Precision cutaneous stimulation in freely moving mice

Survival and adaptation are supported by the somatosensory system, which provides signals from the body surface to drive and refine behavior. It links the skin and central nervous system, recruiting reflexes and shaping behavior through learning and fine-tuning responses. For instance, a noxious stimulus at the skin should generate appropriate responses that minimize harm and increase the odds of survival. However, probing the somatosensory system in freely moving animals presents significant challenges due to the trade-offs between precision and preserving natural behavior.

Current methods for cutaneous stimulation usually necessitate direct physical contact, which restricts the range of behaviors and environments that can be studied. These methods often involve restraining animals or confining them to small chambers where stimuli are applied to their body. While valuable for understanding immediate responses, such approaches fail to capture the complexity of naturalistic behaviors. There is a growing need to study freely moving mice in dynamic, naturalistic environments (Juavinett et al., 2019; Datta et al., 2019; Dennis et al., 2021; Rosenberg et al., 2021; Zong et al., 2022), moving away from restraining and restricting behavior. However, delivering precise cutaneous stimuli to mice navigating complex settings, such as mazes, remains a significant challenge.

Experiments can proceed in more complex environments without researchers in proximity when cutaneous stimuli, such as electric shocks are delivered via a grid floor. This approach has contributed significantly to our understanding over decades, being used in learning studies to probe cells and circuits underpinning aversion, avoidance, fear, expectancy, and memory formation and retrieval. However, the underlying somatosensory processes cannot be resolved due to a lack of spatial precision and control—they indiscriminately stimulate multiple body parts in contact with the floor.

There have been attempts to bridge these gaps by applying localized cutaneous stimuli (e.g. von Frey filaments) to moving rodents. This has been shown to be useful in recent studies of circuits involved in pain processing (Cheng et al., 2017; Gangadharan et al., 2022). However, these approaches require experimenters to be in close proximity to the animals, to continuously observe their movements as they explore, and to manually touch a hind paw at regular intervals (LaBuda and Fuchs, 2000; Uhelski et al., 2012). This can cause observer bias and limit both precision and the complexity of the environments.

To address these challenges, we developed a closed-loop system to automatically deliver spatiotemporally precise cutaneous stimuli in a remote and dynamic manner, targeting mice as they freely explored environments. The system leverages advances in remote transdermal optogenetic stimulation (Schorscher-Petcu et al., 2021) and real-time markerless body part (keypoint) tracking (Kane et al., 2020). The approach moves away from restricted and restrained behavior to enable behaviorally relevant stimulation in more naturalistic environments (Dennis et al., 2021). This addresses the traditional trade-offs between precision and naturalistic environmental settings, providing a framework in which to integrate reflex recruitment, motivation, learning, and decision-making.

To develop closed-loop cutaneous stimulation in mice exploring large environments, we built a system that could rapidly track and target mice and stimulate them remotely (Figure 1). We used real-time pose estimation to monitor body parts and target lasers for spatiotemporally precise thermal stimulation and optogenetic stimulation of genetically defined nociceptor fibers at the skin surface (Schorscher-Petcu et al., 2021; Kane et al., 2020). The approach was demonstrated in large environments, allowing automated cutaneous stimulation in an arena and during goal-directed behavior as mice run through a maze.

Figure 1

Download asset Open asset

A system for closed-loop cutaneous stimulation

We designed a system that automatically tracks targets for cutaneous stimuli across a large environment, leveraging our laser-mirror galvanometer design (Schorscher-Petcu et al., 2021). The environment was built on a large glass platform, allowing us to record exploring mice from below with a camera (Figure 2A and B). The camera enabled real-time tracking with DeepLabCut-Live! (Kane et al., 2020), estimating the keypoints of multiple body parts for every frame of the camera feed. The target body part keypoint x and y coordinates were converted to pre-mapped control signals for x and y galvanometer mirrors to steer lasers as required. This provides a closed-loop system for dynamic control of stimuli according to behaviorally relevant criteria.

Figure 2 with 2 supplements see all

Download asset Open asset

The system was optically precise. The glass platform was close to 1 m above the galvanometers, resulting in a maximum focal length variability of 3.49%, minimizing differences in the size of the laser spot in the stimulation plane. The absolute optical power and power density were uniform across the glass platform (coefficient of variation 0.035 and 0.029, respectively; Figure 2—figure supplement 1A). Laser power could be modulated using the analog control. The laser spot size was set to 2.00±0.08 mm² (mean ± SD; coefficient of variation = 0.039) at the stimulation plane with a series of lenses along the blue light beam path (Schorscher-Petcu et al., 2021). The laser spot could be moved along specific trajectories creating patterns (Figure 2C, Figure 2—figure supplement 1B). We used 10,000 x and y voltage pairs to jump the laser across the stimulation plane and map the voltages to corresponding pixels (Figure 2C). Surface fits resulted in a pixel-voltage mapping dictionary that minimized non-linear distortions. This resulted in a mean average Euclidean error (MAE) of 1.2 pixels (0.54 mm) between predicted and actual laser spot locations in the 500 mm arena. The system and glass platform were stable, showing a displacement equivalent to about half the width of a hind paw (MAE = 1.94±2.61 mm) each week during intensive use of the system (Figure 2—figure supplement 1C), which can be corrected in less than 30 min by remapping.

The system could accurately target moving mice. Real-time estimation of keypoints (Figure 2D and E) was used for closed-loop control of the galvanometer mirror angles, resulting in pairwise correlations of r=0.999 between galvanometer coordinates and hind paw keypoints (Figure 2F). Thus, the laser could be targeted in real time to body parts when certain programmatic criteria were met (see Figure 2—figure supplement 2 for the information flow). For instance, the laser beam could be triggered if the distance of an individual keypoint moves with variance ≤v for time ≥t and keypoint estimation likelihood was ≥l, where v, t, and l are user-defined variables. To determine the targeting accuracy, we used wild-type mice that did not express ChR2 so that blue light pulses did not cause behavioral responses. The latency between acquiring a frame 1100×1100 pixels, estimating keypoints, and targeting a laser was 84±12 ms (mean ± SD using 16,000 trials across four wild-type mice; Figure 2—figure supplement 1D). This delay is sufficient to target paws: during locomotion, the hind paws were static for 350±44 ms in the stance phase and moving for 100±1 ms in the swing phase (Figure 2—figure supplement 1E). The positioning of paws during the stance phase of locomotion creates ‘footprints’ in keypoint space, indicating moments when the paws are momentarily static even as the mouse moves (Figure 2G).

