really long (about 0.8 s for dPAG neurons and 2 s for TeA neurons on average) which seems incompatible with the claim of a direct monosynaptic connection from TeA to dPAG driving running.\n- Anatomy of the recorded neurons (Figure 1f-i): this part would also need clarification. There is no explanation about how the coordinates of each recorded neuron are determined to obtain the distribution map in panel 1g. Figure 1g should indicate orientation of the map (DV/AP) and a scale bar. I find really odd that R-Neurons are so well confined within TeA area considering that movements affect neuronal activity everywhere in the brain (see Steinmetz et al, Nature 2019 and Stringer et al., Science 2019 just as examples). Looking at the image in panel 1f, it seems clear that the anatomy of the recorded neurons could only partially be recovered. That is certainly enough to identify the neurons as pyramidal cells and their respective location, but I do not think the reconstructions in panel 1i are meaningful.\n- Provide more details about the extracellular recording procedure. It seems that the recordings in TeA are performed by inserting the pipette orthogonal relative to the cortex surface, which suppose a close to horizontal angle. How is it achieved practically speaking (size of the craniotomy, recording chamber, reference electrode, how is the brain protected during the recording)? Also describe the protocol for juxtacellular labelling.\n- line 158-163: this sentence does not make sense to me.\n- line 302-303, figure 4g: Backing and stopping should be dissociated. Interestingly, the duration of ‘backing’ appears to be constant, regardless of the duration of the opto-stimulation, but the duration of ‘stopping ‘lasts as long as SC is stimulated!\n- line 360-363: This is a reasonable assumption but it is not shown in the current study. That could go to the Discussion.\n- Provide more details about the extracellular recording procedure. It seems that the recordings in TeA are performed by inserting the pipette orthogonal relative to the cortex surface, which suppose a close to horizontal angle. How is it achieve practically speaking?\nMinor comments:\n- First paragraph of Results (lines 89-94) should move to the Introduction.\n- Line 210: it should be specified that the AAV-Cre virus used here is an anterograde transsynaptic serotype that will drive expression of Cre in the post-synaptic neurons receiving inputs from TeA.\n- Line 281, figure 4e, f: I do not see any sign of ‘ backing away’ when the stimulus is withdrawn?!\n- Clearly indicate on each figure panel when is the stimulus presented (ex: figure 3 a-e2 right panel, 4f, 4g2 and h2 …)\nSylvain Crochet\n(Please do leave my name apparent to the authors)", "reviewer_2": "The study by He et al. aims to investigate the role of the TeA (temporal association area) as a hub for multisensory integration driving escape behavior through microcircuit computations. While the breadth and depth of the experiments—encompassing behavior, anatomy, physiology, and causal manipulations—are impressive, and some findings do indeed support the authors' claims, the overall narrative suffers from significant shortcomings.\nThe manuscript is plagued by numerous inconsistencies in experimental design, logical reasoning, and the interpretation of results. The narrative lacks cohesion, with critical connections between key experiments and conclusions often left underdeveloped or unclear. Instead of building a compelling and logical story, the sheer volume of data and experiments overwhelm and obscure the central message, detracting from the paper's impact.\nAlthough a few specific examples are highlighted below, these issues are pervasive throughout the manuscript. As it stands, the paper is disjointed and challenging to follow, making it difficult to discern a coherent scientific narrative. A thorough and thoughtful revision is required to address these issues before the manuscript can be considered suitable for peer review.\nBelow are a few examples of major concerns with the manuscript (minor comments are far too numerous to list):\nThe central theme of “running” as highlighted in the title and abstract feels misaligned with the actual findings, which seem to emphasize sensory-evoked escape behavior instead.\nFurthermore, the broader significance of the study is poorly articulated. The authors must clarify why this research is interesting and important within the larger context of behavioral neuroscience.\nThe text is frequently unclear, with superficial or anecdotal explanations.\nThere are innumerable inconsistencies between figure labeling and the main text, making interpretation challenging.\nThe English language must undergo significant editing for grammar, style, and overall readability to meet minimal academic standards.\nSome core experimental designs lack clarity and justification. For instance, the claim that AAV-Cre (source) and DIO-virus (target) represent transsynaptic labeling is fundamentally questionable. This combination typically labels neurons projecting from the target to the source region, not synaptically connected downstream cells. This issue alone casts doubt on the interpretation and validity of conclusions drawn from these experiments.\nThe framing suggests a focus on multisensory integration, yet the experiments alternate inconsistently between unisensory and multisensory paradigms, leaving the narrative fragmented.\nThe analysis of interneuron anatomy and connectivity feels redundant and poorly integrated into the overarching story.\nThese problems, combined with the sheer volume of data and experiments, result in a manuscript that is difficult to follow and comprehend. The connections between experiments, results, and conclusions are weak, and the narrative lacks cohesion.\nIn summary, the manuscript requires substantial revision to address these conceptual, technical, and narrative flaws. Without these changes, it remains disjointed and exceedingly difficult to read as a coherent scientific manuscript.", "reviewer_3": "This manuscript by Li et. al presents the interesting and somewhat provocative claim that multi-sensory integration, decision-making, and generation of motor command to initiate walking all occur within an intra-laminar circuit in layer 5 of temporal association cortex (TeA). The work does a careful anatomical characterization of the projection from TeA to periaqueductal grey (PAG) and a series of manipulations of this pathway, with increasing specificity as the manuscript progresses.\nHowever, the only readout is stimulus-evoked locomotion. Locomotion in other contexts would need to be assayed to determine specificity. Furthermore, analyses of locomotor responses to each of the stimuli are not sufficiently presented to interpret the effects. More generally, the results are sloppily presented, with inconsistencies in presentation, a lack of justification for selection of data for illustrations, a lack of detail needed to interpret statistical comparisons, and numerous saturated anatomical images. The intersectional anatomical characterization of input in Figure 3 is valuable, but for a more specialized audience. Key experiments for necessity are missing, which would rule out other pathways, such as ACC, sensory-to-PAG, mesencephalic locomotor region, or cortico-striatal pathways. Along these lines, more broadly, many circuits and pathways are known to be involved in sensory-driven locomotion and thus the results of this paper are a modest advance that would require more careful experiments, analysis, presentation, and interpretation to be persuasive.\nMajor concerns:\nOrganization of the results lacks introductory and other transitional sentences, so it is not clear how one section relates to the next. For example, first the first two sections with headers ‘Open field running model.’ And ‘turntable running model’ describe two behaviors but it is not clear where things are going. Are both going to be used later? What is the purpose of the open field model given the turntable model is described as having some advantages but no disadvantages.\nThe framing around multisensory may create confusion, since the paper does not look at multisensory integration (e.g. stimuli/cues with components/features from multiple senses). Rather, it presents sound, light, or air puff on separate trials. As such, they are studying a set of related associations for the 3 senses in isolation. From this perspective, there are gross overstatements of the results, such as this: “With the above experiments, we have established that the TeA-dPAG pathway is the neural circuit associated with multisensory-induced movement.” And “Therefore, the TeA must have the function of integrating various sensory information.” This is partially addressed by experiments in Figure 4g-l; most of the paper is about uni-sensory motor responses and framing should be adjusted, accordingly.\nThe descriptions of analyses and selection of data to be presented are not clearly motivated or described to give confidence in what is being compared and why.\nThe results are sloppily presented, with inconsistencies in presentation, a lack of justification for selection of data for illustrations, and a lack of detail needed to interpret statistical comparisons. Numerous examples are given below in ‘minor concerns’ section, as well as a few ‘big ones’ here in the major concerns section.\nIt is not clear if ‘Silencing TeA blocks running’ experiments are done in the freely moving or head-fixed paradigm. Similarly, it is not clear in panel l and m of Figure 1 if these speed calculations are done for sound, light, or air puff. There is a smillar issue with the optogenetic experiments, where is is not clear if the running under investigation is triggered by sound, light, or air puff.\nPhysiological recording for the CNO experiments in Figure 1 are lacking, so it is not clear what effect the CNO has had on TeA and other structures.\nAAVretro is known to leak into neighboring cells. It would be helpful to confirm the projection from TeA layer 5a to PAG using a confirmatory method, such as pseudorabies or fluoro-gold.\nSome images are saturated (e.g. 1k,q; 2a1; 3a,c; e1b; e2g; e3a) or show signs of injection damage (2a2), raising concerns about data quality. Images should be adjusted so they are not saturated and extent of tissue damage at injection site should be discussed and/or addressed as needed with new experiments.\nIt seems possible that the CNO and optogenetic experiments in Figures 1 and 2 are a ‘hammer’ and are shutting to locomotion and behavior rather broadly, rather than indicating the specific multimodal integration and transformation to a motor signal that the paper suggests. It would be helpful to determine if locomotion in other contexts (e.g. plus maze exploration) is impacted. Also, CNO injections into virus control animals would be needed to mitigate e.g. tissue damage concerns.\nIn Figure 2, the responses to the 3 different stimuli should be parsed (walking to sound, light, and airpuff).\nThe characterization of neuronal responses in TeA to running versus sound stimuli, although intriguing, do not demonstrate much about the integration process. Furthermore, running related signals are extremely prominent across neocortex, and thus not surprising or very informative regarding this study.\nThe critical experiment of silencing the TeA-dPAG neurons and determining if that blocks sound/light/air-puff induced locomotion appears to be missing. This is the experiment that would test if those neurons are necessary for the behavior. Without this experiment, it seems likely that many other pathways are also involved and/or sufficient.\nMinor concerns:\nFigure 1f shows peak running speed and the authors claim the stimulation intensities were carefully chosen to evoke similar running speeds. It would also help to see average, rather than peak, running speed, given that this peak running speed may be insensitive to differences in evoked running patterns by the stimuli used.\nHow were 10 trials shown in 1c selected? What does these plots look like for different animals and single trial?\nIt is not clear in Fig. 1d-f how the stas were done. There are 7 animals and 7 data points, suggesting each point is an animal. This should be stated. The type of post hoc test is not stated. And the F values are reported as 2,18 for degrees of freedom. How do these numbers relate to the 7 mice and 7 data points?\nWhy are walk bout durations not shown for the freely-moving task in Figure 1?\nIt is not clear if it is the same, or different, 7 animals used in the freely moving versus head-fixed behavior.\nThis statement “However, the characteristics of the running, such as the probability, delay, and peak speed (Fig. 1j), were not ignificantly different between the turntable model and the open field model (Fig. 1c-f), except for the running duration.” Is not justified based on what is shown in panel ‘j’, at least as described. Again, how the F values were determined, and what the groups were, etc., is not clear.\nThe co-localization rate reported in Fig. S1 is ambiguous; they should report both false positive and false negative rates (i.e. were there any retrogradely labeled cells that were negative for camKII/SOM/VIP/and PV? Also, why was AAVretro used for the CaMKII labeling?\nThe purpose of this sentence is unclear: “Due to the scarcity of cholinergic and dopaminergic neurons in the cortex, we did not perform corresponding tests on these neuron types.” Why are cholinergic and dopaminergic neurons singled out for mention? And in fact there are cholinergic neurons in neocortex (Von Engelhardt, J., Eliava, M., Meyer, A. H., Rozov, A., & Monyer, H. (2007). Functional characterization of intrinsic cholinergic interneurons in the cortex. Journal of Neuroscience, 27(21), 5633-5642.).\nStatistical quantification of significant reduction for Figure 4d-f is lacking.\nExperiments in Figure 4m-r would benefit from a CNO or optogenetic manipulation. More broadly, some form of psychometric manipulation to understand how graded responses varies with graded manipulation would strengthen the manuscript.", "reviewer_4": "Li et al. 2025 - An intra-layer microcircuit for multisensory-motor decision and running\nI congratulate the authors on a powerful manuscript. It is thought-provoking and reads nicely with a logical structure. Included are an awe-inspiring array of experiments. Particularly noteworthy are the number of methodological approaches (pharmacological, chemogenetic, and optogenetic) to address the same research question. This contributes to the convincing nature of the manuscript. The figures include appropriate panels showing the data clearly, clear micrographs of the injections and expression patterns, and the legends are complete with relevant statistics and descriptions.\nThe overall claim of the paper can be argued to be quite extraordinary. A single piece of association cortex (TeA) is responsible for routing and integrating not just one, but multiple sensory signals to evoke running behavior, thus encompassing a significant part of the sensorimotor pathway.\nI have two main comments, several additional comments, and minor (editorial) remarks. I hope that by addressing these major comments, the manuscript could present its findings more cohesively while maintaining a balanced and scientifically rigorous narrative. I look forward to seeing this exciting work refined and published.\n1. Clarification of Running Behavior Context and Interpretation.\nI had a hard time understanding how the authors think about running behavior. The introduction starts with reviewing innate defense and escape behaviors that include running. From lines 55-63 it appears that running is equated with escape behaviors – while escape behavior consists of more complex sequences of behavioral elements including running. The results section talks about the running with the TeA circuit being almost directly responsible for the motor command signals being sent. Then in the discussion, the authors present the sensory stimuli as more neutral than the aversive stimuli used on other studies. However, 110 (!) dB white noise auditory stimuli, a bright inescapable light and an air puff in the eye (classic aversive stimulus) can NOT be called neutral. These are classic aversive stimuli. Moreover, it becomes completely puzzling to me how the authors think of (the adaptive role of) systematic running following neutral stimuli. Without any context dependence such a proposed circuit would be highly disadvantageous behavior (i.e. starting to run with the onset of any neutral stimulus). On top of this the dPAG is of course pivotal in innate defense and escape behaviors, including running but also freezing.