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Background: Alcohol consumption and withdrawal alter social behaviour in humans in a sex-dependent manner. The three-chamber test is a widely used paradigm to assess rodents' social behaviour, including sociability and social novelty. Automatic tracking systems are commonly used to score time spent with conspecifics, despite failing to score direct interaction time with conspecifics rather than time in the nearby zone. Thereby, the automatically scored results are usually inaccurate and need manual corrections. New method: New advances in artificial intelligence (AI) have been used recently to analyze complex behaviours. DeepLabCat is a pose-estimation toolkit that allows the tracking of animal body parts. Thus, we used DeepLabCut, to introduce a scoring model of the three-chamber test to investigate alcohol withdrawal effects on social behaviour in mice considering sex and withdrawal periods. We have compared the results of two automatic pose estimation methods: automatic tracking (AnyMaze) and DeepLabCut considering the manual scoring method, the current gold standard. Results: We have found that the automatic tracking method (AnyMaze) has failed to detect the significance of social deficits in female mice during acute withdrawal. However, tracking the animal's nose using DeepLabCut showed a significant social deficit in agreement with manual scoring. Interestingly, this social deficit was shown only in females during acute and recovered by the protracted withdrawal. DLC and manually scored results showed a higher Spearman correlation coefficient and a lower bias in the Bland-Altman analysis. Conclusion: our approach helps improve the accuracy of scoring the three-chamber test while outperforming commercial automatic tracking systems.

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Pain assessment in animal models is essential for understanding mechanisms underlying pathological pain and developing effective pain medicine. The grimace scale (GS), facial expression features in pain such as orbital tightening (OT), is a valuable measure for assessing pain in animal models. However, the classical grimace scale for pain assessment is labor-intensive, subject to subjectivity and inconsistency, and is not a quantitative measure. In the present study, we utilized machine learning with DeepLabCut to annotate the superior and inferior eyelid margins and the medial and lateral canthus of the eyes in animals' video images. Based on the annotation, we quantified the eyelid distance and palpebral fissure width of the animals' eyes so that the degree of OT in animals with pain could be measured and described quantitatively. We established criteria for the inclusion and exclusion of the annotated images for quantifying OT, and validated our quantitative grimace scale (qGS) in the mice with pain caused by capsaicin injections in the orofacial or hindpaw regions, the Nav1.8-ChR2 mice following orofacial noxious stimulation with laser light, and the oxaliplatin-treated mice following tactile stimulation with a von Frey filament. We showed that both the eyelid distance and the palpebral fissure width were shortened significantly in the animals in pain compared to the control animals without nociceptive stimulation. Collectively, the present study has established a quantitative orbital tightening for pain assessment in mice using DeepLabCut, providing a new tool for pain assessment in preclinical studies with mice.

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Traumatic spinal cord injury (SCI) is a devastating condition that impacts over 300,000 individuals in the US alone. Depending on the severity of the injury, SCI can lead to varying degrees of sensorimotor deficits and paralysis. Despite advances in our understanding of the underlying pathological mechanisms of SCI and the identification of promising molecular targets for repair and functional restoration, few therapies have made it into clinical use. To improve the success rate of clinical translation, more robust, sensitive, and reproducible means of functional assessment are required. The gold standards for the evaluation of locomotion in rodents with SCI are the Basso Beattie Bresnahan (BBB) scale and Basso Mouse Scale (BMS). To overcome the shortcomings of current methods, we developed two separate markerless kinematic analysis paradigms in mice, MotorBox and MotoRater, based on deep-learning algorithms generated with the DeepLabCut open-source toolbox. The MotorBox system uses an originally designed, custom-made chamber, and the MotoRater system was implemented on a commercially available MotoRater device. We validated the MotorBox and MotoRater systems by comparing them with the traditional BMS test and extracted metrics of movement and gait that can provide an accurate and sensitive representation of mouse locomotor function post-injury, while eliminating investigator bias and variability. The integration of MotorBox and/or MotoRater assessments with BMS scoring will provide a much wider range of information on specific aspects of locomotion, ensuring the accuracy, rigor, and reproducibility of behavioral outcomes after SCI.

