Dorsomedial Striatal Medium Spiny Neurons Orchestrate Temporally approaching threat Driven Defensive Switching in Fear Conditioning

Dorsomedial Striatal Medium Spiny Neurons Orchestrate Temporally approaching threat Driven Defensive Switching in Fear Conditioning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Dorsomedial Striatal Medium Spiny Neurons Orchestrate Temporally approaching threat Driven Defensive Switching in Fear Conditioning Wenting Wang, Junye Ge, Pengfei Ren, Baijun Chen, Yaning Zhang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7083386/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Defensive responses are evolutionarily conserved adaptive behaviors that species exhibit in response to threats to protect themselves from harm or death. The selection of context-appropriate defensive strategies, particularly the transition between freezing and flight behaviors, constitutes a critical determinant of species survival. By employing the serial-compound stimulus (SCS) Pavlovian fear conditioning, we found that the decision-making process of mice, specifically their inclination to exhibit freezing versus flight in the presence of threats, is primarily determined by learned temporal relationships of conditioned stimulus (CS) and unconditioned stimulus (US). As the threat stimulus approached more closely, mice exhibited enhanced escape behaviors characterized by reduced response latencies and increased response magnitudes. Furthermore, medium spiny neurons (MSNs) within the dorsal medial striatum (DMS) exhibited differential engagement based on threat proximity, as assessed through fiber photometry to record the population response of these neurons during the SCS. Dopamine receptor 2 expressing MSNs (D2 MSNs) predominantly modulated responses to temporally distant threats, while dopamine receptor 1 expressing MSNs (D1 MSNs) primarily regulated responses to imminent threats. Both types of MSNs contribute to the magnitude of the defensive response. These findings suggest that the switching of defensive responses during SCS Pavlovian fear conditioning is primarily determined by the temporal proximity of the CS to the US. This process is regulated through the collaborative interaction between D1 MSNs and D2 MSNs within the DMS region, underscoring the DMS as a critical neural hub for threat assessment and defensive strategy selection. Biological sciences/Neuroscience Health sciences/Diseases Defensive behavior Decision-making Dorsomedial Striatum Freezing Flight Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Defensive behaviors, shaped by natural selection, are automatically activated in humans and other animals to mitigate harm or mortality in dangerous situations[ 1 ]. When confronted with a threat, organisms display a flexible transition between different defensive responses[ 2 ]. Active responses, such as flight, cost more energy and carry greater risks. On the contrary, freezing is a common conservative defensive response, while it means forgoing opportunities to forage or seek potential mates. Therefore, the ability to promptly and accurately switch between freezing and flight in response to approaching threats is critical for survival[ 3 ]. The selection of defensive responses, whether active (e.g., flight) or passive (e.g., freezing), is governed by a dynamic process known as risk assessment (RA). RA involves the evaluation of multiple factors, including the threat proximity on a spatiotemporal scale, which determines the transition between defensive behaviors[ 4 , 5 ]. For example, freezing is typically the initial response to a realized threat (post-encounter mode), whereas flight emerges as the threat becomes imminent (circa-strike defensive mode). This pattern has been consistently observed in both humans and rodents exposed to naturalistic predator threats[ 2 , 4 , 6 ]. However, naturalistic predator threats make it difficult to disentangle the roles of stimulus type and threat proximity in driving defensive behaviors. To address this limitation, Fadok and colleagues developed a modified auditory Pavlovian fear conditioning procedure using a serial-compound stimulus (SCS) [ 7 ]. The SCS consists of a pure tone followed immediately by white noise, each eliciting distinct conditioned responses: freezing to the tone and flight to the white noise. After repeated trials, mice exhibit clear behavioral differentiation, freezing in response to the tone but fleeing upon hearing the white noise. However, whether these responses are driven by the auditory stimulus itself or by the perceived threat proximity encoded within the SCS remains debated[ 8 , 9 ]. The transition between freezing and flight during the SCS involves stimulus–response learning, risk assessment, and decision-making. The dorsal striatum (DS) particularly its medial subdivision (DMS), has emerged as a key candidate for mediating these processes due to its well-established roles in stimulus–response learning, goal-directed behavior, and decision-making[ 10 – 16 ]. These functions have been consistently demonstrated in both rodents [ 17 ] and nonhuman primates[ 18 , 19 ]. Specifically, the DS integrates sensorimotor, cognitive, and motivational/emotional information to facilitate flexible decision-making, particularly in action selection and initiation[ 18 , 20 – 22 ]. Emerging evidence further highlights the distinct roles of dopamine receptor 2 expressing MSNs (D2 MSNs) and dopamine receptor 1 expressing MSNs (D1 MSNs) in the DMS, with the DMS being critical for evaluating options and making decisions prior to outcome delivery[ 23 – 28 ]. Given these functions, the DMS may play a pivotal role in assessing conditioned threats and selecting between freezing and flight behaviors in the SCS paradigm. However, the specific contributions of D1 and D2 MSNs in the DMS to conditioned threat assessment and the decision-making processes underlying freezing and flight behaviors remain poorly understood. In this study, we modified the types of auditory stimuli used in previous studies to investigate the decision-making processes of defensive response in mice. We demonstrated that the switching between freezing and flight behaviors in mice in response to compound stimuli does not depend on the type of auditory stimulus. Instead, it depends on the temporal association strength between the conditioned stimulus (CS) and unconditioned stimulus (US). Moreover, using cell type-specific fiber photometry, we revealed that DMS MSNs regulate threat response latency and amplitude. Specifically, D2 MSNs modulate response latency to weaker threats, while D1 MSNs dominate as threat intensity increases. Both MSN types jointly maintain stable response amplitude across varying threat levels. Our findings demonstrate that the DMS integrates temporal threat information to guide adaptive defensive behaviors, revealing a critical neural mechanism for RA and the flexible selection of defensive responses in dynamic threat environments. Methods Animals All experimental procedures were approved by the Institutional Animal Care and Use Committee of the Fourth Military Medical University (Approval No. IACUC-20230320) and conformed to the Guide for the Care and Use of Laboratory Animals published by the National Institutes of Health. All mice were maintained under a 12-h light/dark cycle at 22–25℃ with food and water under environmentally controlled conditions. C57BL/6J mice were purchased from the Experimental Animal Center of the Fourth Military Medical University. The Drd1-Cre (#037156-JAX) and Adora2a-Cre (#031168-UCD) were obtained from the Jackson Laboratory. All virus injections were administered to mice aged 2 months old, and all behavioral tests were carried out during the light phase. The experimenters were blinded to the genotype and experimental conditions. All the mice employed in the behavioral tests were male. Virus injection and stereotaxic surgery Mice were anesthetized with isoflurane (4% for induction and 1.5% for maintenance), and their heads were fixed in a stereotaxic injection frame (RWD Life Science Inc., China). Injections into the brain were performed using a microinjection needle with a 10 µL microsyringe (Shanghai Gaoge Industry and Trade Co., LTD., China) to deliver the 250 nl of virus (rAAV-hSyn-DIO-GCaMP6s-WPRE-pA, Cat# BC-0238, BrainCase., China; rAAV-hSyn-CaMKII-GCaMP6s -WPRE-hGH-pA, Cat# PT-0110, BrainVTA., China.) at a rate of 30 nL/min using a micro syringe pump (KD Scientific Inc., USA). Following injection, the needle was held at the site for another 15 minutes to allow for diffusion. And an optical fiber (200 µm OD, 0.37 NA) was placed 100 µm above the injection site. The coordinates were defined as dorsal-ventral (DV) from the skull surface, anterior-posterior (AP) from bregma, and medio-lateral (ML) from the midline. The stereotaxic coordinates for the DMS injection were as follows: AP: 0.7 mm; ML: +1.5 mm; and DV: -3 mm. Conditioned flight paradigm Two different contexts were used. Context A (low-threat context, 200D × 400H mm) consisted of a clear cylindrical chamber with a smooth floor, while Context B (high-threat context, 200L×200W×300H mm) consisted of a square enclosure with an electrical grid floor used to deliver alternating current footshocks and a programmable audio generator for auditory stimulus (Shanghai Vanbi Intelligent Technology Co., Ltd.). For the SCS conditioned flight paradigm test, we used the methods previously described [ 7 , 8 ]. Briefly, on day 1, after 4-min habituation in the context A environment, an auditory SCS consisting of a 10 s pure tone (500 ms, 7.5 kHz pips at 1 Hz, 75 dB) and 10 s of white noise (500 ms, pips at 1 Hz, random and composed of frequencies ranging from 1 to 20 kHz, 75 dB) was delivered four times with a 60s ITI (inter-trial interval, ITI). On days 2 and 3, the mice were conditioned five times in context B after a 4-min habituation period by pairing the SCS with the US (footshock 0.9 mA, 1 s) at an average pseudorandom ITI of 180 s, the shock was applied at the end of the last pip. On day 4, the mice were placed back in context A and, after a 4-min habituation period, were presented with the SCS four times at an ITI of 60 s. For the TTSCS conditioned flight paradigm test, we replace the 10 s pure tone and 10 s white noise stimuli in the SCS with two pure tones (HT, high frequency tone, 500 ms, 4 kHz pips at 1 Hz, 75 dB; HT, low frequency tone, 500 ms, 400 Hz pips at 1 Hz, 75 dB) of different frequencies. And for the reverse TTSCS conditioned flight paradigm test, we switched the order of HT and LT. Quantification of defensive behavior All behavioral assessments were video recorded using Tracking Master V4.0 (Shanghai Vanbi Intelligent Technology Co., Ltd.). The body of each animal was extracted from the background, and the center of gravity was used to calculate its speed. Freezing was defined as a complete cessation of movement for at least 1 s and was automatically scored using a frame-by-frame analysis of pixel changes. The flight score was calculated by dividing the average speed during exposure to each CS by the average speed during the 10 s before CS onset (speed CS/speed pre), and then adding 1 point for each jump. The first CS is taken for 10 s, and the second CS is taken for 9 s to exclude the impact of the footshock at the last pip. Escape jumping was scored manually from video files by a blinded observer. For the quantification of defensive behavior in the mice with synchronized recording of neuronal calcium activity, we tracked the mouse's trajectory at a higher frame rate (Noldus Ethovision XT 11) to match the sampling rate of the calcium signals. We extracted the speed during the 10 s before SCS onset and the speed during the SCS period. Fiber photometry recording and data analysis Fiber photometry was used to record calcium signals using a commercialized fiber photometry system (ThinkerTech, China) as described previously[ 45 ]. To record the fluorescence signals, a 470-nm laser beam (OBIS 488LS; Coherent) was reflected off a dichroic mirror (MD498, Thorlabs) that was focused by a 310 objective lens (0.3 NA; Olympus) and coupled to an optical commutator (Doris Lenses). An optical fiber (230 mm OD, 0.37 NA) guided the light between the commutator and the implanted optical fiber. The laser power at the tip of the optical fiber was adjusted to 0.01–0.02 mW to decrease laser bleaching. Fluorescence was bandpass-filtered (MF525-39, Thorlabs), and an amplifier was used to convert the photomultiplier tube current output to a voltage signal. The analog vo00ltage signals were digitalized at 500 Hz and recorded by a Power 1401 digitizer with Spike2 software (CED). For data analysis, the fluorescence change was calculated as Z-score, the Z-score values of the animals in each group were averaged. To precisely analyze the changes in the fluorescence values across the training, we defined the baseline period (-2 to 0 s relative to the CS onset). To quantify the change in the level of calcium signal induced by CS, the area under the curve (AUC) of Z-score in each time window defined was calculated. Quantification and statistical analysis All data were transferred to SPSS 21.0 software (IBM, http://www.spss.com.cn ) for analysis and to OriginPro 2021 software (OriginLab, https://www.originlab.com ) for graphing. For the unpaired data, normality testing was performed by the Shapiro–Wilk test, the homogeneity of variance test was performed by Levene test. Data that met these two conditions were analyzed using a two-tailed unpaired t-test or one-factor ANOVA and Bonferroni correction for post hoc test. Datasets that were not normally distributed were analyzed with a Mann-Whitney U test or Kruskal-Wallis H test and Nemenyi multiple comparisons test. For the paired data, datasets that were normally distributed were analyzed with Two-tailed unpaired t-test, datasets that were not normally distributed were analyzed with Mann-Whitney U test. The significance levels for all tests were set at *p < 0.05, **p < 0.01, and ***p < 0.001. Results The switching between freezing and flight primarily reflect learned temporal relationships of CS1 and CS2 to the US In Pavlovian fear conditioning, a CS is paired with a US, and the passive defensive response (freezing) is the dominant conditioned defensive response to the CS in most conditioning experiments[ 29 ]. To investigate the dynamic switching between freezing and flight behaviors, we employed a modified Pavlovian conditioning paradigm using a two-tone serial compound stimulus (TTSCS) in mice. Specifically, we paired a TTSCS, consisting of a high-frequency tone (HT, 4 kHz, 500 ms, 75 dB) followed by a low-frequency tone (LT, 