Neovascular age-related macular degeneration (nAMD) is a progressive retinal disease that often leads to severe and permanent vision loss. Early initiation of anti-vascular endothelial growth factor (anti-VEGF) therapy has been shown to preserve vision in nAMD patients. Concurrently, treatment outcomes in real-world are inferior to those reported in clinical trials. The most likely reasons observed are fewer treatment-intensity in routine clinical practice than in clinical trials. The other possibility could be the delay in starting treatment and the re-treatment interval. Although a negative impact of aforementioned parameters seems obvious, quantitative impact measures remain elusive in a real-world setting due to a lack of an ‘optimal treatment’ control group. To overcome this shortcoming, we developed, validated, and applied a model to assess and quantify the impact of anti-VEGF administration variables on visual acuity development in a prospective nAMD patient cohort. The model was further applied to probe the impact of the COVID-19 pandemic on visual progressions in nAMD patients. The presented model paves the way to systematically explore and evaluate realistic interventions in the current treatment paradigm, that can be adopted in routine clinical care. Current treatment schemes usually include 3 injections at monthly interval (q4w) followed by an individualized treatment regimen like Pro Re Nata (PRN), Treat and Extend (T&E), or Observe and Plan with regular optical coherence tomography (OCT) controls to detect early reactivation of the disease7. Anecdotally, some physicians do not start treatment with three loading injections but treat and extend immediately from start of treatment.Neovascular age-related macular degeneration (nAMD) is a progressive retinal disease that leads to severe and permanent visual impairment and legal blindness if not treated. Early initiation of anti-vascular endothelial growth factor (anti-VEGF) therapy has shown to preserve vision of patients with this disease10. However, the average number of intravitreal injections applied by PRN or T&E in real-world setting is consistently lower than in RCTs due to multifactorial reasons including the patient, physician, treatment center set up and logistical factors15. Concurrently, VA gains observed in real-world settings stay below those observed in RCT. While about eight injections where applied on average in the first year of PRN and T&E arms within RCTs10, means of 4–7 injections in the first year were observed in real-world studies13.Randomized controlled trials (RCT) showed that individualized PRN and T&E are not inferior to fixed treatment regimens but mitigate treatment burden for the patient16. During the delay, the disease progresses and may cause irreversible damage to the retina. There is increasing evidence that immediate treatment without delay after diagnosis improves and any delay of more than two to three weeks worsens treatment outcomes17. Beside the challenges we see in routine clinical practice, the COVID-19 pandemic exemplified the impact of may have aggravated the situation of undertreatment, since as investigations examinations or injections were omitted or delayed20.Furthermore, treatment start is commonly delayed after the first detection of disease signs. Reasons include a lack of awareness of the disease and its urgency by the patient or primary care giver and logistical constraints at the patient, care giver or physician/center23, but have not been adapted to real-world data from non-interventional studies (NIS) yet. As real-world data are very relevant for day-to-day clinical decisions, our study is needed to close this gap.To better understand impact of number of injections, treatment delay and other baseline characteristics on treatment outcomes, a model was developed to assess and quantify their impact on mean VA progression in a prospective patient cohort. Therefore, the dataset of a non-interventional trial cohort was used to inform such a general model structure and to validate it using a second dataset. Similar models were successfully used in RCT data24 (NCT02194803) was used for model training and scenario development. Datasets from the NIS PACIFIC (NCT04847895) and the RCT HARBOR9 (NCT00891735) were used as independent validation datasets. Table The present modelling study uses datasets from 3 studies: 2NIS and one RCT. The dataset from the NIS OCEAN24 NCT0294803 was25. All trials were conducted in accordance with principles of the Declaration of Helsinki, International Conference on Harmonization E6 Good Clinical Practice Consolidated Guideline, and other regulations as applicable and were compliant with the Health Insurance Portability and Accountability Act of 1996.All patients provided written informed consent before screening or initiation of any study-related procedures. The original OCEAN study protocol was approved by the ethics committee of the Eberhard-Karls-University, Tübingen, Germany. The original PACIFIC study protocol was approved by the ethics committee of the Bavarian State Board of Physicians (Bayerische Landesärztekammer), Germany. The original HARBOR study protocol was approved by independent institutional review boards at all 99 study sites prior to the start of the studyThe model is an indirect response pharmacokinetic (PK)/pharmacodynamic (PD) drug-disease model that simulates individual VA progression of patients with nAMD who are treated with ranibizumab. VA is simulated dependent on patient's baseline data  and the dose and timing of intravitreal ranibizumab injections Fig. . Focus o21 based on data of the RCTs MARINA, ANCHOR, and PIER. It was then extended and applied by Mulyukov et al.22 and Agostini et al23. In the present study, the model is extended and applied for the first time to real-world data. Model training was on the dataset from the NIS OCEAN, validation was on the independent dataset from the NIS PACIFIC and the RCT HARBOR. The present model structure was adopted from Mulyukov et al. and expanded further to a pre-treated patient population.The general model structure was first developed by Holz et al.i at time t, i.Natural progression of the disease without treatment is modelled by the differential equationi at time t, The effect of intravitreal anti-VEGF injections is modelled as effect on The parameterized model was used to predict the observed mean VA progression from baseline in the independent validation datasets of the NIS PACIFIC and the RCT HARBOR, based on observed baseline parameters  and timing of ranibizumab injections. For PACIFIC, individual scale data were used. Each study patient was individually simulated based on its respective patient individual, baseline and injection data until the time of drop out or completion of the study. Patients with missing baseline data were not considered. For HARBOR, age and baseline VA were drawn from normal distributions with mean and standard deviation from the demographics table of the study  No treatment .The real-world injection pattern of the OCEAN study was first compared to two extreme scenarios: (i) A 3l-q8w pattern. Start of the treatment without delay, with 3 loading injections in q4w, followed by maintenance injections with q8w. The first-year injection frequency in this pattern corresponds to the mean injection frequency observed in PRN arms in RCTConsequences of delayed treatment start were explored in a scenario where the delay was reduced to zero for all patients and a scenario where the delay was bound at a maximum of 2 weeks. In both scenarios, the number and timing of injections after the first injection was unchanged compared to the OCEAN study .The effect of an initial loading period with 3 injections q4w was explored in 2 scenarios where treatment delay and the total number of injections per patient until Month 12 were kept as in the OCEAN study, but the timing of injections was modified in 2 ways: (i) scenario with loading period ; (ii) scenario without loading period . In an additional scenario, the real-world treatment pattern was substituted by a 3l-q8w pattern (3 loading injections q4w followed by maintenance injections q8w) as reference of a pattern without undertreatment.27. The baseline characteristics age and baseline VA were randomly drawn from the OCEAN study : adjusted R2 = 0.91; PACIFIC : adjusted R2 = 0.81; HARBOR : adjusted R2 = 0.94.The model accurately predicted mean VA gains in the OCEAN study that was used for training and in the independent validation datasets of the NIS PACIFIC and the RCT HARBOR Fig. . In the 2 was 0.90 and 0.87, respectively. Smaller R2 in these subgroups than in the total population was also due to the smaller sample size.Goodness of fit was also high for patient subgroups with specific injection patterns. In subgroups of the OCEAN study with small  and large  delay between baseline and treatment start, adjusted RKnown effects like the effect of age and the ceiling effect of baseline BCVA on BCVA gain were predicted by the model , whereas 23,000 patients missed one injection with this being in the first month after diagnosis (orange bars). Mean BCVA losses related to those missed injections were 1.2 and 4.3 letters, respectively.About 9000 patients missed two injections with no being missed in the first month after diagnosis (yellow bars), whereas 6000 patients missed two injections with one being in the first month after diagnosis (purple bars). Mean BCVA losses related to those missed injections were 2.5 and 5.7, respectively.Many studies have shown that early treatment initiation, and frequent VEGF inhibitor injections during the maintenance phase results in better outcomes in patients with nAMD. We could confirm these observations using a mathematical prediction model based on real-world data. In addition, the model