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Behavior is fundamental to neuroscience research, providing insights into the mechanisms underlying thoughts, actions and responses. Various model organisms, including mice, flies, and fish, are employed to understand these mechanisms. Zebrafish, in particular, serve as a valuable model for studying anxiety-like behavior, typically measured through the novel tank diving (NTD) assay. Traditional methods for analyzing NTD assays are either manually intensive or costly when using specialized software. To address these limitations, it is useful to develop methods for the automated analysis of zebrafish NTD assays using deep-learning models. In this study, we classified zebrafish based on their anxiety levels using DeepLabCut. Subsequently, based on a training dataset of image frames, we compared deep-learning models to identify the model best suited to classify zebrafish as anxious or non anxious and found that specific architectures, such as InceptionV3, are able to effectively perform this classification task. Our findings suggest that these deep learning models hold promise for automated behavioral analysis in zebrafish, offering an efficient and cost-effective alternative to traditional methods.

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Social interactions are often analyzed by scoring segments of predefined behavior and then statistically assessing numerical and sequential patterns to identify the structure of the encounters. However, this approach can miss the dynamics of the animals' relationship over the course of the encounter, one that often involves invariant bonds, say a nose-to-nose orientation, with many different movements performed by both partners acting to counteract each other's attempts to break or maintain the relationship. Moreover, these invariant bonds can switch from one configuration to another during an interaction, leading from one stable configuration to another. It is this stepwise sequence of configurational stabilities that lead to functional outcomes, such as mating, aggression, or predation. By focusing on the sequence of invariant relational configurations, the deep structure of interactions can be discerned. This deep structure can then be used to differentiate between compensatory movements, no matter how seemingly stereotyped they may appear, from movement patterns which are restricted to a particular form when more than one option is available. A dynamic perspective requires suitable tools for analysis, and such tools are highlighted as needed in describing particular interactions.

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Introduction: Defensive responses to threat-associated cues are commonly evaluated using conditioned freezing or suppression of operant responding. However, rats display a broad range of behaviors and shift their defensive behaviors based on immediacy of threats and context. This study aimed to systematically quantify the defensive behaviors that are triggered in response to threat-associated cues and assess whether they can accurately be identified using DeepLabCut in conjunction with SimBA. Methods: We evaluated behavioral responses to fear using the auditory fear conditioning paradigm. Observable behaviors triggered by threat-associated cues were manually scored using Ethovision XT. Subsequently, we investigated the effects of diazepam (0, 0.3, or 1 mg/kg), administered intraperitoneally before fear memory testing, to assess its anxiolytic impact on these behaviors. We then developed a DeepLabCut + SimBA workflow for ethological analysis employing a series of machine learning models. The accuracy of behavior classifications generated by this pipeline was evaluated by comparing its output scores to the manually annotated scores. Results: Our findings show that, besides conditioned suppression and freezing, rats exhibit heightened risk assessment behaviors, including sniffing, rearing, free-air whisking, and head scanning. We observed that diazepam dose-dependently mitigates these risk-assessment behaviors in both sexes, suggesting a good predictive validity of our readouts. With adequate amount of training data (approximately > 30,000 frames containing such behavior), DeepLabCut + SimBA workflow yields high accuracy with a reasonable transferability to classify well-represented behaviors in a different experimental condition. We also found that maintaining the same condition between training and evaluation data sets is recommended while developing DeepLabCut + SimBA workflow to achieve the highest accuracy. Discussion: Our findings suggest that an ethological analysis can be used to assess fear learning. With the application of DeepLabCut and SimBA, this approach provides an alternative method to decode ongoing defensive behaviors in both male and female rats for further investigation of fear-related neurobiological underpinnings.

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PURPOSE: The development of innovative solutions, such as simulator training and artificial intelligence (AI)-powered tutoring systems, has significantly changed surgical trainees' environments to receive the intraoperative instruction necessary for skill acquisition. In this study, we developed a new objective assessment system using AI for forceps manipulation in a surgical training simulator. METHODS: Laparoscopic exercises were recorded using an iPad®, which provided top and side views. Top-view movies were used for AI learning of forceps trajectory. Side-view movies were used as supplementary information to assess the situation. We used an AI-based posture estimation method, DeepLabCut (DLC), to recognize and positionally measure the forceps in the operating field. Tracking accuracy was quantitatively evaluated by calculating the pixel differences between the annotation points and the points predicted by the AI model. Tracking stability at specified key points was verified to assess the AI model. RESULTS: We selected a random sample to evaluate tracking accuracy quantitatively. This sample comprised 5% of the frames not used for AI training from the complete set of video frames. We compared the AI detection positions and correct positions and found an average pixel discrepancy of 9.2. The qualitative evaluation of the tracking stability was good at the forceps hinge; however, forceps tip tracking was unstable during rotation. CONCLUSION: The AI-based forceps tracking system can visualize and evaluate laparoscopic surgical skills. Improvements in the proposed system and AI self-learning are expected to enable it to distinguish the techniques of expert and novice surgeons accurately. This system is a useful tool for surgeon training and assessment.