\nThe methods lack a lot of detail on the running behavior in the freely moving scenario. When was the sensory stimulus presented and where, on different sides? Did the behavior depend on the current location of the animal? Was the mouse always running away from the stimulated side and express thigmotactic behavior? What was considered the onset of running? Did the mice freeze before running?\nLine 55: Running is usually chosen… I would argue that this is not true and depends on the research. In perceptual decision-making licking, nose poking or forepaw movements are often used as behavioral indices in multisensory decision making tasks. In the context of fear, innate and defense research, freezing is also often used.\nIn sum, the authors need to argue better if and why they think of running as different from escape behavior or not.\nThis brings me to a related point. Brain-wide activity changes associated with the behavioral state of mice have been shown throughout literature; most prominently in (Stringer et al. 2019), and across most studies studies now studying awake behavior in mice. Neurons throughout cortex, and in particular deeper layers correlate with locomotion state and running speed (Niell & Stryker 2010, Saleem et al. 2013, Steinmetz et al. 2019, Musall et al. 2019, Vinck et al. 2015). An alternative interpretation of the signal of deep TeA layers is thus that these neurons correlate with running as any other part of mouse cortex (Fig. 7d supports this – nonChR2 cell modulation of TeA activity when running) and rather than motor command signals, that their outputs reflect the threat-relevance of sensory stimuli to dPAG. Perhaps the authors can discuss to what extent the data support a direct role in motor initiation versus alternative interpretations that consider the context of the behavior, such as that TeA provides the contextual signals necessary to signal a situation in which escape behavior is necessary. To further clarify, I suggest:\n-Providing additional context about the adaptive relevance of the observed behavior.\n-Expanding the methods to include critical details about the running behavior in freely moving animals, such as:\n• How and where stimuli were presented.\n• Whether the animals' location influenced their responses.\n• Onset criteria for running and whether the animals exhibited freezing behavior beforehand.\n2. Latency and Circuit Dynamics\nThe authors present a circuit in which sensory signals coming from respective cortical sensory systems project to TeA. After an intralaminar projection, the outputs of TeA project to dPAG responsible for the running behavior. The latencies of the proposed pathway, tens of milliseconds, is very different from the timescale at which the behavior is elicited (Fig. 1e, 1j). Why does it take 2 seconds to start to run? Are the animals freezing first? Coming back to the first point, is this escape behavior? Methods details are lacking here. Line 1291: What is the smoothing kernel size? How was latency to start of running estimated? How does the smoothing kernel affect latency estimates?\nFrom Figure 1b it appears that the running speed in the freely moving context is not a smoothly increasing function, but rather a small bout in the middle of stimulus presentation. Why do the animals run only the middle second? Then the dynamics in the head fixed scenario, Fig. 1h, are different but still have onset latencies of 1 sec or more. How do the authors account for the difference between the output of TeA (100ms?) and running behavior (1-2 sec?)? Is dPAG integrating over time? Moreover, some neural latencies do not match with the proposed circuit. The latencies to light stimulation for example are not in line with the suggested cortical circuit: VC to TeA, where VC responds at 50-60 ms latencies, but TeA around 400 (Fig. 5h).\nI suggest therefore greater clarity on:\n• Specify how running onset latency was measured (e.g., smoothing kernel size, its effect on estimates).\n• Address why the running speed in Fig. 1b peaks during the middle second of stimulus presentation.\n• Reconcile neural response latencies (e.g., VC to TeA at 50-60 ms vs. 400 ms for TeA, Fig. 5h) with the behavioral timescale. If the authors maintain that the pathways depends on weak connections that need 100s of milliseconds to accumulate, they need to argue how this system is robust to noise, evolving temporal dynamics of the brain and changing behavioral objectives.\nMinor comments\n- I was confused by the usage of multimodal stimuli in the manuscript. The authors also use multisensory and multimodal to refer to effects and situations in which different sensory modalities alone can produce similar effects (e.g. line 200). As the manuscript also involves more classical multisensory experiments with simultaneous sensory stimulation, this can be confusing. My suggestion is to reserve multimodal/multisensory for bimodal or trimodal stimulation. I don’t see what is lost if the term multisensory would be reserved for experiments involving simultaneous sensory stimulation. E.g. line 218: Multisensory inputs converge to TeA   Sensory inputs converge to TeA.\n- There are several conclusions in the results sections that are not (yet) supported by the data. I strongly suggest that the authors adjust their claims to precisely match the data shown. For example:\n- Line 318: “These results indicate…” It has not been shown that the multimodal integration (here I am specifically referring to the synergistic interactions between the modalities) that induces running behavior is taking place in TeA. Only that pathways from early sensory cortex to TeA are involved in eliciting running behavior, and (Fig 4) that multimodal stimuli can interact to produce running behavior. The circuits supporting the integration of different sensory modalities that underlie the change in elicited behavior could still be elsewhere.\n- Line 338: “These experiments indicate…” This statement is problematic. First it makes a claim about the whole research field of multimodal sensory integration, whereas (1) no multimodal stimuli were tested, only sound, and (2) one limited behavior was tested. Second, the statement generalizes to other modalities not tested.\n- Line 533: It believe it was not specifically shown that this weak connection requires an extended time to sufficiently excite downstream neurons (perhaps move to discussion of proposed circuit).\n- Line 406-413: formulate as hypothesis. Only part of the claims here is shown.\n- Line 376: Biologists love categorization, but in this scenario, it is unclear whether it is justified and what is gained. Do the authors believe the categorization into 14 neuron types is biologically important and future research should focus on the differences between subtypes? We know from earlier work (Raposo et al. 2014) that neurons in higher order mouse cortex show mixed selectivity that defy categorization. It is likely that the reported sensory subtypes come from continuous distributions. It is hard to judge, the Methods lack any description of how significant responsiveness was calculated, which statistics were used. If similar to running analyses the standard deviation of the baseline was used, which time frames served as baseline? Moreover, how was running and sensory responsiveness dissociated? (If sensory stimulation causes the mice to run, then to what does the neuron respond?). The presented single neurons are not convincing and highlight the problem. For example, the neuron in Ext. Data Fig 4c, is it responding to light stimulation or running? And the neuron in Ext. Data Fig 4d, responding to air puffs, or increasing its firing rate upon running? Misinterpreting behavioral state related signals as and dissociating them from sensory signals has recently been an important topic in (multi)sensory research (Zagha et al. 2022, Bimbard et al. 2024, Oude Lohuis et al. 2024) Supporting the statement that there are meaningful categories of multisensory responsive neurons would thus require showing distributions of a metric of sensory responsiveness + low-latency sensory-evoked responses that are independent of behavioral state.\nFurther notes:\nLine 64: two sensory information, information has no plural. Similarly, line 221, various information..\nLine 158: retrospectively -> retrogradely?\nFig. 1k, inset of waveform missing for Mus+? 7 TeA cells in 3 animals. Why 7 animals for muscimol, but only 3 animals have cells recorded? Some more clarity in the methods about how many cells were recorded across animals and whether there were exclusion criteria would help interpretability.\nFrom line 192: consistency, why PAG and not dPAG?\nLine 281, ‘distinguish’ is a verb, not a noun\nLine 290, an article is missing ‘AC-TeA projection loop’\nFigure 3, legend missing for panel f\nFig. 3d: how anterograde? Or retrograde?\nFig 3j: The resolution is too poor to judge the overlap in expression. Make sure the resolution is sufficiently high and/or include a closeup.\nLine 324: ‘Even within a single modality’… this is hardly a surprising statement with rich worlds of sensory signals in each modality.\nLine 357: Specify for the lazy reader how this stimulation intensity compares to earlier experiments and why this intensity was used.\nLine 363: ‘Connections’, poor word usage as it might be interpreted anatomically and can be described sticking closer to the results: ‘whose firing rate correlated with running speed’\nLine 401: S-neuron, here and below I suggest to use plural (S-neurons) or S-neuron type.\nLine 411: ‘correspond to’ -> activate?\nLine 441: the virus was retrograde, but was not injected retrograde.\nLine 477. I confused the results of ChR2- cells in the experimental groups with the control animals. Perhaps the authors can include additional phrasing that help distinguish the behavioral results of the optogenetic control cohort with the distinct analysis of ChR2 pos and neg cells.\nFig 6h: the presence of at least one merged neuron can be observed, but a quantification is missing, similar to earlier in the manuscript (Fig 3k).\nLine 564: adjust word order.\nLine 578: What are the differences between the TeA circuit and SC circuit. Do they overlap, play a role in different circumstances? How come TeA inhibiton can block the running behavior with intact SC circuit?\nLine 591: ‘involved in’… Involved in what?\nLine 586-592: I find the positioning of this study in the literature a bit off. In comparison to the cited references, this study also epitomizes on circuit mapping techniques to discover pathways and cell types involved in specific sensorimotor transformations. No proper cellular mechanisms are described. Moreover, the phrasing ‘special technics’ (techniques) suggest a newly introduced technique whereas the study uses commonly available tools and techniques, albeit a comparably sophisticated level of circuit investigation.\nLine 881: degrees -> percentage?\nMethods:\nLine 1000: a retro-pseudotyped virus was used for anterograde tracing?\nLine 1074: What does ‘maintaining a stable state on the recording platform’ mean? Were the mice spontaneously running? What percentage of animals does ‘most animals’ mean and what was done if animals did not ‘maintain a stable state’?\nLine 1289: What does randomized batch processing refer to?\nLine 1276: No fiber photometry data is presented in the study.\nLine 1296: ‘produced apparent’ -> ‘were’?\nLine 1129: Pharmacal -> pharmacological? + revise sentence grammar\nLine 1152: 800 mm -> um?\nLine 1156: Were animals excluded based on expression? If so, on what criteria and how many?\nLine 1383: ‘The ordinates.’ What about the ordinates?\nLine 1315: What is meant by that the raw data is available in the main text?\nLine 1321: Where is the code available?", "reviewer_1_attached": "1 Dear Reviewer,\n3 \"Revised manuscript without track changes-He Li.pdf\" file.\n4\n6\n8\n11 driving different defensive behaviors (running away, stopping, backing away and\n12 rebound running) in the dorsal periaqueductal grey (dPAG), temporal association\n13 areas (TeA) and superior colliculus (SC). The authors use a combination of viral\n14 tracing, projection selective neuronal manipulation and single-unit extracellular\n15 recordings in head-fixed behaving mice to define the neuronal circuits driving\n16 defensive behaviors.\n19 pathways, TeA-to-dPAG and SC-to-dPACG in driving running and backing away,\n20 involving two distinct cell populations in the dPAG. The second paper (Li et al.,\n22 involved in integrating sensory cues from different modality into motor action\n23 (running) showing that L5 IT cells in TeA integrate sensory information from\n24 sensory cortical areas and thalamic nuclei and drive L5PT cells projecting to\n25 dPAG to initiate running.\n27 impressive set of experimental data using state of the art methodologies. I find\n28 particularly attractive the combination of cell-type- and projection- specific\n29 manipulations combined with single-cell electrophysiology that provides a\n30 straightforward functional characterization of the neuronal circuits involved in\n31 simple innate behaviors.\n33 clarity. This comes both from a purely formal aspect with the writing of the paper\n34 – some sentences are just difficult to understand (starting with the title!) - and\n35 from some lack of explanation about the experimental procedures and data\n36 analysis.\n38 neuronal spiking data using more standard approaches to disentangle what part\n39 of the activity is related to sensory cues and movements (not only running speed).\n41 papers, in particular for the neuronal activity (see my comments below).\n42\n44 significantly improved our manuscript. On the basis of your feedback, we have\n45 restructured the overall framework of the manuscript, clarified key concepts, and\n46 improved the logical flow.\n47\n50 consistency, and conceptual clarity of the paper, resulting in better readability and\n51 impact. Furthermore, both the semantics and grammar have been substantially refined.\n52\n54 articles adopted a unified processing method when analyzing whether neurons are\n55 related to motor or sensory functions, further clarifying the types of neurons. All points\n56 have been addressed in the revised version. Regarding the specific point-by-point\n57 responses, they have already been addressed in the replies to another manuscript (Chen\n58 et al. NCOMMS-24-78436-T)."}, "author_response": "1 Dear Reviewer,\n3 \"Revised manuscript without track changes-He Li.pdf\" file.\n4\n6\n8\n11 driving different defensive behaviors (running away, stopping, backing away and\n12 rebound running) in the dorsal periaqueductal grey (dPAG), temporal association\n13 areas (TeA) and superior colliculus (SC). The authors use a combination of viral\n14 tracing, projection selective neuronal manipulation and single-unit extracellular\n15 recordings in head-fixed behaving mice to define the neuronal circuits driving\n16 defensive behaviors.\n19 pathways, TeA-to-dPAG and SC-to-dPACG in driving running and backing away,\n20 involving two distinct cell populations in the dPAG. The second paper (Li et al.,\n22 involved in integrating sensory cues from different modality into motor action\n23 (running) showing that L5 IT cells in TeA integrate sensory information from\n24 sensory cortical areas and thalamic nuclei and drive L5PT cells projecting to\n25 dPAG to initiate running.\n27 impressive set of experimental data using state of the art methodologies. I find\n28 particularly attractive the combination of cell-type- and projection- specific\n29 manipulations combined with single-cell electrophysiology that provides a\n30 straightforward functional characterization of the neuronal circuits involved in\n31 simple innate behaviors.\n33 clarity. This comes both from a purely formal aspect with the writing of the paper\n34 – some sentences are just difficult to understand (starting with the title!) - and\n35 from some lack of explanation about the experimental procedures and data\n36 analysis.\n38 neuronal spiking data using more standard approaches to disentangle what part\n39 of the activity is related to sensory cues and movements (not only running speed).\n41 papers, in particular for the neuronal activity (see my comments below).\n42\n44 significantly improved our manuscript. On the basis of your feedback, we have\n45 restructured the overall framework of the manuscript, clarified key concepts, and\n46 improved the logical flow.\n47\n50 consistency, and conceptual clarity of the paper, resulting in better readability and\n51 impact. Furthermore, both the semantics and grammar have been substantially refined.