400 Hz, 500 ms, 75 dB), with a footshock (US, 0.9 mA) (Fig. 1 A-B). This paradigm is based on the SCS previously described[ 7 ], but uses two distinct pure tones instead of a tone and white noise. After conditioning, mice exhibited active and passive defensive responses during TTSCS on day 3 (Fig. 1 C-E). We quantified the active defensive response (flight) by measuring increases in speed (Fig. 1 E) and the number of escape jumps (Fig. 1 F, middle). We found that mice showed significantly higher flight scores and jump numbers during exposure to the LT compared to the HT (Fig. 1 F, left and middle). Conversely, freezing behavior was more pronounced during HT exposure than LT exposure (Fig. 1 F, right). These results indicate that the second tone, which is temporally closer to the US, elicited a stronger flight response. To further test this hypothesis, we reversed the order of the two pure tones (Figure S2A). Consistent with our initial findings, mice exhibited more flight behavior during exposure to the tone that was closer to the US (Figure S2B-F). We also replicated these results using the classic SCS paradigm (Figure S1A-F), as previously reported[ 7 ]. Collectively, these results suggest that the switching between freezing and flight in response to threats is primarily determined by the temporal proximity of the CS to the US, rather than the inherent properties of the stimulus itself. Mice exhibit faster and stronger active defensive responses as the threat approaches To further investigate the characteristics of the switch in defensive responses as the threat approaches. We defined the first auditory stimulus in the SCS composed of different types of stimuli as T1 and the subsequent second stimulus as T2. We established a velocity-based classifier to determine the state of the mice, following the definitions of freezing and flight reported in previous studies (Fig. 2 A). Our analysis revealed that during the entire SCS period, the proportion of freezing responses gradually decreased, while the proportion of flight responses progressively increased (Fig. 2 A-B). Interestingly, the proportion of freezing increased at the T1 stage and then smoothly decreased, whereas the proportion of flight exhibited peaks with different latencies during the T1, T2, and foot-shock stimulation stages (Fig. 2 B). To better understand the temporal dynamics of these two behaviors, we divided T1 and T2 into three segments of 3 seconds each and calculated the proportion of each behavior within each time segment. We found that freezing predominantly occurred within the 0–3 s, after which the proportion of flight began to increase (Fig. 2 C-D) during T1. Flight behavior accounted for a relatively large proportion in all three intervals during T2 (Fig. 2 E-F). Given that flight is determined by velocity, we averaged the velocity of all mice during the SCS and we found that following exposure to the three different levels of threat stimuli—T1, T2, and foot-shock, mice all exhibited increased velocity, corresponding to three peaks of different shapes (Fig. 2 G). After calculating the latency and amplitude of these three peaks, we found that as the threat approached, the latency of the peaks gradually decreased (Fig. 2 H), while their amplitude progressively increased (Fig. 2 I). In summary, mice flexibly exhibit defensive responses when facing threat, as the threat stimulus approached more closely, mice tended to exhibit more escape behaviors with shorter latencies and larger magnitudes. The involvement of DMS in the defensive responses Previous studies have indicated the involvement of the DMS in decision-making and action. We first examined the calcium activity changes of all MSNs in the DMS during the TTSCS paradigm and found that MSNs exhibited decreased calcium activity throughout the entire SCS stimulation period (Figure S3). To determine whether D1 and D2 MSNs in the DMS are involved in defensive responses during the TTSCS paradigm, we injected rAAV-DIO-GCaMP6s into the DMS of D1-Cre and A2a-Cre mice to record calcium activity (Fig. 3 A). Viral expression and fiber placement were verified (Fig. 3 B). To quantitatively assess calcium signal changes, we established a baseline period (− 2 to 0 s relative to TTSCS onset) and measured calcium activity within 20 s after SCS onset (Fig. 3 C). Using the area under the curve (AUC) as an indicator, we found significant increases in calcium activity in both D1 and D2 MSNs during TTSCS compared to the control recording channel, however, the activation levels of D1 and D2 MSNs did not show significant differences (Fig. 3 D). To further dissect whether the activation of D1 and D2 MSNs differs during T1 and T2, we established a 2-second baseline preceding each stimulus and separately quantified calcium activity changes within the 0–3 s and 3–6 s intervals following stimulus onset. In response to T1, no significant differences were observed in calcium activity changes between the two intervals for D1 MSNs, but the activation of D2 MSNs was significantly higher during the 3–6 second interval compared to the 0–3 second interval (Fig. 3 E). Additionally, the magnitude of calcium signal changes did not significantly differ between D1 and D2 MSNs (Fig. 3 E, right). During the T2 phase, we did not observe any significant differences in calcium activity changes between the two intervals for D1 MSNs and D2 MSNs (Fig. 3 F). D1 and D2 MSNs dynamically participate in decision-making and execution of defensive responses Given that defensive responses can vary among mice within the same behavioral paradigm, we used velocity as an index of defensive responses and performed joint analysis with calcium signals sampled at the same rate. We found that during the T1 stimulus, the latency to peak velocity was highly correlated with the latency to peak calcium activity in D2 MSNs but not in D1 MSNs (Fig. 4 A). Conversely, during the T2 stimulus, the latency to peak velocity was highly correlated with the latency to peak calcium activity in D1 MSNs instead of D2 MSNs (Fig. 4 B). Moreover, during T1 stimulus, the amplitude of peak velocity was highly correlated with the amplitude of peak calcium activity in both D1 and D2 MSNs (Fig. 4 C), and during the T2 stimulus, the amplitude of peak velocity was highly correlated with the amplitude of peak calcium activity in D2 MSNs (Fig. 4 D). Although we did not detect a significant correlation between the amplitude of peak calcium activity amplitude of D1 MSNs and velocity peaks during the T2 stimulus, we observed a positive skewness in the distribution of correlation coefficients between D1 MSNs calcium activity and velocity across the entire time axis (Fig. 4 E-H). This indicates that D1 calcium activity maintains a correlation with velocity throughout the SCS paradigm. These results suggest that MSNs in the DMS are involved in the latency and amplitude of active defensive responses when threats approaching. Specifically, when the threat stimulus is temporally distant, the involvement is primarily associated with D2 MSNs, whereas when the threat stimulus is temporally close, the involvement is mainly associated with D1 MSNs. The amplitude of the active defensive response is associated with the activity of both D1 and D2 MSNs. Discussion The present study indicates that the switching of freezing and flight defensive responses in SCS Pavlovian fear conditioning is driven by the temporal proximity of the auditory stimulus to the US. As the threat stimulus approached more closely, mice exhibit more pronounced flight responses with shorter latencies. MSNs in the DMS are involved in modulating the latency and amplitude of active defensive responses to threats. Specifically, D2 MSNs primarily contribute to the latency of defensive responses when facing relatively weaker threats, whereas their involvement decreases with increasing threat intensity, while the role of D1 MSNs becomes more prominent. The amplitude of the active defensive response is associated with the activity of both D1 and D2 MSNs and remains constant regardless of threat intensity. The mechanisms underlying SCS-driven conditioned responses remain debate. Recent studies have reported that white noise elicits stronger physiological and behavioral responses than pure tones even before conditioning[ 30 , 31 ]. These findings suggest that frequency and sound pressure levels, rather than temporal proximity to the US, drive these responses. However, this notion has been challenged by a work showing that white noise is not inherently aversive to mice. In a reverse SCS paradigm, mice exhibited flight in response to a tone but froze in response to white noise[ 8 ]. In our study, before training, mice exhibited more freezing behavior in response to white noise compared to pure tones ( Figure S1D ). However, after auditory habituation on Day1, mice did not show significant defensive responses to either the habituated white noise or the pure tones. These results suggest that mice may innately exhibit subtle defensive reactions to different auditory stimuli, but these responses can be eliminated through repeated habituation. Following shock pairing, auditory stimuli acquire new roles, eliciting learned defensive behaviors—specifically, the freezing and flight observed in the SCS paradigm. To eliminate the potential confounding effects of white noise, we designed both CS1 and CS2 as pure tones (75 dB) with different frequencies in the TTSCS paradigm and reversed their order in the reverse TTSCS paradigm ( Figure S2 ). The results showed that the switching between different defensive responses was primarily determined by the temporal proximity of the CS to the US, rather than the inherent properties of the CS. This is consistent with previous observations that mice switch defensive responses to naturalistic predator threats based on predator proximity[ 32 – 34 ]. Indeed, mice freeze to avoid detection when a predator is distant but switch to flight when the predator approaches too closely. This may reflect the transition from fear to panic. Our findings in the conditioned procedure confirm this, supporting the correlation between the switching of learned defensive responses and the degree of threat proximity. Interestingly, we also found that as the threat stimulus approached more closely, mice exhibited more escape behaviors with shorter latencies and larger magnitudes of response. This suggests that mice evaluate the CS based on the learned temporal relationships among CS1, CS2, and the US, make decisions, and then execute appropriate defensive responses. Previous studies have shown that amygdala, midbrain, and associated circuits are involved in the execution of defensive behavior[ 7 , 35 – 38 ]. However, most of these studies have focused on the emotional factors of defensive behaviors and the execution of defensive responses themselves, rather than on the decision-making processes underlying them. In this study, we found that the decision-making process is associated with the dynamic responses of D1 and D2 MSNs in the DMS. Specifically, D2 MSNs dominate when the threat is temporally distant, while D1 MSNs take over when the threat is temporally close. The DMS is known to plays a crucial role in decision-making[ 12 , 13 , 16 , 20 ] and complex temporal processing[ 39 ]. Moreover, it receives substantial and highly overlapping inputs from the amygdala and the prefrontal cortex[ 40 , 41 ], both of which regulate defensive behaviors[ 37 , 38 ]. Recent optogenetic experiments suggest that D1 and D2 MSNs in the DMS exert antagonistic control over learning and decision-making[ 42 ]. The direct pathway activation disinhibits brainstem motor structures, and thalamic nuclei targeting the motor cortex, promoting movement. While the indirect pathway activates basal ganglia output nuclei and thus inhibits movements[ 43 , 44 ]. Therefore, it may integrate cognitive and motivational/emotional information from these regions to make decisions in defensive behaviors. Specifically, in the switching of defensive responses, D2 MSNs mediate freezing (a conservative defensive response), while D1 MSNs involve in flight (a riskier defensive response). In conclusion, our findings indicated the switching of defensive responses during SCS Pavlovian fear conditioning is primarily determined by the temporal proximity of the auditory stimulus to the US, rather than the inherent properties of the stimulus, and highlight the DMS region as a key player in the switching of different defensive responses when face to the dynamically changing threat stimuli. Limitation of Study However, our study has certain limitations. Due to limitations of the viral tools, we were unable to simultaneously record calcium signals from both D1 and D2 MSNs in the same mouse. While we identified a correlation between MSNs activity in the DMS and the decision-making processes underlying defensive responses, we did not establish their causal roles. Additionally, we did not further explore how the classic corticostriatal circuits are involved in defensive behaviors. Future research should aim to elucidate the causal and mechanistic roles of D1 and D2 MSNs in corticostriatal circuits, particularly in processing selected aspects of threat information, thereby contributing to defensive responses. Declarations Declaration of interests The authors declare that there are no conflicts of interest. Author contributions W.W., J.G. contributed to experimental design and discussion. J.G., P.R., and B.C. performed the experiments and collected data. Y.Z., B.C., Y.D., and J.X. analyzed data. J.G., W.W., Q.X., and Y.Z. wrote, edited and reviewed the manuscript. All authors read and approved the final manuscript. Acknowledgments This study was supported by Natural Science Foundation of China (82271577, 82071536 to W.W., 82371236 to Y. Z.), Shaanxi Provincial Innovation Chain Project of Key Industries (2023-ZDLSF-47 to W.W), the Natural Science Foundation of Guangdong Province of China (2024A1515012479 to Y. Z.), Key Research and Development Program of Shaanxi Province (2023-YBSF-106 to Q.X.), and the Joint Founding Project of Innovation Research Institute, Xijing Hospital (LHJJ24JH05 to W.W.). References Tseng, Y.-T., et al., Defensive responses: behaviour, the brain and the body. Nature Reviews. Neuroscience, 2023. 24 (11): p. 655-671. Blanchard, D.C., Translating dynamic defense patterns from rodents to people. Neuroscience and Biobehavioral Reviews, 2017. 76 (Pt A): p. 22-28. Yang, X., et al., A simple threat-detection strategy in mice. BMC Biology, 2020. 18 (1): p. 93. Blanchard, D.C., et al., Risk assessment as an evolved threat detection and analysis process. Neuroscience and Biobehavioral Reviews, 2011. 