The little things that run: a general scaling of invertebrate exploratory speed with body mass.

Speed is a key trait of animal movement, and while much is already known about vertebrate speed and how it scales with body mass, studies on invertebrates are sparse, especially across diverse taxonomic groups. Here, we used automated image-based tracking to characterize the exploratory (voluntary) speed of 173 invertebrates comprising 57 species across six taxonomic groups (Arachnida, Chilopoda, Diplopoda, Entognatha, Insecta, Malacostraca) and four feeding types (carnivore, detritivore, herbivore, omnivore). Across all individuals, exploratory speed (mm/s) scaled with body mass (g) following a power-law relationship with a scaling exponent of 0.19 ± 0.04 (mean ± SE) and an intercept of 14.33 ± 1.2. These parameters varied substantially with taxonomic group and feeding type. For the first time, we provide general empirically derived allometric scaling relationships of exploratory speed across broad taxonomic groups of invertebrates. As exploratory speed drives key components of species interactions, such as encounter and attack rates, or competition, our study contributes to a deeper understanding of the role of individual movement in population and community level processes.

Overexpression of synphilin-1 promotes clearance of soluble and misfolded alpha-synuclein without restoring the motor phenotype in aged A30P transgenic mice.

Lewy bodies and neurites are the pathological hallmark of Parkinson's disease. These structures are composed of fibrillized and ubiquitinated alpha-synuclein suggesting that impaired protein clearance is an important event in aggregate formation. The A30P mutation is known for its fast oligomerization, but slow fibrillization rate. Despite its toxicity to neurons, mechanisms involved in either clearance or conversion of A30P alpha-synuclein from its soluble state into insoluble fibrils and their effects in vivo are poorly understood. Synphilin-1 is present in Lewy bodies, interacting with alpha-synuclein in vivo and in vitro and promotes its sequestration into aggresomes, which are thought to act as cytoprotective agents facilitating protein degradation. We therefore crossed animals overexpressing A30P alpha-synuclein with synphilin-1 transgenic mice to analyze its impact on aggregation, protein clearance and phenotype progression. We observed that co-expression of synphilin-1 mildly delayed the motor phenotype caused by A30P alpha-synuclein. Additionally, the presence of N- and C-terminal truncated alpha-synuclein species and fibrils were strongly reduced in double-transgenic mice when compared with single-transgenic A30P mice. Insolubility of mutant A30P and formation of aggresomes was still detectable in aged double-transgenic mice, paralleled by an increase of ubiquitinated proteins and high autophagic activity. Hence, this study supports the notion that co-expression of synphilin-1 promotes formation of autophagic-susceptible aggresomes and consecutively the degradation of human A30P alpha-synuclein. Notably, although synphilin-1 overexpression significantly reduced formation of fibrils and astrogliosis in aged animals, a similar phenotype is present in single- and double-transgenic mice suggesting additional neurotoxic processes in disease progression.

Disentangling rodent behaviors to improve automated behavior recognition.

Automated observation and analysis of behavior is important to facilitate progress in many fields of science. Recent developments in deep learning have enabled progress in object detection and tracking, but rodent behavior recognition struggles to exceed 75-80% accuracy for ethologically relevant behaviors. We investigate the main reasons why and distinguish three aspects of behavior dynamics that are difficult to automate. We isolate these aspects in an artificial dataset and reproduce effects with the state-of-the-art behavior recognition models. Having an endless amount of labeled training data with minimal input noise and representative dynamics will enable research to optimize behavior recognition architectures and get closer to human-like recognition performance for behaviors with challenging dynamics.

CatWalk XT gait parameters: a review of reported parameters in pre-clinical studies of multiple central nervous system and peripheral nervous system disease models.