\n52\n54 articles adopted a unified processing method when analyzing whether neurons are\n55 related to motor or sensory functions, further clarifying the types of neurons. All points\n56 have been addressed in the revised version. Regarding the specific point-by-point\n57 responses, they have already been addressed in the replies to another manuscript (Chen\n58 et al. NCOMMS-24-78436-T).\n59\n60\n61\n62\n65 \"Revised manuscript without track changes-He Li.pdf\" file.\n66\n68\n70 area) as a hub for multisensory integration driving escape behavior through\n71 microcircuit computations. While the breadth and depth of the experiments—\n72 encompassing behavior, anatomy, physiology, and causal manipulations—are\n73 impressive, and some findings do indeed support the authors' claims, the overall\n74 narrative suffers from significant shortcomings.\n76 logical reasoning, and the interpretation of results. The narrative lacks cohesion,\n77 with critical connections between key experiments and conclusions often left\n78 underdeveloped or unclear. Instead of building a compelling and logical story, the\n79 sheer volume of data and experiments overwhelm and obscure the central message,\n80 detracting from the paper's impact.\n82 throughout the manuscript. As it stands, the paper is disjointed and challenging to\n83 follow, making it difficult to discern a coherent scientific narrative. A thorough\n84 and thoughtful revision is required to address these issues before the manuscript\n85 can be considered suitable for peer review.\n86\n88 feedback, we have restructured the overall framework of the manuscript, clarified key\n89 concepts, and improved the logical flow. Additionally, we have redesigned the figures,\n90 provided new experimental data, and conducted additional data analysis. The entire\n91 manuscript has been thoroughly revised and polished by professional English language\n92 editors. Your insightful suggestions have significantly improved the coherence, logical\n93 consistency, and conceptual clarity of the paper, resulting in better readability and\n94 impact. Furthermore, both the semantics and grammar have been substantially refined.\n95\n97 comments are far too numerous to list):\n98 -The central theme of “running” as highlighted in the title and abstract feels\n99 misaligned with the actual findings, which seem to emphasize sensory-evoked\n100 escape behavior instead.\n101\n103 understand that simply equating defensive escape with running is inaccurate and that\n104 this conceptual ambiguity would confuse readers, negatively affecting the paper's\n105 readability and appeal. On the basis of your suggestions, we have revised the conceptual\n106 framework of the entire manuscript and thoroughly rewritten the text, with particular\n107 emphasis on the Title, Abstract, Introduction, and Discussion sections. These\n108 modifications have significantly improved the manuscript's impact, clarity, and overall\n109 readability.\n110\n111 -Some core experimental designs lack clarity and justification. For instance, the\n112 claim that AAV-Cre (source) and DIO-virus (target) represent transsynaptic\n113 labeling is fundamentally questionable. This combination typically labels neurons\n114 projecting from the target to the source region, not synaptically connected\n115 downstream cells. This issue alone casts doubt on the interpretation and validity\n116 of conclusions drawn from these experiments.\n117\n119 required further clarification, and so the manuscript now contains detailed explanations\n120 with supporting references “Among them, AAV2/1 was used for anterograde\n121 monosynaptic tracing82-84.” [Methods (line 806)] As you correctly noted, early\n122 applications of the AAV-Cre and DIO-virus systems were primarily used to label\n123 neurons projecting from the target to the source region. However, emerging evidence\n124 has demonstrated that certain AAV serotypes, particularly AAV1, exhibit anterograde\n125 transsynaptic transduction properties1-5. Notably, Zingg et al.6 systematically\n126 demonstrated that AAV1 and AAV9 are capable of anterograde transsynaptic spread. In\n127 their study, delivery of AAV1-Cre to presynaptic neurons robustly and selectively drove\n129 tracing and functional manipulation of input-defined pathways. This established AAV1\n130 as a validated anterograde transsynaptic tracer, a role that has been subsequently\n131 leveraged by multiple studies to identify neuronal subpopulations receiving direct\n132 inputs (e.g., mapping Y-nucleus neurons receiving X-derived projections).\n133\n135 anterograde transsynaptic labelling. In our experiments, injection of AAV2-1-Cre into\n137 target) successfully labelled TeA dPAG neurons (see Fig. 3b1-b2 and c1-c2), confirming\n138 the efficacy of the system for pathway-specific labelling. We appreciate your critique\n139 and hope that the clarified methodology and cited literature adequately address your\n140 concerns. Please let us know if further elaboration would be helpful.\n141\n142\n144 pathway in turntable model mice: dPAG neurons (b), and dPAG CaMKII neurons (c). 1, Viral\nTeA TeA\n145 injection site and chemogenetic (CNO) and optogenetic (LED) interventions. 2, Viral expression.\n146\n147 -Furthermore, the broader significance of the study is poorly articulated. The\n148 authors must clarify why this research is interesting and important within the\n149 larger context of behavioral neuroscience.\n150\n152 framework was unclear, as it erroneously equated defensive escape with running and\n153 misattributed the multisensory integration observed in the behavioural experiments to\n154 integration in the TeA. These errors inadvertently affected the importance of our study\n155 in elucidating the relevant sensory-to-motor information processing pathway and its\n156 underlying cellular mechanisms. We have thoroughly revised the Introduction and\n158 neuroscience. We are grateful for your insightful comments, which have helped us in\n159 correcting these issues and in improving the scientific rigor of the manuscript.\n160\n161 -The text is frequently unclear, with superficial or anecdotal explanations.\n162 -There are innumerable inconsistencies between figure labeling and the main text,\n163 making interpretation challenging.\n164 -The English language must undergo significant editing for grammar, style, and\n165 overall readability to meet minimal academic standards.\n166\n168 primarily stemming from conceptual ambiguities—notably, the oversimplification of\n169 defensive escape as running and the inaccurate interpretation of experimental results\n170 where behavioural multisensory integration was incorrectly extended to integration\n171 within the TeA. Additionally, we acknowledge the grammatical, stylistic, and overall\n172 readability shortcomings in the original text. With your invaluable guidance, we have\n173 thoroughly revised the manuscript to: 1. Clarify and refine the core concepts underlying\n174 the research; 2. Restructure the logical flow of the arguments; and 3. Correct all\n175 inaccuracies regarding results interpretation. Moreover, we have engaged professional\n176 editors to improve language quality, including grammar, syntax, and overall readability.\n178 scientific rigor and presentation quality. We deeply appreciate your expert suggestions,\n179 which have made these essential revisions possible.\n180\n181 -The framing suggests a focus on multisensory integration, yet the experiments\n182 alternate inconsistently between unisensory and multisensory paradigms, leaving\n183 the narrative fragmented.\n184\n186 our description of the ability of the TeA to perform multisensory integration was\n187 inappropriate. Therefore, we have revised the corresponding content in the main text of\n188 the manuscript. For example, we have moved the text “multisensory integration” and\n189 “Auditory sensory integration depends on the intensity and duration of the stimuli” to\n190 relatively unimportant parts of the manuscript, although we wanted to delete them. We\n191 greatly appreciate your suggestion and apologize for our previous lack of precision in\n192 the statement.\n193\n194 -The analysis of interneuron anatomy and connectivity feels redundant and poorly\n195 integrated into the overarching story.\n196\n198 moved the results investigating the TeA-dPAG projections from the original\n200 dPAG circuit have been moved from the original Supplementary Fig. 1 and 2 to\n202\n204 interspersed with our exploration of the cell types in the TeA-dPAG circuit, creating a\n205 mutually complementary flow of the narrative. At the same time, it prevents readers\n206 from deviating from our main points while preserving logic.\n208\n209 1 Oh, S. W. et al. A mesoscale connectome of the mouse brain. Nature 508, 207-\n210 214,(2014).\n211 2 Salegio, E. A. et al. Axonal transport of adeno-associated viral vectors is\n212 serotype-dependent. Gene Ther 20, 348-352,(2013).\n213 3 Hutson, T. H., Kathe, C. & Moon, L. D. Trans-neuronal transduction of spinal\n214 neurons following cortical injection and anterograde axonal transport of a\n215 bicistronic AAV1 vector. Gene Ther 23, 231-236,(2016).\n216 4 Harris, J. A., Oh, S. W. & Zeng, H. Adeno-associated viral vectors for\n217 anterograde axonal tracing with fluorescent proteins in nontransgenic and cre\n218 driver mice. Curr Protoc Neurosci Chapter 1, Unit 1.20.21-18,(2012).\n219 5 Aschauer, D. F., Kreuz, S. & Rumpel, S. Analysis of transduction efficiency,\n220 tropism and axonal transport of AAV serotypes 1, 2, 5, 6, 8 and 9 in the mouse\n221 brain. PLoS One 8, e76310,(2013).\n222 6 Zingg, B. et al. AAV-Mediated Anterograde Transsynaptic Tagging: Mapping\n225\n226\n229 \"Revised manuscript without track changes-He Li.pdf\" file.\n230\n232\n234 claim that multi-sensory integration, decision-making, and generation of motor\n235 command to initiate walking all occur within an intra-laminar circuit in layer 5 of\n236 temporal association cortex (TeA). The work does a careful anatomical\n237 characterization of the projection from TeA to periaqueductal grey (PAG) and a\n238 series of manipulations of this pathway, with increasing specificity as the\n239 manuscript progresses. However, the only readout is stimulus-evoked locomotion.\n242 sufficiently presented to interpret the effects. More generally, the results are\n243 sloppily presented, with inconsistencies in presentation, a lack of justification for\n244 selection of data for illustrations, a lack of detail needed to interpret statistical\n245 comparisons, and numerous saturated anatomical images. The intersectional\n246 anatomical characterization of input in Figure 3 is valuable, but for a more\n247 specialized audience. Key experiments for necessity are missing, which would rule\n248 out other pathways, such as ACC, sensory-to-PAG, mesencephalic locomotor\n249 region, or cortico-striatal pathways. Along these lines, more broadly, many circuits\n250 and pathways are known to be involved in sensory-driven locomotion and thus the\n251 results of this paper are a modest advance that would require more careful\n252 experiments, analysis, presentation, and interpretation to be persuasive.\n253\n255 -Organization of the results lacks introductory and other transitional sentences,\n256 so it is not clear how one section relates to the next. For example, first the first two\n257 sections with headers ‘Open field running model.’ And ‘turntable running model’\n258 describe two behaviors but it is not clear where things are going. Are both going\n259 to be used later? What is the purpose of the open field model given the turntable\n260 model is described as having some advantages but no disadvantages.\n261\n263 we induced defensive escape behaviours in mice using three types of sensory stimuli.\n265 immobilize the animal's head while allowing it to run, so we adopted a turntable running\n266 model. The comparison between the two setups was intended to validate the use of the\n267 turntable running model for interpreting defensive escape behaviours observed under\n268 freely moving conditions. This part of the statement was also included in the Results\n269 section “To facilitate interventions in the TeA and observe corresponding behavioural\n270 changes in the mice, as well as to record neuronal responses, we transitioned from the\n271 open-field model to a head-fixed, turntable running model.” [Results (line 124-126)]\n272\n273 -The framing around multisensory may create confusion, since the paper does not\n274 look at multisensory integration (e.g. stimuli/cues with components/features from\n275 multiple senses). Rather, it presents sound, light, or air puff on separate trials. As\n276 such, they are studying a set of related associations for the 3 senses in isolation.\n278 “With the above experiments, we have established that the TeA-dPAG pathway is\n279 the neural circuit associated with multisensory-induced movement.” And\n280 “Therefore, the TeA must have the function of integrating various sensory\n281 information.” This is partially addressed by experiments in Figure 4g-l; most of\n282 the paper is about uni-sensory motor responses and framing should be adjusted,\n283 accordingly.\n284\n286 could have led readers to confuse single sensory stimulation with multisensory\n287 integration behaviours while mistakenly attributing the observed multisensory\n288 integration effects observed in the experimental behaviours to the TeA. We have\n289 removed the section describing the latter while acknowledging that our experimental\n290 design using separate, unimodal stimuli (sound, light, air puffs) does not actually test\n291 multisensory integration but rather examines parallel processing of distinct sensory\n292 modalities.\n293\n294 -The descriptions of analyses and selection of data to be presented are not clearly\n295 motivated or described to give confidence in what is being compared and why.\n296 -The results are sloppily presented, with inconsistencies in presentation, a lack of\n297 justification for selection of data for illustrations, and a lack of detail needed to\n298 interpret statistical comparisons.\n299\n301 narrative shortcomings in the original manuscript. To address these issues, we have\n302 undertaken a comprehensive revision: we have restructured the logical flow of the\n303 entire article to clarify key concepts; we have redrawn the figures and provided details\n304 on additional experimental investigations. We have conducted novel data analyses to\n305 improve the methodological rigor of the manuscript, and following a full rewrite of the\n306 manuscript, the paper has been submitted to a professional English editing service to\n307 improve the linguistic precision of the text. For example, in the revised manuscript, we\n308 made significant modifications to the experiments under “Silencing TeA blocks sensory-\n309 induced flight behaviour.” In this study, we employed pharmacological, chemogenetic,\n310 and optogenetic methods to inhibit neuronal activity in the bilateral TeA and observed\n311 changes in flight behaviours induced by different sensory stimuli. To ensure consistency\n312 in the results, we have revised the presentation of the original Fig. 1, which separately\n313 illustrates the changes in flight behaviour induced by three types of sensory stimuli\n314 under each silencing method. Additionally, to provide further details on the statistical\n315 analysis, we explicitly state in the figure legend that one-way repeated-measures\n317 values and degrees of freedom.\n318\n319\n321 showing the spread of muscimol. Scale bar, 100 μm, unless otherwise specified. Bottom,\n324 250–800 μm) in 7 animals. Calibration: 50 μV, 0.5 ms. t = 9.927, P = 6(cid:3400)10-5. Paired t test. m,\n6\n2,12\n326 10-8. L, F = 488.007, P < 10-8. A, F = 366.002, P < 10-8. One-way repeated-measures ANOVA\n2,12 2,12\n327 with Bonferroni post hoc correction. n = 7 animals. o, Top, slices containing the bilateral TeA\n328 showing viral expression. Bottom left, raw traces of current-clamp recordings from an hM4D(Gi)-\n329 expressing TeA cell in a slice preparation. Bottom right, average spontaneous spike frequencies\n330 before and after brain perfusion with CNO and after CNO washout. F = 260.973, P < 10-8. One-\n2,8\n331 way repeated-measures ANOVA with Bonferroni post hoc correction, n = 5 cells from two mice. p,\n6\n334 6), L (t = 13.191, P = 1.2(cid:3400)10--5), and A (A: t = 14.407, P = 7(cid:3400)10--6) before and after CNO injection.\n6 6\n337 by S (t = -0.523, P = 0.620), L (t = 1.127, P = 0.303), and A (A: t = 0.922, P = 0.392) before and\n6 6 6\n338 after CNO injection. Paired t test. n = 7 animals. r, Top, bilateral TeA slices showing viral expression.\n340 with (LED+) LED illumination. Data correspond to 7 TeA cells (at depths of 250–800 μm) from 7\n341 animals. Calibration: 50 μV, 0.5 ms. t = 10.405, P = 1.41(cid:3400)10-4. Paired t test. s, Left, Example flight\n6\n342 speed traces without (LED-) and with (LED+) LED illumination for eNpHR-expressing animals.