35 (4): p. 991-998. Fanselow, M. and L. Lester, A functional behavioristic approach to aversively motivated behavior: Predatory imminence as a determinant of the topography of defensive behavior. Evolution and Learning, 1988. Blanchard, D.C., Sex, defense, and risk assessment: Who could ask for anything more? Neuroscience and Biobehavioral Reviews, 2023. 144 : p. 104931. Fadok, J.P., et al., A competitive inhibitory circuit for selection of active and passive fear responses. Nature, 2017. 542 (7639). Dong, P., et al., A novel cortico-intrathalamic circuit for flight behavior. Nature Neuroscience, 2019. 22 (6): p. 941-949. Furuyama, T., et al., Multiple factors contribute to flight behaviors during fear conditioning. Scientific Reports, 2023. 13 (1): p. 10402. Yin, H.H. and B.J. Knowlton, The role of the basal ganglia in habit formation. Nature Reviews. Neuroscience, 2006. 7 (6): p. 464-476. Ashby, F.G., B.O. Turner, and J.C. Horvitz, Cortical and basal ganglia contributions to habit learning and automaticity. Trends In Cognitive Sciences, 2010. 14 (5): p. 208-215. Hiebert, N.M., et al., Dorsal striatum mediates deliberate decision making, not late-stage, stimulus-response learning. Human Brain Mapping, 2017. 38 (12): p. 6133-6156. Schouppe, N., et al., The role of the striatum in effort-based decision-making in the absence of reward. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience, 2014. 34 (6): p. 2148-2154. Pearson, J.M., K.K. Watson, and M.L. Platt, Decision making: the neuroethological turn. Neuron, 2014. 82 (5): p. 950-965. Sugrue, L.P., G.S. Corrado, and W.T. Newsome, Choosing the greater of two goods: neural currencies for valuation and decision making. Nature Reviews. Neuroscience, 2005. 6 (5): p. 363-375. Hiebert, N.M., et al., Striatum in stimulus-response learning via feedback and in decision making. NeuroImage, 2014. 101 : p. 448-457. Gore, F., et al., Orbitofrontal cortex control of striatum leads economic decision-making. Nature Neuroscience, 2023. 26 (9): p. 1566-1574. Cai, X., S. Kim, and D. Lee, Heterogeneous coding of temporally discounted values in the dorsal and ventral striatum during intertemporal choice. Neuron, 2011. 69 (1): p. 170-182. Hassani, O.K., H.C. Cromwell, and W. Schultz, Influence of expectation of different rewards on behavior-related neuronal activity in the striatum. Journal of Neurophysiology, 2001. 85 (6): p. 2477-2489. Balleine, B.W., M.R. Delgado, and O. Hikosaka, The role of the dorsal striatum in reward and decision-making. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience, 2007. 27 (31): p. 8161-8165. O'Doherty, J., et al., Dissociable roles of ventral and dorsal striatum in instrumental conditioning. Science (New York, N.Y.), 2004. 304 (5669): p. 452-454. Johnson, A., M.A.A. van der Meer, and A.D. Redish, Integrating hippocampus and striatum in decision-making. Current Opinion In Neurobiology, 2007. 17 (6): p. 692-697. Friedman, A., et al., A Corticostriatal Path Targeting Striosomes Controls Decision-Making under Conflict. Cell, 2015. 161 (6): p. 1320-1333. Yin, H.H., et al., The role of the dorsomedial striatum in instrumental conditioning. The European Journal of Neuroscience, 2005. 22 (2): p. 513-523. Gremel, C.M. and R.M. Costa, Orbitofrontal and striatal circuits dynamically encode the shift between goal-directed and habitual actions. Nature Communications, 2013. 4 : p. 2264. Gremel, C.M., et al., Endocannabinoid Modulation of Orbitostriatal Circuits Gates Habit Formation. Neuron, 2016. 90 (6): p. 1312-1324. Bissonette, G.B. and M.R. Roesch, Rule encoding in dorsal striatum impacts action selection. The European Journal of Neuroscience, 2015. 42 (8): p. 2555-2567. Cui, G., et al., Concurrent activation of striatal direct and indirect pathways during action initiation. Nature, 2013. 494 (7436): p. 238-242. Trott, J.M., et al., Conditional and unconditional components of aversively motivated freezing, flight and darting in mice. ELife, 2022. 11 . Hersman, S., et al., Stimulus salience determines defensive behaviors elicited by aversively conditioned serial compound auditory stimuli. ELife, 2020. 9 . Totty, M.S., et al., Behavioral and brain mechanisms mediating conditioned flight behavior in rats. Scientific Reports, 2021. 11 (1): p. 8215. Mobbs, D., et al., When fear is near: threat imminence elicits prefrontal-periaqueductal gray shifts in humans. Science (New York, N.Y.), 2007. 317 (5841): p. 1079-1083. Yilmaz, M. and M. Meister, Rapid innate defensive responses of mice to looming visual stimuli. Current Biology : CB, 2013. 23 (20): p. 2011-2015. Li, Z., et al., Corticostriatal control of defense behavior in mice induced by auditory looming cues. Nature Communications, 2021. 12 (1): p. 1040. Tovote, P., et al., Midbrain circuits for defensive behaviour. Nature, 2016. 534 (7606): p. 206-212. Wang, L., I.Z. Chen, and D. Lin, Collateral pathways from the ventromedial hypothalamus mediate defensive behaviors. Neuron, 2015. 85 (6): p. 1344-1358. Gross, C.T. and N.S. Canteras, The many paths to fear. Nature Reviews. Neuroscience, 2012. 13 (9): p. 651-658. Borkar, C.D., et al., Top-down control of flight by a non-canonical cortico-amygdala pathway. Nature, 2024. 625 (7996): p. 743-749. Emmons, E.B., et al., Rodent Medial Frontal Control of Temporal Processing in the Dorsomedial Striatum. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience, 2017. 37 (36): p. 8718-8733. Wall, N.R., et al., Differential innervation of direct- and indirect-pathway striatal projection neurons. Neuron, 2013. 79 (2): p. 347-360. Hunnicutt, B.J., et al., A comprehensive excitatory input map of the striatum reveals novel functional organization. ELife, 2016. 5 . Cox, J. and I.B. Witten, Striatal circuits for reward learning and decision-making. Nature Reviews. Neuroscience, 2019. 20 (8): p. 482-494. DeLong, M.R., Primate models of movement disorders of basal ganglia origin. Trends In Neurosciences, 1990. 13 (7): p. 281-285. Albin, R.L., A.B. Young, and J.B. Penney, The functional anatomy of basal ganglia disorders. Trends In Neurosciences, 1989. 12 (10): p. 366-375. Ge, J., et al., Ventral zona incerta parvalbumin neurons modulate sensory-induced and stress-induced self-grooming via input-dependent mechanisms in mice. IScience, 2024. 27 (7): p. 110165. Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files figs1202571201.tif.jpg Figure S1. SCS Pavlovian conditioning paradigm (A) The conditioning stimulus is a SCS paired with a 1s footshock during conditioning. (B) Schematic of the behavioral protocol used to induce conditioned flight behavior. (C) Comparison of the flight score between tone (blue) and white noise (red) across sessions. Day1, Friedman's M test (n = 11 mice): χ 2 = 9.091, df = 1, p = 0.003; Day2, Friedman's M test (n = 11 mice): χ 2 = 6.564, df = 1, p = 0.010; Day3, Friedman's M test (n = 12 mice): χ 2 = 13.067, df = 1, p = 0.000; Day4, Friedman's M test (n = 12 mice): χ 2 = 5.333, df = 1, p = 0.021. (D) Comparison of the freezing between tone (blue) and white noise (red) across sessions. Day1, Friedman's M test (n = 10 mice): χ 2 = 16.892, df = 1, p = 0. 000; Day2, Friedman's M test (n = 12 mice): χ 2 = 0.067, df = 1, p = 0.796; Day3, Friedman's M test (n = 12 mice): χ 2 = 4.267, df = 1, p = 0.039; Day4, Friedman's M test (n = 12 mice): χ 2 = 3.596, df = 1, p = 0.058. (E) Average speed curve (mean ± s.e.m., n = 7 mice) on day 3; note the increase in speed during the onset of white noise. (F) Left, flight scores on day 3 , Wilcoxon signed-rank test (n = 12 mice): Z = -2.824, p = 0.005. Right, freezing behavior on day 3, Wilcoxon signed-rank test (n = 12 mice): Z = -2.434, p = 0.015. figs2202571201.tif.jpg Figure S2. Reverse TTSCS Pavlovian conditioning paradigm (A) The conditioning stimulus is a reverse TTSCS paired with a 1s footshock during conditioning. (B) Comparison of the flight score between HT (red) and LT (blue) across sessions. Day1, Friedman's M test (n = 16 mice): χ 2 = 0.342, df = 1, p = 0.558; Day2, Friedman's M test (n = 16 mice): χ 2 = 2.909, df = 1, p = 0.088; Day3, Friedman's M test (n = 16 mice): χ 2 = 29.225, df = 1, p = 0.000; Day4, Friedman's M test (n = 16 mice): χ 2 = 4.056, df = 1, p = 0.044. (C) Comparison of the freezing between HT (red) and LT (blue) across sessions. Day1, Friedman's M test (n = 16 mice): χ 2 = 33.882, df = 1, p = 0.000; Day2, Friedman's M test (n = 16 mice): χ 2 = 20.045, df = 1, p = 0.000; Day3, Friedman's M test (n = 16 mice): χ 2 = 0.551, df = 1, p = 0.458; Day4, Friedman's M test (n = 16 mice): χ 2 = 33.136, df = 1, p = 0.000. (D) Average speed curve (mean ± s.e.m., n = 16 mice) on day 3; note the increase in speed during the onset of HT. (E) Flight scores on day 3 , Wilcoxon signed-rank test (n = 16 mice): Z = -3.942, p = 0.000. (F) Freezing behavior on day 3, Wilcoxon signed-rank test (n = 16 mice): t = -1.466, p = 0.143. figs3202571201.tif.jpg Figure S3. The involvement of MSNs during TTSCS Pavlovian conditioning paradigm (A) Left, schematic representation of the setup of fiber photometry to record calcium activity from MSNs infected with rAAV-CaMKII--GCaMP6s virus in the C57/BL6 mice. Right, expression of GCaMP6s and placement of the fiber optics were verified post-mortem. 200 μm for the big image and 20 μm for blown-up images. (B) Representative images, counterstained with DAPI (blue), showing GCaMP6s (green) colocalizing with DARPP-32 protein (red). Scale bar, 50 μm. (C) The percentage of colocalized DARPP-32 and GCaMP6s relative to GCaMP6s. (D) All results of calcium activity (left) and averaged traces of each mouse (right) in MSNs in the DMS on day 1. (E) Averaged traces of calcium activity in MSNs (left) and the area under the curve (AUC) of calcium changes (right) on day 1. Two-tailed unpaired t-test (n = 6 mice): t = 2.778, df = 5, p = 0.039; Two-tailed unpaired t-test (n = 6 mice): t = 0.548, df = 5, p = 0.607. (F) All results of calcium activity (left) and averaged traces of each mouse (right) in MSNs in the DMS on day 3. (G) Averaged traces of calcium activity in MSNs (left) and the area under the curve (AUC) of calcium changes (right) on day 3. Two-tailed unpaired t-test (n = 6 mice): t = 3.140, df = 5, p = 0.026; Two-tailed unpaired t-test (n = 6 mice): t = 3.752, df = 5, p = 0.013. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: revise 09 Sep, 2025 Review # 3 received at journal 06 Sep, 2025 Review # 1 received at journal 18 Aug, 2025 Reviewer # 3 agreed at journal 14 Aug, 2025 Review # 2 received at journal 10 Aug, 2025 Reviewer # 2 agreed at journal 28 Jul, 2025 Reviewer # 1 agreed at journal 23 Jul, 2025 Reviewers invited by journal 18 Jul, 2025 Editor assigned by journal 15 Jul, 2025 Submission checks completed at journal 15 Jul, 2025 First submitted to journal 14 Jul, 2025 Unknown event 10 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7083386","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":487587871,"identity":"9eced458-0ce7-460a-a49f-cdb00c230614","order_by":0,"name":"Wenting Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYJCCAx///JdjbwCzmYnRwMz4cGYDszHPARK0MBtzNjAn9hCtxeBG/jFpxh1s6T1ip9MkGCqsExvYzx4goCWZTbrwDE9uj3TuNgmGM+mJDTx5CXi1mN0GapnBJpG7H6SFse1wYoMEjwFhLTxsBuk8YC3/iNPCbMzblpAA0dJAhBb7+48NH844c8AQ6JfNFgnH0o3beHLwa5HsOfjgwIeKA/JAWzbe+FBjLdvPfga/FlSQAMRsJKgfBaNgFIyCUYADAACpSkSmFVmcWwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-8344-0102","institution":"Department of Neurobiology, School of Basic Medicine, Fourth Military Medical University","correspondingAuthor":true,"prefix":"","firstName":"Wenting","middleName":"","lastName":"Wang","suffix":""},{"id":487587872,"identity":"296b7550-4ac6-4374-941e-2a07b26e30c7","order_by":1,"name":"Junye Ge","email":"","orcid":"https://orcid.org/0000-0002-8168-439X","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Junye","middleName":"","lastName":"Ge","suffix":""},{"id":487587873,"identity":"0139cd01-ae36-4e41-baac-38f1c7009073","order_by":2,"name":"Pengfei Ren","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Pengfei","middleName":"","lastName":"Ren","suffix":""},{"id":487587874,"identity":"e912c818-5838-41f4-bb35-662d178057d6","order_by":3,"name":"Baijun Chen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Baijun","middleName":"","lastName":"Chen","suffix":""},{"id":487587875,"identity":"5c887684-abef-4f81-8d6c-9096107ff70d","order_by":4,"name":"Yaning Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yaning","middleName":"","lastName":"Zhang","suffix":""},{"id":487587876,"identity":"0eddd6bf-8a39-4dbc-8589-7f428ca1914e","order_by":5,"name":"Yiwen Deng","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yiwen","middleName":"","lastName":"Deng","suffix":""},{"id":487587877,"identity":"f443deb4-00c4-46b2-a8d8-293db4bba4ce","order_by":6,"name":"Jinwei Xu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jinwei","middleName":"","lastName":"Xu","suffix":""},{"id":487587878,"identity":"9afc18c2-e1e1-423d-9086-053e6b796da0","order_by":7,"name":"Ying Zang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Zang","suffix":""},{"id":487587879,"identity":"0a0380c5-3c9b-4dfa-8d47-d90ec80e096a","order_by":8,"name":"Shengxi Wu","email":"","orcid":"https://orcid.org/0000-0002-3210-9567","institution":"Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shengxi","middleName":"","lastName":"Wu","suffix":""},{"id":487587880,"identity":"8da2c729-bd01-4872-a49b-5f7d4f59891a","order_by":9,"name":"Qian Xue","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Xue","suffix":""}],"badges":[],"createdAt":"2025-07-09 11:35:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7083386/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7083386/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87396995,"identity":"53144b95-3a87-4cbb-9d94-15311856c13a","added_by":"auto","created_at":"2025-07-23 11:00:03","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":365967,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTwo-tone serial compound stimulus Pavlovian conditioning paradigm\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The conditioning stimulus is a two-tone serial compound stimulus (TTSCS) paired with a 1s footshock during conditioning.\u003c/p\u003e\n\u003cp\u003e(B) Schematic of the behavioral protocol used to induce conditioned flight behavior.