enriched those observation by a quantification, which is challenging to measure in a clean way, when considering a real world setting by nature and, hence, to date remained scarce.natural course of disease’ where the population was not-treated and a theoretical optimum scenario where treatment was simulated at an intensity observed under PRN schemes in clinical trials. Compared to the simulated optimum, mean VA in the real-world cohort increased less steeply during the first months of treatment and declined slowly after an initial peak.The model analysis is based on real-world data of the NIS OCEAN, conducted between 2011 and 2016 in Germany. Mean VA gain in this real-world cohort laid in-between a ‘An early start of treatment appeared to be decisive for the magnitude of the first improvement in VA. Initial mean VA gain was significantly larger in a scenario where treatment started without delay and a scenario where the delay was bound to a reasonable maximum of 2 weeks than in the real-world cohort. The difference between these scenarios did not diminish after the initial peak VA. Overall, 2 weeks delay until treatment start in the real-world cohort caused on average 2.5 letters loss compared to immediate treatment start. Four weeks delay or more caused on average 5 letters loss or more. The latter corresponds to a line and may be decisive for the ability of the patient to read.29. Also current guidelines recommend an interval of max. 14 days between first contact with a doctor due to symptoms and first injection30. Our simulation results confirm that these recent improvements saved vision for patients who would have waited longer in the past.Mean delay to treatment start was 20.3 days in the real-world cohort used in this study. For 29.1% of patients, the delay was longer than 4 weeks. Since the time of this study, awareness for the need to start treatment as early as possible has increased. In a newer real-world study conducted from 2015 to 2021, mean delay was only 8.5 days17. However, the analyses underline how important it can be for both studies and the clinical routine treatment to distinguish between the points in time  on the one hand, and to avoid any possible cause of delay on the other. Physiologically, the disease progresses during the delay, causing non-reversible damage to the retina and loss of vision. This may affect the potential for VA improvement in 2 ways: First, vision that was lost before treatment start may not be regained due to irreversible damage. Second, the improvement rate after onset of treatment may be slower as more VA is lost due to irreversible than due to reversible damage. In most previous correlation studies, only the second mechanism is captured, since the baseline for VA change was defined as treatment start, i.e., after the delay17. In the present study, only the first mechanism is considered, as baseline for VA change was defined before the delay but the improvement rate after treatment start was not varied in the model. Taking these results together, both mechanisms may be in place and the effect of delayed treatment start may even be larger than estimated in the present study.A correlation between delay to treatment start and VA outcome was established in several observational studies14. Many studies have shown that the frequency of injections and gained vision in real-world settings stayed below the potential that was demonstrated in clinical trials13. Our simulation results confirm this observation of relative undertreatment. In addition, they point to a new aspect: Accepting that the optimum of a complete loading period and sufficient frequency of maintenance injections may not be reached in real-world settings, the frequency of maintenance injections appears to be equally or even more important to preserve vision as a complete loading period. This was shown by a scenario with loading period and equally spaced injections, thereafter, compared to a scenario with equally spaced injections during the entire observation period (without loading period). The overall number of injections was equal in both scenarios; consequently, the injection frequency after the first 3 months was higher in the scenario without loading period. Simulations showed that after about 10 months of treatment, the scenario with higher injection frequency outweighed the benefit of a loading period.After initial improvement in VA, regular monitoring and an adequately high frequency of injections are crucial to maintain or further improve the gained visionMore generally, the mathematical structure of the model implies that omitting or delaying an injection is more harmful when disease activity is high, and VA is low. This occurs before treatment start and in the situation of undertreatment during the maintenance phase. During the loading period, when disease activity could already be slowed down, delaying the treatment interval may be less detrimental.Lastly, the model was successfully applied to estimate the impact of missed injections due to COVID-19 pandemic. The overall impact, averaged over the studied population, was predicted low with a mean loss of 0.8 letters. While the majority of patients in the simulated population was not affected, some patients lost 5 letters or more. Apparently, the impact of missed or delayed injections varies largely between individuals, depending on when the delay occurred (at which degree of disease activity), and how long and how often injections were omitted or delayed.The model was developed to simulate mean VA outcome in a sufficiently large population under explicit treatment patterns. Accordingly, the model is not capable to predict individual patient outcomes. Reason for this limitation is that the individual variation between patients is captured only by the baseline parameters age, baseline VA and pre-treatment status and by the pattern of individual injections. Therefore, no conclusion can be made about individual patients who do not respond to the modelled treatment patterns as expected for the population mean.Simulation results beyond a medium timescale (> ~ 18 months) are subject to large deviations. Reason is that long-term data are scarce compared to early data and model validity decreases with time. For this reason, only data until 18 months after start of treatment are considered in all scenarios. Long-term data of about 5 years would be needed to generate a robust model that is capable of long-term prediction.The model is based on NIS data where documentation quality and observation artifacts may degrade the informative power of the data. In particular drop-outs may cause a bias due to potential elimination of worst disease courses.The model was parametrized using solely data from patients, treated with ranibizumab. Thus, only ranibizumab treatment effects can be simulated with confidence using this model.The validated VA prediction model is a valuable tool to predict population level VA response to individual treatment patterns in patients with nAMD treated with anti-VEGF. It provides the opportunity to systematically explore specified treatment patterns in real-world settings where there is usually no control group. Model scenarios may be of systematic nature or depict realistic situations such as the COVID-19 pandemic scenario. The good validation performance of the model provides confidence in simulation results when the model is applied within its purpose and limits. We hope that the model may become a widely used tool to improve treatment patterns for nAMD with reasonable effort.Supplementary Information.


Previous research has successfully used feature integration theory to operationalise the predictions of Perceptual Load Theory, while simultaneously testing the predictions of both models. Building on this work, we test the extent to which these models hold up in a 3D world. In two experiments, participants responded to a target stimulus within an array of shapes whose apparent depth was manipulated using a combination of monoscopic and stereoscopic cues. The search task was designed to test the predictions of (a) feature integration theory, as the target was identified by a single feature or a conjunction of features and embedded in search arrays of varying size, and (b) perceptual load theory, as the task included congruent and incongruent distractors presented alongside search tasks imposing high or low perceptual load. Findings from both experiments upheld the predictions of feature integration theory, regardless of 2D/3D condition. Longer search times in conditions with a combination of monoscopic and stereoscopic depth cues suggests that binding features into three-dimensional objects requires greater attentional effort. This additional effort should have implications for perceptual load theory, yet our findings did not uphold its predictions; the effect of incongruent distractors did not differ between conjunction search trials  and feature search trials . Individual differences in susceptibility to the effects of perceptual load were evident and likely explain the absence of load effects. Overall, our findings suggest that feature integration theory may be useful for predicting attentional performance in a 3D world. In order to be useful, cognitive theories must be simplified models of a complex world, from which we can derive specific predictions. In order to be meaningful, these models must also be scalable and applicable to real world situations. Models of visual search are principally based around perception of two-dimensional (2D) stimuli such as shapes on a computer screen, but are used to make inferences about our interaction with the three-dimensional (3D) world around us. When attempting to test the ecological validity of such models, researchers typically add complexity in a controlled