\n6 6 6\n344 = 8.175, P = 1.8(cid:3400)10-4) without and with LED illumination. Paired t test. n = 7 animals. t, Left,\n345 example flight speed traces obtained without (LED-) and with (LED+) LED illumination for EGFP\n346 control animals. Right, peak speed induced by S (t = 18.192, P = 2(cid:3400)10-6), L (t = 13.191, P =\n6 6\n347 1.2(cid:3400)10-5), and A (A: t = 14.407, P = 7(cid:3400)10-6) without and with LED illumination. Paired t test. n =\n6\n348 7 animals.\n349\n350 -Numerous examples are given below in ‘minor concerns’ section, as well as a few\n351 ‘big ones’ here in the major concerns section.\n352 -It is not clear if ‘Silencing TeA blocks running’ experiments are done in the freely\n353 moving or head-fixed paradigm. Similarly, it is not clear in panel l and m of Figure\n354 1 if these speed calculations are done for sound, light, or air puff. There is a smillar\n355 issue with the optogenetic experiments, where is is not clear if the running under\n356 investigation is triggered by sound, light, or air puff.\n357\n359 experimental paradigms. We would like to clarify the following important\n360 methodological details: The experiments in the “Silencing TeA blocks sensory-induced\n361 flight behaviour.” section (including both muscimol silencing, shown in Fig. 1m and n,\n362 and optogenetic inhibition, shown in Fig. 1s and t) were indeed conducted with the\n363 head-fixed turntable paradigm. As explained in our response to previous comments, we\n364 transitioned all subsequent experiments to the turntable model after initial validation in\n365 an open field setting, owing to the advantages of the former for precise neural\n366 manipulation and recording. For all the silencing experiments (muscimol,\n367 chemogenetic, and optogenetic), we systematically recorded and analysed the\n368 responses to all three sensory modalities (sound, light, and air puff) separately. These\n369 stimulus-specific results are presented in Fig. 1l-t.\n370\n371 -Physiological recording for the CNO experiments in Figure 1 are lacking, so it is\n372 not clear what effect the CNO has had on TeA and other structures.\n373\n375 information to Fig. 1 (see Fig. 1o).\n376\n377\n379 showing viral expression. Bottom left, raw traces of current-clamp recordings from an hM4D(Gi)-\n380 expressing TeA cell in a slice preparation. Bottom right, average spontaneous spike frequencies\n381 before and after brain perfusion with CNO and after CNO washout. F = 260.973, P < 10-8. One-\n2,8\n382 way repeated-measures ANOVA with Bonferroni post hoc correction, n = 5 cells from two mice.\n383\n384 -AAVretro is known to leak into neighboring cells. It would be helpful to confirm\n385 the projection from TeA layer 5a to PAG using a confirmatory method, such as\n386 pseudorabies or fluoro-gold.\n387\n390 would improve our validation of the TeA-dPAG circuit. In fact, multiple findings in our\n391 manuscript consistently support the hypothesis that TeA layer 5a neurons project to the\n392 dPAG, including Fig. 3d2 and e2, Fig. 6a and h, and Extended Data Fig. 1b. In direct\n393 response to your suggestion, we have performed additional verification experiments by\n394 injecting the retrograde tracer CTB into the dPAG. The results (see Figure below)\n395 confirm our original findings, again demonstrating the projection of TeA layer 5a\n396 neurons to the dPAG.\n397\n398\ndPAG\n401 optogenetic (LED) interventions. 2, Viral expression. e, Blockade of TeA CaMKII neurons. 1,\ndPAG\n403\nSens dPAG\nSens\n406 including TeA, TeA, TeA, TeA, and TeA, and TeA neurons in L5a (mCherry, red)\nAC S1 V2L MGB DLG dPAG\n407 in the TeA. g, Schematics of the virus injection sites. h, Left, images showing TeA neurons\nSens\n408 (EYFP), TeA neurons and neurons labelled in the upper layer (mCherry).\ndPAG\n409\ndPAG\n412 transgenic mice (bottom). Yellow reflects labelling overlap in the neurons.\n413\n414 a, Injection site for CTB 488 in the dPAG. b, Images showing CTB 488-labelled TeA neurons.\ndPAG\n415\n416 -Some images are saturated (e.g. 1k,q; 2a1; 3a,c; e1b; e2g; e3a) or show signs of\n417 injection damage (2a2), raising concerns about data quality. Images should be\n418 adjusted so they are not saturated and extent of tissue damage at injection site\n419 should be discussed and/or addressed as needed with new experiments.\n420\n422 suggestions for improvement. We have carefully reviewed all the listed images (Figs.\n423 1k and q, 2a1, and 3a, c, e1b, e2g, and e3a) and have adjusted the exposure levels to\n424 eliminate saturation while maintaining the necessary signal clarity. All saturated images\n425 or those showing signs of injection damage have been replaced in the revised\n426 manuscript. Regarding the latter images, we would like to clarify that this damage was\n427 not caused by the mouse intracranial cannula implantation surgeries. Instead, it resulted\n428 from the removal of the cannulas after the experiments to verify their placement\n429 accuracy. Moreover, the extent of this damage was relatively minor. We also noted this\n430 damage ourselves. To further confirm whether blocking TeA dPAG neurons affects\n431 sensory stimulus-induced flight behaviour, we conducted additional experiments,\n432 illustrated in Fig. 3d, where we bilaterally injected optogenetic viruses into the dPAG\n433 and delivered yellow LED light to the bilateral TeA to specifically inhibit these neurons.\n435 experimental findings in Fig. 3a are reliable.\n436\n437\n438\n440 unless otherwise specified. r, Bilateral TeA slices showing viral expression. Fig. 2 b, Left, injection\n441 site for AAVretro in the dPAG. Right, mCherry-labelled TeA neurons. Fig. 3 a, Blockade of the\ndPAG\n442 fibres from the TeA to the dPAG in turntable model mice. 1, Viral injection site and chemogenetic\n443 (CNO) and optogenetic (LED) interventions. 2, Viral expression. Fig. 5 a, Viral injection site in the\n446 ( TeA) source regions. Extended Data Fig. 1 i, PV neurons in the PAG. Extended Data Fig. 3\nSens\n447 a, Injection sites of AAV2/9-hSyn-mCherry into the AC, S1, V2L, MGB, and DLG.\n448\n449 -It seems possible that the CNO and optogenetic experiments in Figures 1 and 2\n450 are a ‘hammer’ and are shutting to locomotion and behavior rather broadly,\n451 rather than indicating the specific multimodal integration and transformation to\n452 a motor signal that the paper suggests. It would be helpful to determine if\n453 locomotion in other contexts (e.g. plus maze exploration) is impacted. Also, CNO\n454 injections into virus control animals would be needed to mitigate e.g. tissue\n455 damage concerns.\n456\n459 to selectively block TeA dPAG and TeA dPAG neurons and examined the effects of this\n460 blockade on spontaneous locomotor activity. By blocking TeA dPAG (Fig. 3 e1 and e2)\n461 and TeA dPAG neurons (Fig. 3 f1 and f2) separately, we observed that both manipulations\n462 affected the ability of the mice to freely explore (Fig. 3e3 and f3). The results revealed\n463 that inhibiting TeA dPAG or TeA dPAG neurons induced bradykinesia, as indicated by\n464 significant decreases in both total distance moved and movement speed (Fig. 3e4 and\n465 f4, upper panels), and akinesia, as indicated by a sharp increase in immobility (Fig. 3e4\n466 and f4, lower panels, left). These results indicate that CaMKII-positive projections\n467 through TeA layer 5a to the dPAG determine running behaviour. Furthermore, we have\n468 now included the CNO control figure that was initially omitted for brevity.\n469\n470\ndPAG\n472 neurons. 1, Virus injection site and chemogenetic (CNO) intervention. 2, Viral expression. 3,\n474 hM4D(Gi)-expressing animals. 4, Performance of hM4D and control mice in the open field test:\n475 distance (F = 5.192, P = 0.0351), speed (F = 15.68, P = 0.0009), immobility time (F =\n1,18 1,18 1,18\n476 8.107, P = 0.0107), and time spent exploring the centre zone F = 0.9949, P = 0.3318) before\n1,18\n477 (CNO-) and after (CNO+) CNO intraperitoneal injections. Two-way ANOVA with Bonferroni post\n478 hoc correction. n = 10 animals/group. f, Blockade of dPAG CaMKII neurons. 1, Virus injection\nTeA\n479 site and chemogenetic (CNO) intervention. 2, Viral expression. 3, Representative traces before\n480 (CNO-) and after (CNO+) CNO intraperitoneal (i.p.) injection in hM4D(Gi)-expressing animals. 4,\n1,18\n482 speed F = 51.17, P < 10-4), immobility time (F = 78.19, P < 10-4), and time spent exploring\n1,18 1,18\n483 the centre zone (F = 0.7787, P = 0.3892) before (CNO-) and after (CNO+) intraperitoneal\n1,18\n484 injection. Two-way ANOVA with Bonferroni post hoc correction. n = 10 animals/group.\n485\n486 -In Figure 2, the responses to the 3 different stimuli should be parsed (walking to\n487 sound, light, and airpuff).\n488\n491 already presented separate analyses for each sensory modality (sound, light, air puffs)\n492 in Fig. 1m, n, p, q, s, and t. The data shown in these figure panels demonstrate that TeA-\n493 dPAG inhibition blocks flight behaviours induced by all three stimulus types similarly,\n494 with no significant differences observed among the modalities. Given the consistent\n495 effects across all sensory stimuli (i.e., complete blockade of flight behaviour regardless\n496 of modality), we sought to maintain a focus on the core finding that TeA-dPAG circuit\n497 activity is essential for sensory-induced flight regardless of sensory modality and thus\n498 avoided the redundant presentation of identical effects across multiple panels.\n499\n500\n502 averages (10 trials). n, Peak speeds. S, F = 854.417, P < 10-8. L, F = 488.007, P < 10-8. A,\n2,12 2,12\n2,12\n504 n = 7 animals. p, Left, example flight speed traces before (CNO-) and 30 min after CNO\n505 intraperitoneal injection (i.p., CNO+) in hM4D(Gi)-expressing animals. Right, peak speed induced\n506 by S (t = 18.192, P = 2(cid:3400)10--6), L (t = 13.191, P = 1.2(cid:3400)10--5), and A (A: t = 14.407, P = 7(cid:3400)10--6)\n6 6 6\n507 before and after CNO injection. Paired t test. n = 7 animals. q, Left, Example flight speed traces\n508 before (CNO-) and 30 min after CNO intraperitoneal injection (i.p., CNO+) in mCherry control\n509 animals. Right, peak speed induced by S (t = -0.523, P = 0.620), L (t = 1.127, P = 0.303), and A\n6 6\n510 (A: t = 0.922, P = 0.392) before and after CNO injection. Paired t test. n = 7 animals. s, Left,\n6\n512 expressing animals. Right, peak speed induced by S (t = 12.179, P = 1.9(cid:3400)10-5), L (t = 6.209, P =\n6 6\n513 0.001), and A (A: t = 8.175, P = 1.8(cid:3400)10-4) without and with LED illumination. Paired t test. n = 7\n6\n514 animals. t, Left, example flight speed traces obtained without (LED-) and with (LED+) LED\n515 illumination for EGFP control animals. Right, peak speed induced by S (t = 18.192, P = 2(cid:3400)10-6),\n6\n6 6\n518\n519 -The characterization of neuronal responses in TeA to running versus sound\n520 stimuli, although intriguing, do not demonstrate much about the integration\n521 process. Furthermore, running related signals are extremely prominent across\n522 neocortex, and thus not surprising or very informative regarding this study.\n523\n525 neuronal response data. We have removed all claims about the role of the TeA in\n526 multisensory integration. We initially considered removing this section, but given its\n527 potential implications regarding multisensory integration behaviours, we have decided\n528 to retain it as supplementary information.\n529\n531 widespread throughout the neocortex (as demonstrated in Steinmetz et al.1 and Stringer\n532 et al.2), our study reveals novel insights into the functional heterogeneity of neurons in\n533 neocortical TeA layer 5a. Although running-related signals are very\n534 prominent in the neocortex, their characteristics are not necessarily correlated\n535 specifically with the the animal’s running speed. Furthermore, the results of\n536 pharmacological, chemogenetic, and optogenetic interventions collectively confirmed\n537 that the TeA is a key hub for flight behaviours induced by various sensory stimuli.\n539 sensory-related neurons (S-neurons; Fig. 4c-d) in this area. A distinct population of S-\n540 neurons demonstrated firing rates that were correlated with running speed (SR-neurons;\n542 are predominantly localized in L5a and appear to integrate different sensory\n543 information into running signals, ultimately driving running behaviour. To our\n544 knowledge, this represents the first demonstration of such functional specialization in\n546 transformation.\n547\n549 recording pipette. Sound, light, and air puff stimuli were delivered to the mouse. The running speed\n550 was monitored in real time by the rotary encoder. b-d, Example characteristics for three types of\n552 (red). (b), A running-related neuron (R-neuron). Right panel, top, peristimulus time histogram\n553 (PSTH) of the neuron during presentation of S, L, and A. Right panel, bottom, firing rate plotted\n554 versus the original running speed. Insert, discharge events and running events extracted following\n555 adjustment of the running speed data to align the firing and running speed peaks. (c), A sensory-\n556 related neuron (S-neuron) whose firing rate was correlated with running speed (SR-neuron;\n557 responds to sound stimulation (SS)). (d), A sensory-related neuron whose firing rate was\n558 uncorrelated with running speed (unSR; responds to SS). The discharge characteristics are plotted\n559 as the firing rate (black). The ordinates for firing rate and running speed are on the right and left,\n560 respectively.\n561\n562 -The critical experiment of silencing the TeA-dPAG neurons and determining if\n563 that blocks sound/light/air-puff induced locomotion appears to be missing. This is\n564 the experiment that would test if those neurons are necessary for the behavior.\n566 involved and/or sufficient.\n567\n569 results in Fig. 2 in the original manuscript. However, we did present them in the original\n571 dPAG pathway in controlling flight behaviours, we injected the AAV-hSyn-hM4D(Gi)-\n572 mCherry virus into the bilateral TeA to express hM4D in TeA cells (Fig. 3a1). Local\n573 injection of CNO (with 5% BDA) into the dPAG blocked the TeA-dPAG projection fibres\n574 (Fig. 3a2), resulting in a significant cessation of the flight behaviours induced by the\n575 three different stimuli. In contrast, local injection of CNO had a minimal effect on\n576 control animals expressing only AAV-hSyn-mCherry without hM4D(Gi) (Fig. 3 a3 and\n577 a4). To specifically block the TeA-dPAG pathway, we locally injected Cre (AAV-Cre-\n579 hM4D expression in TeA dPAG cells (Fig. 3b2). Intraperitoneal injections of CNO\n580 specifically inhibited TeA dPAG cells and blocked the flight behaviours induced by the\n581 three sensory stimuli, whereas no blockade was observed in the control group injected\n582 with hM4D-free virus (Fig. 3b3 and b4).” [Results (line 219-231)] and the updated Fig.\n583 3). Additionally, to further clarify whether the TeA-dPAG circuit is related to running,\n584 we conducted an open field test in which chemogenetic inhibition was used to\n585 selectively block TeA dPAG and TeA dPAG neurons and examine the effects of this\n586 blockade on spontaneous locomotor activity. By blocking TeA dPAG (Fig. 3 e1 and e2)\n587 and TeA dPAG neurons (Fig. 3 g1 and g2) separately, we observed that both\n588 manipulations affected the ability of the mice to freely explore (Fig. 3e3 and f3). The\n589 results revealed that inhibiting TeA dPAG or TeA dPAG neurons induced bradykinesia, as\n590 indicated by significant decreases in both total distance moved and movement speed\n591 (Fig. 3e4 and f4, upper panels), and akinesia, as indicated by a sharp increase in\n592 immobility (Fig. 3e4 and f4, lower panels, left). These results indicate that CaMKII-\n593 positive projections through TeA L5a to the dPAG determine running behaviours.\n594\n595\n596\n598 pathway in turntable model mice: fibres from the TeA to the dPAG (a), dPAG neurons (b),\nTeA\n599 dPAG CaMKII neurons (c), and TeA CaMKII neurons (d). 1, Viral injection site and\nTeA dPAG\n600 chemogenetic (CNO) and optogenetic (LED) interventions. 2, Viral expression. 3, Example flight\n601 speed trace following random S, L, and A presentation before (CNO- or LED-) and after (CNO+ or\n603 (30 trials). 4, Peak flight speeds for a (F = 46.54, P < 10-4), b (F = 69.73, P < 10-4), c (F =\n1,12 1,12 1,12\n604 112.0, P < 10-4), and d (F = 62.16, P < 10-4), two-way ANOVA with Bonferroni post hoc\n1,12\n605 correction. n = 7 animals/group. e, Blockade of TeA CaMKII neurons. 1, Virus injection site\ndPAG\n606 and chemogenetic (CNO) intervention. 2, Viral expression. 