Mice move around at variable speeds while exploring, which can be categorized (Figure 2H). During these movements, locomotor gait is defined by the coordinated sequence and timing of stance and swing phases across all four limbs. Each paw alternates between the stance and swing phase; thus, as the mouse moves forward, the individual paws are stationary during their respective stance phases. When the mouse was stationary (58±7% of the time), the hind paws were static in 99.8±0.1% frames, and this remains high even as the mouse increases its bodily speed: 95.7±0.1% frames for ‘low’ speed (28±4% of the time), 79.3±0.1% frames at ‘medium’ speed (8±2% of the time), and 60±0.3% at ‘high’ speed (6±1% of the time). Therefore, even during locomotion, the hind paws are static long enough for stimulation with short-latency body part tracking (Figure 2I and J). The laser was successfully targeted to the hind paws with a high ‘hit accuracy’ when the mouse was moving at low speeds (95.5±2.6%; Figure 2K). Hit accuracy refers to the percentage of trials in which the laser successfully targeted (‘hit’) the intended hind paw. As expected, the hit accuracy was reduced during high-speed running. We found zero keypoint confusion across all speeds, with 657 out of 657 trials successfully targeting the correct hind paw. The optical system targeted the hind paws of wild-type mice exploring an open arena 650±30 times within 5 min at 30 frames per second (fps, n=4 mice), providing multiple stimuli in short periods of time. We also targeted the forepaws, but confusion between them resulted in 5.7±2.2% targets being directed to the incorrect paw. This resulted in a low hit accuracy compared to the hind paws (Figure 2—figure supplement 1F). The laser spots were delivered with high accuracy to the targeted body parts, showing a small error of ≈1.3 mm MAE (Figure 2L). As laser targeting relies on real-time tracking to direct the laser to the specified body part, this metric includes any errors introduced by tracking and targeting. Thus, this design resulted in a fully automated system that facilitates spatiotemporally precise optical targeting of freely moving mice in a large environment.

Cutaneous stimulation in large environments drives behavioral responses

We next used the closed-loop system to automatically deliver optogenetic cutaneous stimuli and examine the resultant behavior. A large circular arena was chosen to encourage free exploration and movement (Figure 3A and B), and behavior was examined by pose estimation, reconstructing the movement trajectories of body parts in the arena (Figure 3C). Brief transdermal optogenetic stimulation of nociceptors was used to achieve minimal cutaneous stimulation: mice expressed the blue-light-sensitive opsin, ChR2, in nociceptors innervating the skin (Trpv1::ChR2; Schorscher-Petcu et al., 2021; Browne et al., 2017; Black et al., 2020). Mice explored the arena, and when stationary were stimulated on the hind paw with brief 10 ms pulses of light, delivered at intervals of at least 10 mins (Figure 3B). We found that the system accurately targeted the laser to the hind paw (Figure 3D).

Figure 3

Download asset Open asset

The precise cutaneous input gave rise to motor output as coordinated behavior in freely moving mice. The brief stimuli caused paw withdrawals along with coordinated whole-body behaviors, such as rapid head orientation and body repositioning (Figure 3E and F). These behaviors were quantified as time-locked keypoint traces for each body part (Figure 3E and F). Littermate control mice that do not express ChR2 did not exhibit responses to optogenetic cutaneous stimuli, including paw withdrawals or whole-body behaviors (n=10 mice). Therefore, the system enables fully automated and precise optical delivery of cutaneous stimuli to freely moving mice while simultaneously recording and quantifying behavior.

Multi-animal stimulation for automatic nociceptive testing

The system could deliver cutaneous stimuli across a large space, evoking behavioral responses. Next, we demonstrate the flexibility of the system design and establish automatic nociceptive testing across multiple mice simultaneously. A method for random-access targeting was developed (Figure 4A) to target nine mice (3×3 configuration) in individual chambers; we detected idle mice by analyzing motion energy in each chamber, then rapidly selected and cropped to one chamber for real-time pose estimation and stimulation (Figure 4B). This reduced the computational burden by decreasing image resolution, compared to running real-time pose estimation across the whole environment (Kane et al., 2020). The process operated as a loop, ensuring that automated stimuli were spaced by at least 1 min apart for each mouse.

Figure 4

Download asset Open asset

To test the system, we used: (1) thermal stimulation with a 10 s pulse of infrared light (785 nm) on the hind paw of wild-type mice; and (2) optogenetic stimulation of cutaneous nociceptors with a 3 ms pulse of blue light (473 nm) on the hind paw of Trpv1::ChR2 mice. We varied the intensity of the optogenetic stimuli using 10 Hz pulse trains (0.5–8 mW/mm²) compared to a single pulse at higher intensity (40 mW/mm²).

Thermal and optogenetic stimulation induced similar nocifensive behaviors, including paw responses and whole-body movements (Figure 4C). This can be seen from traces of hind paw movement following thermal or optogenetic stimuli (Figure 4D–F). Consistent with previous studies, we observed the whole-body behaviors, such as head orienting concurrent with local withdrawal (Browne et al., 2017; Blivis et al., 2017). In the case of thermal stimulation, we analyzed the local motion energy of the stimulated hind paw movements to quantify the response. Following stimulation onset, mice showed a mean response latency of 4.74±2.48 s (mean ± SD, 188 response trials; Figure 4E). Cumulative distributions further demonstrated nocifensive behaviors in response to infrared stimulation (Figure 4G) and illustrated the temporal resolution afforded by transdermal optogenetics compared to infrared stimulation. Optogenetic stimulation-induced response latencies followed the rank order of stimulus intensity, demonstrating the dynamic range possible with this system (0.5 mW/mm²: 2.98±3.19 s, 1.0 mW/mm²: 2.84±3.19 s, 2.0 mW/mm²: 1.69±2.14 s, 4.0 mW/mm²: 1.72±2.25 s, 8.0 mW/mm²: 1.51±2.19 s, 40.0 mW/mm²: 0.81±1.65 s, mean ± SD; Figure 4G). In contrast, littermate control mice did not exhibit responses (Schorscher-Petcu et al., 2021; Browne et al., 2017).

This demonstrates that the system is both versatile and precise, enabling the study of local paw withdrawals and whole-body movements, including head orientation and body repositioning. The platform is large enough to accommodate 25 mice (5×5 configuration) in a single session, allowing for high-throughput automated experiments.

Closed-loop cutaneous stimulation in mice running through a maze

We demonstrate that freely moving mice can be stimulated as they were running through a maze environment. The ability to deliver cutaneous stimuli to mice moving through behaviorally relevant tasks has previously been a significant challenge, and experimenters typically apply stimuli to moving mice manually (Cheng et al., 2017; Gangadharan et al., 2022; LaBuda and Fuchs, 2000; Uhelski et al., 2012).

To motivate movement in a task, we built a novel maze that encourages alternation between two rewards at separate locations. The maze had one-way doors, ensuring that after making a decision to turn left or right, mice could obtain a reward but are required to circle back to the maze’s start point to re-initiate the action-reward cycle (Figure 5A and B). The reward ports were activated by a brief nose poke and delivered a drop of sucrose water, followed by a timeout period. In addition to the timeout, the reward ports were only reset once the mouse exited the reward chamber through a one-way door, as determined by real-time keypoint tracking. The combination of a long timeout and required exit from the reward chamber rendered it more time-efficient to cycle between the two reward chambers and encouraged running along the corridors.