manner, one variable at a time. In this paper we attempt to scale two models of visual search – Treisman’s feature integration theory and Lavie’s perceptual load theory– by incorporating monoscopic and stereoscopic cues to depth into an existing paradigm in which both models can be tested simultaneously. Both feature integration theory and perceptual load theory are simplified models of the type described above, from which useful predictions about the world have been generated. By the simple addition of a third dimension we literally add depth to the models, and test their validity in a three-dimensional world.Feature integration theory  is a modThe premise of perceptual load theory  is that Although perceptual load theory has been influential over the past twenty years, the concept of perceptual load has been criticised for its vagueness and lack of a clear operational definition . Murphy The lateral separation of human eyes means that each has a slightly different view of the world and the disparity between these views occurs along a single horizontal dimension. This binocular disparity is greater for objects that are closer to us than those that are farther away. Using this rule, the visual system can map objects along three dimensions of the visual field; a process referred to as stereoscopic vision or stereopsis . In an eStereoscopic vision (requiring two eyes) is not the only way the visual system derives spatial and depth information from a visual scene. Monocular signals  are common and powerful cues to depth , allowinAnother approach exploring depth in feature integration theory has investigated the way in which apparent distance  acts as a visual feature , 2017. FImportantly, previous research has not yet compared the effects of viewing the same stimuli in monoscopic 2D and stereoscopic 3D on visual attention, or separated those effects from the role of monocular cues to depth such as colour and shading. If stereopsis and other binocular cues to depth  improve our ability to act upon the world more precisely, they may benefit performance in visually demanding experimental trials. On the other hand, if additional visual features  are required to produce a 3D percept, it follows that attention must be deployed to bind these features together; this would result in slowed visual search for stimuli containing these features.In the present study we explore whether monocular and binocular cues to depth make unique demands of the visual system that limit the ecological validity of feature integration theory and perceptual load theory. We report two experiments, in which we replicated the paradigm used by M = 24.41 years, SD = 6.79 years). All had normal or corrected-to-normal vision and all were screened for colour-blindness and for stereoscopic depth perception.Forty-two volunteers  were recruited from the University College Dublin campus to participate in the experiment. Participants were aged between 18 and 52 years (The experiment was programmed and run using E-Prime 2.0 (Psychology Software Tools Inc.). A 47-inch LG Smart Cinema 3D television screen (resolution = 1920 × 1080) was used to present the stimuli in 2D or passive polarised stereoscopic 3D. For the stereoscopic 3D condition, the depth control was set to two points above the TV default, which creates a baseline crossed disparity of 12 pixels (∼0.62% of the screen width). All 3D stimuli appeared on a single depth plane with a sense of 3D space around them. To control for screen luminance effects, participants in both the 2D and 3D groups wore passive-polarised 3D glasses designed for use with this television.The experimental design followed The experiment involved the search for a target stimulus within an array, while ignoring a congruent or incongruent distractor. In the 2D condition, participants were instructed to search for the target (red triangle) in an array of yellow and red shapes presented to the left of a central fixation point (a white cross), while ignoring a distractor to the right of the fixation point. Participants responded by indicating whether the target triangle was pointing up or down (using the arrows on a keyboard). In low load, feature search trials, the non-target stimuli in the search array were all yellow triangles, and the target was thus distinguished by a single feature (colour). In high load, conjunction search trials, the non-target stimuli were red squares, yellow squares and yellow triangles; a conjunction of colour (red) and shape (triangle) was thus required to distinguish the target. All shapes were created in Microsoft Office 365 ProPlus. The red shapes had an RGB value of  and the yellow shapes had an RGB value of .To supplement the binocular stereoscopic effect in the 3D condition, the above described stimuli were adapted for the 3D condition to include apparent (monocular) depth cues; this was achieved by replacing the standard two-dimensional stimuli (squares and triangles) with two-dimensional representations of three dimensional objects (cubes and cones). Shading effects were used to create the illusion of depth, as shown in The target red triangle/cone could appear in an array of 2, 4, 6, 8, or 10 shapes and the orientation of the distractor stimulus could be congruent  or incongruent with the target . See Participants were seated 140 cm from the television screen at a desk equipped with a keyboard. After providing written consent, participants were instructed to don the 3D glasses and the task was explained to them. All lighting in the laboratory was switched off during testing. Participants first completed 30 practice trials and were given the opportunity to clarify the requirements of the task. This also allowed the experimenter to confirm that the 3D effect was perceived by participants in the 3D condition. Participants then completed three blocks of 100 trials, comprising 15 repetitions of each combination of search type, congruency and array size. Trials were presented randomly within blocks, and participants were given the opportunity to take a break following completion of each block. Participants were instructed to maintain fixation on the central white cross, and to respond to the orientation of the target stimulus as quickly and accurately as possible. The stimuli in each trial remained onscreen until a response was recorded. No performance feedback was provided.The study protocol was approved by the Human Research Ethics Committee (Humanities) of University College Dublin.https://osf.io/ep5gv/. Data from one individual in the 3D condition were removed from analysis as their accuracy across all conditions was below chance (mean = 32%). Performance in the search task was generally very accurate, with mean accuracy among the remaining 41 participants of 96.5% (SD = 3.6%).All data are available in the Open Science Framework repository, at 1.Median reaction time (RT) for each participant was extracted for correct search trials within each experimental condition. Median reaction time data are often preferred for such analyses as they are less susceptible than mean data to the effects of outliers . A 2  than low load/feature searches . A main effect of array size was also observed; planned contrasts showed a significant linear trend, indicating that RT increased in line with the number of distractors in the array . These main effects were qualified by a significant interaction, depicted in R2 = 0.26, F  = 142.5, p < 0.001] and the feature search task , however, the conjunction search task had a slope of 20.85 ms/item, which was significantly steeper than the feature search slope of 6.75 ms/item, t(816) = 6.37, p < 0.001. Each additional distractor in the display therefore increased reaction time by a greater magnitude in the conjunction search than the feature search.A main effect of search type was observed such that responses were slower in high load/conjunction searches  and 3D conditions . There was also no main effect of distractor congruency  and no interaction between congruency and search type/perceptual load, as would be predicted by perceptual load theory. No other significant interaction effects were detected.There was no significant difference in mean reaction time between participants in the 2D  and stimulus shading (flat vs. shaded shapes).Finally, Experiment 1 employed a relatively small sample size and was thus somewhat underpowered. This issue is also remedied in Experiment 2.M = 22.85, SD = 6.72) were recruited from the University College Dublin campus. All participants had normal or corrected-to-normal vision, none were colour-blind, and all were screened for stereoscopic depth perception to ensure that participants allocated to the 3D condition had stereoscopic vision. Power analysis was conducted in G∗Power 3.1  in the within/between-subjects interaction terms; this power analysis assumed an average correlation of 0.9 among repeated measures, based on data reported in One hundred and thirty participants  factorial design with three within-subjects variables  and two between-subjects variables (stereoscopic depth: 2D/3D and stimulus shading: flat/shaded). Participants were randomly allocated to view the task in monoscopic 2D or stereoscopic 3D, and to view either flat or shaded shapes.In the task for Experiment 2, the distractor stimulus was presented in the centre of the screen surrounded by a white box, with the target appearing among an array of shapes presented in a ring around the distractor see . This adAll other features of the design, task and procedure were identical to those used in Experiment 1.https://osf.io/ep5gv/. The data from one participant were removed from analysis due a technical error during data recording. Mean search accuracy for the remaining 129 participants was 97.3% (SD = 2.6%). Median RT for each participant was extracted for