Automated gait assessment tests are used in studies of disorders characterized by gait impairment. CatWalk XT is one of the first commercially available automated systems for analyzing the gait of rodents and is currently the most used system in peer-reviewed publications. This automated gait analysis system can generate a large number of gait parameters. However, this creates a new challenge in selecting relevant parameters that describe the changes within a particular disease model. Here, for the first time, we performed a multi-disorder review on published CatWalk XT data. We identify commonly reported CatWalk XT gait parameters derived from 91 peer-reviewed experimental studies in mice, covering six disorders of the central nervous system (CNS) and peripheral nervous system (PNS). The disorders modeled in mice were traumatic brain injury (TBI), stroke, sciatic nerve injury (SNI), spinal cord injury (SCI), Parkinson's disease (PD), and ataxia. Our review consisted of parameter selection, clustering, categorization, statistical evaluation, and data visualization. It suggests that certain gait parameters serve as potential indicators of gait dysfunction across multiple disease models, while others are specific to particular models. The findings also suggest that the more site-specific the injury is, the fewer parameters are reported to characterize its gait abnormalities. This study strives to present a clearly organized picture of gait parameters used in each one of the different mouse models, potentially helping novel CatWalk XT users to apply this information to similar or related mouse models they are working on.

Emerging Role of Translational Digital Biomarkers Within Home Cage Monitoring Technologies in Preclinical Drug Discovery and Development.

In drug discovery and development, traditional assessment of human patients and preclinical subjects occurs at limited time points in potentially stressful surroundings (i.e., the clinic or a test arena), which can impact data quality and welfare. However, recent advances in remote digital monitoring technologies enable the assessment of human patients and preclinical subjects across multiple time points in familiar surroundings. The ability to monitor a patient throughout disease progression provides an opportunity for more relevant and efficient diagnosis as well as improved assessment of drug efficacy and safety. In preclinical in vivo animal models, these digital technologies allow for continuous, longitudinal, and non-invasive monitoring in the home environment. This manuscript provides an overview of digital monitoring technologies for use in preclinical studies including their history and evolution, current engagement through use cases, and impact of digital biomarkers (DBs) on drug discovery and the 3Rs. We also discuss barriers to implementation and strategies to overcome them. Finally, we address data consistency and technology standards from the perspective of technology providers, end-users, and subject matter experts. Overall, this review establishes an improved understanding of the value and implementation of digital biomarker (DB) technologies in preclinical research.

Measuring Behavior in the Home Cage: Study Design, Applications, Challenges, and Perspectives.

The reproducibility crisis (or replication crisis) in biomedical research is a particularly existential and under-addressed issue in the field of behavioral neuroscience, where, in spite of efforts to standardize testing and assay protocols, several known and unknown sources of confounding environmental factors add to variance. Human interference is a major contributor to variability both within and across laboratories, as well as novelty-induced anxiety. Attempts to reduce human interference and to measure more "natural" behaviors in subjects has led to the development of automated home-cage monitoring systems. These systems enable prolonged and longitudinal recordings, and provide large continuous measures of spontaneous behavior that can be analyzed across multiple time scales. In this review, a diverse team of neuroscientists and product developers share their experiences using such an automated monitoring system that combines Noldus PhenoTyper® home-cages and the video-based tracking software, EthoVision® XT, to extract digital biomarkers of motor, emotional, social and cognitive behavior. After presenting our working definition of a "home-cage", we compare home-cage testing with more conventional out-of-cage tests (e.g., the open field) and outline the various advantages of the former, including opportunities for within-subject analyses and assessments of circadian and ultradian activity. Next, we address technical issues pertaining to the acquisition of behavioral data, such as the fine-tuning of the tracking software and the potential for integration with biotelemetry and optogenetics. Finally, we provide guidance on which behavioral measures to emphasize, how to filter, segment, and analyze behavior, and how to use analysis scripts. We summarize how the PhenoTyper has applications to study neuropharmacology as well as animal models of neurodegenerative and neuropsychiatric illness. Looking forward, we examine current challenges and the impact of new developments. Examples include the automated recognition of specific behaviors, unambiguous tracking of individuals in a social context, the development of more animal-centered measures of behavior and ways of dealing with large datasets. Together, we advocate that by embracing standardized home-cage monitoring platforms like the PhenoTyper, we are poised to directly assess issues pertaining to reproducibility, and more importantly, measure features of rodent behavior under more ethologically relevant scenarios.

Deep learning improves automated rodent behavior recognition within a specific experimental setup.

Automated observation and analysis of rodent behavior is important to facilitate research progress in neuroscience and pharmacology. Available automated systems lack adaptivity and can benefit from advances in AI. In this work we compare a state-of-the-art conventional rat behavior recognition (RBR) system to an advanced deep learning method and evaluate its performance within and across experimental setups. We show that using a multi-fiber network (MF-Net) in conjunction with data augmentation strategies within-setup dataset performance improves over the conventional RBR system. Two new methods for video augmentation were used: video cutout and dynamic illumination change. However, we also show that improvements do not transfer to videos in different experimental setups, for which we discuss possible causes and cures.