Figure 5

Download asset Open asset

Mice were trained over three sessions in the maze. In the first training session, they rapidly navigated the one-way doors with little delay after a few attempts. This quickly led to the use of reward ports in the chambers on either side of the maze. The first reward was collected after 440±117 s (4 mice). By the third training session, this time decreased to 82±38 s, suggesting rapid learning. During the reward port timeout, mice typically explored the corridor connected to the reward chamber or exited it to explore the entry corridor. By the third day of training, the number of completed trials had increased for all mice. On average, mice completed 64±24 rewarded trials in the third training session (<2 hr in duration), although individual performance varied considerably. For example, one mouse completed only 10 rewarded trials in the third session, and another mouse completed 97 trials (Figure 5C).

Mice could be stimulated while they were running. Example movement trajectories from one mouse are shown in Figure 5D. Using Trpv1::ChR2 mice, we demonstrated the system’s utility in studying localized cutaneous hypersensitivity. We employed a widely used model of inflammatory pain with a unilateral injection of complete Freund’s adjuvant (CFA) in the hind paw. A brief (3 ms) nociceptive stimulus was successfully targeted to the contralateral (right, non-injected) paw with negligible confusion between paws (705/706 stimuli targeted the correct hind paw). This allows future studies in which phasic and tonic pain can be separated. Despite ongoing hypersensitivity and phasic stimuli, mice remained actively engaged in the task, collecting rewards sequentially from each side, as per their training (Figure 5F and G). Their rapid movement along the stimulation corridors (average maximum speed of 142±26 mm/s, 4 mice) required precise targeting (Figure 5E).

Stimulation during movement revealed a frequency-dependent disruption of path coherence and speed. High-frequency stimulation resulted in reduced locomotor speeds (p=0.037 with paired t-test, n=4 mice) and more variable trajectories compared to during low-frequency stimulation (Figure 5H). Once through the stimulation corridors, the mice successfully collected a reward in almost all trials.

We then examined how behavior depends on the behavioral state prior to stimulation. Before stimulation, two behavioral states were identified (Figure 5I): one represents faster locomotion in a direct heading (fast-direct state), while the other represents slower movements with headings that were less coherent (slow-assess state). The two clusters had significantly different speeds and coherence (p<0.0001 for both; Welch’s t-test), where the 79 fast-direct trials (68.1%) showed speed of 90.8±28.3 mm/s (mean ± SD) and coherence of 0.9±0.1 (mean ± SD; arbitrary units) and the 37 slow-assess trials (31.9%) had lower speed of 32.5±13.9 mm/s and less direct headings with a coherence of 0.1±0.1. Stimulation of the two states produced robust opposing changes in behavior (Figure 5J and K). Slow-assess states become significantly more coherent as the stimulus drives a redirection of heading. Fast-direct states were significantly slowed and less direct, potentially representing a pause for information gain. In this case, fast-direct states shift towards slow-assess, and slow-assess shifts towards fast-assess, where intermediate states may together represent a form of vigilant behavior. This demonstrates movement state-dependence changes behavior. Indeed, the speed after stimulation was proportional to the initial speed in the fast-direct trials; a positive linear correlation (Pearson’s r=0.23, p=0.046) indicates that the disruption of behavior partly scales with the initial movement state (Figure 5L).

We thus demonstrate automatic control of precise cutaneous stimuli as mice move through naturalistic environments.

The somatosensory system provides a critical link between the brain, the body, and its immediate external environment. The complex ways in which this system supports movement, learning, and action in rodents have historically posed substantial methodological challenges. Traditional methodologies have varied widely, encompassing both innovative and practical approaches—from stimulation of whiskers or skin in head-fixed animals (Black et al., 2020; Pai et al., 2022; Buetfering et al., 2022; Guo et al., 2014; Hong et al., 2018; Campagner et al., 2019; Esmaeili et al., 2020; Schwaller et al., 2021; Emanuel et al., 2021) to the more straightforward manual touching of paws in mice (Cheng et al., 2017; Gangadharan et al., 2022; Uhelski et al., 2012; Gan et al., 2021). These approaches, however, have inherent limitations in replicating the dynamic and complex interactions experienced in naturalistic environmental settings. In response to these limitations, we have developed a system that enables cutaneous stimulation in more complex environments. This closed-loop system automatically tracks, targets, and stimulates mice remotely to study the somatosensory system in naturalistic environmental settings.

Generating cutaneous inputs in freely moving mice requires stimuli that are spatially and temporally precise. We achieved millisecond-timescale stimulation of small skin areas using transdermal optogenetics (‘remote touch’; Schorscher-Petcu et al., 2021). Opsins were genetically targeted to specific afferent fibers innervating skin and activated with light targeted precisely via a laser in free space. This beam path was aligned to a second laser system and employed for thermal stimulation on a timescale of seconds. Delivery of these stimuli was controlled by a feedback system consisting of three main components: (1) real-time tracking infers the keypoints of various body parts, continuously transmitting this data to the controller; (2) user-defined rules determine the stimulation conditions (spatial, temporal, and physiological state-dependence), providing control signals for the actuators; and (3) mirror galvanometers target the beam path to specified key points and signals trigger light delivery, following which real-time tracking is then resumed. The processing latency can be further reduced by minimizing the frame resolution, narrowing to a region of interest (ROI), increasing the processing power, using alternative networks, or combinations thereof. We demonstrate an approach to reduce the pixel count by cropping to an ROI using our random-access targeting method (Figure 4). One possible version of this would be centering a region on the mouse centroid as it moves. This would dramatically reduce the computational load to allow shorter processing latencies. With a reduced processing latency, high-speed video can be processed, which enables the approach to generalize to other body parts that move more rapidly. Additionally, predictive tracking can compensate for the remaining delay by estimating a keypoint location from its recent trajectory (e.g. constant-velocity extrapolation or a Kalman filter). This can reduce effective targeting error for continuously moving features, such as the snout. However, we demonstrate here that this system can precisely target the hind paw for stimulation, even as the mice are in motion.