3, Representative traces before (CNO-)\n607 and after (CNO+) intraperitoneal (i.p.) injections in hM4D(Gi)-expressing animals. 4, Performance\n608 of hM4D and control mice in the open field test: distance (F = 5.192, P = 0.0351), speed (F =\n1,18 1,18\n609 15.68, P = 0.0009), immobility time (F = 8.107, P = 0.0107), and time spent exploring the centre\n1,18\n610 zone F = 0.9949, P = 0.3318) before (CNO-) and after (CNO+) CNO intraperitoneal injections.\n1,18\n612 dPAG CaMKII neurons. 1, Virus injection site and chemogenetic (CNO) intervention. 2, Viral\nTeA\n613 expression. 3, Representative traces before (CNO-) and after (CNO+) CNO intraperitoneal (i.p.)\n614 injection in hM4D(Gi)-expressing animals. 4, Performance of hM4D and control mice in the open\n615 field test: distance (F = 27.70, P < 10-4), speed F = 51.17, P < 10-4), immobility time (F =\n1,18 1,18 1,18\n616 78.19, P < 10-4), and time spent exploring the centre zone (F = 0.7787, P = 0.3892) before (CNO-)\n1,18\n617 and after (CNO+) intraperitoneal injection. Two-way ANOVA with the Bonferroni post hoc correction.\n618 n = 10 animals/group.\n619\n621\n622 -Figure 1f shows peak running speed and the authors claim the stimulation\n623 intensities were carefully chosen to evoke similar running speeds. It would also\n624 help to see average, rather than peak, running speed, given that this peak running\n625 speed may be insensitive to differences in evoked running patterns by the stimuli\n626 used.\n627\n629 evaluated multiple kinematic parameters, including average speed, acceleration, and\n630 total distance. However, we ultimately selected peak speed as our primary metric on\n631 the basis of two key observations:\n632 1. Neural correlations: We found that instantaneous running speed showed the\n633 strongest correlation with neuronal firing rate (Chen et al., Figs. 1-2), suggesting that\n634 peak speed best reflects the neural dynamics of interest.\n635 2. Field standard: This choice aligns with established practices in comparable studies\n636 employing similar behavioural paradigms, including Evans et al.3, Xiong et al.4, and\n639 duration (Fig. 1g). We believe that this approach provides the most biologically\n640 meaningful representation of locomotor capability across behavioural states.\n641\n2,12\n644 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001, n.s. not significant.\n645\n646 -How were 10 trials shown in 1c selected? What does these plots look like for\n647 different animals and single trial?\n648\n650 the average movement trajectories from 10 stimulus repetitions for the representative\n651 animal shown in Fig. 1b. These trajectories were originally positioned below Fig. 1b\n652 but were moved to Fig. 1c to improve the layout of the figure.\n653\n654\n657\n658 -It is not clear in Fig. 1d-f how the stas were done. There are 7 animals and 7 data\n659 points, suggesting each point is an animal. This should be stated. The type of post\n660 hoc test is not stated. And the F values are reported as 2,18 for degrees of freedom.\n662\n664 unclear but also that we used incorrect statistical methods. In the revised version, we\n665 have improved the related descriptions in both the figure legend and methods section\n666 and report the results of one-way repeated-measures ANOVA with Bonferroni post hoc\n667 correction for statistical analysis. Accordingly, we have systematically reviewed and\n668 corrected the statistical methods throughout the manuscript. We are grateful for this\n669 important critique, which has significantly improved the rigor and transparency of our\n670 statistical reporting. The revised manuscript now provides complete documentation of\n671 all analyses to enable full evaluation of our findings.\n672\n673 -Why are walk bout durations not shown for the freely-moving task in Figure 1?\n674\n676\n677\n2,12\n680 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001, n.s. not significant.\n681\n682 -It is not clear if it is the same, or different, 7 animals used in the freely moving\n683 versus head-fixed behavior.\n684\n686 manuscript that the freely moving and head-fixed experiments were conducted using\n687 different cohorts of animals (7 mice per condition) to avoid potential confounding.\n688\n689 -This statement “However, the characteristics of the running, such as the\n690 probability, delay, and peak speed (Fig. 1j), were not significantly different\n691 between the turntable model and the open field model (Fig. 1c-f), except for the\n692 running duration.” Is not justified based on what is shown in panel ‘j’, at least as\n693 described. Again, how the F values were determined, and what the groups were,\n694 etc., is not clear.\n695\n697 statistical reporting and interpretation. We acknowledge the confusion caused by our\n698 original statement and have implemented the following revisions. We now present a\n699 more precise and data-driven description; in both behavioural paradigms (open field\n700 and turntable), the presentation of sound stimuli consistently resulted in the highest\n701 probability of eliciting escape behaviours, the shortest response latencies, and the\n702 shortest running durations, while no significant differences were observed in peak\n703 running speeds among the sound, light, and air puff stimuli.\n704\n705 -The co-localization rate reported in Fig. S1 is ambiguous; they should report both\n706 false positive and false negative rates (i.e. were there any retrogradely labeled cells\n707 that were negative for camKII/SOM/VIP/and PV? Also, why was AAVretro used\n708 for the CaMKII labeling?\n709\n711 labelling. Below, we explain our experimental rationale in detail.\n713 (AAVretro-hSyn-EYFP) combined with immunohistochemistry. This strategy would\n714 have involved retrograde labelling of hSyn-expressing neurons followed by anti-\n716 methodology presented in the original Extended Fig. 2 b-c.\n718 while these antibodies showed excellent labelling efficiency throughout the thalamic\n719 regions (original Extended Fig. 2c), their labelling in cortical areas was less satisfactory.\n721 the superficial cortical layers and showed strong edge effects, whereas deeper cortical\n722 layers presented significantly weaker staining.\n723\n724 a, Images showing the anti-CaMKII-EYFP-labelled neurons, and hSyn-mCherry-labelled TeA\ndPAG\n725 neurons. Scale bar, 100 μm.\n730 patterns. Unfortunately, CamKIIα-Cre mice are not available in our laboratory.\n732 established and widely adopted tool for specific neuronal labelling. To validate\n733 inhibitory neuronal populations, we used Cre×Ai14 mice in combination with\n736 in the TeA, we employed an alternative experimental approach. Specifically, we\n737 injected AAVretro-hSyn-EYFP together with AAVretro-CaMKIIα (glutamatergic)-\n738 mCherry into wild-type mice. Four weeks postinjection, we performed\n739 immunofluorescence staining with an alternative anti-CaMKII antibody to quantify the\n740 proportion of glutamatergic neurons relative to virally labelled neurons.\n742 previous attempts, some challenges persisted. Notably, as shown in the attached figure,\n743 the distribution of labelled neurons in the cortext remained uneven relative to reference\n744 data from the Allen Brain Atlas. This observation suggests potential regional variability\n745 in antibody penetration or expression efficiency, particularly in deeper cortical layers.\n746\n747 b, Images showing the staining results of anti-CaMKII antibody in TeA. Scale bar, 100 μm. c,\n751 that 53% of TeA dPAG hSyn-positive neurons were labelled with the anti-CaMKII\n752 antibody, whereas 55% of TeA dPAG CaMKII-positive neurons exhibited anti-CaMKII\n753 immunoreactivity. Notably, 73% of TeA dPAG hSyn-positive neurons exhibited\n754 colabeling with CaMKII, representing a lower colocalization rate than the 86.59%\n755 reported in our original manuscript. This difference likely reflects our adjustment of the\n756 viral titres, as in our previous experiment, we employed AAVretro-CaMKII-mCherry\n757 (8.07(cid:3400)1012 vg/mL, BrainVTA) at a higher titre than that used for AAVretro-hSyn-EYFP\n758 (5.18(cid:3400)1012 vg/mL, BrainVTA), whereas in the current study, we employed AAVretro-\n761 more than 50% of AAVretro-CaMKII-mCherry-targeted neurons without evidence of\n762 overlabelling artefacts related to the viral titre, suggesting that virus-mediated labelling\n763 is more efficient than antibody-based detection for this neuronal population.\n764\n765 d, Images showing EYFP- and mCherry- labelled TeA neurons, and the anti-CaMKII-labelled\ndPAG\n766 neurons in wild-type mice. Scale bar, 100 μm.\n768 mice and performed immunofluorescence staining with an anti-GAD67 antibody four\n769 weeks postinjection to quantify the proportion of inhibitory neurons among the virus-\n770 labelled neurons. Analysis of 9 brain sections (3 sections per mouse from 3 mice)\n771 revealed a colabelling rate of only 3.82% between virus-labelled hSyn-positive neurons\n772 and GAD67-immunoreactive neurons. This exceptionally low positive rate strongly\n773 suggests that the vast majority of TeA L5a neurons projecting to the dPAG are\n774 glutamatergic in nature. The minimal overlap observed between virus labelling and\n776 character of this neural pathway.\n777\n778\n779 e, Images showing hSyn-mCherry -labelled TeA neurons, and anti-GAD67-labelled neurons.\ndPAG\n781\n782 -The purpose of this sentence is unclear: “Due to the scarcity of cholinergic and\n783 dopaminergic neurons in the cortex, we did not perform corresponding tests on\n784 these neuron types.” Why are cholinergic and dopaminergic neurons singled out\n785 for mention? And in fact there are cholinergic neurons in neocortex (Von\n789\n791 cortex. We apologize for our initial incorrect statement about their absence in cortical\n792 layers. Upon reviewing the manuscript you have cited, we recognize that while\n793 cholinergic neurons are present in the neocortex, they are predominantly localized to\n796 projection contains cholinergic neurons in the current study.\n798 dopaminergic neurons, this reflected our initial exploratory efforts using antibody\n799 staining to examine these populations in the TeA, although we acknowledge that the\n800 limitations of this approach in cortical tissue led to unreliable results. We agree that\n801 discussing these specific neuron types without definitive data could be potentially\n802 distracting; therefore, in our revised manuscript, we have removed this speculative\n803 content to maintain a focus on our core findings.\n804\n805 -Statistical quantification of significant reduction for Figure 4d-f is lacking.\n806\n808 analysis for quantifying the reduction in the figure legend (Fig. 5j-l), including details\n809 on the specific tests used, sample sizes, and significance thresholds.\n810\n811\n813 (blue) of running induced by sound (j), light (k), and air puff (l) stimulation before (dark) and after\n814 (undertint) CNO i.p. injection, n = 5 animals for each sensory area. Numbers: percentage reduction\n815 in the peak speed and running probability for CNO+ animals relative to CNO- animals. j-speed:\n4 4 4 4\n817 3.701, P = 0.021; DLG: t = 4.480, P = 0.011. j-probability: AC: t = 10.59, P = 5(cid:3400)10-4; MGB: t\n4 4 4\n818 = 21.78, P < 10-4; S1: t = 9.914, P = 6(cid:3400)10-4; V2L: t = 8.257, P = 0.001; DLG: t = 5.568, P =\n4 4 4\n819 0.005. k-speed: AC: t = 4.351, P = 0.012; MGB: t = 0.937, P = 0.402; S1: t = 1.580, P = 0.189;\n4 4 4\n4 4 4\n4 4 4 4\n4 4 4\n823 3(cid:3400)10-4; V2L: t = 0.560, P = 0.606; DLG: t = 1.335, P = 0.253. l-probability: AC: t = 0.703, P\n4 4 4\n824 = 0.521; MGB: t = 0.222, P = 0.835; S1: t = 7.882, P = 0.001; V2L: t = 0.247, P = 0.817; DLG:\n4 4 4\n825 t = 1.855, P = 0.137. Paired t test.\n4\n826\n827 -Experiments in Figure 4m-r would benefit from a CNO or optogenetic\n828 manipulation. More broadly, some form of psychometric manipulation to\n829 understand how graded responses varies with graded manipulation would\n830 strengthen the manuscript.\n831\n833 we fully recognize the mistake of extrapolating the multisensory integration observed\n834 in behavioural experiments to imply the integration function of the TeA. While we\n835 initially considered removing this section entirely, we decided to retain it as\n836 supplementary information, as it may still provide some useful insights into\n837 multisensory integration behaviour.\n839\n840 1 Steinmetz, N. A., Zatka-Haas, P., Carandini, M. & Harris, K. D. Distributed\n841 coding of choice, action and engagement across the mouse brain. Nature 576,\n842 266-273,(2019).\n843 2 Stringer, C. et al. Spontaneous behaviors drive multidimensional, brainwide\n844 activity. Science 364, 255,(2019).\n845 3 Evans, D. A. et al. A synaptic threshold mechanism for computing escape\n846 decisions. Nature 558, 590-594,(2018).\n847 4 Xiong, X. R. et al. Auditory cortex controls sound-driven innate defense\n848 behaviour through corticofugal projections to inferior colliculus. Nat Commun\n849 6, 7224,(2015).\n850 5 Wang, H. et al. Direct auditory cortical input to the lateral periaqueductal gray\n851 controls sound-driven defensive behavior. PLoS Biol 17, e3000417,(2019).\n853 and backing away behaviours”. Submitted as a companion paper.\n854\n855\n856\n859 \"Revised manuscript without track changes-He Li.pdf\" file.\n860\n862\n864 running\n866 reads nicely with a logical structure. Included are an awe-inspiring array of\n867 experiments. Particularly noteworthy are the number of methodological\n868 approaches (pharmacological, chemogenetic, and optogenetic) to address the same\n869 research question. This contributes to the convincing nature of the manuscript.\n871 micrographs of the injections and expression patterns, and the legends are\n872 complete with relevant statistics and descriptions.\n874 piece of association cortex (TeA) is responsible for routing and integrating not just\n875 one, but multiple sensory signals to evoke running behavior, thus encompassing a\n876 significant part of the sensorimotor pathway.\n878 remarks. I hope that by addressing these major comments, the manuscript could\n879 present its findings more cohesively while maintaining a balanced and\n880 scientifically rigorous narrative. I look forward to seeing this exciting work refined\n881 and published.\n882 1. Clarification of Running Behavior Context and Interpretation.\n885 include running. From lines 55-63 it appears that running is equated with escape\n886 behaviors – while escape behavior consists of more complex sequences of\n887 behavioral elements including running. The results section talks about the running\n888 with the TeA circuit being almost directly responsible for the motor command\n889 signals being sent.\n890\n892 a clearer understanding of the precise meaning of \"escape behaviours\" allowing us to\n893 properly distinguish between \"flight\" and \"running.\" Accordingly, we have thoroughly\n894 revised the manuscript to reflect these conceptual distinctions more accurately\n895 throughout the text. In this revised version, we describe a more precise behavioural\n896 classification: all sensory-induced locomotion is now consistently referred to as \"flight\n897 behaviour\", whereas the specific motor output mediated by the TeA-dPAG circuit is\n898 distinctly characterized as \"running\". This distinction reflects our improved\n899 understanding of these behavioural categories, with \"flight\" representing broader\n900 defensive responses to threat stimuli and \"running\" describing the specific locomotor\n901 component controlled by the abovementioned neural pathway.\n902\n903 -Then in the discussion, the authors present the sensory stimuli as more neutral\n904 than the aversive stimuli used on other studies. However, 110 (!) dB white noise\n905 auditory stimuli, a bright inescapable light and an air puff in the eye (classic\n906 aversive stimulus) can NOT be called neutral. These are classic aversive stimuli.\n908 adaptive role of) systematic running following neutral stimuli. Without any\n909 context dependence such a proposed circuit would be highly disadvantageous\n910 behavior (i.e. starting to run with the onset of any neutral stimulus). On top of this\n911 the dPAG is of course pivotal in innate defense and escape behaviors, including\n912 running but also freezing.\n913\n915 original manuscript. Our use of the term \"neutral stimulus\" was initially intended to\n916 emphasize that the observed behaviour was specifically running rather than an escape\n917 behaviour induced by aversive stimuli. However, in the current revision, we have\n918 clearly separated and refined our descriptions. We now explicitly refer to the stimuli as\n919 \"aversive\", as our experiments indeed employed a sensory-induced flight model in the\n920 mice. These changes ensure precise alignment between our methodological approach\n921 and the behavioural paradigm we investigated.