\u003c/p\u003e\n\u003cp\u003e(C) Comparison of the flight score between HT (blue) and LT (red) across sessions. Day1, \u003cem\u003eFriedman's M test\u003c/em\u003e (n = 7 mice): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 3.571, \u003cem\u003edf\u003c/em\u003e = 1,\u003cem\u003e p\u003c/em\u003e = 0.059; Day2, \u003cem\u003eFriedman's M test\u003c/em\u003e (n = 7 mice): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 6.429, \u003cem\u003edf\u003c/em\u003e = 1,\u003cem\u003e p\u003c/em\u003e = 0.011; Day3, \u003cem\u003eFriedman's M test\u003c/em\u003e (n = 7 mice): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 24.029, \u003cem\u003edf\u003c/em\u003e = 1,\u003cem\u003e p\u003c/em\u003e = 0.000; Day4, \u003cem\u003eFriedman's M test\u003c/em\u003e (n = 7 mice): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 9.143, \u003cem\u003edf\u003c/em\u003e = 1,\u003cem\u003e p\u003c/em\u003e = 0.002.\u003c/p\u003e\n\u003cp\u003e(D) Comparison of the freezing between HT (blue) and LT (red) across sessions. Day1, \u003cem\u003eFriedman's M test\u003c/em\u003e (n = 7 mice): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 0.067, \u003cem\u003edf\u003c/em\u003e = 1,\u003cem\u003e p\u003c/em\u003e = 0.796; Day2, \u003cem\u003eFriedman's M test\u003c/em\u003e (n = 7 mice): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 5.765, \u003cem\u003edf\u003c/em\u003e = 1,\u003cem\u003e p\u003c/em\u003e = 0.016; Day3, \u003cem\u003eFriedman's M test\u003c/em\u003e (n = 7 mice): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 24.029, \u003cem\u003edf\u003c/em\u003e = 1,\u003cem\u003e p\u003c/em\u003e = 0.000; Day4, \u003cem\u003eFriedman's M test\u003c/em\u003e (n = 7 mice): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 2.462, \u003cem\u003edf\u003c/em\u003e = 1,\u003cem\u003e p\u003c/em\u003e = 0.117.\u003c/p\u003e\n\u003cp\u003e(E) Average speed curve (mean ± s.e.m., n = 7 mice) on day 3; note the increase in speed during the onset of LT.\u003c/p\u003e\n\u003cp\u003e(F) Left, flight scores on day 3\u003cem\u003e, Wilcoxon signed-rank test\u003c/em\u003e (n = 7 mice): \u003cem\u003eZ =\u003c/em\u003e -2.366, \u003cem\u003ep =\u003c/em\u003e 0.018. Middle, the number of jump escape responses on day 3, \u003cem\u003eWilcoxon signed-rank test\u003c/em\u003e (n = 7 mice): \u003cem\u003eZ =\u003c/em\u003e -2.023, \u003cem\u003ep =\u003c/em\u003e 0.043. Right, freezing behavior on day 3,\u003cem\u003e two-tailed paired t-test\u003c/em\u003e (n = 7 mice): \u003cem\u003et =\u003c/em\u003e 4.353, \u003cem\u003edf =\u003c/em\u003e 6, \u003cem\u003ep =\u003c/em\u003e 0.005.\u003c/p\u003e","description":"","filename":"fig1202571201.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7083386/v1/7ee85ea6c23dba46e83ceb49.jpg"},{"id":87397255,"identity":"688f06fa-65f5-4e6c-8bce-31e1793c78d2","added_by":"auto","created_at":"2025-07-23 11:08:03","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":557946,"visible":true,"origin":"","legend":"\u003cp\u003e(I) Comparison of the amplitude of the velocity peaks caused by the increase in speed in response to three different types of stimuli (T1, T2 and foot-shock). \u003cem\u003eKruskal-Wallis H test \u003c/em\u003eand\u003cem\u003e Nemenyi multiple comparisons test \u003c/em\u003efor post hoc test (n = 34 mice in the freezing, flight, and normal group): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 41.363, \u003cem\u003edf =\u003c/em\u003e 2, \u003cem\u003ep =\u003c/em\u003e 0.000; \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eT1\u0026amp;T2\u003c/sub\u003e = -24.647, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e T1\u0026amp;T2\u003c/sub\u003e = 0.002; \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e T1\u0026amp;shock\u003c/sub\u003e= -46.118, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e T1\u0026amp;shock\u003c/sub\u003e = 0.000; \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e T2\u0026amp;shock\u003c/sub\u003e = -21.471, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e T2\u0026amp;shock\u003c/sub\u003e = 0.008.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe distribution of freezing and flight responses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The type of defensive response exhibited by each mouse (n = 34 mice).\u003c/p\u003e\n\u003cp\u003e(B) The density distribution of the two defensive responses (red: flight; blue: freezing).\u003c/p\u003e\n\u003cp\u003e(C-D) Comparison of the proportion of different types of defensive responses during three intervals (0-3s, 3-6s, 6-9s) while the T1 stimulus is being presented. Left (C), \u003cem\u003eKruskal-Wallis H test \u003c/em\u003eand\u003cem\u003e Nemenyi multiple comparisons test \u003c/em\u003efor post hoc test (n = 34 mice in the freezing, flight, and normal group): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 53.609, \u003cem\u003edf =\u003c/em\u003e 2, \u003cem\u003ep =\u003c/em\u003e 0.000; \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003efreezing\u0026amp;normal\u003c/sub\u003e = 33.412, \u003cem\u003ep\u003c/em\u003e\u003csub\u003efreezing\u0026amp;normal\u003c/sub\u003e = 0.000; \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eflight\u0026amp;normal\u003c/sub\u003e = 13.294, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e flight\u0026amp;normal\u003c/sub\u003e = 0.129; \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003efreezing\u0026amp;flight\u003c/sub\u003e = 46.706, \u003cem\u003ep\u003c/em\u003e\u003csub\u003efreezing\u0026amp;flight\u003c/sub\u003e = 0.000. Middle (C), \u003cem\u003eKruskal-Wallis H test \u003c/em\u003eand\u003cem\u003e Nemenyi multiple comparisons test \u003c/em\u003efor post hoc test (n = 34 mice in the freezing, flight, and normal group): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 21.592, \u003cem\u003edf =\u003c/em\u003e 2, \u003cem\u003ep =\u003c/em\u003e 0.000; \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003efreezing\u0026amp;normal\u003c/sub\u003e = 31.559, \u003cem\u003ep\u003c/em\u003e\u003csub\u003efreezing\u0026amp;normal\u003c/sub\u003e = 0.000; \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eflight\u0026amp;normal\u003c/sub\u003e = -17.324, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e flight\u0026amp;normal\u003c/sub\u003e = 0.033; \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003efreezing\u0026amp;flight\u003c/sub\u003e = 14.235, \u003cem\u003ep\u003c/em\u003e\u003csub\u003efreezing\u0026amp;flight\u003c/sub\u003e = 0.109; Right (C), \u003cem\u003eKruskal-Wallis H test \u003c/em\u003eand\u003cem\u003e Nemenyi multiple comparisons test \u003c/em\u003efor post hoc test (n = 34 mice in the freezing, flight, and normal group): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 16.021, \u003cem\u003edf =\u003c/em\u003e 2, \u003cem\u003ep =\u003c/em\u003e 0.000; \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003efreezing\u0026amp;normal\u003c/sub\u003e = 27.074, \u003cem\u003ep\u003c/em\u003e\u003csub\u003efreezing\u0026amp;normal\u003c/sub\u003e = 0.000; \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eflight\u0026amp;normal\u003c/sub\u003e = 14.662, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eflight\u0026amp;normal\u003c/sub\u003e = 0.091; \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003efreezing\u0026amp;flight\u003c/sub\u003e = -12.412, \u003cem\u003ep\u003c/em\u003e\u003csub\u003efreezing\u0026amp;flight\u003c/sub\u003e = 0.200.\u003c/p\u003e\n\u003cp\u003e(E-F) Comparison of the proportion of different types of defensive responses during three intervals (0-3s, 3-6s, 6-9s) while the T2 stimulus is being presented. Left (E), \u003cem\u003eKruskal-Wallis H test \u003c/em\u003eand\u003cem\u003e Nemenyi multiple comparisons test \u003c/em\u003efor post hoc test (n = 34 mice in the freezing, flight, and normal group): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 30.913, \u003cem\u003edf =\u003c/em\u003e 2, \u003cem\u003ep =\u003c/em\u003e 0.000; \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003efreezing\u0026amp;normal\u003c/sub\u003e = 11.588, \u003cem\u003ep\u003c/em\u003e\u003csub\u003efreezing\u0026amp;normal\u003c/sub\u003e = 0.266; \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eflight\u0026amp;normal\u003c/sub\u003e = -36.985, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e flight\u0026amp;normal\u003c/sub\u003e = 0.000; \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003efreezing\u0026amp;flight\u003c/sub\u003e = -25.397, \u003cem\u003ep\u003c/em\u003e\u003csub\u003efreezing\u0026amp;flight\u003c/sub\u003e = 0.001. Middle (E), \u003cem\u003eKruskal-Wallis H test \u003c/em\u003eand\u003cem\u003e Nemenyi multiple comparisons test \u003c/em\u003efor post hoc test (n = 34 mice in the freezing, flight, and normal group): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 21.020, \u003cem\u003edf =\u003c/em\u003e 2, \u003cem\u003ep =\u003c/em\u003e 0.000; \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003efreezing\u0026amp;normal\u003c/sub\u003e = 4.721, \u003cem\u003ep\u003c/em\u003e\u003csub\u003efreezing\u0026amp;normal\u003c/sub\u003e = 1.000; \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eflight\u0026amp;normal\u003c/sub\u003e = -28.809, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eflight\u0026amp;normal\u003c/sub\u003e = 0.000; \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003efreezing\u0026amp;flight\u003c/sub\u003e = -24.088, \u003cem\u003ep\u003c/em\u003e\u003csub\u003efreezing\u0026amp;flight\u003c/sub\u003e = 0.001. Right (E), \u003cem\u003eKruskal-Wallis H test \u003c/em\u003eand\u003cem\u003e Nemenyi multiple comparisons test \u003c/em\u003efor post hoc test (n = 34 mice in the freezing, flight, and normal group): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 9.813, \u003cem\u003edf =\u003c/em\u003e 2, \u003cem\u003ep =\u003c/em\u003e 0.007; \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003efreezing\u0026amp;normal\u003c/sub\u003e = 6.971, \u003cem\u003ep\u003c/em\u003e\u003csub\u003efreezing\u0026amp;normal\u003c/sub\u003e = 0.909; \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eflight\u0026amp;normal\u003c/sub\u003e = -20.824, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eflight\u0026amp;normal\u003c/sub\u003e = 0.006; \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003efreezing\u0026amp;flight\u003c/sub\u003e = -13.853, \u003cem\u003ep\u003c/em\u003e\u003csub\u003efreezing\u0026amp;flight\u003c/sub\u003e = 0.122.\u003c/p\u003e\n\u003cp\u003e(G) The average velocity traces (bottom) and velocity change rate traces (top) of all mice.\u003c/p\u003e\n\u003cp\u003e(H) Comparison of the latency of the velocity peaks caused by the increase in speed in response to three different types of stimuli (T1, T2 and foot-shock). \u003cem\u003eKruskal-Wallis H test \u003c/em\u003eand\u003cem\u003e Nemenyi multiple comparisons test \u003c/em\u003efor post hoc test (n = 34 mice in the freezing, flight, and normal group): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 73.036, \u003cem\u003edf =\u003c/em\u003e 2, \u003cem\u003ep =\u003c/em\u003e 0.000; \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eT1\u0026amp;T2\u003c/sub\u003e = 21.294, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e T1\u0026amp;T2\u003c/sub\u003e = 0.008; \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e T1\u0026amp;shock\u003c/sub\u003e= 59.662, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e T1\u0026amp;shock\u003c/sub\u003e = 0.000; \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e T2\u0026amp;shock\u003c/sub\u003e = 38.368, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e T2\u0026amp;shock\u003c/sub\u003e = 0.000.\u003c/p\u003e","description":"","filename":"fig2202571201.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7083386/v1/2b6f90c1bded45de01851672.jpg"},{"id":87397256,"identity":"69641036-2afd-4d39-bf1a-a334a113b2a9","added_by":"auto","created_at":"2025-07-23 11:08:03","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":547621,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eActivation of D1 and D2 MSNs in the DMS during defensive behavior\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Schematic representation of the setup of fiber photometry to record calcium activity from D1 or D2 MSNs infected with rAAV-hSyn-DIO-GCaMP6s virus in the D1 Cre or A2a Cre mice.\u003c/p\u003e\n\u003cp\u003e(B) Expression of GCaMP6s (green) and placement of the fiber optics were verified postmortem in the D1 Cre mice (left) and A2a Cre mice (right). Scale bar, 200 µm for the big image and 20 µm for blown-up images.\u003c/p\u003e\n\u003cp\u003e(C) All results of calcium activity in D1 MSNs (top) and D2 MSNs (bottom) in the DMS during flight conditioning on day 3.\u003c/p\u003e\n\u003cp\u003e(D) Averaged traces of calcium activity (left) in D1 MSNs (red) and D2 MSNs (blue) and the area under the curve (AUC) of calcium changes (right) during flight conditioning on day 3. \u003cem\u003eWilcoxon signed-rank test\u003c/em\u003e (n = 39 trials from 6 mice): \u003cem\u003eZ =\u003c/em\u003e -4.298, \u003cem\u003ep =\u003c/em\u003e 0.000; \u003cem\u003eWilcoxon signed-rank test\u003c/em\u003e(n = 64 trials from 11 mice): \u003cem\u003eZ =\u003c/em\u003e -5.437, \u003cem\u003ep =\u003c/em\u003e 0.000; \u003cem\u003eMann-Whitney U test\u003c/em\u003e(n = 39 trials from 6 mice in D1 MSNs group and n = 64 trials from 11 mice in D2 MSNs group): \u003cem\u003eZ =\u003c/em\u003e -0.462, \u003cem\u003ep =\u003c/em\u003e 0.644.\u003c/p\u003e\n\u003cp\u003e(E) Left, averaged trace of calcium activity in D1 MSNs (red) and D2 MSNs (blue) during T1. Right, the AUC of calcium activity changes within the intervals of 0-3 s (grey) and 3-6 s (D1 MSNs, red; D2 MSNs, blue). \u003cem\u003eWilcoxon signed-rank test\u003c/em\u003e (n = 39 trials from 6 mice): \u003cem\u003eZ =\u003c/em\u003e \u003cem\u003eZ =\u003c/em\u003e -1.856, \u003cem\u003ep =\u003c/em\u003e 0.063; \u003cem\u003eWilcoxon signed-rank test\u003c/em\u003e (n = 64 trials from 11 mice): \u003cem\u003eZ =\u003c/em\u003e \u003cem\u003eZ =\u003c/em\u003e -2.478, \u003cem\u003ep =\u003c/em\u003e 0.013; 0-3 s, \u003cem\u003eMann-Whitney U test\u003c/em\u003e(n = 39 trials from 6 mice in D1 MSNs group and n = 64 trials from 11 mice in D2 MSNs group): \u003cem\u003eZ =\u003c/em\u003e -0.637, \u003cem\u003ep =\u003c/em\u003e 0.524; 3-6 s, \u003cem\u003eMann-Whitney U test\u003c/em\u003e(n = 39 trials from 6 mice in D1 MSNs group and n = 64 trials from 11 mice in D2 MSNs group): \u003cem\u003eZ =\u003c/em\u003e -0.699, \u003cem\u003ep =\u003c/em\u003e 0.485.