We demonstrate the versatility of the system from automated multi-animal nociceptive testing to sparse stimulation of freely moving mice in a circular arena. We show that stimuli could be targeted with high accuracy and resulted in immediate behavioral responses that could be mapped. The system was used to deliver cutaneous stimuli to mice running through a maze. Mice were trained on an alternation task with stimuli applied en route to the reward, thus separating choice, punishment, and reward in a naturalistic environment. Mice with a model of inflammatory pain still readily engaged in this task during noxious cutaneous input (punishment). The recruitment of reflexes during locomotion caused immediate evaluative behavior that temporarily disrupted goal-directed behavior. Achieving such localized stimulation has been challenging with traditional methods: electric grid floors generate whole body stimuli that are considered incompatible with models of chronic pain that generate unilateral hind paw hypersensitivity; and manual stimulation lacks capacity, reliability, and is potentially confounded by experimenter and observer biases. Our system addresses these issues, allowing for the dissociation of touch, phasic pain, and tonic pain to better understand their relationships with behavior.

Stimulation of the body and paws enables the study of pain, touch, thermoception, and movement (Caterina et al., 1997; Decosterd and Woolf, 2000; Chiu et al., 2013; Ranade et al., 2014; Moy et al., 2018; Corder et al., 2019; Boyle et al., 2019; Juarez-Salinas et al., 2019; Choi et al., 2020; Gatto et al., 2021; Drake et al., 2021; Middleton et al., 2022). Paw stimulation is also ubiquitous in aversive learning and memory, using a crude shock stimulation with a grid floor (Klein et al., 2021; Bonapersona et al., 2022), and can now be carried out with spatiotemporal precision. The stimulation can be static or dynamic and localized to small areas on the body in an automated manner. Automation improves the precision of stimulus delivery compared to traditional manual methods; it reduces labor and enhances the reliability of the data. All experiments were conducted remotely from an adjacent room to minimize potential observer effects and biases on the mice. Automated nociceptive assays have principally focused on the initial rapid movements elicited by stimulation of mice in small chambers (Dedek et al., 2023; Iredale et al., 2023; Burdge et al., 2023). Here, we provide an approach to examine how these rapid movements are embedded within complex behavior in naturalistic environments, opening new ways to investigate nociception and somatosensation more broadly.

We have previously mapped fast behavioral responses in stationary mice, demonstrating that fast responses to noxious stimuli are not localized and fixed, but are widespread and contextual (Browne et al., 2017) and that these are coordinated where whole-body movements depend on the initial posture (Schorscher-Petcu et al., 2021). Here, we show state dependence of behavioral responses in mice that are moving, where noxious stimulation causes fast-direct moving mice to slow and mice already assessing their location to shift to a direct heading. This could be considered a state-dependent modulation or switching to a form of vigilance and suggests that even the most rapid protective responses are modulated online by the current movement state, ensuring behavior remains appropriate to context.

While we demonstrate the utility of the optical system using nociceptive stimuli, this system can deliver various cutaneous inputs by targeting specific afferents for selective opsin expression, whether they are thermoreceptive, chemoreceptive, or mechanosensitive (Schorscher-Petcu et al., 2021; Dedek et al., 2023; Iredale et al., 2023; Burdge et al., 2023; Mickle and Gereau, 2018). Such ‘pure’ stimuli do not occur in nature but offer crucial spatial, temporal, and genetic precision (Klein et al., 2021; Madden et al., 2016; Cheah et al., 2017). Our system enables delivery of multiple wavelengths of light separately or together, to support combinations of opsins from the vast optogenetic toolbox. Opsins can be used to activate or silence neurons, with a range of kinetic properties, diverse light wavelength profiles allowing multi-color manipulations, or control different downstream signaling effectors (Emiliani et al., 2022). Thermal stimuli are also used routinely in research (Pai et al., 2022; Dedek et al., 2023; Mitchell et al., 2010) but slow thermal dissipation can require mice to be stationary for consistent stimulation. Automation provides opportunities for the development of analgesics, particularly for integrating reflexes with spontaneous, free operant behaviors. Indeed, cutaneous stimulation in naturalistic environments can be readily combined with approaches to quantify behavior (Mathis et al., 2018; Jones et al., 2020; Wiltschko et al., 2020; Hsu and Yttri, 2021; Zhang et al., 2022; Weinreb et al., 2024; Bohic et al., 2023; Pereira et al., 2020).

The system has many applications for the study of sensorimotor transformations, perception, memory, learning, and action. It is flexible enough to trigger stimulation based on various states, including periods of inactivity or locomotion, at specific spatial locations and with precise timing. Future work made possible by this system is expected to include examining how cutaneous input can interrupt and modulate specific swing phases (Gatto et al., 2021), self-grooming, posture states (Browne et al., 2017), and other spontaneous behavioral syllables (Wiltschko et al., 2020). It can facilitate investigations of naturalistic learning, whether through mazes, social interactions, or engagement with the environment or objects (Choi and Kim, 2010; Lai et al., 2024), and of sleep fragmentation (Alexandre et al., 2024), anxiety (Orefice et al., 2019; La Vu et al., 2020), fear (Klein et al., 2021), and stress (Bonapersona et al., 2022). Finally, it has the potential to provide free operant methods for analgesic development for chronic pain. The system may be combined with existing tools to record neural activity in freely moving mice, such as fiber photometry, miniscopes, or large-scale electrophysiology, and manipulations of this neural activity, such as optogenetics and chemogenetics. This can allow mechanistic dissection of cell and circuit biology in the context of naturalistic behaviors.

Establishing how behavior is shaped by somatosensation requires that mice can be stimulated while freely behaving. We describe a system that addresses this need, delivering cutaneous stimuli in a manner that is precise, remote, state-dependent, dynamic, and fully automated to target freely behaving mice that are actively exploring complex environments.

The manuscript is a Tools and Resources study that provides a new method. Code to use this method is openly accessible at https://github.com/browne-lab/closed-loop-somatosensory-stimulation (Parkes et al., 2026). Data is available on Dryad at https://doi.org/10.5061/dryad.rr4xgxdhs.

1. 1. Parkes I
   2. Schorscher-Petcu A
   3. Gan Q
   4. Browne LE

   (2026) Dryad Digital Repository

   Precision cutaneous stimulation in freely moving mice.

   https://doi.org/10.5061/dryad.rr4xgxdhs

1. 1. Alexandre C
   2. Miracca G
   3. Holanda VD
   4. Sharma A
   5. Kourbanova K
   6. Ferreira A
   7. Bicca MA
   8. Zeng X
   9. Nassar VA
   10. Lee S
   11. Kaur S
   12. Sarma SV
   13. Sacré P
   14. Scammell TE
   15. Woolf CJ
   16. Latremoliere A

   (2024) Nociceptor spontaneous activity is responsible for fragmenting non-rapid eye movement sleep in mouse models of neuropathic pain

   Science Translational Medicine 16:eadg3036.

   https://doi.org/10.1126/scitranslmed.adg3036

   • PubMed
   • Google Scholar
2. Preprint

   1. Bakermans JJW
   2. Behrens TEJ

   (2021) Controlling precedence in sequential stimulus presentation with euler tours

   PsyArXiv.