\n922\n923 -The methods lack a lot of detail on the running behavior in the freely moving\n924 scenario. When was the sensory stimulus presented and where, on different sides?\n926 always running away from the stimulated side and express thigmotactic behavior?\n928\n930 suggestions, we have thoroughly revised both the Methods and Results sections of our\n931 manuscript to incorporate these important details. The updated version now provides\n932 comprehensive descriptions addressing all the points you raised, ensuring a clearer\n933 presentation of our experimental procedures and findings. Below is a detailed schematic\n934 diagram of our open field escape model setup:\n935\n936\n937\n938 -Line 55: Running is usually chosen… I would argue that this is not true and\n939 depends on the research. In perceptual decision-making licking, nose poking or\n940 forepaw movements are often used as behavioral indices in multisensory decision\n941 making tasks. In the context of fear, innate and defense research, freezing is also\n942 often used.\n943\n945 model involving mice. We fully acknowledge that it was conceptually inappropriate to\n946 equate defensive flight behaviours with simple running, despite the superficial\n947 similarities in their locomotor manifestations. The two behaviours clearly represent\n948 distinct categories with different underlying mechanisms and ethological implications.\n950 text to ensure precise behavioural classification. Specifically, we now consistently use\n951 \"flight\" to describe the defensive escape response and reserve \"running\" to refer to the\n952 specific locomotor component mediated by the TeA-dPAG circuit.\n953\n954 -In sum, the authors need to argue better if and why they think of running as\n955 different from escape behavior or not.\n956 -This brings me to a related point. Brain-wide activity changes associated with the\n957 behavioral state of mice have been shown throughout literature; most prominently\n958 in (Stringer et al. 2019), and across most studies studies now studying awake\n959 behavior in mice. Neurons throughout cortex, and in particular deeper layers\n960 correlate with locomotion state and running speed (Niell & Stryker 2010, Saleem\n961 et al. 2013, Steinmetz et al. 2019, Musall et al. 2019, Vinck et al. 2015). An\n962 alternative interpretation of the signal of deep TeA layers is thus that these neurons\n963 correlate with running as any other part of mouse cortex (Fig. 7d supports this –\n964 nonChR2 cell modulation of TeA activity when running) and rather than motor\n965 command signals, that their outputs reflect the threat-relevance of sensory stimuli\n966 to dPAG. Perhaps the authors can discuss to what extent the data support a direct\n967 role in motor initiation versus alternative interpretations that consider the context\n968 of the behavior, such as that TeA provides the contextual signals necessary to signal\n969 a situation in which escape behavior is necessary.\n970\n972 running and escape behaviour. After careful consideration, we would like to clarify our\n973 conceptual framework. “Escape behaviour” represents a complete process from\n974 sensation to movement, whereas “running” is a specific manifestation of this process.\n976 running behaviours, as evidenced by both the activation patterns of these neurons (Fig.\n977 7) and their correlations with running speed (Chen et al.). While these neurons receive\n978 threat-related sensory inputs from Sens TeA neurons, we classify TeA dPAG neurons as\n979 motor command neurons on the basis of two key observations: (1) they directly control\n980 running behaviours, and (2) their firing patterns encode running speed rather than\n981 sensory information. The speed-dependent firing of both TeA dPAG and TeA dPAG neurons\n982 (Chen et al.) further supports their role in motor execution rather than sensory\n983 processing. Therefore, we maintain that TeA dPAG neurons function as the final motor\n984 command executors within this circuit.\n985\n987 -Providing additional context about the adaptive relevance of the observed\n988 behavior.\n989 -Expanding the methods to include critical details about the running behavior in\n990 freely moving animals, such as:\n991 • How and where stimuli were presented.\n992 • Whether the animals' location influenced their responses.\n993 • Onset criteria for running and whether the animals exhibited freezing behavior\n994 beforehand.\n995\n997 incorporated into the revised manuscript. Furthermore, each of the specific concerns\n998 raised has been systematically addressed in our previous point-by-point responses. The\n999 current version of the manuscript now provides comprehensive documentation of these\n1000 experimental details, ensuring full transparency and reproducibility of our findings.\n1002 we provide the following explanation. In this study, using a classic sensory stimulus-\n1003 induced escape model, we did not observe typical freezing behaviours (such as\n1004 complete immobility, cessation of locomotion, immobility accompanied by shaking, or\n1005 halting during an intermediate action state) either before or during the animal’s flight\n1006 behaviours. In a previous study referenced in our manuscript that employs similar\n1007 experimental models1, the authors explicitly stated that no freezing was observed in the\n1008 latency period during sound-induced escape behaviours; instead, escape was triggered\n1009 directly. Furthermore, our experimental video recordings revealed that mice typically\n1010 exhibit brief vigilance (e.g., head turning, ear twitching) after sensory stimulation,\n1011 followed by the initiation of running, rather than entering a motionless state.\n1012\n1014 manifest as immobility, cessation of locomotion, or immobility accompanied by\n1015 shaking. However, the brief pauses observed during the latency period in our study are\n1016 more consistent with vigilance or a state of decision-making regarding movement rather\n1017 than the active motor suppression characteristic of freezing.\n1019 and the onset of running behaviours (as shown in Figs. 1-2 of Chen et al.), which aligns\n1020 with the theory that the activity of “running units” alone is insufficient to directly trigger\n1021 the cessation of behaviours. Therefore, brief pauses during the latency period are more\n1022 likely to reflect neural signal integration or motor preparation rather than active\n1023 freezing-induced suppression.\n1024\n1025 2. Latency and Circuit Dynamics\n1026\n1027 -The authors present a circuit in which sensory signals coming from respective\n1028 cortical sensory systems project to TeA. After an intralaminar projection, the\n1029 outputs of TeA project to dPAG responsible for the running behavior. The latencies\n1030 of the proposed pathway, tens of milliseconds, is very different from the timescale\n1031 at which the behavior is elicited (Fig. 1e, 1j). Why does it take 2 seconds to start to\n1032 run? Are the animals freezing first? Coming back to the first point, is this escape\n1033 behavior? Methods details are lacking here. Line 1291: What is the smoothing\n1034 kernel size? How was latency to start of running estimated? How does the\n1035 smoothing kernel affect latency estimates?\n1036\n1038 seconds1-3, which contrasts sharply with the much faster response times of sensory\n1039 neurons. Sensory-driven behaviours require at least three processing stages: sensory\n1040 detection, sensory-to-behaviour encoding, and motor command execution. However,\n1041 the identities of the neurons that mediate sensory-to-behaviour encoding and motor\n1042 execution, as well as their connectivity, remain unclear. To our knowledge, Evans et al.2\n1043 (Fig. 1c, right panel; 1b, bottom) demonstrated that the synapses between the superior\n1044 colliculus (SC) and periaqueductal grey (PAG) driving running behaviours are slow-\n1045 acting and require prolonged latencies (Fig. 2d and g). Our study was specifically\n1046 designed to investigate how sensory-induced running behaviours emerge through these\n1047 stages (sensory detection-sensory to behaviour encoding-motor execution). However,\n1048 neither our work nor that of Evans et al.9 has resolved the mechanistic basis for these\n1049 long latencies.\n1051 sensory stimulus-induced flight behaviour in animals is not stable but instead decreases\n1052 gradually as running speed increases. Under high-speed running conditions, the latency\n1053 remains very low. Additionally, as shown in Figs. 1 and 2 of Chen et al., both TeA and\n1054 dPAG running-related neurons show significant time differences between neuronal\n1055 firing and the running behaviours of the animals. Integrating our findings from the\n1056 companion manuscript (Chen et al., Fig. 2h) with prior literature4, we note that dPAG\n1057 neurons exhibit an average delay of ~0.8 s between firing onset and running initiation.\n1059 under natural conditions (i.e., without artificial interventions such as high-frequency\n1060 optogenetic activation) inherently require prolonged transmission times across these\n1061 neural stages.\n1063 smoothly record latency during running behaviours.\n1064\n1067\n1069 colliculus and periaqueductal gray. d, Average calcium response for active dPAG cells, aligned\n1070 to escape and sorted by onset (57/138 cells, N=3, 55 trials). g, Same as d for dmSC (177/218 active\n1071 cells, N=8, 111 trials; mean onset= -1.51±0.17s, P=3.5x10-12 Wilcoxon test comparison with 0s).\n[FIGURE REDACTED]\n1072\n1074 neurons (gray), originally shown in Fig. 1o. Gaussian fitting curve for dPAG; R2 = 0.983, P =\n1075 1.5(cid:3400)10-4. Figure 3 j, Running latency versus St. Fre. (every 10 trials, error bars: SDs). k, Running\n1076 latency versus running speed.\n1077\n1078 -From Figure 1b it appears that the running speed in the freely moving context is\n1079 not a smoothly increasing function, but rather a small bout in the middle of\n1080 stimulus presentation. Why do the animals run only the middle second? Then the\n1081 dynamics in the head fixed scenario, Fig. 1h, are different but still have onset\n1082 latencies of 1 sec or more.\n1083\n1085 an adaptive motor pattern for responding to sudden threats; in naturalistic free-field\n1086 conditions, escape behaviours triggered by sensory or fear stimuli drive animals to\n1087 rapidly reach safety, causing the initiation of running to decouple from the duration of\n1088 the stimulus. As the perceived threat diminishes following successful escape, the\n1089 running speed correspondingly decreases. To address your second question further, we\n1090 would like to highlight a critical distinction between the behavioural paradigms: in the\n1091 head-fixed scenario, the mice are physically constrained on a treadmill and thus cannot\n1092 rapidly escape from potential threats as they would in free-field conditions. This\n1093 physical limitation results in significantly prolonged running durations under the head-\n1094 fixed paradigm. Moreover, as demonstrated in Fig. 4 of the study by Chen et al., the\n1095 running duration was positively correlated with stimulus duration within certain\n1096 parameter values under the head-fixed condition. Additionally, our data in\n1098 on the stimulation duration. These differences underscore how physical constraints\n1099 fundamentally alter the temporal dynamics of the threat response behaviours between\n1100 these experimental models.\n[FIGURE REDACTED]\n1101\ndPAG\n1103 durations of 5, 10, and 15 s and a frequency of 40 Hz in the TeA. Panels 1-4: (1) Movement speed\n1104 heatmap, (2) average speed versus time, (3) response duration versus stimulation duration, (4)\n1105 statistical comparison of the peak speed for backing away or rebound running behaviours. F =\n(2, 8)\n1106 0.212, P = 0.813. One-way repeated-measures ANOVA with Bonferroni post hoc correction (error\n1107 bars: SDs).\n1108\n1110 to sounds with different durations (in s). k, duration (Freq: F = 1.485, P = 0.265. Dur: F =\n2,12 2,12\n1111 197.738, P < 10-6. Int: F = 195.134, P < 10-6.) of running induced by sound stimuli of different\n2,12\n1112 durations (Dur). One-way repeated-measures ANOVA with Bonferroni post hoc correction; n = 7\n1113 animals.\n1114\n1115 -How do the authors account for the difference between the output of TeA (100ms?)\n1116 and running behavior (1-2 sec?)? Is dPAG integrating over time? Moreover, some\n1117 neural latencies do not match with the proposed circuit. The latencies to light\n1118 stimulation for example are not in line with the suggested cortical circuit: VC to\n1120\n1122 we acknowledge that while the TeA responds to sensory signals within milliseconds\n1123 (Fig. 4h), the latency for the production of flight behaviours is significantly longer (1–\n1124 2 s). This delay likely reflects both the inherent response time of dPAG-mediated motor\n1125 initiation and a potential temporal integration process for sensory signals (Fig. 7c and\n1126 d). Our data reveal an interesting parallel: by recording neuronal discharge and\n1127 activating ChR2+- AC TeA SR neurons and ChR2+-TeA dPAG , a long-latency monosynaptic\n1128 processing from SR neurons to R neurons was observed (approximately 332 ms, as\n1129 shown in Fig. 4j, or 343 ms, as shown in Fig. 7j). While we initially considered\n1130 alternative polysynaptic pathways, the complete behavioural blockade of TeA dPAG\n1131 neurons upon chemogenic inhibition (Fig. 7k-n) confirmed that Sens TeA neurons must\n1132 act through this direct pathway to trigger running behaviours. These findings suggest a\n1133 slow integration process in which sensory signals gradually accumulate in TeA dPAG\n1134 neurons before the latter reach the firing threshold. Although we have not fully\n1135 elucidated the underlying mechanism, recent work2,5 supports our hypothesis that the\n1136 connection between Sens TeA and TeA dPAG neurons in the mouse TeA may involve weak\n1137 synapses requiring hundreds of milliseconds of temporal summation for effective signal\n1138 transmission.\n1139\n1141 running behavior (1-2 sec?),\" we would like to clarify that, as shown in Fig. 1 and 2 of\n1143 difference between neuronal firing and the animal’s running behaviour. This\n1144 observation suggests that the neural activity in these regions precedes and drives the\n1145 running output, which is consistent with the role of these neurons in sensorimotor\n1146 integration during flight responses. The time difference may reflect the time required\n1147 for signal propagation through downstream circuits or the integration of sensory inputs\n1148 before sustained running behaviours are initiated. Furthermore, in Chen et al. (Fig. 3i),\n1149 we further clarified that the TeA-dPAG transmission delay closely matches the latency\n1150 observed between TeA and dPAG neuronal activity, as shown in Fig. 2. This delay is\n1151 attributable to the monosynaptic connection from the TeA to the dPAG. This finding\n1152 reinforces the conclusion that the TeA directly and rapidly drives activity in dPAG\n1153 neurons during flight behaviours, with temporal dynamics governed by single-synaptic\n1154 transmission rather than polysynaptic circuits.\n1155\n1157 ms, SR-SS: 31.86 ± 13.19 ms, U = 192.5, z = -3.217, P = 0.001, Mann‒Whitney U test. unSR-LS:\n1158 414.8 ± 37.13 ms, SR-LS: 568.3 ± 70.11 ms, t = -5.456, P = 1.5(cid:3400)10-5, two-sample t test. unSR-\n23\n55\n1160 of the stimulus–running latency for each type of neuron. unSR-SS: 2.51 ± 0.11 s, SR-SS: 2.49 ±\n1161 0.18 s, t = 0.315, P = 0.754, two-sample t test. unSR-LS: 2.87 ± 0.15 s, SR-LS: 2.92 ± 0.19 s, t\n38 12\n1162 = -0.402, P = 0.695, two-sample t test. unSR-AP: 2.60 ± 0.21 s, SR-AP: 2.52 ± 0.19 s, t = 0.774,\n32\n1164 neuron. unSR-SS: 2.49 ± 0.11 s, unSR-LS: 2.45 ± 0.13 s, unSR-AP: 2.56 ± 0.21 s, SR-SS: 2.45 ±\n1165 0.18 s, SR-LS: 2.38 ± 0.15 s, SR-AP: 2.47 ± 0.19 s, R: 2.12 ± 0.24 s. K-W = 47.16, P < 10-8, Kruskal‒\nAC\n1167 neurons expressing ChR2 (c), TeA neurons not expressing ChR2 (d) that respond to the speed\ndPAG\n1168 of running behaviours evoked by blue LED light stimulation (20 Hz, 5 s, 10 trials). j, Time\n1169 difference between firing and running events. F = 45.16, P < 10-8, one-way ANOVA with\n2,72\nAC dPAG\ndPAG\n1172 optogenetic (LED) interventions. TeA and TeA neurons expressing ChR2-EYFP; TeA\nAC S1 dPAG\n1173 neurons expressing hM4D(Gi). l, m, Example LED light (20 Hz, 5 s, 9 trials)-induced speed raster\n1174 (l) and running speed trace (m) (10 trials) before (Pre), during (CNO), and recovery (Rec) from the\n1175 effect of CNO i.p. injections n, Induced peak speed for the Pre, CNO, and Rec periods. Pre: 6.149\n1176 ± 1.179 cm/s, CNO: 0.9806 ± 0.6357 cm/s, Rec: 6.27 ± 1.366 cm/s, F = 142.444, P < 10-6, one-\n2,12\n1177 way repeated-measures ANOVA with Bonferroni post hoc correction. n = 7 animals.