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This study aimed to evaluate clinical utility of 2D-markerless motion analysis (2DMMA) from a single camera during a reaching-sideways-task in individuals with dyskinetic cerebral palsy (DCP) by determining (1) concurrent validity by correlating 2DMMA against marker-based 3D-motion analysis (3DMA) and (2) construct validity by assessing differences in 2DMMA features between DCP and typically developing (TD) peers. 2DMMA key points were tracked from frontal videos of a single camera by DeepLabCut and accuracy was assessed against human labelling. Shoulder, elbow and wrist angles were calculated from 2DMMA and 3DMA (as gold standard) and correlated to assess concurrent validity. Additionally, execution time and variability features such as mean point-wise standard deviation of the angular trajectories (i.e. shoulder elevation, elbow and wrist flexion/extension) and wrist trajectory deviation by mean overshoot and convex hull were calculated from key points. 2DMMA features were compared between the DCP group and TD peers to assess construct validity. Fifty-one individuals (30 DCP;21 TD; age:5-24 years) participated. An accuracy of approximately 1.5 cm was reached for key point tracking. While significant correlations were found for wrist (ρ = 0.810;p < 0.001) and elbow angles (ρ = 0.483;p < 0.001), 2DMMA shoulder angles were not correlated (ρ = 0.247;p = 0.102) to 3DMA. Wrist and elbow angles, execution time and variability features all differed between groups (Effect sizes 0.35-0.81;p < 0.05). Videos of a reaching-sideways-task processed by 2DMMA to assess upper extremity movements in DCP showed promising validity. The method is especially valuable to assess movement variability.

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Background: Caffeoylquinic acid (CQA), which is abundant in coffee beans and Centella asiatica, reportedly improves cognitive function in Alzheimer's disease (AD) model mice, but its effects on neuroinflammation, neuronal loss, and the amyloid-ß (Aß) plaque burden have remained unclear. Objective: To assess the effects of a 16-week treatment with CQA on recognition memory, working memory, Aß levels, neuronal loss, neuroinflammation, and gene expression in the brains of 5XFAD mice, a commonly used mouse model of familial AD. Methods: 5XFAD mice at 7 weeks of age were fed a 0.8% CQA-containing diet for 4 months and then underwent novel object recognition (NOR) and Y-maze tests. The Aß levels and plaque burden were analyzed by enzyme-linked immunosorbent assay and immunofluorescent staining, respectively. Immunostaining of markers of mature neurons, synapses, and glial cells was analyzed. AmpliSeq transcriptome analysis and quantitative reverse-transcription-polymerase chain reaction were performed to assess the effect of CQA on gene expression levels in the cerebral cortex of the 5XFAD mice. Results: CQA treatment for 4 months improved recognition memory and ameliorated the reduction of mature neurons and synaptic function-related gene mRNAs. The Aß levels, plaque burden, and glial markers of neuroinflammation seemed unaffected. Conclusions: These findings suggest that CQA treatment mitigates neuronal loss and improves cognitive function without reducing Aß levels or neuroinflammation. Thus, CQA is a potential therapeutic compound for AD, improving cognitive function via as-yet unknown mechanisms independent of reductions in Aß or neuroinflammation.

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This study emphasizes the benefits of open-source software such as DeepLabCut (DLC) and R to automate, customize and enhance data analysis of motor behavior. We recorded 2 different spinocerebellar ataxia type 6 mouse models while performing the classic beamwalk test, tracked multiple body parts using the markerless pose-estimation software DLC and analyzed the tracked data using self-written scripts in the programming language R. The beamwalk analysis script (BAS) counts and classifies minor and major hindpaw slips with an 83% accuracy compared to manual scoring. Nose, belly and tail positions relative to the beam, as well as the angle at the tail base relative to the nose and tail tip were determined to characterize motor deficits in greater detail. Our results found distinct ataxic abnormalities such as an increase in major left hindpaw slips and a lower belly and tail position in both SCA6 ataxic mouse models compared to control mice at 18 months of age. Furthermore, a more detailed analysis of various body parts relative to the beam revealed an overall lower body position in the SCA684Q compared to the CT-longQ27PC mouse line at 18 months of age, indicating a more severe ataxic deficit in the SCA684Q group.