   https://doi.org/10.31234/osf.io/y8r6k

   • Google Scholar
3. 1. Black CJ
   2. Allawala AB
   3. Bloye K
   4. Vanent KN
   5. Edhi MM
   6. Saab CY
   7. Borton DA

   (2020) Automated and rapid self-report of nociception in transgenic mice

   Scientific Reports 10:13215.

   https://doi.org/10.1038/s41598-020-70028-8

   • PubMed
   • Google Scholar
4. 1. Blivis D
   2. Haspel G
   3. Mannes PZ
   4. O’Donovan MJ
   5. Iadarola MJ

   (2017) Identification of a novel spinal nociceptive-motor gate control for Aδ pain stimuli in rats

   eLife 6:e23584.

   https://doi.org/10.7554/eLife.23584

   • PubMed
   • Google Scholar
5. 1. Bohic M
   2. Pattison LA
   3. Jhumka ZA
   4. Rossi H
   5. Thackray JK
   6. Ricci M
   7. Mossazghi N
   8. Foster W
   9. Ogundare S
   10. Twomey CR
   11. Hilton H
   12. Arnold J
   13. Tischfield MA
   14. Yttri EA
   15. St John Smith E
   16. Abdus-Saboor I
   17. Abraira VE

   (2023) Mapping the neuroethological signatures of pain, analgesia, and recovery in mice

   Neuron 111:2811–2830.

   https://doi.org/10.1016/j.neuron.2023.06.008

   • PubMed
   • Google Scholar
6. 1. Bonapersona V
   2. Schuler H
   3. Damsteegt R
   4. Adolfs Y
   5. Pasterkamp RJ
   6. van den Heuvel MP
   7. Joëls M
   8. Sarabdjitsingh RA

   (2022) The mouse brain after foot shock in four dimensions: temporal dynamics at a single-cell resolution

   PNAS 119:e2114002119.

   https://doi.org/10.1073/pnas.2114002119

   • PubMed
   • Google Scholar
7. 1. Boyle KA
   2. Gradwell MA
   3. Yasaka T
   4. Dickie AC
   5. Polgár E
   6. Ganley RP
   7. Orr DPH
   8. Watanabe M
   9. Abraira VE
   10. Kuehn ED
   11. Zimmerman AL
   12. Ginty DD
   13. Callister RJ
   14. Graham BA
   15. Hughes DI

   (2019) Defining a spinal microcircuit that gates myelinated afferent input: implications for tactile allodynia

   Cell Reports 28:526–540.

   https://doi.org/10.1016/j.celrep.2019.06.040

   • PubMed
   • Google Scholar
8. 1. Browne LE
   2. Latremoliere A
   3. Lehnert BP
   4. Grantham A
   5. Ward C
   6. Alexandre C
   7. Costigan M
   8. Michoud F
   9. Roberson DP
   10. Ginty DD
   11. Woolf CJ

   (2017) Time-resolved fast mammalian behavior reveals the complexity of protective pain responses

   Cell Reports 20:89–98.

   https://doi.org/10.1016/j.celrep.2017.06.024

   • PubMed
   • Google Scholar
9. 1. Buetfering C
   2. Zhang Z
   3. Pitsiani M
   4. Smallridge J
   5. Boven E
   6. McElligott S
   7. Häusser M

   (2022) Behaviorally relevant decision coding in primary somatosensory cortex neurons

   Nature Neuroscience 25:1225–1236.

   https://doi.org/10.1038/s41593-022-01151-0

   • PubMed
   • Google Scholar
10. 1. Burdge J
    2. Jhumka A
    3. Ogundare S
    4. Baer N
    5. Fulton S
    6. Bistis B
    7. Foster W
    8. Toussaint A
    9. Li M
    10. Morizawa YM
    11. Yadessa L
    12. Khan A
    13. Delinois A
    14. Mayiseni W
    15. Loran N
    16. Yang G
    17. Abdus-Saboor I

    (2023) Remote automated delivery of mechanical stimuli coupled to brain recordings in behaving mice

    eLife 13:RP99614.

    https://doi.org/10.7554/eLife.99614.1

    • Google Scholar
11. 1. Campagner D
    2. Evans MH
    3. Chlebikova K
    4. Colins-Rodriguez A
    5. Loft MSE
    6. Fox S
    7. Pettifer D
    8. Humphries MD
    9. Svoboda K
    10. Petersen RS

    (2019) Prediction of choice from competing mechanosensory and choice-memory cues during active tactile decision making

    The Journal of Neuroscience 39:3921–3933.

    https://doi.org/10.1523/JNEUROSCI.2217-18.2019

    • PubMed
    • Google Scholar
12. 1. Caterina MJ
    2. Schumacher MA
    3. Tominaga M
    4. Rosen TA
    5. Levine JD
    6. Julius D

    (1997) The capsaicin receptor: a heat-activated ion channel in the pain pathway

    Nature 389:816–824.

    https://doi.org/10.1038/39807

    • PubMed
    • Google Scholar
13. 1. Cavanaugh DJ
    2. Chesler AT
    3. Jackson AC
    4. Sigal YM
    5. Yamanaka H
    6. Grant R
    7. O’Donnell D
    8. Nicoll RA
    9. Shah NM
    10. Julius D
    11. Basbaum AI

    (2011) Trpv1 reporter mice reveal highly restricted brain distribution and functional expression in arteriolar smooth muscle cells

    The Journal of Neuroscience 31:5067–5077.

    https://doi.org/10.1523/JNEUROSCI.6451-10.2011

    • PubMed
    • Google Scholar
14. 1. Cheah M
    2. Fawcett JW
    3. Andrews MR

    (2017) Assessment of thermal pain sensation in rats and mice using the hargreaves test

    Bio-Protocol 7:e2506.

    https://doi.org/10.21769/BioProtoc.2506

    • PubMed
    • Google Scholar
15. 1. Cheng L
    2. Duan B
    3. Huang T
    4. Zhang Y
    5. Chen Y
    6. Britz O
    7. Garcia-Campmany L
    8. Ren X
    9. Vong L
    10. Lowell BB
    11. Goulding M
    12. Wang Y
    13. Ma Q

    (2017) Identification of spinal circuits involved in touch-evoked dynamic mechanical pain

    Nature Neuroscience 20:804–814.

    https://doi.org/10.1038/nn.4549

    • PubMed
    • Google Scholar
16. 1. Chiu IM
    2. Heesters BA
    3. Ghasemlou N
    4. Von Hehn CA
    5. Zhao F
    6. Tran J
    7. Wainger B
    8. Strominger A
    9. Muralidharan S
    10. Horswill AR
    11. Bubeck Wardenburg J
    12. Hwang SW
    13. Carroll MC
    14. Woolf CJ

    (2013) Bacteria activate sensory neurons that modulate pain and inflammation

    Nature 501:52–57.