\n[FIGURE REDACTED]\n1178\n1180 neurons (gray), originally shown in Fig. 1o. Gaussian fitting curve for dPAG; R2 = 0.983, P =\n1181 1.5(cid:3400)10-4. Figure 3 i, In vivo loose-patch recordings from a TeA CaMKIIα neuron expressing ChR2\n1182 (ChR2+, left top) (a) and a dPAG neuron not expressing ChR2 (ChR2-) ( dPAG) (left bottom)\nTeA fibre\n1183 (error bars: SDs) (b). The light stimulation pulse duration was 10 ms (scale bar, 100 ms, 50 pA).\n1185 potentials.\n1186\n1188 • Specify how running onset latency was measured (e.g., smoothing kernel size,\n1189 its effect on estimates).\n1190 • Address why the running speed in Fig. 1b peaks during the middle second of\n1191 stimulus presentation.\n1192 • Reconcile neural response latencies (e.g., VC to TeA at 50-60 ms vs. 400 ms for\n1194 pathways depends on weak connections that need 100s of milliseconds to\n1195 accumulate, they need to argue how this system is robust to noise, evolving\n1196 temporal dynamics of the brain and changing behavioral objectives.\n1197\n1199 addressed in our preceding responses. We sincerely appreciate your insightful queries,\n1200 which have significantly contributed to improving the clarity and rigor of our\n1201 manuscript.\n1202\n1204\n1205 - I was confused by the usage of multimodal stimuli in the manuscript. The authors\n1206 also use multisensory and multimodal to refer to effects and situations in which\n1207 different sensory modalities alone can produce similar effects (e.g. line 200). As the\n1208 manuscript also involves more classical multisensory experiments with\n1209 simultaneous sensory stimulation, this can be confusing. My suggestion is to\n1210 reserve multimodal/multisensory for bimodal or trimodal stimulation. I don’t see\n1211 what is lost if the term multisensory would be reserved for experiments involving\n1212 simultaneous sensory stimulation. E.g. line 218: Multisensory inputs converge to\n1214\n1217 be applied to the experiments presented in Extended Fig. 4a-f, where combinations of\n1218 sensory modalities were systematically tested. While we employed three distinct\n1219 sensory stimuli (sound, light, and air puffs) throughout the study, these stimuli were\n1220 applied in isolation across separate experiments rather than as simultaneous\n1221 multisensory inputs. To improve the precision of the concepts described in the text, we\n1222 have comprehensively reviewed all the multisensory-related descriptions in the\n1223 manuscript and have made corresponding revisions to clarify this distinction.\n1224\n1225\n1227 subthreshold or suprathreshold unisensory (b, sound: 30 dB, 12 k; light: 1 lux and air puff: 0.4 L/min)\n1228 or multisensory stimuli in different combinations for inducing running shown as rasters (c) and\n1229 running speed traces (d, 5 trials) and corresponding plots of the probability of running (e, F =\n2,12\n1230 176.448, P < 10-6) and peak speed (f, F = 35.425, P = 9 (cid:3400)10-6). One-way repeated-measures\n2,12\n1232\n1233 - There are several conclusions in the results sections that are not (yet) supported\n1234 by the data. I strongly suggest that the authors adjust their claims to precisely\n1235 match the data shown. For example:\n1236\n1237 - Line 318: “These results indicate…” It has not been shown that the multimodal\n1238 integration (here I am specifically referring to the synergistic interactions between\n1239 the modalities) that induces running behavior is taking place in TeA. Only that\n1240 pathways from early sensory cortex to TeA are involved in eliciting running\n1241 behavior, and (Fig 4) that multimodal stimuli can interact to produce running\n1242 behavior. The circuits supporting the integration of different sensory modalities\n1243 that underlie the change in elicited behavior could still be elsewhere.\n1244\n1246 the relevant statement in the main text to “These results indicate that multisensory\n1247 inputs contribute to the overall modulation of running behaviours.” [Results (line 458-\n1248 459)] This change ensures that the text is precisely aligned with the experimental design\n1249 and terminology, as only the experiments in Extended Fig. 4a-f explicitly involved\n1250 concurrent multisensory stimulation.\n1251\n1252 - Line 338: “These experiments indicate…” This statement is problematic. First it\n1253 makes a claim about the whole research field of multimodal sensory integration,\n1254 whereas (1) no multimodal stimuli were tested, only sound, and (2) one limited\n1255 behavior was tested. Second, the statement generalizes to other modalities not\n1256 tested.\n1257\n1259 relevant section in the main text to state the following: “The results of these experiments\n1260 indicated that auditory stimulus integration depends more on the intensity and duration\n1261 of the stimulus rather than on the specific (frequency) characteristics.” [Results (line\n1262 479-482)]\n1263\n1264 - Line 533: It believe it was not specifically shown that this weak connection\n1265 requires an extended time to sufficiently excite downstream neurons (perhaps\n1266 move to discussion of proposed circuit).\n1267\n1269 lack direct experimental evidence to conclusively establish that the connection between\n1271 synaptic connections requiring prolonged temporal integration. In light of this\n1272 limitation, we have moved the corresponding text to the Discussion section, where it is\n1273 presented as a plausible hypothesis supported by our current findings and data from the\n1274 literature9 (Chen et al.).\n1275\n1276 - Line 406-413: formulate as hypothesis. Only part of the claims here is shown.\n1277\n1279 the basis of these results, we hypothesized that at least three types of neurons are\n1280 involved in information processing from sensation to running behaviour. unSR-neurons\n1281 respond to sensory stimuli and are localized upstream of SR-neurons, which translate\n1282 sensory information into a running signal and relay this signal to R-neurons, which\n1283 then are responsible for driving running behaviours. R-neurons might be TeA dPAG\n1284 neurons, whereas SR-neurons might be those that receive projections from input sources,\n1285 hereafter referred to as Sens TeA. Both types of neurons are located in L5, but they play\n1286 different roles in the sensory-to-motor transformation process.” [Results (line 347-354)]\n1288 implemented to improve clarity and precision.\n1289\n1290 - Line 376: Biologists love categorization, but in this scenario, it is unclear whether\n1291 it is justified and what is gained. Do the authors believe the categorization into 14\n1292 neuron types is biologically important and future research should focus on the\n1293 differences between subtypes? We know from earlier work (Raposo et al. 2014)\n1294 that neurons in higher order mouse cortex show mixed selectivity that defy\n1295 categorization. It is likely that the reported sensory subtypes come from\n1296 continuous distributions. It is hard to judge, the Methods lack any description of\n1297 how significant responsiveness was calculated, which statistics were used. If\n1298 similar to running analyses the standard deviation of the baseline was used, which\n1299 time frames served as baseline? Moreover, how was running and sensory\n1300 responsiveness dissociated? (If sensory stimulation causes the mice to run, then to\n1301 what does the neuron respond?). The presented single neurons are not convincing\n1302 and highlight the problem. For example, the neuron in Ext. Data Fig 4c, is it\n1303 responding to light stimulation or running? And the neuron in Ext. Data Fig 4d,\n1304 responding to air puffs, or increasing its firing rate upon running? Misinterpreting\n1305 behavioral state related signals as and dissociating them from sensory signals has\n1306 recently been an important topic in (multi)sensory research (Zagha et al. 2022,\n1308 are meaningful categories of multisensory responsive neurons would thus require\n1309 showing distributions of a metric of sensory responsiveness + low-latency sensory-\n1310 evoked responses that are independent of behavioral state.\n1311\n1313 our initial classification of neurons into 14 subtypes on the basis solely of their\n1314 responses to different sensory modalities was an overly simplistic approach. To address\n1315 this limitation, we have revised our classification system throughout the manuscript\n1316 (see updated Fig. 4e-g). Neurons are now divided into two primary categories: R-\n1317 neurons (nonsensory-responsive, running-related) and S-neurons (sensory-responsive).\n1319 unrelated) and SR-neurons (sensory-responsive and running-related).\n1320\n1321\n1323 correlation coefficients between the firing rate and running speed (e, F = 971.4, P < 10-8), depth\n5,286\n1324 location (f, F = 18.588, P < 10-8, unSR vs. SR: P < 10-8; SR vs. R: P = 0.465), and peak spike\n2,101\n1325 rate (g, F = 51.552, P < 10-8, unSR vs. SR: P < 10-8; SR vs. R: P = 1.1(cid:3400)10-5), one-way ANOVA\n2,186\n1326 with Bonferroni post hoc correction.\n1327\n1329\n1331 various information..\n1332\n1334\n1336\n1338 manuscript. Please refer to the revised text “To further confirm the projections of the\n1340 labelled TeA cells specifically in layer 5a (L5a) (Fig. 2b).” [Results (line 185-188)]\n1341\n1343 animals for muscimol, but only 3 animals have cells recorded? Some more clarity\n1344 in the methods about how many cells were recorded across animals and whether\n1345 there were exclusion criteria would help interpretability.\n1346\n1348 the erroneous statement regarding “Statistics for 7 TeA cells (at depths of 250-800 μm)\n1349 in three animals” has been corrected. Please refer to the revised text “Statistics for 7\n1351 (line 1448-1449)]\n1352\n1354\n1356 and have made the corresponding changes to the text. Please refer to the updated\n1357 description “Specific inhibition of the TeA-dPAG circuit blocks running behaviours.”\n1358 [Results (line 219)]\n1359\n1361\n1363 manuscript. Please refer to the revised text “Although TeA neurons seemed to receive\n1364 multiple sensory inputs, including auditory (Au1, AuD, AuV, MGB), visual (V1, V2L,\n1366 different types of sensory information in the TeA exert varying degrees of influence on\n1367 driving running behaviours.” [Results (line 419-423)]\n1368\n1370\n1373 now stating: “ AC TeA neurons were suppressed.” [Results (line 428)]\n1374\n1376\n1379 legend for Fig. 3f (now Supplementary Fig. 3e) has been edited accordingly to\n1380 correspond with the new figure organization. All the panel labels and methodological\n1381 descriptions in the supplementary figure legends have been rigorously revised to ensure\n1382 consistency with the new figures themselves.\n1383\n1385\n1389 and subcortical regions (AC, S1, VC, MGB, DLG) in different mice. Concurrently,\n1391 and Flp/fDIO orthogonal recombination systems, this strategy enables specific labelling\n1392 of TeA neuron populations receiving anterograde projections from distinct sensory-\n1393 related brain regions. The revised methodology now explicitly emphasizes the\n1394 anterograde tracing capability of these viral vectors and their combined use for the\n1395 pathway-specific investigation of the circuitry of the TeA.\n1396\n1398 resolution is sufficiently high and/or include a closeup.\n1399\n1401 specified regions to improve the visual clarity and detail.\n1402\n1404 inputs.\n1405\n1407 with rich worlds of sensory signals in each modality.\n1408\n1410 main text accordingly. For details, please refer to the revised text “The related stimuli\n1411 can also vary in terms of characteristics such as type, intensity, and duration within a\n1412 single sensation.” [Results (line 463-464)]\n1413\n1415 earlier experiments and why this intensity was used.\n1416\n1418 and air-puff stimuli) during in vivo electrophysiological recordings in awake, behaving\n1419 mice to investigate how TeA neurons encode sensory processing and running. By\n1420 systematically reducing the stimulus intensity from that in prior protocols, we achieved\n1421 a critical balance: while such stimuli remained effective in eliciting movement, they\n1422 resulted in reduced success rates and lower locomotor velocities. This approach allowed\n1423 us to address two critical aspects: 1. Sensory-evoked neuronal responses under\n1424 behaviourally relevant conditions. 2. Running-associated firing patterns during low-\n1425 intensity running. The attenuated stimulus paradigm facilitated simultaneous\n1426 acquisition of TeA neuronal activity during running over long periods, allowing the\n1427 precise dissection of sensory-induced versus motor-related neural dynamics. This\n1428 methodological refinement significantly improved our ability to classify neuronal\n1429 subtypes (e.g., S-neurons versus R-neurons) on the basis of their dual modulation by\n1430 sensory inputs and behavioural outputs.\n1431\n1433 and can be described sticking closer to the results: ‘whose firing rate correlated\n1434 with running speed’\n1435\n1437 original description. We should clarify that not all recorded neuronal activities can be\n1438 characterized as running-related, as we identified a subset of neurons in the TeA (unSR-\n1439 neurons) whose firing patterns were not correlated with any quantitative aspects of the\n1440 running behaviours. In response to your concern, we have revised the relevant section\n1441 in the main text to state the following: “We recorded action potentials (APs) from a total\n1442 of 104 single neurons and recorded the generation of flight behaviours in the mice\n1443 during neuronal firing.” This change emphasizes the observational nature of our data\n1444 while avoiding overinterpretation of causal relationships. [Results (line 294-296)]\n1445\n1447 type.\n1448\n1450 throughout the manuscript following a comprehensive review.\n1451\n1453\n1455 this idea by revising the text to the following: “R-neurons might be TeA dPAG neurons,”\n1456 [Results (line 351)] This terminology reflects the functional identity of these neurons\n1457 as motor command neurons, defined by their direct control of running behaviour and\n1458 speed-associated firing patterns, as demonstrated in our experiments. We have removed\n1459 the ambiguous phrase \"correspond to\" and revised all related descriptions to ensure that\n1460 the text regarding this concept is suitably precise.\n1461\n1463\n1465 manuscript. Please refer to the revised text “Concurrently, we injected AAVretro-CMV-\n1467\n1469 control animals. Perhaps the authors can include additional phrasing that help\n1470 distinguish the behavioral results of the optogenetic control cohort with the\n1471 distinct analysis of ChR2 pos and neg cells.\n1472\n1474 provided more thorough explanations of the neuronal designations in the revised text:\n1476 the auditory cortex (AC) in the experimental paradigm depicted in Fig. 7a and express\n1478 neurons in the experimental paradigm illustrated in Fig. 7a. As these neurons do not\n1479 receive direct anterograde inputs from the AC, they do not express ChR2. ChR2+-\n1482\n1484 expression) failed to evoke running behaviours. Consequently, we did not record\n1485 neuronal activity from ChR2-negative cells in control animals during optical\n1486 stimulation, as these cells were functionally irrelevant to the observed behavioural\n1487 paradigm.\n1488\n1489\nAC dPAG\n1491 and LED light delivery sites. g, Running speed (F = 96.822, P < 10-8, n = 6 animals/group). One-\n2,15\n1492 way ANOVA with Bonferroni post hoc correction.\n1493\n1495 quantification is missing, similar to earlier in the manuscript (Fig 3k).\n1496\n1498 been incorporated into the main figures. For details, please refer to Fig. 6h.\n1499\n1500\nSens dPAG Sens\ndPAG\n1503 neurons with overlapping labelled inputs from different sources. n = 9, 9, and 9 slices from 3, 3, and\n1504 3 animals, respectively.\n1505\n1507\n1509\n1511 overlap, play a role in different circumstances? How come TeA inhibiton can block\n1512 the running behavior with intact SC circuit?\n1513\n1515 addressed in our companion manuscript (Chen et al., Figs. 4-6). Our findings revealed\n1516 that dPAG neurons receiving projections from the TeA ( TeA dPAG) and superior\n1517 colliculus ( SC dPAG) constitute two largely nonoverlapping neuronal populations (Chen\n1518 et al., Fig. 4a-c). Optogenetic activation of the SC-dPAG circuit induces backwards\n1519 movement and stopping behaviours during stimulation, followed by rebound running\n1520 upon cessation, whereas TeA-dPAG activation elicits forwards running (Chen et al., Fig.