\u003c/p\u003e\n\u003cp\u003e(F) Left, averaged trace of calcium activity in D1 MSNs (red) and D2 MSNs (blue) during T2. Right, the AUC of calcium activity changes within the intervals of 0-3 s (grey) and 3-6 s (D1 MSNs, red; D2 MSNs, blue). \u003cem\u003eWilcoxon signed-rank test\u003c/em\u003e (n = 39 trials from 6 mice): \u003cem\u003eZ =\u003c/em\u003e \u003cem\u003eZ =\u003c/em\u003e -0.628, \u003cem\u003ep =\u003c/em\u003e 0.530; \u003cem\u003eWilcoxon signed-rank test\u003c/em\u003e (n = 64 trials from 11 mice): \u003cem\u003eZ =\u003c/em\u003e \u003cem\u003eZ =\u003c/em\u003e -1.951, \u003cem\u003ep =\u003c/em\u003e 0.051; 0-3 s, \u003cem\u003eMann-Whitney U test\u003c/em\u003e(n = 39 trials from 6 mice in D1 MSNs group and n = 64 trials from 11 mice in D2 MSNs group): \u003cem\u003eZ =\u003c/em\u003e -1.828, \u003cem\u003ep =\u003c/em\u003e 0.068; 3-6 s, \u003cem\u003eMann-Whitney U test\u003c/em\u003e(n = 39 trials from 6 mice in D1 MSNs group and n = 64 trials from 11 mice in D2 MSNs group): \u003cem\u003eZ =\u003c/em\u003e -0.575, \u003cem\u003ep =\u003c/em\u003e 0.565.\u003c/p\u003e","description":"","filename":"fig3202571201.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7083386/v1/b26d4f4a949cffbca8ba95b4.jpg"},{"id":87397257,"identity":"708a41f8-419c-4068-a487-45564c2f62af","added_by":"auto","created_at":"2025-07-23 11:08:03","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":597021,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis between velocity and calcium activity of D1 and D2 MSNs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Scatter plot showing the relationship between the calcium signal peak time of MSNs (D1: left, red; D2: right, blue) and velocity peak time. Left, \u003cem\u003ePearson correlation coefficient \u003c/em\u003e(\u003cem\u003er\u003c/em\u003e) = 0.177, \u003cem\u003ep\u003c/em\u003e = 0.280. Right, \u003cem\u003ePearson correlation coefficient \u003c/em\u003e(\u003cem\u003er\u003c/em\u003e) = 0.382, \u003cem\u003ep\u003c/em\u003e = 0.005. Data points represent individual measurements, and the solid line indicates the linear regression fit.\u003c/p\u003e\n\u003cp\u003e(B) Scatter plot showing the relationship between the calcium signal peak time of MSNs (D1: left, red; D2: right, blue) and velocity peak time. Left, \u003cem\u003ePearson correlation coefficient \u003c/em\u003e(\u003cem\u003er\u003c/em\u003e) = 0.350, \u003cem\u003ep\u003c/em\u003e = 0.029. Right, \u003cem\u003ePearson correlation coefficient \u003c/em\u003e(\u003cem\u003er\u003c/em\u003e) = -0.094, \u003cem\u003ep\u003c/em\u003e = 0.503. Data points represent individual measurements, and the solid line indicates the linear regression fit.\u003c/p\u003e\n\u003cp\u003e(C) Scatter plot showing the relationship between the calcium signal peak time of MSNs (D1: left, red; D2: right, blue) and velocity peak time. Left, \u003cem\u003ePearson correlation coefficient \u003c/em\u003e(\u003cem\u003er\u003c/em\u003e) = 0.344, \u003cem\u003ep\u003c/em\u003e = 0.032. Right, \u003cem\u003ePearson correlation coefficient \u003c/em\u003e(\u003cem\u003er\u003c/em\u003e) = 0.576, \u003cem\u003ep\u003c/em\u003e = 0.000. Data points represent individual measurements, and the solid line indicates the linear regression fit.\u003c/p\u003e\n\u003cp\u003e(D) Scatter plot showing the relationship between the calcium signal peak time of MSNs (D1: left, red; D2: right, blue) and velocity peak time. Left, \u003cem\u003ePearson correlation coefficient \u003c/em\u003e(\u003cem\u003er\u003c/em\u003e) = -0.194, \u003cem\u003ep\u003c/em\u003e = 0.236. Right, \u003cem\u003ePearson correlation coefficient \u003c/em\u003e(\u003cem\u003er\u003c/em\u003e) = 0.494, \u003cem\u003ep\u003c/em\u003e = 0.000. Data points represent individual measurements, and the solid line indicates the linear regression fit.\u003c/p\u003e\n\u003cp\u003e(E) The distribution of correlation coefficients between calcium signals (red: D1 MSNs; blue: D2 MSNs) and velocity in 0-3 s (top), 3-6 s (middle), and 6-10 s (bottom).\u003c/p\u003e\n\u003cp\u003e(F) Correlation coefficients between calcium signals and velocity (top) and the proportion of correlation coefficients with significant differences (bottom) in -2-0 s, 0-3 s, 3-6 s, and 6-10 s during T1.\u003c/p\u003e\n\u003cp\u003e(G) The distribution of correlation coefficients between calcium signals (red: D1 MSNs; blue: D2 MSNs) and velocity in 0-3 s (top), 3-6 s (middle), and 6-10 s (bottom).\u003c/p\u003e\n\u003cp\u003e(H) Correlation coefficients between calcium signals and velocity (top) and the proportion of correlation coefficients with significant differences (bottom) in 0-3 s, 3-6 s, and 6-10 s during T2.\u003c/p\u003e","description":"","filename":"fig4202571201.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7083386/v1/94407e7108a8745aafaf423b.jpg"},{"id":87399200,"identity":"fcae23ec-fdc3-42a3-ac0c-f0e97eff5825","added_by":"auto","created_at":"2025-07-23 11:32:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3062031,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7083386/v1/69fd919b-f950-4e84-b420-918c824c4354.pdf"},{"id":87397005,"identity":"05d551bb-2965-4340-9d2f-5176dfc6dcab","added_by":"auto","created_at":"2025-07-23 11:00:03","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":325317,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1. SCS Pavlovian conditioning paradigm\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The conditioning stimulus is a SCS paired with a 1s footshock during conditioning.\u003c/p\u003e\n\u003cp\u003e(B) Schematic of the behavioral protocol used to induce conditioned flight behavior.\u003c/p\u003e\n\u003cp\u003e(C) Comparison of the flight score between tone (blue) and white noise (red) across sessions. Day1, \u003cem\u003eFriedman's M test\u003c/em\u003e (n = 11 mice): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 9.091, \u003cem\u003edf\u003c/em\u003e = 1,\u003cem\u003e p\u003c/em\u003e = 0.003; Day2, \u003cem\u003eFriedman's M test\u003c/em\u003e (n = 11 mice): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 6.564, \u003cem\u003edf\u003c/em\u003e = 1,\u003cem\u003e p\u003c/em\u003e = 0.010; Day3, \u003cem\u003eFriedman's M test\u003c/em\u003e (n = 12 mice): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 13.067, \u003cem\u003edf\u003c/em\u003e = 1,\u003cem\u003e p\u003c/em\u003e = 0.000; Day4, \u003cem\u003eFriedman's M test\u003c/em\u003e (n = 12 mice): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 5.333, \u003cem\u003edf\u003c/em\u003e = 1,\u003cem\u003e p\u003c/em\u003e = 0.021.\u003c/p\u003e\n\u003cp\u003e(D) Comparison of the freezing between tone (blue) and white noise (red) across sessions. Day1, \u003cem\u003eFriedman's M test\u003c/em\u003e (n = 10 mice): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 16.892, \u003cem\u003edf\u003c/em\u003e = 1,\u003cem\u003e p\u003c/em\u003e = 0. 000; Day2, \u003cem\u003eFriedman's M test\u003c/em\u003e (n = 12 mice): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 0.067, \u003cem\u003edf\u003c/em\u003e = 1,\u003cem\u003e p\u003c/em\u003e = 0.796; Day3, \u003cem\u003eFriedman's M test\u003c/em\u003e (n = 12 mice): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 4.267, \u003cem\u003edf\u003c/em\u003e = 1,\u003cem\u003e p\u003c/em\u003e = 0.039; Day4, \u003cem\u003eFriedman's M test\u003c/em\u003e (n = 12 mice): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 3.596, \u003cem\u003edf\u003c/em\u003e = 1,\u003cem\u003e p\u003c/em\u003e = 0.058.\u003c/p\u003e\n\u003cp\u003e(E) Average speed curve (mean ± s.e.m., n = 7 mice) on day 3; note the increase in speed during the onset of white noise.\u003c/p\u003e\n\u003cp\u003e(F) Left, flight scores on day 3\u003cem\u003e, Wilcoxon signed-rank test\u003c/em\u003e (n = 12 mice): \u003cem\u003eZ =\u003c/em\u003e -2.824, \u003cem\u003ep =\u003c/em\u003e 0.005. Right, freezing behavior on day 3,\u003cem\u003e Wilcoxon signed-rank test\u003c/em\u003e (n = 12 mice): \u003cem\u003eZ =\u003c/em\u003e -2.434, \u003cem\u003ep =\u003c/em\u003e 0.015.\u003c/p\u003e","description":"","filename":"figs1202571201.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7083386/v1/4df134a6fd60aa2c19d30e75.jpg"},{"id":87398114,"identity":"dcf00abb-0aee-47c1-8973-57a565d78ceb","added_by":"auto","created_at":"2025-07-23 11:16:03","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":300946,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S2. Reverse TTSCS Pavlovian conditioning paradigm\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The conditioning stimulus is a reverse TTSCS paired with a 1s footshock during conditioning.\u003c/p\u003e\n\u003cp\u003e(B) Comparison of the flight score between HT (red) and LT (blue) across sessions. Day1, \u003cem\u003eFriedman's M test\u003c/em\u003e (n = 16 mice): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 0.342, \u003cem\u003edf\u003c/em\u003e = 1,\u003cem\u003e p\u003c/em\u003e = 0.558; Day2, \u003cem\u003eFriedman's M test\u003c/em\u003e (n = 16 mice): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 2.909, \u003cem\u003edf\u003c/em\u003e = 1,\u003cem\u003e p\u003c/em\u003e = 0.088; Day3, \u003cem\u003eFriedman's M test\u003c/em\u003e (n = 16 mice): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 29.225, \u003cem\u003edf\u003c/em\u003e = 1,\u003cem\u003e p\u003c/em\u003e = 0.000; Day4, \u003cem\u003eFriedman's M test\u003c/em\u003e (n = 16 mice): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 4.056, \u003cem\u003edf\u003c/em\u003e = 1,\u003cem\u003e p\u003c/em\u003e = 0.044.\u003c/p\u003e\n\u003cp\u003e(C) Comparison of the freezing between HT (red) and LT (blue) across sessions. Day1, \u003cem\u003eFriedman's M test\u003c/em\u003e (n = 16 mice): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 33.882, \u003cem\u003edf\u003c/em\u003e = 1,\u003cem\u003e p\u003c/em\u003e = 0.000; Day2, \u003cem\u003eFriedman's M test\u003c/em\u003e (n = 16 mice): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 20.045, \u003cem\u003edf\u003c/em\u003e = 1,\u003cem\u003e p\u003c/em\u003e = 0.000; Day3, \u003cem\u003eFriedman's M test\u003c/em\u003e (n = 16 mice): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 0.551, \u003cem\u003edf\u003c/em\u003e = 1,\u003cem\u003e p\u003c/em\u003e = 0.458; Day4, \u003cem\u003eFriedman's M test\u003c/em\u003e (n = 16 mice): \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 33.136, \u003cem\u003edf\u003c/em\u003e = 1,\u003cem\u003e p\u003c/em\u003e = 0.000.\u003c/p\u003e\n\u003cp\u003e(D) Average speed curve (mean ± s.e.m., n = 16 mice) on day 3; note the increase in speed during the onset of HT.\u003c/p\u003e\n\u003cp\u003e(E) Flight scores on day 3\u003cem\u003e, Wilcoxon signed-rank test\u003c/em\u003e (n = 16 mice): \u003cem\u003eZ =\u003c/em\u003e -3.942, \u003cem\u003ep =\u003c/em\u003e 0.000.\u003c/p\u003e\n\u003cp\u003e(F) Freezing behavior on day 3,\u003cem\u003e Wilcoxon signed-rank test\u003c/em\u003e (n = 16 mice): \u003cem\u003et =\u003c/em\u003e -1.466, \u003cem\u003ep =\u003c/em\u003e 0.143.\u003c/p\u003e","description":"","filename":"figs2202571201.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7083386/v1/12c773ce4a11567e5029973d.jpg"},{"id":87397002,"identity":"237b9347-16a6-4ee0-a68c-2a8ee922ab26","added_by":"auto","created_at":"2025-07-23 11:00:03","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":446679,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S3. The involvement of MSNs during TTSCS Pavlovian conditioning paradigm\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Left, schematic representation of the setup of fiber photometry to record calcium activity from MSNs infected with rAAV-CaMKII--GCaMP6s virus in the C57/BL6 mice. Right, expression of GCaMP6s and placement of the fiber optics were verified post-mortem. 200 μm for the big image and 20 μm for blown-up images.\u003c/p\u003e\n\u003cp\u003e(B) Representative images, counterstained with DAPI (blue), showing GCaMP6s (green) colocalizing with DARPP-32 protein (red). Scale bar, 50 μm.\u003c/p\u003e\n\u003cp\u003e(C) The percentage of colocalized DARPP-32 and GCaMP6s relative to GCaMP6s.\u003c/p\u003e\n\u003cp\u003e(D) All results of calcium activity (left) and averaged traces of each mouse (right) in MSNs in the DMS on day 1.\u003c/p\u003e\n\u003cp\u003e(E) Averaged traces of calcium activity in MSNs (left) and the area under the curve (AUC) of calcium changes (right) on day 1. Two-tailed unpaired t-test (n = 6 mice): \u003cem\u003et =\u003c/em\u003e 2.778, \u003cem\u003edf\u003c/em\u003e= 5, \u003cem\u003ep =\u003c/em\u003e 0.039; Two-tailed unpaired t-test (n = 6 mice): \u003cem\u003et =\u003c/em\u003e 0.548, \u003cem\u003edf\u003c/em\u003e = 5, \u003cem\u003ep =\u003c/em\u003e0.607.\u003c/p\u003e\n\u003cp\u003e(F) All results of calcium activity (left) and averaged traces of each mouse (right) in MSNs in the DMS on day 3.\u003c/p\u003e\n\u003cp\u003e(G) Averaged traces of calcium activity in MSNs (left) and the area under the curve (AUC) of calcium changes (right) on day 3. Two-tailed unpaired t-test (n = 6 mice): \u003cem\u003et =\u003c/em\u003e 3.140, \u003cem\u003edf\u003c/em\u003e= 5, \u003cem\u003ep =\u003c/em\u003e 0.026; Two-tailed unpaired t-test (n = 6 mice): \u003cem\u003et =\u003c/em\u003e 3.752, \u003cem\u003edf\u003c/em\u003e = 5, \u003cem\u003ep =\u003c/em\u003e0.013.\u003c/p\u003e","description":"","filename":"figs3202571201.