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Obsessive-compulsive disorder (OCD) is a debilitating psychiatric disorder characterized by intrusive obsessive thoughts and compulsive behaviors. Multiple studies have shown the association of polymorphisms in the SLC1A1 gene with OCD. The most common of these OCD-associated polymorphisms increases the expression of the encoded protein, excitatory amino acid transporter 3 (EAAT3), a neuronal glutamate transporter. Previous work has shown that increased EAAT3 expression results in OCD-relevant behavioral phenotypes in rodent models. In this study, we created a novel mouse model with targeted, reversible overexpression of Slc1a1 in forebrain neurons. The mice do not have a baseline difference in repetitive behavior but show increased hyperlocomotion following a low dose of amphetamine (3 mg/kg) and increased stereotypy following a high dose of amphetamine (8 mg/kg). We next characterized the effect of amphetamine on striatal cFos response and found that amphetamine increased cFos throughout the striatum in both control and Slc1a1-overexpressing (OE) mice, but Slc1a1-OE mice had increased cFos expression in the ventral striatum relative to controls. We used an unbiased machine classifier to robustly characterize the behavioral response to different doses of amphetamine and found a unique response to amphetamine in Slc1a1-OE mice, relative to controls. Lastly, we found that the differences in striatal cFos expression in Slc1a1-OE mice were driven by cFos expression specifically in D1 neurons, as Slc1a1-OE mice had increased cFos in D1 ventral medial striatal neurons, implicating this region in the exaggerated behavioral response to amphetamine in Slc1a1-OE mice.

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Cigarette smoking during pregnancy is known to be associated with the incidence of attention-deficit/hyperactive disorder (ADHD). Recent developments in deep learning algorithms enable us to assess the behavioral phenotypes of animal models without cognitive bias during manual analysis. In this study, we established prenatal nicotine exposure (PNE) mice and evaluated their behavioral phenotypes using DeepLabCut and SimBA. We optimized the training parameters of DeepLabCut for pose estimation and succeeded in labeling a single-mouse or two-mouse model with high fidelity during free-moving behavior. We applied the trained network to analyze the behavior of the mice and found that PNE mice exhibited impulsivity and a lessened working memory, which are characteristics of ADHD. PNE mice also showed elevated anxiety and deficits in social interaction, reminiscent of autism spectrum disorder (ASD). We further examined PNE mice by evaluating adult neurogenesis in the hippocampus, which is a pathological hallmark of ASD, and demonstrated that newborn neurons were decreased, specifically in the ventral part of the hippocampus, which is reported to be related to emotional and social behaviors. These results support the hypothesis that PNE is a risk factor for comorbidity with ADHD and ASD in mice.

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BACKGROUND: Non-radiographical techniques have been suggested to measure the spine curvature at the sagittal plane. However, a neural network has not been used to measure the curvature. METHODS: A single video camera captured images of a standing posture at the sagittal plane from twenty healthy males. Six marker positions along the spine's contour in each image were identified for measuring inclination, thoracic kyphosis, and lumbar lordosis angles. We estimated three inflection points around the neck, hip, and between the neck and hip, followed by identifying two adjacent marker positions per inflection point to compute its tangent. The angular deviation of each tangent line from the horizontal was computed to measure inclination angles. Thoracic kyphosis and lumbar lordosis angles were computed by the angular difference between the two adjacent tangents. A deep neural network was trained with 500,000 iterations using the labeled images from 18 participants (388 and 44 images for training and test set) and then evaluated using the unseen images (2 participants, 48 images; evaluation set). FINDINGS: The mean total training and test errors were <2 pixels (∼ 0.6 cm). The total error in the evaluation set was qualitatively comparable (∼ 3 pixels = âˆ¼ 0.9 cm), suggesting the model performance was maintained in the unseen data. The angle values between labeled and network-predicted marker positions were similar in the evaluation set. INTERPRETATION: The network training with a relatively small number of images was successful based on the small error values observed in the evaluation set. The model may be an affordable, automated, and non-contact measurement tool for the human spine curvature.