correct search trials within each experimental condition. Overall median RT was significantly longer in Experiment 2  than Experiment 1 , suggesting that the central placement of the distractor increased the difficulty of the task.All data are available in the Open Science Framework repository, at 2. A main effect of search type was observed on median reaction time, such that search was slower in high load/conjunction search trials  than in low load/feature search trials . A main effect of array size was also observed, such that RT increased with each additional item in the array . Critically, there was also a significant interaction between search type and array size. Linear regression analysis showed that RT increased with array size for conjunction trials  while the slope stayed relatively flat for feature search trials ; the slope of the line was significantly steeper in the conjunction search condition, t (1296) = 8.54, p < 0.001. Thus, the predictions of feature integration theory were upheld.A 2 (search type) × 2 (distractor congruency) × 5 (array size) × 2 (stereoscopic depth) × 2 (stimulus shading) ANOVA was performed. The full results of this analysis may be seen in 4M = 744.47 ms, SE = 9.33) than congruent trials , indicating that the distractor stimuli were perceived and interfered with search performance. However, no significant interaction between load and congruency was observed, contrary to the predictions of perceptual load theory. Post hoc tests indicated a significant difference in RT between congruent and incongruent trials under both low load and high load conditions, whereas perceptual load theory would predict that this effect should be eliminated or greatly reduced under high load  than flat shapes . These effects are qualified by an interaction between these two display variables. Post hoc Tukey tests revealed that RT in response to the 3D shaded condition  was significantly higher than RT in the 3D flat , 2D flat  or 2D shaded conditions ; none of the other pairwise comparisons showed significant differences.There was no main effect of stereoscopic depth (2D vs. 3D), however, a main effect of stimulus shading was observed, such that longer RTs were observed in response to shaded shapes  depth cues relative to simply presenting the stimuli in 2D ; they alSimilarly, the absence of a difference between the 2D flat and 3D flat conditions rules out an explanation in terms of stereopsis alone, or any methodological artefact arising from the use of the 3D TV screen. Computing depth from binocular disparity and creating a coherent percept from two distinct channels of visual input is an effortful and time-consuming process . One marThe predictions of perceptual load theory were not upheld in this study: in contrast with the experiments reported by The data presented in this paper do, however, contribute to the validity of feature integration theory as a model for evaluating visual search in a three-dimensional world. The predictions of feature integration theory were upheld by findings from both experiments in the current study. Although this was the case regardless of 2D/3D condition, longer search times were observed in conditions with a combination of monoscopic and stereoscopic depth cues. This suggests that binding features into three-dimensional objects requires greater attentional effort. In the interests of ecological validity, we call for this line of work to be extended beyond the visual domain and to consider the integration of other sensory information as a part of a multimodal process.https://osf.io/ep5gv/.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: Open Science Framework, The studies involving human participants were reviewed and approved by the University College Dublin Human Research Ethics Committee (Humanities). The patients/participants provided their written informed consent to participate in this study.CG and BR designed the study, with the assistance of JB and AH. CG programmed the experimental paradigms and conducted the statistical analysis. JB, AH, SeK, SH, and StK collected the data and assisted with analysis. CG wrote the manuscript with BR. All authors approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Somatosensory stimuli guide and shape behavior, from immediate protective reflexes to longer-term learning and higher-order processes related to pain and touch. However, somatosensory inputs are challenging to control in awake mammals due to the diversity and nature of contact stimuli. Application of cutaneous stimuli is currently limited to relatively imprecise methods as well as subjective behavioral measures. The strategy we present here overcomes these difficulties, achieving ‘remote touch’ with spatiotemporally precise and dynamic optogenetic stimulation by projecting light to a small defined area of skin. We mapped behavioral responses in freely behaving mice with specific nociceptor and low-threshold mechanoreceptor inputs. In nociceptors, sparse recruitment of single-action potentials shapes rapid protective pain-related behaviors, including coordinated head orientation and body repositioning that depend on the initial body pose. In contrast, activation of low-threshold mechanoreceptors elicited slow-onset behaviors and more subtle whole-body behaviors. The strategy can be used to define specific behavioral repertoires, examine the timing and nature of reflexes, and dissect sensory, motor, cognitive, and motivational processes guiding behavior. To safely navigate their world, animals need to be able to tell apart a gentle touch from an eye-watering pinch, detect cold water or sense the throbbing pain stemming from an infected cut. These ‘somatic’ sensations are relayed through thousands of nerve endings embedded in the skin and other tissues. Yet the neurological mechanisms that underpin these abilities are complex and still poorly understood.Indeed, these nerve endings can be stimulated by extreme temperatures, harmful chemicals, friction or even internal signals such as inflammation. One event can also recruit many different types of endings: a cut for example, will involve responses to mechanical pressure, tissue damage and local immune response. To disentangle these different actors and how they affect behavior, scientists need to develop approaches that allow them to deliver specific stimuli with increased precision, and to monitor the impact on an animal.To achieve this goal, Schorscher-Petcu et al. used mice in which blue light could trigger specific types of nerve endings. For instance, depending on the genetic background of the animals, a laser could either activate nerve endings involved in pain or gentle touch. Crucially, this could be done from a distance by beaming light with exquisite precision onto the paws of the mice without physically touching or disturbing the animals.How the mice responded could then be observed without any interference. Their behavior was analyzed using a combination of high-speed videos, computer-driven recording systems, and machine learning. This revealed subtle changes in behavior that had not been detected before, spotting microscopic movements of the stimulated paw and mapping simultaneous whole-body movements such as changes in posture or head orientation. The approach therefore allows scientists to assess the impact of touch, pain or temperature sensation in freely behaving mice. It could also be harnessed to develop much needed treatments against chronic pain. The survival of an organism depends on its ability to detect and respond appropriately to its environment. Afferent neurons innervating the skin provide sensory information to guide and refine behavior . CutaneoCompared to visual, olfactory, and auditory stimuli, somatosensory inputs are challenging to deliver in awake unrestrained mammals. This is due to the nature of stimuli that require contact and the diversity of stimulus features encoded by afferents that innervate skin. Cutaneous afferent neurons are functionally and genetically heterogeneous, displaying differential tuning, spike thresholds, adaptation rates, and conduction velocities . The arbThe behaviors that are evoked by cutaneous stimuli are also typically measured with limited and often subjective means. Manual scoring introduces unnecessary experimenter bias and omits key features of behavior. Behavioral assays have traditionally focused on a snapshot of the stimulated body part rather than dynamics of behavior involving the body as a whole . Recent Here we developed an approach to project precise optogenetic stimuli onto the skin of freely behaving mice . The str2) arena; (4) stimuli can be controlled with a computer interface from outside the behavior room; (5) stimulation patterns, lines, and points are generated by rapidly scanning the stimuli between predefined locations; (6) stimulation size can be controlled down to ≥150 μm diameter; (7) stimuli are temporally precise to control individual action potentials using sub-millisecond time-locked pulses; and (8) behavioral responses are recorded at high speed at the stimulated site and across the whole body simultaneously. An optical system was assembled to meet these specific criteria  that somatosensory stimuli are delivered non-invasively without touching or approaching the mice; (2) localization of stimuli are spatially precise and accurate (<10 μm); (3) freely moving mice can be targeted anywhere within a relatively large (400 cmcriteria .2 (150 μm beam diameter) at the surface of this floor. This was defocused to provide a range of calibrated stimulation spot sizes up to 2.307 mm2, with separable increments that were stable over long periods of time ; the path was descanned through the galvanometers so that the alignment between the laser and camera is fixed . The camTrpv1-IRES-Cre (TRPV1Cre) and R26-CAG-LSL-ChR2-tdTomato mice to obtain a line (TRPV1Cre::ChR2) in which ChR2 is selectively expressed in a broad class of nociceptors innervating glabrous skin . Once a hind limb motor response was initiated, it developed rapidly, lifting from the glass with rise times that show the vigor of the motor response was also dependent on nociceptive input size . This indicates that global behaviors are also proportional to the relative size of the nociceptive input; the recruited nociceptors firing a single-action potential . The high-speed video recordings of stimulation trials were analyzed using this network. Specific nociceptive input at the hind paw  causes behavior that initiates simultaneously across the body. Inspection of the movements of each body part relative to the baseline pose . Examining the relative distance between the nose and stimulated hind paw shows a reliably short latency (Examining specific features of these behaviors over a slightly longer period (300 ms) provides further insights. Displacement of each body part relative to their baseline position reveals the response timing, extent, and coordination . The sti latency , indicat latency . This co latency  and prin latency , we find latency . The pre latency  and comp latency . We haveSlc17a7-IRES2-Cre-D (Vglut1Cre) mice with R26-CAG-LSL-ChR2-tdTomato mice to generate a line (Vglut1Cre::ChR2) that express ChR2 in LTMRs  precisely delivered to the hind paw of these mice rarely elicited hind paw responses , with the earliest response occurring at 206 ms after stimulation (Cre::ChR2 mice (fastest response: 19 ms). Trains of five pulses, however, frequently elicited responses, showing mean paw withdrawal probabilities of 0.31 ± 0.09  for 5 Hz and 0.40 ± 0.10  for 10 Hz trains (The vesicular glutamate transporter-1 (Vglut1) is a known marker of Aβ-LTMRs . To demoin LTMRs . A recenAβ-LTMRs . Here, wmulation , which iz trains . Increasz trains . While tz trains , left, iz trains . We findz trains , right. z trains .Cre::ChR2 line, whole-body behaviors in response to optogenetic stimulation of Vglut1Cre::ChR2 mice were subtle: visual inspection of high-speed whole-body behavior videos revealed that responses were mostly limited to small hind paw lifts or shifts towards the center of the body in cases where the stimulated paw was initially further away from the body. In most instances, these movements did not disturb balance or alter the animal’s posture. Interestingly, we observed that whisking and, to a lesser extent, circular movements of the upheld forepaws would precede hind paw responses and initiate as early as the first pulse, even in trials that would not proceed to withdrawal. We speculate that mice may perceive the stimulation early on, but only act on this after a delay.The magnitude of whole-body motion was not altered by increasing frequencies . In contWe describe a strategy for remote, precise, dynamic somatosensory input and behavioral mapping in awake unrestrained mice. The approach can remotely deliver spatiotemporally accurate optogenetic stimuli to the skin with predefined size, geometry, duration, timing, and location, while simultaneously monitoring behavior in the millisecond timescale. Microscale optogenetic stimulation can be used to simulate patterns, edges, and moving points on the skin. Responses to these precisely defined points and patterns can be mapped using machine vision approaches. The design is modular, for example, additional lasers for multicolor optogenetic control or naturalistic infrared stimuli can be added and complementary machine vision analysis approaches readily implemented. As an example, we combine this with DeepLabCut , for marWe validated the system in two transgenic mouse lines, providing optical control of broad-class Aδ and C-nociceptors, and Aβ-LTMRs. Advances in transcriptional profiling have identified a vast array of genetically defined primary afferent neuron populations involved in specific aspects of temperature, mechanical, and itch sensation . SelectiWe find that the probabilistic recruitment of nociceptors determines the behavioral response probability, latency, and magnitude. We propose that the aggregate number of first action potentials arriving from nociceptors to the spinal cord can be utilized to optimize the timing and extent of rapid protective responses. These first action potentials could be summated by spinal neurons so that appropriate behaviors are selected based on thresholds. Resultant fast behaviors are diverse but include coordinated head orientation and body repositioning that depends on the initial pose. In contrast, responses to optogenetic activation of Aβ-LTMRs occurred with slower onset, lower probability, and resulted in more subtle whole-body movements. Using a fixed number of pulses, we find that responses from multiple Aβ-LTMR inputs can be explained by the sum rule of probabilities rather than frequency-dependence . This doIn summary, we have developed a strategy to precisely control afferents in the skin without touching or approaching them by projecting light to optogenetically generate somatosensory input in patterns, lines, or points. This is carried out non-invasively in awake freely behaving mice in a way that is remote yet precise. Remote control of temporally and spatially precise input addresses the many limitations of manually applied contact stimuli. The timing, extent, directionality, and coordination of resultant millisecond-timescale behavioral responses can be investigated computationally with specific sensory inputs. This provides a way to map behavioral responses, circuits, and cells recruited by defined afferent inputs and dissect the neural basis of processes associated with pain and touch. This strategy thus enables the investigation of sensorimotor, perceptual, cognitive, and motivational processes that guide and shape behavior in health and disease.2 to 2.307 mm2, by translating this lens along the beam path at set points to defocus the laser spot at the 200 mm × 200 mm stimulation plane. The beam size can be altered manually using this rotating lens tube per design, but this is modular and could be altered by the user. To ensure a relatively flat field in the stimulation plane, the galvanometer housing aperture was placed at a distance of 400 mm from its center. In this configuration, the corners of the stimulation plane were at a distance of 424 mm from the galvanometer housing aperture and variability of the focal length was below 1.5%.Optical elements, optomechanical components, mirror galvanometers, the diode laser, LEDs, controllers, machine vision cameras, and structural parts for the optical platform are listed in the table in Optical power density was kept constant by altering the laser power according to the laser spot area. Neutral density (ND) filters were used so that the power at the laser aperture was above a minimum working value (≥8 mW) and to minimize potential changes in the beam profile at the stimulation plane. The laser and mirror galvanometers were controlled through a multifunction DAQ  using custom software written in LabVIEW. The software displays the NIR-FTIR camera feed, whose path through the mirror galvanometers is shared with the laser beam, so that they are always in alignment with one another. Computationally adjusting mirror galvanometer angles causes identical shifts in both the descanned NIR-FTIR image field of view and intended laser stimulation site, so that the laser can be targeted to user-identified locations. Shaped stimulation patterns were achieved by programmatically scaling the mirror galvanometer angles to the glass stimulation plane using a calibration grid array . The timings of laser pulse trains were synchronized with the mirror galvanometers to computationally implement predefined shapes and lines using small angle steps that could be as short as 300 µs. The custom software also synchronized image acquisition from the two cameras, so that time-locked high-speed local paw responses were recorded . Time-locked global whole-body responses were recorded above video-frame rate  or at high speed  across the entire stimulation platform.2 diameters and ellipticities for each laser spot size over three replicates at all nine coordinates. The averages of replicates were used to calculate the area of the eight different laser spot sizes that were measured in each of the nine coordinates  to nine defined coordinates at different locations covering the stimulation plane. The laser power was set to a value that approximates powers used in behavioral experiments (40 mW). The laser power was then attenuated with an ND filter to match the operating range of the beam profiler. Using Thorlabs Beam Software, Gaussian fits were used to determine x-axis and y-axis 1/erdinates  and thenrdinates  B.1 = 0.0185 mm2, S2 = 0.0416 mm2, S3 = 0.0898 mm2, S4 = 0.176 mm2, S5 = 0.308 mm2, S6 = 0.577 mm2, S7 = 1.155 mm2, S8 = 2.307 mm2. These measurements were repeated 6 months after extensive use of the optical system to ensure stability over time -tdTomato, Ai27D; Trpv1 gene in one allele -tdTomato at the Rosa26 locus (RRID:2 regardless of spot size. The software was then employed to trigger a laser pulse of defined duration (between 100 μs and 30 ms) and simultaneously acquire high-speed  NIR-FTIR recordings of the stimulated paw, as well as a global view of the mice with a second camera (400 frames/s or 40 frames/s)  and duration (3 ms). In addition to the single-pulse stimulation, these mice received a train of five pulses applied at 5, 10, or 20 Hz. The recording time for each trial was extended to 2000 ms to accommodate for the longer stimulation period. For each protocol, Vglut1Cre::ChR2 mice were stimulated in 10 trials, split equally between the two hind paws. Data was collected