\n1521 5g-h). Mechanistically, we demonstrated that SC dPAG CaMKII neurons mediate\n1522 backwards running, whereas SC dPAG SOM neurons transiently inhibit TeA dPAG\n1524 postactivation (triggering rebound running). Critically, inhibition of the TeA-dPAG\n1525 circuit does not impair SC-dPAG-mediated behaviours, confirming the functional\n1526 independence of these parallel pathways.\n1527\n[FIGURE REDACTED]\n1528\nTeA SC\n1530 labelling (red) (b), and statistics of merged cells (yellow) (c). Figure 5 g, Behavioural responses for\n1531 f, respectively, plotted like b. h, Statistics for f, respectively, plotted like c. Rebound running (F\n(2,\n1532 = 112.013, P < 0.0001) and backing away (F = 1.780, P = 0.210). Control group rebound\n12) (2, 12)\n1533 running (F = 2.547, P = 0.120) and backing away (F = 0.476, P = 0.633).\n(2, 12) (2, 12)\n1534\n1536\n1538 has been revised as follows: “This study represents the first observation of these three\n1539 distinct types of neurons, providing new insights into and methodologies for future\n1540 motor and behaviour research.” [Discussion (line 706-707)]\n1541\n1543 comparison to the cited references, this study also epitomizes on circuit mapping\n1544 techniques to discover pathways and cell types involved in specific sensorimotor\n1545 transformations. No proper cellular mechanisms are described. Moreover, the\n1546 phrasing ‘special technics’ (techniques) suggest a newly introduced technique\n1547 whereas the study uses commonly available tools and techniques, albeit a\n1548 comparably sophisticated level of circuit investigation.\n1549\n1551 on the role of neural circuits, wherein different functions are typically performed by\n1552 distinct neural nuclei, forming pathways. However, whether a single neural nucleus in\n1553 the mouse brain can directly convert sensory input into motor output, particularly the\n1554 distributions of sensory neurons, sensory–motor decision neurons, and motor\n1555 command neurons at the cellular level, remains unknown.” [Discussion (line 677-\n1556 682)]\n1557\n1559 mapping techniques to discover pathways and cell types involved in specific\n1560 sensorimotor transformations.\n1561\n1563 ensure precise terminology and alignment with standard scientific nomenclature.\n1564\n1566\n1568 manuscript. Please refer to the revised text “Numbers: percentage reduction in the peak\n1569 speed and running probability for CNO+ animals relative to CNO- animals.” [Main\n1570 figure legends (line 1561-1562)]\n1571\n1573\n1575\n1577 (2.6×10¹³ vg/mL, OBiO), which functions as a retrograde tracer, to line 796 in the\n1578 revised manuscript. This revision ensures accurate categorization of the viruses on the\n1579 basis of their functional properties and experimental applications.\n1580\n1583 animals’ mean and what was done if animals did not ‘maintain a stable state’?\n1584\n1586 rotating disk voluntarily. A small subset of mice (approximately 5% of the cohort)\n1587 exhibited persistent motor incoordination or complete inability to run despite training,\n1588 likely due to incomplete adaptation to head fixation. These mice were excluded from\n1589 the study analyses to ensure behavioural consistency and data validity.\n1590\n1592\n1594 running speeds were pooled and processed in randomized batches after alignment with\n1595 the pseudorandom sequence of sensory stimuli (designed to induce heterogeneous\n1596 running behaviours across animals). The batch-processed data were subsequently\n1597 mapped to their corresponding stimuli and categorized according to the experimental\n1598 condition.” Please refer to the updated manuscript. [Methods (line 1144-1148)]\n1599\n1601\n1603\n1605\n1607 manuscript. Please refer to the revised text “We defined “running behaviour” as\n1608 movements in which the running speed of the mouse exceeded 0.5 cm/s, and the onset\n1609 of the running behaviour was defined as the point at which this speed threshold was\n1610 first exceeded.” [Methods (line 1157-1160)]\n1611\n1613\n1615 manuscript. Please refer to the revised text “For pharmacological manipulation of the\n1617\n1618\n1620\n1622 manuscript. Please refer to the revised text “no hChR2- or eNpHR3.0-expressing neural\n1623 fibres or structures other than the target structure were present in the light pathway\n1624 within 800 μm from the end of the optic fibre.” [Methods (line 963-965)]\n1625\n1627 how many?\n1628\n1630 excluded from the study. Following each experimental session, we rigorously\n1631 confirmed viral expression in all the animals and excluded those for whom the\n1632 injections were unsuccessful. While we did not systematically quantify the exact\n1633 amount of excluded data, we confirm that these animals represented a small percentage\n1634 of the total.\n1635\n1637\n1639\n1641\n1644\n1646\n1648 publication, with the repository link to be inserted here: [GitHub URL:\n1649 https://github.com/LiHe0606?tab=repositories.]. Please refer to the updated manuscript\n1650 (lines 1186).\n1652\n1653 1 Xiong, X. R. et al. Auditory cortex controls sound-driven innate defense\n1654 behaviour through corticofugal projections to inferior colliculus. Nat Commun\n1655 6, 7224,(2015).\n1656 2 Evans, D. A. et al. A synaptic threshold mechanism for computing escape\n1657 decisions. Nature 558, 590-594,(2018).\n1658 3 Wang, H. et al. Direct auditory cortical input to the lateral periaqueductal gray\n1659 controls sound-driven defensive behavior. PLoS Biol 17, e3000417,(2019).\n1660 4 Deng, H., Xiao, X. & Wang, Z. Periaqueductal Gray Neuronal Activities\n1662 7588,(2016).\n1663 5 Shapson-Coe, A. et al. A petavoxel fragment of human cerebral cortex\n1664 reconstructed at nanoscale resolution. Science 384, eadk4858,(2024).\n1666 and backing away behaviours”. Submitted as a companion paper."}, {"round": 2, "reviewer_comments": {"reviewer_1": "In this revised manuscript, the authors have addressed most of my comments. But there remains a few changes to be made before this paper can be accepted for publication:\n- I would suggest a more specific title that includes explicit reference to the TeA and mouse model.\n- line 286-287: “Three types of neurons in the TeA may regulate signalling that running behaviour” ???\n- Figure 4 : report real mean or median values, not the ones computed from Gaussian fitting.\n- Lines 381-397: I maintain that it is not correct to realign each running event to the preceding neuronal event with variable delays! This section is flawed and unnecessary, and should be removed. The report of the max correlation coefficients (r max) and lags from the cross-correlations is sufficient.\n- As indicated by the authors “the low overlap rate between hSyn and CaMKII could result from differences in the efficiency of the expression of the two types of viruses”. Indeed, the images in the Extended Data Figure 4d clearly show a significant number pyramidal cells only labeled with EYFP (green, hSyn promoter) and not by mCherry (CaMKIIa promoter). Therefore, I do not think that this part of the data is really informative.\n- Lines 506-508: Incorrect phrasing. In these experiments the inhibitory receptor (hM4D-Gi) is expressed in TeA neurons receiving inputs from sensory cortical or thalamic areas. So technically, the CNO will not block the sensory inputs from cortex or thalamus but instead block the activity downstream to SENS-TeA neurons.\n- Lines 510-511: No. These results show that SENS-TeA neurons are somewhat modality specific (at least partially).\n- Lines 570-584: In the end only 5 cells of each group were reconstructed. The authors should clearly indicate that they could only reconstruct the dendritic arborization from a small subset of neurons that were isolated enough. The authors should include the images provided in the rebuttal (line 157-158) as Extended Figure.\n- Lines 634-636 and Figure 7: Considering ChR2- cells in the experiments labelling the cells receiving inputs from AC, it is quite speculative to consider them as TeA-dPAG neurons. It would by more rigorous and less confusing to call them 'AC-TeA-ChR2-'\nSylvain Crochet", "reviewer_4": "This revised version of the manuscript presents clear improvements. However, the manuscript remains problematic in the disentanglement of sensory and motor related signals. In sum, I am not convinced about the presence of clear sensory-evoked activity, which presents a key aspect of the proposed model where within TeA diverse sensory signals are transformed into motor command signals.\nFirst, the new raster plots are unclear to me. For example:\nExtended Data Fig. 2 | S- and R-neurons in the TeA. a, Responses of the R-neurons to three types of sensory stimuli: auditory (sound), visual (light), and tactile (air puff). Top panel, Population raster plot and the corresponding PSTH of firing activity. Bottom panel, Superimposed running speed curves from all trials (n = 156 trials). The blue bars indicate the stimulus period (duration: 5 s).\nIn the legend it says response of the R-neurons and Population raster plot, but these must be individual example neurons (not a population of neurons) with each row representing individual trials, is that correct? A figure that shows the responses across all neurons of a certain category is then lacking. (If it is not, and each row is the mean activity across trials for one neuron and it is indeed a population plot, then there is just one spike for some neurons, and the legend is incorrect as these must be different numbers of trials for neurons recorded on different days.)\nExtended data figure 2f and 2g show 2 example S neurons that do not show stimulus-locked onset latency, arguing against strongly sensory driven activity. Similarly, Extended Data Fig 3 shows only example neurons and only for 1-3 individual stimulus presentations, too little to judge stimulus-locked sensory-evoked activity that is independent of behavioral changes.\nI understand from the Results section that the identification of SR and S neuron is assessed based on trials without any movement, but shockingly I can not find a good explanation of how S, SR and unSR neurons were categorized in the Methods section, including this information about trial selection.\nBy now, claiming strictly sensory evoked activity in awake behaving mice is not easy, and the default null hypothesis is widespread activity changes related to running, but also whisking, postural changes and other facial motion indices.\nI am happy to see the discussion. Given that the stimuli used cause behaviors other than locomotion such as head twitching and orienting behaviors at least in the open area, it would be a natural interpretation that the activity observed is related to non-locomotion related behavioral changes.\nIn sum, with more and more methods and new figures coming to the table in this MS, I am not assured by the presented unconvincing example neurons that there are clear auditory, tactile and visual responses.\nA convincing result for me would be a simple heatmap where each row is the activity of one S neuron (66 SR and 9 unSR cells) aligned to stimulus onset and averaged for all trials without locomotion. This should show clear stimulus locked increase in activity across both populations.\nAdditionally, it would be even more convincing to show that these responses also occur in the absence of other movements, or at least have different temporal dynamics. However, given the high rate of sound-induced behavioral changes reported in the discussion there might be too few trials without any sensory-induced behavioral changes.\nIn that case it is warranted that the authors add a statement in the Discussion that they can not fully exclude that the reported sensory-related activity is (partially) related to observed movements other than locomotion."}, "author_response": "Dear Reviewer #1,\nThe line numbers referenced in our point-by-point responses correspond to the\n\"Revised manuscript without track changes-He Li.pdf\" file.\n\nReviewer #1 (Remarks to the Author):\nIn this revised manuscript, the authors have addressed most of my comments. But\nthere remains a few changes to be made before this paper can be accepted for\npublication:\n- I would suggest a more specific title that includes explicit reference to the TeA\nand mouse model.\nThank you for your valuable suggestion. As suggested, we have revised the title to\nexplicitly include reference to both TeA and the mouse model used. The new title is:\n“An intralayer microcircuit in the temporal association cortex underlies sensory-\ninduced escape in mice”. (line 1–2)\nThe running title: “Running title: A TeA intralayer microcircuit for escape”. (line 4)\n- line 286-287: “Three types of neurons in the TeA may regulate signalling that\nrunning behaviour” ???\nWe have revised this subheading in light of the subsequent results. The updated heading:\n“Identification and characterization of three functional neuron types in the TeA region”.\n[Results line (286-287)]\n- Figure 4 : report real mean or median values, not the ones computed from\nGaussian fitting.\nWe have updated Figure 4 to present the real mean values. All relevant results are now\nreported using the actual mean values rather than those derived from Gaussian fitting.\nFig. 4 | Sensation-related and running-related neurons in the TeA. f, Left panel, Distribution of\nthe maximum correlation coefficient (r ) from the analysis of all recorded TeA R-neurons\nmax\n(Extended Data Fig. 2d), where the r for each neuron exceeded the 95% confidence interval\nmax\nderived from permutation testing (P < 0.05 for all cells). The red dashed line represents the Gaussian\nfit curve (R² = 0.9324, P = 3.66(cid:3400)10-6). Right panel, Distribution of the optimal time lag from the\nanalysis of R-neurons (R² = 0.9165, P = 1.125(cid:3400)10-14). j, The same as (f), left panel, analysis of all\nrecorded SR-neurons (Extended Data Fig. 2e), where the r for each neuron exceeded the 95%\nmax\nconfidence interval derived from permutation testing (P < 0.05 for all cells). (n = 66 cells, R² =\n0.6075, P = 2.1947(cid:3400)10-6). Right panel, Analysis of SR-neurons (R² = 0.7285, P = 3.8492(cid:3400)10-6).\n- Lines 381-397: I maintain that it is not correct to realign each running event to\nthe preceding neuronal event with variable delays! This section is flawed and\nunnecessary, and should be removed. The report of the max correlation\ncoefficients (r max) and lags from the cross-correlations is sufficient.\nThank you for your valuable comment. For the sake of clarity and focus, we have\nremoved the section describing the realignment of each running event to its preceding\nneuronal event with variable delays. The revised manuscript now only reports the key\noutcomes, specifically the maximum correlation coefficients (r ) and the\nmax\ncorresponding lag values obtained from the cross-correlation analyses.\n- As indicated by the authors “the low overlap rate between hSyn and CaMKII\ncould result from differences in the efficiency of the expression of the two types of\nviruses”. Indeed, the images in the Extended Data Figure 4d clearly show a\nsignificant number pyramidal cells only labeled with EYFP (green, hSyn promoter)\nand not by mCherry (CaMKIIa promoter). Therefore, I do not think that this part\nof the data is really informative.\nWe thank you for this insightful comment. While we had complementary data from\nboth GAD67 immunostaining and the dual-virus labeling, the key conclusion that these\nprojections are predominantly glutamatergic is primarily supported by the definitive\nGAD67 staining. The dual-virus experiment, although illustrative, introduces\nunnecessary complexity given the known technical variations. To streamline the\nnarrative and focus on the core findings, we have restructured this section. We now first\ndirectly establish that \"the projections are predominantly glutamatergic\" using the\nconclusive GAD67 immunostaining data. The dual-virus imaging and its statistics are\nretained but presented as a supplementary observation, accompanied by a note on its\ntechnical limitations. This entire section has been significantly condensed and serves\nspecifically to justify our rationale for selecting the hSyn promoter for subsequent\nfunctional manipulations.\nThe revised text in the Results section: \" We also investigated the neuronal types of TeA\nneurons receiving sensory projections ( TeA neurons). AAV-Cre was injected into\nSens\ndifferent sensory cortices, and AAV-hSyn-DIO-mCherry was injected into the TeA to\nlabel neurons receiving distinct sensory inputs in the TeA (Extended Data Fig. 4c).\nDirect identification of these neurons was performed via GAD67 (a marker for\nGABAergic neurons) immunostaining (Extended Data Fig. 4d). The results revealed an"}]

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