    https://doi.org/10.1038/nature12479

    • PubMed
    • Google Scholar
17. 1. Choi JS
    2. Kim JJ

    (2010) Amygdala regulates risk of predation in rats foraging in a dynamic fear environment

    PNAS 107:21773–21777.

    https://doi.org/10.1073/pnas.1010079108

    • PubMed
    • Google Scholar
18. 1. Choi S
    2. Hachisuka J
    3. Brett MA
    4. Magee AR
    5. Omori Y
    6. Iqbal NUA
    7. Zhang D
    8. DeLisle MM
    9. Wolfson RL
    10. Bai L
    11. Santiago C
    12. Gong S
    13. Goulding M
    14. Heintz N
    15. Koerber HR
    16. Ross SE
    17. Ginty DD

    (2020) Parallel ascending spinal pathways for affective touch and pain

    Nature 587:258–263.

    https://doi.org/10.1038/s41586-020-2860-1

    • PubMed
    • Google Scholar
19. 1. Corder G
    2. Ahanonu B
    3. Grewe BF
    4. Wang D
    5. Schnitzer MJ
    6. Scherrer G

    (2019) An amygdalar neural ensemble that encodes the unpleasantness of pain

    Science 363:276–281.

    https://doi.org/10.1126/science.aap8586

    • PubMed
    • Google Scholar
20. 1. Datta SR
    2. Anderson DJ
    3. Branson K
    4. Perona P
    5. Leifer A

    (2019) Computational neuroethology: a call to action

    Neuron 104:11–24.

    https://doi.org/10.1016/j.neuron.2019.09.038

    • PubMed
    • Google Scholar
21. 1. Decosterd I
    2. Woolf CJ

    (2000) Spared nerve injury: an animal model of persistent peripheral neuropathic pain

    Pain 87:149–158.

    https://doi.org/10.1016/S0304-3959(00)00276-1

    • PubMed
    • Google Scholar
22. 1. Dedek C
    2. Azadgoleh MA
    3. Prescott SA

    (2023) Reproducible and fully automated testing of nocifensive behavior in mice

    Cell Reports Methods 3:100650.

    https://doi.org/10.1016/j.crmeth.2023.100650

    • PubMed
    • Google Scholar
23. 1. Dennis EJ
    2. El Hady A
    3. Michaiel A
    4. Clemens A
    5. Tervo DRG
    6. Voigts J
    7. Datta SR

    (2021) Systems neuroscience of natural behaviors in rodents

    The Journal of Neuroscience 41:911–919.

    https://doi.org/10.1523/JNEUROSCI.1877-20.2020

    • PubMed
    • Google Scholar
24. 1. Drake RAR
    2. Steel KA
    3. Apps R
    4. Lumb BM
    5. Pickering AE

    (2021) Loss of cortical control over the descending pain modulatory system determines the development of the neuropathic pain state in rats

    eLife 10:e65156.

    https://doi.org/10.7554/eLife.65156

    • PubMed
    • Google Scholar
25. 1. Emanuel AJ
    2. Lehnert BP
    3. Panzeri S
    4. Harvey CD
    5. Ginty DD

    (2021) Cortical responses to touch reflect subcortical integration of LTMR signals

    Nature 600:680–685.

    https://doi.org/10.1038/s41586-021-04094-x

    • PubMed
    • Google Scholar
26. 1. Emiliani V
    2. Entcheva E
    3. Hedrich R
    4. Hegemann P
    5. Konrad KR
    6. Lüscher C
    7. Mahn M
    8. Pan ZH
    9. Sims RR
    10. Vierock J
    11. Yizhar O

    (2022) Optogenetics for light control of biological systems

    Nature Reviews. Methods Primers 2:55.

    https://doi.org/10.1038/s43586-022-00136-4

    • PubMed
    • Google Scholar
27. 1. Esmaeili V
    2. Tamura K
    3. Foustoukos G
    4. Oryshchuk A
    5. Crochet S
    6. Petersen CC

    (2020) Cortical circuits for transforming whisker sensation into goal-directed licking

    Current Opinion in Neurobiology 65:38–48.

    https://doi.org/10.1016/j.conb.2020.08.003

    • PubMed
    • Google Scholar
28. 1. Gan Z
    2. Li H
    3. Naser PV
    4. Oswald MJ
    5. Kuner R

    (2021) Suppression of neuropathic pain and comorbidities by recurrent cycles of repetitive transcranial direct current motor cortex stimulation in mice

    Scientific Reports 11:9735.

    https://doi.org/10.1038/s41598-021-89122-6

    • PubMed
    • Google Scholar
29. 1. Gangadharan V
    2. Zheng H
    3. Taberner FJ
    4. Landry J
    5. Nees TA
    6. Pistolic J
    7. Agarwal N
    8. Männich D
    9. Benes V
    10. Helmstaedter M
    11. Ommer B
    12. Lechner SG
    13. Kuner T
    14. Kuner R

    (2022) Neuropathic pain caused by miswiring and abnormal end organ targeting

    Nature 606:137–145.

    https://doi.org/10.1038/s41586-022-04777-z

    • PubMed
    • Google Scholar
30. 1. Gatto G
    2. Bourane S
    3. Ren X
    4. Di Costanzo S
    5. Fenton PK
    6. Halder P
    7. Seal RP
    8. Goulding MD

    (2021) A functional topographic map for spinal sensorimotor reflexes

    Neuron 109:91–104.

    https://doi.org/10.1016/j.neuron.2020.10.003

    • PubMed
    • Google Scholar
31. 1. Guo ZV
    2. Li N
    3. Huber D
    4. Ophir E
    5. Gutnisky D
    6. Ting JT
    7. Feng G
    8. Svoboda K

    (2014) Flow of cortical activity underlying a tactile decision in mice

    Neuron 81:179–194.

    https://doi.org/10.1016/j.neuron.2013.10.020

    • PubMed
    • Google Scholar
32. 1. Hong YK
    2. Lacefield CO
    3. Rodgers CC
    4. Bruno RM

    (2018) Sensation, movement and learning in the absence of barrel cortex

    Nature 561:542–546.

    https://doi.org/10.1038/s41586-018-0527-y

    • PubMed
    • Google Scholar
33. 1. Hsu AI
    2. Yttri EA

    (2021) B-SOiD, an open-source unsupervised algorithm for identification and fast prediction of behaviors

    Nature Communications 12:5188.

    https://doi.org/10.1038/s41467-021-25420-x

    • PubMed
    • Google Scholar
34. Preprint

    1. Iredale JA
    2. Pearl AJ
    3. Callister RJ
    4. Dayas CV
    5. Manning EE
    6. Graham BA

    (2023) 'Optical von-Frey’ method to determine nociceptive thresholds: a novel paradigm for preclinical pain assessment and analgesic screening

    bioRxiv.