from 12 Vglut1Cre::ChR2 mice and 8 littermate controls lacking Cre recombinase split into five cohorts. In all experiments, the behavioral withdrawal of the stimulated hind paw was also manually recorded by the experimenter.Prior to the first experimental day, mice underwent two habituation sessions during which each mouse was individually placed in a plexiglass chamber  on a mesh wire floor for 1 hr, then on a glass platform for another hour. On the experimental day, mice were again placed on the mesh floor for 1 hr, then up to six mice were transferred to six enclosures  positioned on the 200 mm × 200 mm glass stimulation platform. Mice were allowed to settle down and care was taken to stimulate mice that were calm, still, and awake in an ‘idle’ state. The laser was remotely targeted to the hind paw glabrous skin using the descanned NIR-FTIR image feed. The laser spot size was manually set using the calibrated L1 housing, while laser power and neutral density filters were used to achieve a power density of 40 mW/mmrames/s) . RecordiCre::ChR2 mice were stimulated on the heel of the hind paw with each of the following protocols: (1) a single 1 ms pulse with spot size S7 (1.155 mm2); (2) a single 1 ms pulse with spot size S4 (0.176 mm2); (3) seven 1 ms pulses with spot size S4, superimposed on the same stimulation site and spaced by 500 μs intervals; (4) seven 1 ms pulses with spot size S4, spaced by 500 μs intervals and spatially displacing stimuli with 0.3791 mm jumps such as to draw a small hexagon; (5) seven 1 ms pulses with spot size S4, spaced by 500 μs intervals and spatially displacing stimuli with 0.5687 mm jumps such as to draw a hexagon expanded by 50% compared to the previous shape; and (6) seven 1 ms pulses with spot size S4, spaced by 500 μs intervals and spatially displacing stimuli with 0.3791 mm jumps such as to draw a straight line. The power density of the stimulations was kept constant at 40 mW/mm2 as before. Seven mice, split into two cohorts, received 10 stimulations per protocol (five on each hind paw) after a baseline epoch of 500 ms. An additional cohort of four littermates lacking Cre recombinase were stimulated in the same way and served as negative controls. Finally, three TRPV1Cre::ChR2 mice were stimulated  with a single pulse adjacent to the hind paw, five times on each side, in order to control for potential off-target effects. The NIR-FTIR signal was recorded at 500 frames/s.TRPV1Cre::ChR2 mice received between 10 and 20 single-shot laser pulse stimulations of 10 ms each, at least 1 min apart and equally split between right and left hind paw and using spot size S8 (2.31 mm2). The first 10 trials that exceeded quality control were used . Each trial consisted of a 500 ms baseline and 4000 ms after-stimulus recording epoch.To obtain recordings optimized for markerless tracking with DeepLabCut, a single acrylic chamber  was centered on the glass stimulation platform of the system. Rapid movements were recorded at 400 frames/s using a below-view camera . Two white and two infrared LED panels illuminated the sides of the behavioral chamber in order to optimize lighting for these short exposure times and achieve high contrast images. NIR-FTIR was not used in this configuration. TRPV16, 1 ms pulse, 8 ms response latency). In addition to this two-step workflow using Fiji/Python to process AVI files and then RStudio to analyze the resulting output, alternative code was written in Python 3, which combines both steps and also computes individual pixel latencies and motion energy using NumPy and Pandas packages. A median filter (radius = 2 pixels) was applied to the NIR-FTIR recordings used to create the representative time series in Cre::ChR2 experiments in High-speed NIR-FTIR recordings were saved as uncompressed AVI files. A Python script was implemented in Fiji to verify the integrity of the high-speed NIR-FTIR recordings and extract average 8-bit intensity values from all frames within a circular region of interest on the stimulation site (60 pixels diameter). This output was then fed into RStudio to calculate the average intensity and associated standard deviation of the baseline recording (first 500 ms). A hind paw response was defined as a drop of intensity equal to or below the mean of the baseline minus five times its standard deviation. Paw response latency was defined as time between the start of the pulse and the time at which a hind paw response was first detected. For purposes of quality control, only recordings with a baseline NIR-FTIR intensity mean ≥ 3 and a standard deviation/mean of the baseline ratio ≥23 were retained for analysis. Another criterion was that response latencies are not 10 ms or shorter since this would be too short to be generated by the stimulus itself. Only one trial out of 2369 trials did not meet this criterion , the 8-bit value at a given frame (nF) was subtracted from the corresponding pixeln at the previous frame (n-1F). If the resulting absolute value was ≤5, 0 would be assigned to the pixel. If the absolute resulting value was >5, 1 would be assigned to the pixel. The threshold was chosen to discard background noise from the recording. The pixel binary values were then summed for each frame pair to obtain binarized motion energy. Normalized binarized motion energy was calculated by subtracting each post-stimulus frame binarized motion energy from the average baseline binarized motion energy. As an alternative to this analysis strategy, we have developed code in Python that processes the video files and calculates motion energy. The peak normalized binarized motion energy was determined and only trials displaying a peak response ≥5 standard deviations of the baseline mean were retained for further analysis and plotting. For TRPV1Cre::ChR2 mice, the analysis was restricted to a time window of 100 ms after stimulus onset (first three frame pairs proceeding the stimulus frame) to enable time-locking to the stimulus. Between 41 and 47 videos from eight mice were analyzed per spot size. For experiments with Vglut1Cre::ChR2 mice, the peak normalized binary motion energy exceeding five standard deviations of the baseline mean was determined for the entire 1.5 s recording epoch proceeding stimulus onset. Between 51 and 80 trials from 11 to 12 mice were analyzed per stimulation frequency.Videos of the entire stimulation platform were cropped into individual mouse chambers (200 × 315 pixels) and then analyzed using RStudio to quantify the amount of whole-body movements, including those stemming from the response of the stimulated limb, herein referred to as global behavior (GB). GB was approximated as the binarized motion energy: the summed number of pixels changing by more than five 8-bit values between two subsequent frames (Pixel Change). Briefly, for each pixelDeepLabCut (version 2.0.1) was installed on a computer  with an Anaconda virtual environment and was coupled to Tensorflow-GPU .All recordings were automatically cropped with Python MoviePy package and compressed with standard compression using the H.264 format, then saved in mp4 format. This compression method was previously shown to result in robust improvement of processing rate with minimal compromise on detection error.DeepLabCut was used with default network and training settings. Pilot stimulation trials were collected for initial training with 1,030,000 iterations from 253 labeled images from 50 videos. The videos were selected to represent the whole range of behavioral responses and conditions . Out of the 25 videos, 15 were selected from the most vigorous responses, 5 were selected from less vigorous responses, and 5 from control mice. Ground truth images were selected manually, aiming to include the most variable images from each video (up to 14 frames per video). 18 body parts were labeled, namely the nose, approximate center of the mouse, two points on each sides of the torso and one point at each side of the neck, the fore paws, distal and proximal points on the hind paw, between the hind limbs, and three points on the tail. While most of these labels were not used in subsequent analysis, labeling more body parts on the image enhanced performance. The resulting network output was visually assessed. Erroneously labeled frames were manually corrected and used to retrain the network while also adding new recordings. Four sequential retraining sessions with 1,030,000 iterations each were conducted adding a total of 109 frames from 38 videos. This resulted in a reduction in the pixel root mean square error (RMSE) from 4.97 down to 2.66 on the test set, which is comparable to human ground truth variability quantified elsewhere.Only labels of interest were used for analysis. These were ipsilateral and contralateral hind paws , the tail base, and the nose labels. To minimize error, points were removed if they (1) were labeled with less than 0.95 p-cutoff confidence by DeepLabCut, (2) jumped at least 10 pixels in one single frame compared to the previous frame, (3) had not returned on the subsequent frame, and (4) were from the five stimulation frames. Code for data processing was written in Python using the NumPy and Pandas packages. Additional post-hoc quality control was performed on the network output to identify and remove poorly labeled trials. To this end, heatmaps of distances between labels were created and inspected for dropped labels and sudden changes in distance. Trials identified in this manner were then manually inspected and removed if more than 10% of labels were missing or more than 10 frames were mislabeled. In total, 4.7% of trials were discarded. Only the first eight trials for each of the 10 mice that met this video quality control were used in analysis.Disabling NIR-FTIR illumination reduces the baseline saturation and thus allowed us to