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7083386/v1/4aa3bf1e839e8cc37b8f950c.jpg"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Dorsomedial Striatal Medium Spiny Neurons Orchestrate Temporally approaching threat Driven Defensive Switching in Fear Conditioning","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDefensive behaviors, shaped by natural selection, are automatically activated in humans and other animals to mitigate harm or mortality in dangerous situations[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. When confronted with a threat, organisms display a flexible transition between different defensive responses[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Active responses, such as flight, cost more energy and carry greater risks. On the contrary, freezing is a common conservative defensive response, while it means forgoing opportunities to forage or seek potential mates. Therefore, the ability to promptly and accurately switch between freezing and flight in response to approaching threats is critical for survival[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe selection of defensive responses, whether active (e.g., flight) or passive (e.g., freezing), is governed by a dynamic process known as risk assessment (RA). RA involves the evaluation of multiple factors, including the threat proximity on a spatiotemporal scale, which determines the transition between defensive behaviors[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. For example, freezing is typically the initial response to a realized threat (post-encounter mode), whereas flight emerges as the threat becomes imminent (circa-strike defensive mode). This pattern has been consistently observed in both humans and rodents exposed to naturalistic predator threats[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, naturalistic predator threats make it difficult to disentangle the roles of stimulus type and threat proximity in driving defensive behaviors. To address this limitation, Fadok and colleagues developed a modified auditory Pavlovian fear conditioning procedure using a serial-compound stimulus (SCS) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The SCS consists of a pure tone followed immediately by white noise, each eliciting distinct conditioned responses: freezing to the tone and flight to the white noise. After repeated trials, mice exhibit clear behavioral differentiation, freezing in response to the tone but fleeing upon hearing the white noise. However, whether these responses are driven by the auditory stimulus itself or by the perceived threat proximity encoded within the SCS remains debated[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe transition between freezing and flight during the SCS involves stimulus–response learning, risk assessment, and decision-making. The dorsal striatum (DS) particularly its medial subdivision (DMS), has emerged as a key candidate for mediating these processes due to its well-established roles in stimulus–response learning, goal-directed behavior, and decision-making[\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14 CR15\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e–\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These functions have been consistently demonstrated in both rodents [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and nonhuman primates[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Specifically, the DS integrates sensorimotor, cognitive, and motivational/emotional information to facilitate flexible decision-making, particularly in action selection and initiation[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e–\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Emerging evidence further highlights the distinct roles of dopamine receptor 2 expressing MSNs (D2 MSNs) and dopamine receptor 1 expressing MSNs (D1 MSNs) in the DMS, with the DMS being critical for evaluating options and making decisions prior to outcome delivery[\u003cspan additionalcitationids=\"CR24 CR25 CR26 CR27\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e–\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Given these functions, the DMS may play a pivotal role in assessing conditioned threats and selecting between freezing and flight behaviors in the SCS paradigm. However, the specific contributions of D1 and D2 MSNs in the DMS to conditioned threat assessment and the decision-making processes underlying freezing and flight behaviors remain poorly understood.\u003c/p\u003e\u003cp\u003eIn this study, we modified the types of auditory stimuli used in previous studies to investigate the decision-making processes of defensive response in mice. We demonstrated that the switching between freezing and flight behaviors in mice in response to compound stimuli does not depend on the type of auditory stimulus. Instead, it depends on the temporal association strength between the conditioned stimulus (CS) and unconditioned stimulus (US). Moreover, using cell type-specific fiber photometry, we revealed that DMS MSNs regulate threat response latency and amplitude. Specifically, D2 MSNs modulate response latency to weaker threats, while D1 MSNs dominate as threat intensity increases. Both MSN types jointly maintain stable response amplitude across varying threat levels. Our findings demonstrate that the DMS integrates temporal threat information to guide adaptive defensive behaviors, revealing a critical neural mechanism for RA and the flexible selection of defensive responses in dynamic threat environments.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eAnimals\u003c/b\u003e\u003c/p\u003e\u003cp\u003e All experimental procedures were approved by the Institutional Animal Care and Use Committee of the Fourth Military Medical University (Approval No. IACUC-20230320) and conformed to the Guide for the Care and Use of Laboratory Animals published by the National Institutes of Health. All mice were maintained under a 12-h light/dark cycle at 22–25℃ with food and water under environmentally controlled conditions. C57BL/6J mice were purchased from the Experimental Animal Center of the Fourth Military Medical University. The Drd1-Cre (#037156-JAX) and Adora2a-Cre (#031168-UCD) were obtained from the Jackson Laboratory. All virus injections were administered to mice aged 2 months old, and all behavioral tests were carried out during the light phase. The experimenters were blinded to the genotype and experimental conditions. All the mice employed in the behavioral tests were male.\u003c/p\u003e\u003cp\u003e\u003cb\u003eVirus injection and stereotaxic surgery\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMice were anesthetized with isoflurane (4% for induction and 1.5% for maintenance), and their heads were fixed in a stereotaxic injection frame (RWD Life Science Inc., China). Injections into the brain were performed using a microinjection needle with a 10 µL microsyringe (Shanghai Gaoge Industry and Trade Co., LTD., China) to deliver the 250 nl of virus (rAAV-hSyn-DIO-GCaMP6s-WPRE-pA, Cat# BC-0238, BrainCase., China; rAAV-hSyn-CaMKII-GCaMP6s -WPRE-hGH-pA, Cat# PT-0110, BrainVTA., China.) at a rate of 30 nL/min using a micro syringe pump (KD Scientific Inc., USA). Following injection, the needle was held at the site for another 15 minutes to allow for diffusion. And an optical fiber (200 µm OD, 0.37 NA) was placed 100 µm above the injection site. The coordinates were defined as dorsal-ventral (DV) from the skull surface, anterior-posterior (AP) from bregma, and medio-lateral (ML) from the midline. The stereotaxic coordinates for the DMS injection were as follows: AP: 0.7 mm; ML: +1.5 mm; and DV: -3 mm.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConditioned flight paradigm\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTwo different contexts were used. Context A (low-threat context, 200D × 400H mm) consisted of a clear cylindrical chamber with a smooth floor, while Context B (high-threat context, 200L×200W×300H mm) consisted of a square enclosure with an electrical grid floor used to deliver alternating current footshocks and a programmable audio generator for auditory stimulus (Shanghai Vanbi Intelligent Technology Co., Ltd.).\u003c/p\u003e\u003cp\u003eFor the SCS conditioned flight paradigm test, we used the methods previously described [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Briefly, on day 1, after 4-min habituation in the context A environment, an auditory SCS consisting of a 10 s pure tone (500 ms, 7.5 kHz pips at 1 Hz, 75 dB) and 10 s of white noise (500 ms, pips at 1 Hz, random and composed of frequencies ranging from 1 to 20 kHz, 75 dB) was delivered four times with a 60s ITI (inter-trial interval, ITI). On days 2 and 3, the mice were conditioned five times in context B after a 4-min habituation period by pairing the SCS with the US (footshock 0.9 mA, 1 s) at an average pseudorandom ITI of 180 s, the shock was applied at the end of the last pip. On day 4, the mice were placed back in context A and, after a 4-min habituation period, were presented with the SCS four times at an ITI of 60 s.\u003c/p\u003e\u003cp\u003eFor the TTSCS conditioned flight paradigm test, we replace the 10 s pure tone and 10 s white noise stimuli in the SCS with two pure tones (HT, high frequency tone, 500 ms, 4 kHz pips at 1 Hz, 75 dB; HT, low frequency tone, 500 ms, 400 Hz pips at 1 Hz, 75 dB) of different frequencies. And for the reverse TTSCS conditioned flight paradigm test, we switched the order of HT and LT.\u003c/p\u003e\u003cp\u003e\u003cb\u003eQuantification of defensive behavior\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll behavioral assessments were video recorded using Tracking Master V4.0 (Shanghai Vanbi Intelligent Technology Co., Ltd.). The body of each animal was extracted from the background, and the center of gravity was used to calculate its speed. Freezing was defined as a complete cessation of movement for at least 1 s and was automatically scored using a frame-by-frame analysis of pixel changes. The flight score was calculated by dividing the average speed during exposure to each CS by the average speed during the 10 s before CS onset (speed CS/speed pre), and then adding 1 point for each jump. The first CS is taken for 10 s, and the second CS is taken for 9 s to exclude the impact of the footshock at the last pip. Escape jumping was scored manually from video files by a blinded observer.\u003c/p\u003e\u003cp\u003eFor the quantification of defensive behavior in the mice with synchronized recording of neuronal calcium activity, we tracked the mouse's trajectory at a higher frame rate (Noldus Ethovision XT 11) to match the sampling rate of the calcium signals. We extracted the speed during the 10 s before SCS onset and the speed during the SCS period.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFiber photometry recording and data analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFiber photometry was used to record calcium signals using a commercialized fiber photometry system (ThinkerTech, China) as described previously[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. To record the fluorescence signals, a 470-nm laser beam (OBIS 488LS; Coherent) was reflected off a dichroic mirror (MD498, Thorlabs) that was focused by a 310 objective lens (0.3 NA; Olympus) and coupled to an optical commutator (Doris Lenses). An optical fiber (230 mm OD, 0.37 NA) guided the light between the commutator and the implanted optical fiber. The laser power at the tip of the optical fiber was adjusted to 0.01–0.02 mW to decrease laser bleaching. Fluorescence was bandpass-filtered (MF525-39, Thorlabs), and an amplifier was used to convert the photomultiplier tube current output to a voltage signal. The analog vo00ltage signals were digitalized at 500 Hz and recorded by a Power 1401 digitizer with Spike2 software (CED).\u003c/p\u003e\u003cp\u003eFor data analysis, the fluorescence change was calculated as Z-score, the Z-score values of the animals in each group were averaged. To precisely analyze the changes in the fluorescence values across the training, we defined the baseline period (-2 to 0 s relative to the CS onset). To quantify the change in the level of calcium signal induced by CS, the area under the curve (AUC) of Z-score in each time window defined was calculated.\u003c/p\u003e\u003cp\u003e\u003cb\u003eQuantification and statistical analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll data were transferred to SPSS 21.0 software (IBM, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.spss.com.cn\u003c/span\u003e\u003cspan address=\"http://www.spss.com.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for analysis and to OriginPro 2021 software (OriginLab, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.originlab.com\u003c/span\u003e\u003cspan address=\"https://www.originlab.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for graphing. For the unpaired data, normality testing was performed by the Shapiro–Wilk test, the homogeneity of variance test was performed by Levene test. Data that met these two conditions were analyzed using a two-tailed unpaired t-test or one-factor ANOVA and Bonferroni correction for post hoc test. Datasets that were not normally distributed were analyzed with a Mann-Whitney U test or Kruskal-Wallis H test and Nemenyi multiple comparisons test. For the paired data, datasets that were normally distributed were analyzed with Two-tailed unpaired t-test, datasets that were not normally distributed were analyzed with Mann-Whitney U test. The significance levels for all tests were set at *p \u0026lt; 0.05, **p \u0026lt; 0.01, and ***p \u0026lt; 0.001.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eThe switching between freezing and flight primarily reflect learned temporal relationships of CS1 and CS2 to the US\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn Pavlovian fear conditioning, a CS is paired with a US, and the passive defensive response (freezing) is the dominant conditioned defensive response to the CS in most conditioning experiments[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. To investigate the dynamic switching between freezing and flight behaviors, we employed a modified Pavlovian conditioning paradigm using a two-tone serial compound stimulus (TTSCS) in mice. Specifically, we paired a TTSCS, consisting of a high-frequency tone (HT, 4 kHz, 500 ms, 75 dB) followed by a low-frequency tone (LT, 400 Hz, 500 ms, 75 dB), with a footshock (US, 0.9 mA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B). This paradigm is based on the SCS previously described[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], but uses two distinct pure tones instead of a tone and white noise. After conditioning, mice exhibited active and passive defensive responses during TTSCS on day 3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-E). We quantified the active defensive response (flight) by measuring increases in speed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE) and the number of escape jumps (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF, middle). We found that mice showed significantly higher flight scores and jump numbers during exposure to the LT compared to the HT (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF, left and middle). Conversely, freezing behavior was more pronounced during HT exposure than LT exposure (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF, right). These results indicate that the second tone, which is temporally closer to the US, elicited a stronger flight response. To further test this hypothesis, we reversed the order of the two pure tones (Figure S2A). Consistent with our initial findings, mice exhibited more flight behavior during exposure to the tone that was closer to the US (Figure S2B-F). We also replicated these results using the classic SCS paradigm (Figure S1A-F), as previously reported[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Collectively, these results suggest that the switching between freezing and flight in response to threats is primarily determined by the temporal proximity of the CS to the US, rather than the inherent properties of the stimulus itself.