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Transcranial direct current stimulation (tDCS) is increasingly being used for Parkinson's disease (PD); however, the evaluation of its clinical impact remains complex owing to the heterogeneity of patients and treatments. Therefore, we used a unilateral 6-hydroxydopamine-induced PD rat model to investigate whether anodal tDCS of the primary motor cortex (M1) alleviates PD motor deficits. Before tDCS treatment, unilateral PD rats preferentially used the forelimb ipsilateral to the lesion in the exploratory cylinder test and showed reduced locomotor activity in the open field test. In addition, PD-related clumsy forelimb movements during treadmill walking were detected using deep learning-based video analysis (DeepLabCut). When the 5-day tDCS treatment began, the forelimb-use asymmetry was ameliorated gradually, and locomotor activity increased to pre-lesion levels. tDCS treatment also normalized unnatural forelimb movement during walking and restored a balanced gait. However, these therapeutic effects were rapidly lost or gradually disappeared when the tDCS treatment was terminated. Histological analysis at the end of the experiment revealed that the animals had moderately advanced PD, with 40-50% of dopamine neurons and fibers preserved on the injured side compared with those on the intact side. Although it remains a challenge to elucidate the neural mechanisms of the transient improvement in motor function induced by tDCS, the results of this study provide evidence that tDCS of the M1 produces positive behavioral outcomes in PD animals and provides the basis for further clinical research examining the application of tDCS in patients with PD.

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Daphnia magna is one species of water flea that has been used for a long time for ecotoxicity studies. In addition, Daphnia has a myogenic heart that is very useful for cardiotoxicity studies. Previous attempts to calculate the cardiac parameter endpoints in Daphnia suffer from the drawback of tedious operation and high variation due to manual counting errors. Even the previous method that utilized deep learning to help the process suffer from either overestimation of parameters or the need for specialized equipment to perform the analysis. In this study, we utilized DeepLabCut software previously used for animal pose tracking and demonstrated that ResNet_152 was the best fit for training the network. The trained network also showed comparable results with ImageJ and Kymograph, which was mostly done manually. In addition to that, several macro scripts in either Excel or Python format were developed to help summarize the data for faster analysis. The trained network was then challenged to analyze the potential cardiotoxicity of imidacloprid and pendimethalin in D. magna, and it showed that both pesticides cause alteration in their cardiac performance. Overall, this method provides a simple and automatic method to analyze the cardiac performance of Daphnia by utilizing DeepLabCut. The method proposed in this paper can contribute greatly to scientists conducting fast and accurate cardiotoxicity measurements when using Daphnia as a model.

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Infusion reactions are a major risk for advanced therapeutics (e.g., engineered proteins nanoparticles, etc.), which can trigger the complement cascade, anaphylaxis, and other life-threatening immune responses. However, during the early phases of development, it is uncommon to assess for infusion reactions, given the labor involved in measuring multiple physiological parameters in rodents. Therefore, we sought to develop an automated quantification of rodent locomotion to serve as a sensitive screening tool for infusion reactions, with minimal added labor-time for each experiment. Here we present the detailed methods for building a motion tracking cage for mice, requiring ∼$100 of materials, ∼2 h to build and set up completely, and employing freely available software (DeepLabCut). The distance-walked after injection was first shown to have the predicted effects for stimulants (caffeine), sedatives (ketamine), and toxins (lipopolysaccharide). Additionally, the distance-walked more sensitively detected the effects of these compounds than did pulse oximetry-based measurements of the classical vital signs of heart rate, respiratory rate, and blood oxygen saturation. Finally, we examined a nanomedicine formulation that has been in preclinical development, liposomes targeted to the cell adhesion molecule ICAM. While this formulation has been studied across dozens of publications, it has not previously been noted to produce an infusion reaction. However, the automated motion tracking cage showed that ICAM-liposomes markedly reduce the distance-walked, which we confirmed by measuring the other vital signs. Importantly, the motion tracking cage added < 5 min of labor time per 5-mouse condition, while pulse oximetry with a neck cuff (by far the most stable oximetry signal in mice) required âˆ¼ 100 min of labor time. Thus, automated measurement of distance-walked can indeed serve as a "sixth vital sign" for detecting infusion reactions during preclinical testing. Additionally, the device to measure distance-walked is easy and cheap to build and requires negligible labor time for each experiment, enabling distance-walked to be recorded in nearly every infusion experiment.