automate stimulated paw detection using pixel saturation from the stimulation laser. To determine which of the left or right paw had been stimulated in a given trial, the number of saturated pixels within a 60 × 60 pixels window close to the hind paw label was compared 7.5 ms prior and 5 ms after stimulus onset.Movement latencies of hind paws and head (nose) were computed based on significant changes from the baseline position. Baseline positions were calculated as the average x and y values from 10 consecutive frames prior to stimulus onset. A post-stimulus response was considered to be meaningful if the position of the label changed by at least 0.5 pixels (~0.16 mm) compared to baseline and continued moving at a rate of at least 0.5 pixel/frame for the subsequent 10 frames.We carried out dimensionality reduction on x and y values for six body parts  determined at a single time point. These were egocentrically aligned using the tail base as the origin, and the stimulated paw always on the right. PCA was carried out by extracting the first three PCs using these 12 features at 115 ms after stimulus onset. The PCA was cross-validated by pseudo-randomly splitting the 80 trials into training and test datasets (80:20). The training dataset showed 49.5, 27.4, and 12.3% variance was explained by PC1, PC2, and PC3, respectively. The same PCs explained 53.5, 23.2, and 10.1% variance in the test dataset. PCA of these 80 trials together (at 115 ms) gave explained variance values 50.4% (PC1), 26.5% (PC2), and 11.9% (PC3). Projecting the time courses onto these same PCs resulted in explained variance values 37.1% (PC1), 24.3% (PC2), and 16.7% (PC3). In all cases, the shifts seen in PC1–3 were similar to that shown in GB was analyzed within a 1 ms time frame following stimulation by computing the binarized motion energy relative to a baseline reference frame 5 ms prior to stimulation as described above. Here, the threshold for pixel change was set to seven 8-bit values. The binarized motion energy (sum of pixel binaries) of a given frame was normalized to the total number of pixels within that frame after removing those frames that had been affected by the stimulation laser pulse. The global response latency of movement initiation was determined as the time when binarized motion energy was greater than 10 times the standard deviation at baseline. Termination of movement was determined as the time point when binarized motion energy returned below 10 times standard deviation from baseline following the first movement bout.Cre::ChR2 mice was removed from the dataset because it displayed saturating responses to Protocol 3 preventing comparison of values across a dynamic range. Response latencies, response rise times, and response durations were computed using a hierarchical bootstrap procedure  and analyzed using Friedman’s non-parametric test for within-subject repeated measures followed by Dunn’s signed-rank test for multiple comparisons . In thisrocedure  modifiedrocedure  was examrocedure . Experim In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:We acknowledge the effort and time the authors took to address all of the comments made by reviewers which have improved this paper. We strongly believe that this approach will be widely adopted in the somatosensory research community to deliver "remote optogenetic touch" to freely behaving animals in a highly spatial and temporal fashion. The addition of a non-nociceptive line to this paper, with distinct behavioral outputs from the nociceptive line, is an added major strength of this work.Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our board of Reviewing Editors and the evaluation has been overseen by Andrew King as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Scanned optogenetic control of mammalian somatosensory input to map input-specific behavioral outputs" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife.The manuscript by Schorscher-Petcu is a very innovative study approaching an important problem in pain and somatosensory neuroscience – precise and remote delivery of sensory stimuli. This work is timely, and there are many clear applications to understanding peripheral somatosensory encoding using this strategy. The rationale for such a tool developed here is widely agreed upon in the field, and if others can easily adopt this strategy, this could become the standard for peripheral optogenetic stimulation of the hind paw. Nonetheless, the reviewers would like the authors to address the points listed here before consideration of publication in 1. The strength of this paper was in the technique and not the new biology uncovered. We would like the authors to remove any language of a "sparse code" proposition because the data presented here do not support such a claim.2. More detail on how to use this system so that new users can use this off-the-shelf. In particular: we had a hard time evaluating the hierarchical bootstrap procedure, which references a pre-print. Is this method really ensuring that the results are more rigorous? How do the authors define a withdraw? More detail and commentary on how this approach interfaces with Deep Lab Cut. In general, focus more on the technique and less on biology.3. For widespread applicability and to determine the range, strengths, and weaknesses of this new tool to the pain field, the authors should extend their behavioral analyses. The reviewers preferred the authors to do as they mention in the discussion, which is to add an additional somatosensory line (perhaps a non-pain line) and see how their platform performs in comparison to Trpv1-ChR2. The less preferred option if the authors are not able to breed new somatosensory lines in reasonable time, is to try the Trpv1-ChR2 line in different contexts (inflammatory and/or neuropathic pain). In either case, at baseline or during chronic pain states in the Trpv1-ChR2 line, the authors should use an analgesic and show that their tool is modular and can detect decreases in pain-related signatures. The authors should take care to have N numbers closer to 10 animals per group, as the N of 4 in their studies is on the lower side. 1. The strength of this paper was in the technique and not the new biology uncovered. We would like the authors to remove any language of a "sparse code" proposition because the data presented here do not support such a claim.We thank the reviewers and agree that as a technical report we should focus on the technical aspects. In demonstrating the utility of the system, we reveal biological insights that should be described clearly and fully. The language used is critical, so based on the reviewers comments we have removed the term "sparse code". We agree that "code" unintentionally implied that we show noxious stimuli are encoded naturally in this manner in this combination of broad-class nociceptors. More precisely, we are reporting that the relative size of a single action potential nociceptor volley can determine the nature, timing and extent of resultant protective behaviors. This relationship between nociceptive input and behavior has not been demonstrated before and so it is important to describe this. We do however retain the use of the term "sparse" as this summarizes the optogenetic stimulus for the reasons below.First, the input is spatially sparse – stimuli activate a small percentage of nociceptors in the total population. In Figure 2D, we show even an area as small as 0.6 mm² can tune the behavioral response. We now emphasize this point in the manuscript “The stimulus used for this protocol was S6, 0.577 mm² in area, which corresponds to less than 1% of the glabrous paw area and highlights the sensitivity of the nociceptive system”. Second, the input is temporally sparse – the stimuli only generate a single action potential in each nociceptor and this is enough to generate coordinated behaviors. That this is temporally sparse is in line with common usage of the term . This builds on our previous work that showed that a single pulse only generates a single action potential in each nociceptor.We confirm that the subtle difference in laser rise times have no implications on generalizing the previous findings. Indeed, the TRPV1-lineage used here have long refractory periods , as measured by patch clamp recordings . We had previously reported that even a 10 ms optogenetic stimulation of this mouse line does not generate more than a single action potential , but this is perhaps not prominent enough in the previous paper. In the system we describe here, the diode laser delivers the same wavelength and intensity of light as before. The light reaches a stable intensity in less than 1 microsecond, which allowed us to precisely compare the effects of short pulses as the ChR2 kinetics  and are what limits the rate of depolarization, not these lasers. Figure 2E uses 1 ms and 3 ms pulses only, and Figure 2F uses 1 ms pulses. We show that paw response probability increases from 0.3 ms up to 10 ms , which is not long enough to generate more than one action potential. We now point out alongside Figure 2D in the legend that the 30 ms pulse duration may result in more than one action potential but it is important to note this does not alter the interpretation as the probability already plateaued at 10 ms.Sparse recruitment of broad-class nociceptors was achieved by gaining precise control over the pulse width and spatial properties (size and shape) of the stimuli. It is not known whether certain pulse durations are more selective for the fibers they activate, so we have used a range of approaches to control stimulation size. We find that the larger the stimulus, irrespective of increasing the spot size or increasing the duration of the pulse, the shorter the response latencies. As illumination area increases so does the