    https://doi.org/10.1101/2023.11.02.565390

    • Google Scholar
35. 1. Jones JM
    2. Foster W
    3. Twomey CR
    4. Burdge J
    5. Ahmed OM
    6. Pereira TD
    7. Wojick JA
    8. Corder G
    9. Plotkin JB
    10. Abdus-Saboor I

    (2020) A machine-vision approach for automated pain measurement at millisecond timescales

    eLife 9:e57258.

    https://doi.org/10.7554/eLife.57258

    • PubMed
    • Google Scholar
36. 1. Juarez-Salinas DL
    2. Braz JM
    3. Etlin A
    4. Gee S
    5. Sohal V
    6. Basbaum AI

    (2019) GABAergic cell transplants in the anterior cingulate cortex reduce neuropathic pain aversiveness

    Brain 142:2655–2669.

    https://doi.org/10.1093/brain/awz203

    • PubMed
    • Google Scholar
37. 1. Juavinett AL
    2. Bekheet G
    3. Churchland AK

    (2019) Chronically implanted neuropixels probes enable high-yield recordings in freely moving mice

    eLife 8:e47188.

    https://doi.org/10.7554/eLife.47188

    • PubMed
    • Google Scholar
38. 1. Kane GA
    2. Lopes G
    3. Saunders JL
    4. Mathis A
    5. Mathis MW

    (2020) Real-time, low-latency closed-loop feedback using markerless posture tracking

    eLife 9:e61909.

    https://doi.org/10.7554/eLife.61909

    • PubMed
    • Google Scholar
39. 1. Klein AS
    2. Dolensek N
    3. Weiand C
    4. Gogolla N

    (2021) Fear balance is maintained by bodily feedback to the insular cortex in mice

    Science 374:1010–1015.

    https://doi.org/10.1126/science.abj8817

    • PubMed
    • Google Scholar
40. 1. LaBuda CJ
    2. Fuchs PN

    (2000) A behavioral test paradigm to measure the aversive quality of inflammatory and neuropathic pain in rats

    Experimental Neurology 163:490–494.

    https://doi.org/10.1006/exnr.2000.7395

    • PubMed
    • Google Scholar
41. 1. Lai AT
    2. Espinosa G
    3. Wink GE
    4. Angeloni CF
    5. Dombeck DA
    6. MacIver MA

    (2024) A robot-rodent interaction arena with adjustable spatial complexity for ethologically relevant behavioral studies

    Cell Reports 43:113671.

    https://doi.org/10.1016/j.celrep.2023.113671

    • PubMed
    • Google Scholar
42. 1. La Vu M
    2. Tobias BC
    3. Schuette PJ
    4. Adhikari A

    (2020) To approach or avoid: an introductory overview of the study of anxiety using rodent assays

    Frontiers in Behavioral Neuroscience 14:145.

    https://doi.org/10.3389/fnbeh.2020.00145

    • PubMed
    • Google Scholar
43. 1. Madden VJ
    2. Catley MJ
    3. Grabherr L
    4. Mazzola F
    5. Shohag M
    6. Moseley GL

    (2016) The effect of repeated laser stimuli to ink-marked skin on skin temperature-recommendations for a safe experimental protocol in humans

    PeerJ 4:e1577.

    https://doi.org/10.7717/peerj.1577

    • PubMed
    • Google Scholar
44. 1. Madisen L
    2. Mao T
    3. Koch H
    4. Zhuo J
    5. Berenyi A
    6. Fujisawa S
    7. Hsu YWA
    8. Garcia AJ
    9. Gu X
    10. Zanella S
    11. Kidney J
    12. Gu H
    13. Mao Y
    14. Hooks BM
    15. Boyden ES
    16. Buzsáki G
    17. Ramirez JM
    18. Jones AR
    19. Svoboda K
    20. Han X
    21. Turner EE
    22. Zeng H

    (2012) A toolbox of Cre-dependent optogenetic transgenic mice for light-induced activation and silencing

    Nature Neuroscience 15:793–802.

    https://doi.org/10.1038/nn.3078

    • PubMed
    • Google Scholar
45. 1. Mathis A
    2. Mamidanna P
    3. Cury KM
    4. Abe T
    5. Murthy VN
    6. Mathis MW
    7. Bethge M

    (2018) DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

    Nature Neuroscience 21:1281–1289.

    https://doi.org/10.1038/s41593-018-0209-y

    • PubMed
    • Google Scholar
46. 1. Mickle AD
    2. Gereau RW

    (2018) A bright future? Optogenetics in the periphery for pain research and therapy

    Pain 159 Suppl 1:S65–S73.

    https://doi.org/10.1097/j.pain.0000000000001329

    • PubMed
    • Google Scholar
47. 1. Middleton SJ
    2. Perini I
    3. Themistocleous AC
    4. Weir GA
    5. McCann K
    6. Barry AM
    7. Marshall A
    8. Lee M
    9. Mayo LM
    10. Bohic M
    11. Baskozos G
    12. Morrison I
    13. Löken LS
    14. McIntyre S
    15. Nagi SS
    16. Staud R
    17. Sehlstedt I
    18. Johnson RD
    19. Wessberg J
    20. Wood JN
    21. Woods CG
    22. Moqrich A
    23. Olausson H
    24. Bennett DL

    (2022) Nav1.7 is required for normal C-low threshold mechanoreceptor function in humans and mice

    Brain 145:3637–3653.

    https://doi.org/10.1093/brain/awab482

    • PubMed
    • Google Scholar
48. 1. Mitchell K
    2. Bates BD
    3. Keller JM
    4. Lopez M
    5. Scholl L
    6. Navarro J
    7. Madian N
    8. Haspel G
    9. Nemenov MI
    10. Iadarola MJ

    (2010) Ablation of rat TRPV1-expressing Adelta/C-fibers with resiniferatoxin: analysis of withdrawal behaviors, recovery of function and molecular correlates

    Molecular Pain 6:94.

    https://doi.org/10.1186/1744-8069-6-94

    • PubMed
    • Google Scholar
49. 1. Moy JK
    2. Kuhn JL
    3. Szabo-Pardi TA
    4. Pradhan G
    5. Price TJ

    (2018) eIF4E phosphorylation regulates ongoing pain, independently of inflammation, and hyperalgesic priming in the mouse CFA model

    Neurobiology of Pain 4:45–50.

    https://doi.org/10.1016/j.ynpai.2018.03.001

    • PubMed
    • Google Scholar
50. 1. Nath T
    2. Mathis A
    3. Chen AC
    4. Patel A
    5. Bethge M
    6. Mathis MW

    (2019) Using DeepLabCut for 3D markerless pose estimation across species and behaviors

    Nature Protocols 14:2152–2176.