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMice exhibit faster and stronger active defensive responses as the threat approaches\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo further investigate the characteristics of the switch in defensive responses as the threat approaches. We defined the first auditory stimulus in the SCS composed of different types of stimuli as T1 and the subsequent second stimulus as T2. We established a velocity-based classifier to determine the state of the mice, following the definitions of freezing and flight reported in previous studies (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Our analysis revealed that during the entire SCS period, the proportion of freezing responses gradually decreased, while the proportion of flight responses progressively increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B). Interestingly, the proportion of freezing increased at the T1 stage and then smoothly decreased, whereas the proportion of flight exhibited peaks with different latencies during the T1, T2, and foot-shock stimulation stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). To better understand the temporal dynamics of these two behaviors, we divided T1 and T2 into three segments of 3 seconds each and calculated the proportion of each behavior within each time segment. We found that freezing predominantly occurred within the 0\u0026ndash;3 s, after which the proportion of flight began to increase (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D) during T1. Flight behavior accounted for a relatively large proportion in all three intervals during T2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eE-F). Given that flight is determined by velocity, we averaged the velocity of all mice during the SCS and we found that following exposure to the three different levels of threat stimuli\u0026mdash;T1, T2, and foot-shock, mice all exhibited increased velocity, corresponding to three peaks of different shapes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). After calculating the latency and amplitude of these three peaks, we found that as the threat approached, the latency of the peaks gradually decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eH), while their amplitude progressively increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eI). In summary, mice flexibly exhibit defensive responses when facing threat, as the threat stimulus approached more closely, mice tended to exhibit more escape behaviors with shorter latencies and larger magnitudes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe involvement of DMS in the defensive responses\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePrevious studies have indicated the involvement of the DMS in decision-making and action. We first examined the calcium activity changes of all MSNs in the DMS during the TTSCS paradigm and found that MSNs exhibited decreased calcium activity throughout the entire SCS stimulation period (Figure S3). To determine whether D1 and D2 MSNs in the DMS are involved in defensive responses during the TTSCS paradigm, we injected rAAV-DIO-GCaMP6s into the DMS of D1-Cre and A2a-Cre mice to record calcium activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Viral expression and fiber placement were verified (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). To quantitatively assess calcium signal changes, we established a baseline period (\u0026minus;\u0026thinsp;2 to 0 s relative to TTSCS onset) and measured calcium activity within 20 s after SCS onset (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Using the area under the curve (AUC) as an indicator, we found significant increases in calcium activity in both D1 and D2 MSNs during TTSCS compared to the control recording channel, however, the activation levels of D1 and D2 MSNs did not show significant differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). To further dissect whether the activation of D1 and D2 MSNs differs during T1 and T2, we established a 2-second baseline preceding each stimulus and separately quantified calcium activity changes within the 0\u0026ndash;3 s and 3\u0026ndash;6 s intervals following stimulus onset. In response to T1, no significant differences were observed in calcium activity changes between the two intervals for D1 MSNs, but the activation of D2 MSNs was significantly higher during the 3\u0026ndash;6 second interval compared to the 0\u0026ndash;3 second interval (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Additionally, the magnitude of calcium signal changes did not significantly differ between D1 and D2 MSNs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, right). During the T2 phase, we did not observe any significant differences in calcium activity changes between the two intervals for D1 MSNs and D2 MSNs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eD1 and D2 MSNs dynamically participate in decision-making and execution of defensive responses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGiven that defensive responses can vary among mice within the same behavioral paradigm, we used velocity as an index of defensive responses and performed joint analysis with calcium signals sampled at the same rate. We found that during the T1 stimulus, the latency to peak velocity was highly correlated with the latency to peak calcium activity in D2 MSNs but not in D1 MSNs (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Conversely, during the T2 stimulus, the latency to peak velocity was highly correlated with the latency to peak calcium activity in D1 MSNs instead of D2 MSNs (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Moreover, during T1 stimulus, the amplitude of peak velocity was highly correlated with the amplitude of peak calcium activity in both D1 and D2 MSNs (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), and during the T2 stimulus, the amplitude of peak velocity was highly correlated with the amplitude of peak calcium activity in D2 MSNs (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Although we did not detect a significant correlation between the amplitude of peak calcium activity amplitude of D1 MSNs and velocity peaks during the T2 stimulus, we observed a positive skewness in the distribution of correlation coefficients between D1 MSNs calcium activity and velocity across the entire time axis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eE-H). This indicates that D1 calcium activity maintains a correlation with velocity throughout the SCS paradigm. These results suggest that MSNs in the DMS are involved in the latency and amplitude of active defensive responses when threats approaching. Specifically, when the threat stimulus is temporally distant, the involvement is primarily associated with D2 MSNs, whereas when the threat stimulus is temporally close, the involvement is mainly associated with D1 MSNs. The amplitude of the active defensive response is associated with the activity of both D1 and D2 MSNs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study indicates that the switching of freezing and flight defensive responses in SCS Pavlovian fear conditioning is driven by the temporal proximity of the auditory stimulus to the US. As the threat stimulus approached more closely, mice exhibit more pronounced flight responses with shorter latencies. MSNs in the DMS are involved in modulating the latency and amplitude of active defensive responses to threats. Specifically, D2 MSNs primarily contribute to the latency of defensive responses when facing relatively weaker threats, whereas their involvement decreases with increasing threat intensity, while the role of D1 MSNs becomes more prominent. The amplitude of the active defensive response is associated with the activity of both D1 and D2 MSNs and remains constant regardless of threat intensity.\u003c/p\u003e\u003cp\u003eThe mechanisms underlying SCS-driven conditioned responses remain debate. Recent studies have reported that white noise elicits stronger physiological and behavioral responses than pure tones even before conditioning[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These findings suggest that frequency and sound pressure levels, rather than temporal proximity to the US, drive these responses. However, this notion has been challenged by a work showing that white noise is not inherently aversive to mice. In a reverse SCS paradigm, mice exhibited flight in response to a tone but froze in response to white noise[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In our study, before training, mice exhibited more freezing behavior in response to white noise compared to pure tones (\u003cb\u003eFigure S1D\u003c/b\u003e). However, after auditory habituation on Day1, mice did not show significant defensive responses to either the habituated white noise or the pure tones. These results suggest that mice may innately exhibit subtle defensive reactions to different auditory stimuli, but these responses can be eliminated through repeated habituation. Following shock pairing, auditory stimuli acquire new roles, eliciting learned defensive behaviors\u0026mdash;specifically, the freezing and flight observed in the SCS paradigm. To eliminate the potential confounding effects of white noise, we designed both CS1 and CS2 as pure tones (75 dB) with different frequencies in the TTSCS paradigm and reversed their order in the reverse TTSCS paradigm (\u003cb\u003eFigure S2\u003c/b\u003e). The results showed that the switching between different defensive responses was primarily determined by the temporal proximity of the CS to the US, rather than the inherent properties of the CS. This is consistent with previous observations that mice switch defensive responses to naturalistic predator threats based on predator proximity[\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Indeed, mice freeze to avoid detection when a predator is distant but switch to flight when the predator approaches too closely. This may reflect the transition from fear to panic. Our findings in the conditioned procedure confirm this, supporting the correlation between the switching of learned defensive responses and the degree of threat proximity.\u003c/p\u003e\u003cp\u003eInterestingly, we also found that as the threat stimulus approached more closely, mice exhibited more escape behaviors with shorter latencies and larger magnitudes of response. This suggests that mice evaluate the CS based on the learned temporal relationships among CS1, CS2, and the US, make decisions, and then execute appropriate defensive responses. Previous studies have shown that amygdala, midbrain, and associated circuits are involved in the execution of defensive behavior[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, most of these studies have focused on the emotional factors of defensive behaviors and the execution of defensive responses themselves, rather than on the decision-making processes underlying them. In this study, we found that the decision-making process is associated with the dynamic responses of D1 and D2 MSNs in the DMS. Specifically, D2 MSNs dominate when the threat is temporally distant, while D1 MSNs take over when the threat is temporally close. The DMS is known to plays a crucial role in decision-making[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and complex temporal processing[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Moreover, it receives substantial and highly overlapping inputs from the amygdala and the prefrontal cortex[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], both of which regulate defensive behaviors[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Recent optogenetic experiments suggest that D1 and D2 MSNs in the DMS exert antagonistic control over learning and decision-making[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The direct pathway activation disinhibits brainstem motor structures, and thalamic nuclei targeting the motor cortex, promoting movement. While the indirect pathway activates basal ganglia output nuclei and thus inhibits movements[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Therefore, it may integrate cognitive and motivational/emotional information from these regions to make decisions in defensive behaviors. Specifically, in the switching of defensive responses, D2 MSNs mediate freezing (a conservative defensive response), while D1 MSNs involve in flight (a riskier defensive response).\u003c/p\u003e\u003cp\u003eIn conclusion, our findings indicated the switching of defensive responses during SCS Pavlovian fear conditioning is primarily determined by the temporal proximity of the auditory stimulus to the US, rather than the inherent properties of the stimulus, and highlight the DMS region as a key player in the switching of different defensive responses when face to the dynamically changing threat stimuli.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitation of Study\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHowever, our study has certain limitations. Due to limitations of the viral tools, we were unable to simultaneously record calcium signals from both D1 and D2 MSNs in the same mouse. While we identified a correlation between MSNs activity in the DMS and the decision-making processes underlying defensive responses, we did not establish their causal roles. Additionally, we did not further explore how the classic corticostriatal circuits are involved in defensive behaviors. Future research should aim to elucidate the causal and mechanistic roles of D1 and D2 MSNs in corticostriatal circuits, particularly in processing selected aspects of threat information, thereby contributing to defensive responses.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eDeclaration of interests\u003c/h2\u003e\u003cp\u003eThe authors declare that there are no conflicts of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e\u003cp\u003eW.W., J.G. contributed to experimental design and discussion. J.G., P.R., and B.C. performed the experiments and collected data. Y.Z., B.C., Y.D., and J.X. analyzed data. J.G., W.W., Q.X., and Y.Z. wrote, edited and reviewed the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eThis study was supported by Natural Science Foundation of China (82271577, 82071536 to W.W., 82371236 to Y. Z.), Shaanxi Provincial Innovation Chain Project of Key Industries (2023-ZDLSF-47 to W.W), the Natural Science Foundation of Guangdong Province of China (2024A1515012479 to Y. Z.), Key Research and Development Program of Shaanxi Province (2023-YBSF-106 to Q.X.), and the Joint Founding Project of Innovation Research Institute, Xijing Hospital (LHJJ24JH05 to W.W.).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTseng, Y.-T., et al., \u003cem\u003eDefensive responses: behaviour, the brain and the body.\u003c/em\u003e Nature Reviews. Neuroscience, 2023. \u003cstrong\u003e24\u003c/strong\u003e(11): p. 655-671.