probability that Aδ and C fibres are activated. Our analysis of hind paw latencies was focused on Aδ responses. This is possible due to the stimuli being time-locked and the differential conduction velocities of these fibres – the responses typically initiate before 35 ms when the C-fiber action potentials (1.5 m/s) will only just be arriving at the spinal cord. We have previously shown using electrophysiology that TRPV1-Cre::ChR2 drives expression of ChR2 in broad-class nociceptors including Aδ and C fibers . This is also in line with evidence from Usoskin et al.,  showing the PEP2 population expresses TRPV1, and discussed by Caterina and Julius  and Mitchell et al., (Mol Pain 2010).2. More detail on how to use this system so that new users can use this off-the-shelf. In particular: we had a hard time evaluating the hierarchical bootstrap procedure, which references a pre-print. Is this method really ensuring that the results are more rigorous? How do the authors define a withdraw? More detail and commentary on how this approach interfaces with Deep Lab Cut. In general, focus more on the technique and less on biology.We thank the reviewers and have made changes throughout. Specific responses and actions are given below.Bootstrap. The bootstrap procedure allows statistical error to be calculated and provides better estimates of central tendency in skewed distributions. Here, we use hierarchical bootstrap, which as the now peer-reviewed article from Sober and colleagues points out , is an extensively used statistical method for nested data. We modified this to ensure that each mouse contributes equally to the number of trials in the sample, to avoid bias. By running our own analysis of this procedure, we confirm that the results are more rigorous; for 7 mice with between 2 and 6 responses, an additional 1.6-3.1% of bootstrap estimates from 1000 simulated experiments (with skewed distributions) fall within 1% of the population median. We have added this to the manuscript.Targeting stimuli. The laser is fixed to the center of view in the targeting camera, which is displayed in real time in the GUI. This is due to the "descanned" configuration of the laser and targeting camera both using the same path through the galvanometers. Moving the target location of the laser moves the field of view that is displayed. The experimenter chooses what exact location of the mouse to stimulate by aiming the laser with this camera feed. We used a computer mouse cursor to rapidly target stimuli. On triggering a trial, the same camera records 0.5 seconds before the stimulus and 1.5 second after stimulus onset. These high-speed (up to 1000 fps) videos are used to verify if any movement occurred prior to the stimulation. There are occasions when the mouse moves before the stimulation is delivered, which is unavoidable in freely moving mice but the pre-stimulus baseline from all recordings are examined for movement using our code. In practical terms, we assess whether there has been movement occurring during the 0.5 seconds preceding the stimulus by calculating the mean signal intensity for this epoch and dividing it by its standard deviation. If the animal is still, the standard deviation will be small and the incurring ratio large. If the mouse moved, the standard deviation will be high and the ratio small. We empirically determined a threshold value, which we found to reliably indicate whether the animal is not moving up until the delivery of the stimulation. If there is any movement, the trial will not be used thus removing the possibility of recordings with unintended stimulus delivery.To provide clarification on the targeting of stimuli, we have added further description to the Results section Design and assembly of the optical stimulation approach, which now states:“The camera-feed is displayed in the user interface and enables the operator to use this image to target the laser to the desired location”.This makes it clearer how the laser is remotely targeted. The reader may also refer to the Materials and methods section for a more detailed description:“The software displays the NIR-FTIR camera feed, whose path through the mirror galvanometers is shared with the laser beam, so that they are always in alignment with one another. Computationally adjusting mirror galvanometer angles causes identical shifts in both the descanned NIR-FTIR image field of view and intended laser stimulation site, so that the laser can be targeted to user-identified locations.”Automated analysis of optogenetically evoked local withdrawal events. By this definition, a paw response does not require the paw to lift / withdraw. We examine the extent of the response and can categorize partial and full responses. For example, if only part of the paw moves without lifting from the floor this is not a full paw withdrawal but still considered a response and it contains information that can be included in the analysis. The responses are detected using frustrated total internal reflection of infrared light at 1,000 frames per second. These resultant videos reveal nuance that would not be detected with the human eye. The videos are analyzed with code written in R and Python. We extract each of the 25,600 pixels for each frame and statistically analyze changes in each pixel or the intensity across a region of interest. The code is freely available. The videos are inspected by eye to validate findings. We have made changes in the manuscript that make this clearer.Paw responses. We define a hind paw response as a decrease in the intensity equal to or below the mean of the baseline minus five times its standard deviation. This is stated in Materials and methods section Optical system design, components and assembly and Patterned stimulation protocols.Open science. We provide all information required to build the system: a solidworks assembly, part list, software and analysis code, along with technical specifications of the system in the Results section and Materials and methods sections DeepLabCut. We have added additional details and discussion regarding the implementation of DeepLabCut to the Results section Sparse nociceptor stimulation triggers coordinated postural adjustments, and in the Discussion.3. For widespread applicability and to determine the range, strengths, and weaknesses of this new tool to the pain field, the authors should extend their behavioral analyses. The reviewers preferred the authors to do as they mention in the discussion, which is to add an additional somatosensory line (perhaps a non-pain line) and see how their platform performs in comparison to Trpv1-ChR2. The less preferred option if the authors are not able to breed new somatosensory lines in reasonable time, is to try the Trpv1-ChR2 line in different contexts (inflammatory and/or neuropathic pain). In either case, at baseline or during chronic pain states in the Trpv1-ChR2 line, the authors should use an analgesic and show that their tool is modular and can detect decreases in pain-related signatures. The authors should take care to have N numbers closer to 10 animals per group, as the N of 4 in their studies is on the lower side.This is an excellent suggestion. We have included an additional somatosensory Cre driver line, which took time to obtain and breed but substantially improves the manuscript. The somatosensory line we use is the Vglut1-Cre::ChR2-tdTomato line, which was originally characterized by Ru-Rong Ji and colleagues  in a relevant context. Here, we show that behavioral responses are specific to this stimulus. We therefore demonstrate the system is sensitive enough to detect behavioral responses associated with specific non-nociceptive and nociceptive input, and these responses can be readily discriminated.Centrally acting analgesics  may or may not suppress single-shot optogenetic stimuli; the interpretation of such an experiment does not address if pain is or is not experienced by such a stimulus, as they typically have other effects on arousal and locomotion that confound responses to mild inputs. A grooming or sleeping mouse, for example, does not respond to noxious stimuli as often as when it is at rest . We have previously shown that local lidocaine injection can block the optogenetic-evoked responses in these TRPV1-Cre::ChR2 mice . This blocks the input from the periphery. Further, the editor has previously developed a mouse pain scale and showed clearly that optogenetic stimulation causes pain-like responses . Therefore, experiments with analgesics would not contribute further to the existing understanding here. Computational classification of pain vs non-pain behaviors is an exciting area of research but is outside of the purpose of this paper. Our goal is to describe an approach for "remote touch" in freely behaving mice and provide some clear examples of its utility.We have substantially extended our behavioral analysis of the TRPV1-Cre::ChR2 mice not to discriminate between pain vs non-pain, but based on the excellent suggestion to examine whole-body movement “…using a vector instead of a scalar”. We have extended the DeepLabCut analysis and reworked Figure 4, providing in depth computational analysis of the behavioral trajectories, demonstrating coordinated head orienting and body reposition. This further demonstrates the analysis and insights that can be achieved using this technical advance.Regarding n numbers, all experiments used between 7 and 12 mice with one exception; the patterned stimulation experiment in Figure 2F used 6 mice, and 3 littermate controls and 4 off-target controls to confirm that these stimuli were indeed specific. These controls together show no responses, supporting what is already known with optogenetic stimulation of this mouse line. We have made the numbers of mice used clearer throughout the manuscript.