    https://doi.org/10.1038/s41596-019-0176-0

    • PubMed
    • Google Scholar
51. 1. Orefice LL
    2. Mosko JR
    3. Morency DT
    4. Wells MF
    5. Tasnim A
    6. Mozeika SM
    7. Ye M
    8. Chirila AM
    9. Emanuel AJ
    10. Rankin G
    11. Fame RM
    12. Lehtinen MK
    13. Feng G
    14. Ginty DD

    (2019) Targeting peripheral somatosensory neurons to improve tactile-related phenotypes in ASD models

    Cell 178:867–886.

    https://doi.org/10.1016/j.cell.2019.07.024

    • PubMed
    • Google Scholar
52. 1. Pai J
    2. Ogasawara T
    3. Bromberg-Martin ES
    4. Ogasawara K
    5. Gereau RW
    6. Monosov IE

    (2022) Laser stimulation of the skin for quantitative study of decision-making and motivation

    Cell Reports Methods 2:100296.

    https://doi.org/10.1016/j.crmeth.2022.100296

    • PubMed
    • Google Scholar
53. Software

    1. Parkes I
    2. Browne L
    3. browne-lab

    (2026) Closed-loop-somatosensory-stimulation, version 5506ebb

    GitHub.

    https://github.com/browne-lab/closed-loop-somatosensory-stimulation
54. 1. Pereira TD
    2. Tabris N
    3. Li J
    4. Ravindranath S
    5. Papadoyannis ES
    6. Wang ZY
    7. Turner DM
    8. McKenzie-Smith G
    9. Kocher SD
    10. Falkner AL
    11. Shaevitz JW
    12. Murthy M

    (2020) SLEAP: multi-animal pose tracking

    Nature Methods 19:486–495.

    https://doi.org/10.1038/s41592-022-01426-1

    • PubMed
    • Google Scholar
55. 1. Ranade SS
    2. Woo S-H
    3. Dubin AE
    4. Moshourab RA
    5. Wetzel C
    6. Petrus M
    7. Mathur J
    8. Bégay V
    9. Coste B
    10. Mainquist J
    11. Wilson AJ
    12. Francisco AG
    13. Reddy K
    14. Qiu Z
    15. Wood JN
    16. Lewin GR
    17. Patapoutian A

    (2014) Piezo2 is the major transducer of mechanical forces for touch sensation in mice

    Nature 516:121–125.

    https://doi.org/10.1038/nature13980

    • PubMed
    • Google Scholar
56. 1. Rosenberg M
    2. Zhang T
    3. Perona P
    4. Meister M

    (2021) Mice in a labyrinth show rapid learning, sudden insight, and efficient exploration

    eLife 10:e66175.

    https://doi.org/10.7554/eLife.66175

    • PubMed
    • Google Scholar
57. 1. Schorscher-Petcu A
    2. Takács F
    3. Browne LE

    (2021) Scanned optogenetic control of mammalian somatosensory input to map input-specific behavioral outputs

    eLife 10:e62026.

    https://doi.org/10.7554/eLife.62026

    • PubMed
    • Google Scholar
58. 1. Schwaller F
    2. Bégay V
    3. García-García G
    4. Taberner FJ
    5. Moshourab R
    6. McDonald B
    7. Docter T
    8. Kühnemund J
    9. Ojeda-Alonso J
    10. Paricio-Montesinos R
    11. Lechner SG
    12. Poulet JFA
    13. Millan JM
    14. Lewin GR

    (2021) USH2A is a Meissner’s corpuscle protein necessary for normal vibration sensing in mice and humans

    Nature Neuroscience 24:74–81.

    https://doi.org/10.1038/s41593-020-00751-y

    • PubMed
    • Google Scholar
59. 1. Uhelski ML
    2. Morris-Bobzean SA
    3. Dennis TS
    4. Perrotti LI
    5. Fuchs PN

    (2012) Evaluating underlying neuronal activity associated with escape/avoidance behavior in response to noxious stimulation in adult rats

    Brain Research 1433:56–61.

    https://doi.org/10.1016/j.brainres.2011.11.016

    • PubMed
    • Google Scholar
60. 1. Weinreb C
    2. Pearl JE
    3. Lin S
    4. Osman MAM
    5. Zhang L
    6. Annapragada S
    7. Conlin E
    8. Hoffmann R
    9. Makowska S
    10. Gillis WF
    11. Jay M
    12. Ye S
    13. Mathis A
    14. Mathis MW
    15. Pereira T
    16. Linderman SW
    17. Datta SR

    (2024) Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics

    Nature Methods 21:1329–1339.

    https://doi.org/10.1038/s41592-024-02318-2

    • PubMed
    • Google Scholar
61. 1. Wiltschko AB
    2. Tsukahara T
    3. Zeine A
    4. Anyoha R
    5. Gillis WF
    6. Markowitz JE
    7. Peterson RE
    8. Katon J
    9. Johnson MJ
    10. Datta SR

    (2020) Revealing the structure of pharmacobehavioral space through motion sequencing

    Nature Neuroscience 23:1433–1443.

    https://doi.org/10.1038/s41593-020-00706-3

    • PubMed
    • Google Scholar
62. 1. Zhang Z
    2. Roberson DP
    3. Kotoda M
    4. Boivin B
    5. Bohnslav JP
    6. González-Cano R
    7. Yarmolinsky DA
    8. Turnes BL
    9. Wimalasena NK
    10. Neufeld SQ
    11. Barrett LB
    12. Quintão NLM
    13. Fattori V
    14. Taub DG
    15. Wiltschko AB
    16. Andrews NA
    17. Harvey CD
    18. Datta SR
    19. Woolf CJ

    (2022) Automated preclinical detection of mechanical pain hypersensitivity and analgesia

    Pain 163:2326–2336.

    https://doi.org/10.1097/j.pain.0000000000002680

    • PubMed
    • Google Scholar
63. 1. Zong W
    2. Obenhaus HA
    3. Skytøen ER
    4. Eneqvist H
    5. de Jong NL
    6. Vale R
    7. Jorge MR
    8. Moser MB
    9. Moser EI

    (2022) Large-scale two-photon calcium imaging in freely moving mice

    Cell 185:1240–1256.

    https://doi.org/10.1016/j.cell.2022.02.017

    • PubMed
    • Google Scholar

Author details

1. Isobel Parkes

   Wolfson Institute for Biomedical Research, University College London, London, United Kingdom

   Contribution

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

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6569-618X

2. Ara Schorscher-Petcu

   Wolfson Institute for Biomedical Research, University College London, London, United Kingdom

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5808-5172

3. Qinyi Gan

   Wolfson Institute for Biomedical Research, University College London, London, United Kingdom

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0009-0006-3962-5501

4. Liam E Browne

   Wolfson Institute for Biomedical Research, University College London, London, United Kingdom

   Contribution

   Conceptualization, Resources, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   liam.browne@ucl.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5693-7703

• 788

  views

• 31

  downloads

• 0

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.