\u003c/li\u003e\n\u003cli\u003eBlanchard, D.C., \u003cem\u003eTranslating dynamic defense patterns from rodents to people.\u003c/em\u003e Neuroscience and Biobehavioral Reviews, 2017. \u003cstrong\u003e76\u003c/strong\u003e(Pt A): p. 22-28.\u003c/li\u003e\n\u003cli\u003eYang, X., et al., \u003cem\u003eA simple threat-detection strategy in mice.\u003c/em\u003e BMC Biology, 2020. \u003cstrong\u003e18\u003c/strong\u003e(1): p. 93.\u003c/li\u003e\n\u003cli\u003eBlanchard, D.C., et al., \u003cem\u003eRisk assessment as an evolved threat detection and analysis process.\u003c/em\u003e Neuroscience and Biobehavioral Reviews, 2011. \u003cstrong\u003e35\u003c/strong\u003e(4): p. 991-998.\u003c/li\u003e\n\u003cli\u003eFanselow, M. and L. Lester, \u003cem\u003eA functional behavioristic approach to aversively motivated behavior: Predatory imminence as a determinant of the topography of defensive behavior.\u003c/em\u003e Evolution and Learning, 1988.\u003c/li\u003e\n\u003cli\u003eBlanchard, D.C., \u003cem\u003eSex, defense, and risk assessment: Who could ask for anything more?\u003c/em\u003e Neuroscience and Biobehavioral Reviews, 2023. \u003cstrong\u003e144\u003c/strong\u003e: p. 104931.\u003c/li\u003e\n\u003cli\u003eFadok, J.P., et al., \u003cem\u003eA competitive inhibitory circuit for selection of active and passive fear responses.\u003c/em\u003e Nature, 2017. \u003cstrong\u003e542\u003c/strong\u003e(7639).\u003c/li\u003e\n\u003cli\u003eDong, P., et al., \u003cem\u003eA novel cortico-intrathalamic circuit for flight behavior.\u003c/em\u003e Nature Neuroscience, 2019. \u003cstrong\u003e22\u003c/strong\u003e(6): p. 941-949.\u003c/li\u003e\n\u003cli\u003eFuruyama, T., et al., \u003cem\u003eMultiple factors contribute to flight behaviors during fear conditioning.\u003c/em\u003e Scientific Reports, 2023. \u003cstrong\u003e13\u003c/strong\u003e(1): p. 10402.\u003c/li\u003e\n\u003cli\u003eYin, H.H. and B.J. Knowlton, \u003cem\u003eThe role of the basal ganglia in habit formation.\u003c/em\u003e Nature Reviews. Neuroscience, 2006. \u003cstrong\u003e7\u003c/strong\u003e(6): p. 464-476.\u003c/li\u003e\n\u003cli\u003eAshby, F.G., B.O. Turner, and J.C. Horvitz, \u003cem\u003eCortical and basal ganglia contributions to habit learning and automaticity.\u003c/em\u003e Trends In Cognitive Sciences, 2010. \u003cstrong\u003e14\u003c/strong\u003e(5): p. 208-215.\u003c/li\u003e\n\u003cli\u003eHiebert, N.M., et al., \u003cem\u003eDorsal striatum mediates deliberate decision making, not late-stage, stimulus-response learning.\u003c/em\u003e Human Brain Mapping, 2017. \u003cstrong\u003e38\u003c/strong\u003e(12): p. 6133-6156.\u003c/li\u003e\n\u003cli\u003eSchouppe, N., et al., \u003cem\u003eThe role of the striatum in effort-based decision-making in the absence of reward.\u003c/em\u003e The Journal of Neuroscience : the Official Journal of the Society For Neuroscience, 2014. \u003cstrong\u003e34\u003c/strong\u003e(6): p. 2148-2154.\u003c/li\u003e\n\u003cli\u003ePearson, J.M., K.K. Watson, and M.L. Platt, \u003cem\u003eDecision making: the neuroethological turn.\u003c/em\u003e Neuron, 2014. \u003cstrong\u003e82\u003c/strong\u003e(5): p. 950-965.\u003c/li\u003e\n\u003cli\u003eSugrue, L.P., G.S. Corrado, and W.T. Newsome, \u003cem\u003eChoosing the greater of two goods: neural currencies for valuation and decision making.\u003c/em\u003e Nature Reviews. Neuroscience, 2005. \u003cstrong\u003e6\u003c/strong\u003e(5): p. 363-375.\u003c/li\u003e\n\u003cli\u003eHiebert, N.M., et al., \u003cem\u003eStriatum in stimulus-response learning via feedback and in decision making.\u003c/em\u003e NeuroImage, 2014. \u003cstrong\u003e101\u003c/strong\u003e: p. 448-457.\u003c/li\u003e\n\u003cli\u003eGore, F., et al., \u003cem\u003eOrbitofrontal cortex control of striatum leads economic decision-making.\u003c/em\u003e Nature Neuroscience, 2023. \u003cstrong\u003e26\u003c/strong\u003e(9): p. 1566-1574.\u003c/li\u003e\n\u003cli\u003eCai, X., S. Kim, and D. Lee, \u003cem\u003eHeterogeneous coding of temporally discounted values in the dorsal and ventral striatum during intertemporal choice.\u003c/em\u003e Neuron, 2011. \u003cstrong\u003e69\u003c/strong\u003e(1): p. 170-182.\u003c/li\u003e\n\u003cli\u003eHassani, O.K., H.C. Cromwell, and W. Schultz, \u003cem\u003eInfluence of expectation of different rewards on behavior-related neuronal activity in the striatum.\u003c/em\u003e Journal of Neurophysiology, 2001. \u003cstrong\u003e85\u003c/strong\u003e(6): p. 2477-2489.\u003c/li\u003e\n\u003cli\u003eBalleine, B.W., M.R. Delgado, and O. Hikosaka, \u003cem\u003eThe role of the dorsal striatum in reward and decision-making.\u003c/em\u003e The Journal of Neuroscience : the Official Journal of the Society For Neuroscience, 2007. \u003cstrong\u003e27\u003c/strong\u003e(31): p. 8161-8165.\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Doherty, J., et al., \u003cem\u003eDissociable roles of ventral and dorsal striatum in instrumental conditioning.\u003c/em\u003e Science (New York, N.Y.), 2004. \u003cstrong\u003e304\u003c/strong\u003e(5669): p. 452-454.\u003c/li\u003e\n\u003cli\u003eJohnson, A., M.A.A. van der Meer, and A.D. Redish, \u003cem\u003eIntegrating hippocampus and striatum in decision-making.\u003c/em\u003e Current Opinion In Neurobiology, 2007. \u003cstrong\u003e17\u003c/strong\u003e(6): p. 692-697.\u003c/li\u003e\n\u003cli\u003eFriedman, A., et al., \u003cem\u003eA Corticostriatal Path Targeting Striosomes Controls Decision-Making under Conflict.\u003c/em\u003e Cell, 2015. \u003cstrong\u003e161\u003c/strong\u003e(6): p. 1320-1333.\u003c/li\u003e\n\u003cli\u003eYin, H.H., et al., \u003cem\u003eThe role of the dorsomedial striatum in instrumental conditioning.\u003c/em\u003e The European Journal of Neuroscience, 2005. \u003cstrong\u003e22\u003c/strong\u003e(2): p. 513-523.\u003c/li\u003e\n\u003cli\u003eGremel, C.M. and R.M. Costa, \u003cem\u003eOrbitofrontal and striatal circuits dynamically encode the shift between goal-directed and habitual actions.\u003c/em\u003e Nature Communications, 2013. \u003cstrong\u003e4\u003c/strong\u003e: p. 2264.\u003c/li\u003e\n\u003cli\u003eGremel, C.M., et al., \u003cem\u003eEndocannabinoid Modulation of Orbitostriatal Circuits Gates Habit Formation.\u003c/em\u003e Neuron, 2016. \u003cstrong\u003e90\u003c/strong\u003e(6): p. 1312-1324.\u003c/li\u003e\n\u003cli\u003eBissonette, G.B. and M.R. Roesch, \u003cem\u003eRule encoding in dorsal striatum impacts action selection.\u003c/em\u003e The European Journal of Neuroscience, 2015. \u003cstrong\u003e42\u003c/strong\u003e(8): p. 2555-2567.\u003c/li\u003e\n\u003cli\u003eCui, G., et al., \u003cem\u003eConcurrent activation of striatal direct and indirect pathways during action initiation.\u003c/em\u003e Nature, 2013. \u003cstrong\u003e494\u003c/strong\u003e(7436): p. 238-242.\u003c/li\u003e\n\u003cli\u003eTrott, J.M., et al., \u003cem\u003eConditional and unconditional components of aversively motivated freezing, flight and darting in mice.\u003c/em\u003e ELife, 2022. \u003cstrong\u003e11\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eHersman, S., et al., \u003cem\u003eStimulus salience determines defensive behaviors elicited by aversively conditioned serial compound auditory stimuli.\u003c/em\u003e ELife, 2020. \u003cstrong\u003e9\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eTotty, M.S., et al., \u003cem\u003eBehavioral and brain mechanisms mediating conditioned flight behavior in rats.\u003c/em\u003e Scientific Reports, 2021. \u003cstrong\u003e11\u003c/strong\u003e(1): p. 8215.\u003c/li\u003e\n\u003cli\u003eMobbs, D., et al., \u003cem\u003eWhen fear is near: threat imminence elicits prefrontal-periaqueductal gray shifts in humans.\u003c/em\u003e Science (New York, N.Y.), 2007. \u003cstrong\u003e317\u003c/strong\u003e(5841): p. 1079-1083.\u003c/li\u003e\n\u003cli\u003eYilmaz, M. and M. Meister, \u003cem\u003eRapid innate defensive responses of mice to looming visual stimuli.\u003c/em\u003e Current Biology : CB, 2013. \u003cstrong\u003e23\u003c/strong\u003e(20): p. 2011-2015.\u003c/li\u003e\n\u003cli\u003eLi, Z., et al., \u003cem\u003eCorticostriatal control of defense behavior in mice induced by auditory looming cues.\u003c/em\u003e Nature Communications, 2021. \u003cstrong\u003e12\u003c/strong\u003e(1): p. 1040.\u003c/li\u003e\n\u003cli\u003eTovote, P., et al., \u003cem\u003eMidbrain circuits for defensive behaviour.\u003c/em\u003e Nature, 2016. \u003cstrong\u003e534\u003c/strong\u003e(7606): p. 206-212.\u003c/li\u003e\n\u003cli\u003eWang, L., I.Z. Chen, and D. Lin, \u003cem\u003eCollateral pathways from the ventromedial hypothalamus mediate defensive behaviors.\u003c/em\u003e Neuron, 2015. \u003cstrong\u003e85\u003c/strong\u003e(6): p. 1344-1358.\u003c/li\u003e\n\u003cli\u003eGross, C.T. and N.S. Canteras, \u003cem\u003eThe many paths to fear.\u003c/em\u003e Nature Reviews. Neuroscience, 2012. \u003cstrong\u003e13\u003c/strong\u003e(9): p. 651-658.\u003c/li\u003e\n\u003cli\u003eBorkar, C.D., et al., \u003cem\u003eTop-down control of flight by a non-canonical cortico-amygdala pathway.\u003c/em\u003e Nature, 2024. \u003cstrong\u003e625\u003c/strong\u003e(7996): p. 743-749.\u003c/li\u003e\n\u003cli\u003eEmmons, E.B., et al., \u003cem\u003eRodent Medial Frontal Control of Temporal Processing in the Dorsomedial Striatum.\u003c/em\u003e The Journal of Neuroscience : the Official Journal of the Society For Neuroscience, 2017. \u003cstrong\u003e37\u003c/strong\u003e(36): p. 8718-8733.\u003c/li\u003e\n\u003cli\u003eWall, N.R., et al., \u003cem\u003eDifferential innervation of direct- and indirect-pathway striatal projection neurons.\u003c/em\u003e Neuron, 2013. \u003cstrong\u003e79\u003c/strong\u003e(2): p. 347-360.\u003c/li\u003e\n\u003cli\u003eHunnicutt, B.J., et al., \u003cem\u003eA comprehensive excitatory input map of the striatum reveals novel functional organization.\u003c/em\u003e ELife, 2016. \u003cstrong\u003e5\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eCox, J. and I.B. Witten, \u003cem\u003eStriatal circuits for reward learning and decision-making.\u003c/em\u003e Nature Reviews. Neuroscience, 2019. \u003cstrong\u003e20\u003c/strong\u003e(8): p. 482-494.\u003c/li\u003e\n\u003cli\u003eDeLong, M.R., \u003cem\u003ePrimate models of movement disorders of basal ganglia origin.\u003c/em\u003e Trends In Neurosciences, 1990. \u003cstrong\u003e13\u003c/strong\u003e(7): p. 281-285.\u003c/li\u003e\n\u003cli\u003eAlbin, R.L., A.B. Young, and J.B. Penney, \u003cem\u003eThe functional anatomy of basal ganglia disorders.\u003c/em\u003e Trends In Neurosciences, 1989. \u003cstrong\u003e12\u003c/strong\u003e(10): p. 366-375.\u003c/li\u003e\n\u003cli\u003eGe, J., et al., \u003cem\u003eVentral zona incerta parvalbumin neurons modulate sensory-induced and stress-induced self-grooming via input-dependent mechanisms in mice.\u003c/em\u003e IScience, 2024. \u003cstrong\u003e27\u003c/strong\u003e(7): p. 110165.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Defensive behavior, Decision-making, Dorsomedial Striatum, Freezing, Flight","lastPublishedDoi":"10.21203/rs.3.rs-7083386/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7083386/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDefensive responses are evolutionarily conserved adaptive behaviors that species exhibit in response to threats to protect themselves from harm or death. The selection of context-appropriate defensive strategies, particularly the transition between freezing and flight behaviors, constitutes a critical determinant of species survival. By employing the serial-compound stimulus (SCS) Pavlovian fear conditioning, we found that the decision-making process of mice, specifically their inclination to exhibit freezing versus flight in the presence of threats, is primarily determined by learned temporal relationships of conditioned stimulus (CS) and unconditioned stimulus (US). As the threat stimulus approached more closely, mice exhibited enhanced escape behaviors characterized by reduced response latencies and increased response magnitudes. Furthermore, medium spiny neurons (MSNs) within the dorsal medial striatum (DMS) exhibited differential engagement based on threat proximity, as assessed through fiber photometry to record the population response of these neurons during the SCS. Dopamine receptor 2 expressing MSNs (D2 MSNs) predominantly modulated responses to temporally distant threats, while dopamine receptor 1 expressing MSNs (D1 MSNs) primarily regulated responses to imminent threats. Both types of MSNs contribute to the magnitude of the defensive response. These findings suggest that the switching of defensive responses during SCS Pavlovian fear conditioning is primarily determined by the temporal proximity of the CS to the US. This process is regulated through the collaborative interaction between D1 MSNs and D2 MSNs within the DMS region, underscoring the DMS as a critical neural hub for threat assessment and defensive strategy selection.\u003c/p\u003e","manuscriptTitle":"Dorsomedial Striatal Medium Spiny Neurons Orchestrate Temporally approaching threat Driven Defensive Switching in Fear Conditioning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-23 10:59:58","doi":"10.21203/rs.3.rs-7083386/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-09-09T09:27:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-09-06T15:10:29+00:00","index":3,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-08-18T15:31:51+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-08-14T19:41:04+00:00","index":3,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-08-11T03:39:08+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-07-29T02:51:43+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-07-23T21:43:09+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-07-18T17:43:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-15T12:18:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-15T12:15:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Translational Psychiatry","date":"2025-07-14T10:00:42+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2025-07-10T13:42:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"63e7e6b4-9305-4e11-9a99-3c1d47271bca","owner":[],"postedDate":"July 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":51769645,"name":"Biological sciences/Neuroscience"},{"id":51769646,"name":"Health sciences/Diseases"}],"tags":[],"updatedAt":"2026-05-13T10:00:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-23 10:59:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7083386","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7083386","identity":"rs-7083386","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}