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Objectively measuring animal behavior is vital to understanding the neural circuits underlying pain. Recent progress in machine vision has presented unprecedented scope in behavioral analysis. Here, we apply DeepLabCut (DLC) to dissect mouse behavior on the thermal-plate test - a commonly used paradigm to ascertain supraspinal contributions to noxious thermal sensation and pain hypersensitivity. We determine the signature characteristics of the pattern of mouse movement and posture in 3D in response to a range of temperatures from innocuous to noxious on the thermal-plate test. Next, we test how acute chemical and chronic inflammatory injuries sensitize mouse behaviors. Repeated exposure to noxious temperatures on the thermal plate can induce learning. In this study, we design a novel assay and formulate an analytical pipeline to facilitate the dissection of plasticity mechanisms in pain circuits in the brain. Last, we record and test how activating Tacr1 expressing PBN neurons (PBNTacr1) - a population responsive to sustained noxious stimuli- affects mouse behavior on the thermal plate test. Taken together, we demonstrate that by tracking a single body part of a mouse, we can reveal the behavioral signatures of mice exposed to noxious surface temperatures, report the alterations of the same when injured, and determine if a molecularly and anatomically defined pain-responsive circuit plays a role in the reflexive hypersensitivity to thermal pain.

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Understanding the neural basis of emotions is a critical step to uncover the biological substrates of neuropsychiatric disorders. To study this aspect in freely behaving mice, neuroscientists have relied on the observation of ethologically relevant bodily cues to infer the affective content of the subject, both in neutral conditions or in response to a stimulus. The best example of that is the widespread assessment of freezing in experiments testing both conditioned and unconditioned fear responses. While robust and powerful, these approaches come at a cost: they are usually confined within selected time windows, accounting for only a limited portion of the complexity of emotional fluctuation. Moreover, they often rely on visual inspection and subjective judgment, resulting in inconsistency across experiments and questionable result interpretations. To overcome these limitations, novel tools are arising, fostering a new avenue in the study of the mouse naturalistic behavior. In this work we developed a computational tool [stimulus-evoked behavioral tracking in 3D for rodents (SEB3R)] to automate and standardize an ethologically driven observation of freely moving mice. Using a combination of machine learning-based behavioral tracking and unsupervised cluster analysis, we identified statistically meaningful postures that could be used for empirical inference on a subsecond scale. We validated the efficacy of this tool in a stimulus-driven test, the whisker nuisance (WN) task, where mice are challenged with a prolonged and invasive whisker stimulation, showing that identified postures can be reliably used as a proxy for stimulus-driven fearful and explorative behaviors.

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The hypothalamus plays a crucial role in the modulation of social behavior by encoding internal states. The hypothalamic hypocretin/orexin neurons, initially identified as regulators of sleep and appetite, are important for emotional and motivated behaviors. However, their role in social behavior remains unclear. Using fiber photometry and behavioral analysis, we show here that hypocretin neurons differentially encode social discrimination based on the nature of social encounters. The optogenetic inhibition of hypocretin neuron activity or blocking of hcrt-1 receptors reduces the amount of time mice are engaged in social interaction in males but not in females. Reduced hcrt-1 receptor signaling during social interaction is associated with altered activity in the insular cortex and ventral tegmental area in males. Our data implicating hypocretin neurons as sexually dimorphic regulators within social networks have significant implications for the treatment of neuropsychiatric diseases with social dysfunction, particularly considering varying prevalence among sexes.

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We studied the role of rat whisker/snout tactile sense during oral grasping, comparing control data with those obtained, respectively, 1-3 and 5-7 days after bilateral long or short whisker trimming and 3-5 and 8-10 days after bilateral infraorbital nerve (ION) severing. Two behavioural phases were identified: whisker-snout contact by nose-N or lip-L and snout-tongue contact. The second phase involved either: snout passing over stationary pellet (Still pellet); pellet rolling as the snout passed over it (Rolling pellet); pellet being pushed forward by the snout (Pushed pellet); or pellet being hit and pushed away (Hit/Lost pellet). In controls, success was 100%, with N-contact prevailing over L-contact in the first phase and Still pellet in the second. In long whisker-trimmed versus controls, success was still 100%, but L-contact increased in frequency, Pushed pellet prevailed and the second phase duration increased. In short whisker-trimmed versus controls, success remained 100%, with increased L-contact frequency; the first phase duration did not change, but the second phase increased since in pushed trials, the pellet rolled around the snout. In ION-severed versus controls, both phases changed drastically: L-contact frequency increased, Pushed pellet prevailed and contact was persistently maintained; Hit/Lost pellet emerged, Still and Rolling pellets disappeared and the oral-grasping sequence was not triggered. These results suggest that long and short whiskers, respectively, optimize the first and second phases of snout-pellet interaction and that whisker/snout sense is necessary to trigger oral grasping. Kinematic trajectory analysis supports the conclusion that movement from whisker to snout contact is an orientation response.

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