An Integrated System for the Automated Recording and Analysis of Insect Behavior in T-maze Arrays.

Host-plant resistance to insects like thrips and aphids is a complex trait that is difficult to phenotype quickly and reliably. Here, we introduce novel hardware and software to facilitate insect choice assays and automate the acquisition and analysis of movement tracks. The hardware consists of an array of individual T-mazes allowing simultaneous release of up to 90 insect individuals from their individual cage below each T-maze with choice of two leaf disks under a video camera. Insect movement tracks are acquired with computer vision software (EthoVision) and analyzed with EthoAnalysis, a novel software package that allows for automated reporting of highly detailed behavior parameters and statistical analysis. To validate the benefits of the system we contrasted two Arabidopsis accessions that were previously analyzed for differential resistance to western flower thrips. Results of two trials with 40 T-mazes are reported and we show how we arrived at optimized settings for the different filters and statistics. The statistics are reported in terms of frequency, duration, distance and speed of behavior events, both as sum totals and event averages, and both for the total trial period and in time bins of 1 h. Also included are higher level analyses with subcategories like short-medium-long events and slow-medium-fast events. The time bins showed how some behavior elements are more descriptive of differences between the genotypes during the first hours, whereas others are constant or become more relevant at the end of an 8 h recording. The three overarching behavior categories, i.e., choice, movement, and halting, were automatically corrected for the percentage of time thrips were detected and 24 out of 38 statistics of behavior parameters differed by a factor 2-6 between the accessions. The analysis resulted in much larger contrasts in behavior traits than reported previously. Compared to leaf damage assays on whole plants or detached leaves that take a week or more to complete, results were obtained in 8 h, with more detail, fewer individuals and higher significance. The potential value of the new integrated system, named EntoLab, for discovery of genetic traits in plants and insects by high throughput screening of large populations is discussed.

Reproducibility via coordinated standardization: a multi-center study in a Shank2 genetic rat model for Autism Spectrum Disorders.

Inconsistent findings between laboratories are hampering scientific progress and are of increasing public concern. Differences in laboratory environment is a known factor contributing to poor reproducibility of findings between research sites, and well-controlled multisite efforts are an important next step to identify the relevant factors needed to reduce variation in study outcome between laboratories. Through harmonization of apparatus, test protocol, and aligned and non-aligned environmental variables, the present study shows that behavioral pharmacological responses in Shank2 knockout (KO) rats, a model of synaptic dysfunction relevant to autism spectrum disorders, were highly replicable across three research centers. All three sites reliably observed a hyperactive and repetitive behavioral phenotype in KO rats compared to their wild-type littermates as well as a dose-dependent phenotype attenuation following acute injections of a selective mGluR1 antagonist. These results show that reproducibility in preclinical studies can be obtained and emphasizes the need for high quality and rigorous methodologies in scientific research. Considering the observed external validity, the present study also suggests mGluR1 as potential target for the treatment of autism spectrum disorders.

Learning to recognize rat social behavior: Novel dataset and cross-dataset application.

BACKGROUND: Social behavior is an important aspect of rodent models. Automated measuring tools that make use of video analysis and machine learning are an increasingly attractive alternative to manual annotation. Because machine learning-based methods need to be trained, it is important that they are validated using data from different experiment settings. NEW METHOD: To develop and validate automated measuring tools, there is a need for annotated rodent interaction datasets. Currently, the availability of such datasets is limited to two mouse datasets. We introduce the first, publicly available rat social interaction dataset, RatSI. RESULTS: We demonstrate the practical value of the novel dataset by using it as the training set for a rat interaction recognition method. We show that behavior variations induced by the experiment setting can lead to reduced performance, which illustrates the importance of cross-dataset validation. Consequently, we add a simple adaptation step to our method and improve the recognition performance. COMPARISON WITH EXISTING METHODS: Most existing methods are trained and evaluated in one experimental setting, which limits the predictive power of the evaluation to that particular setting. We demonstrate that cross-dataset experiments provide more insight in the performance of classifiers. CONCLUSIONS: With our novel, public dataset we encourage the development and validation of automated recognition methods. We are convinced that cross-dataset validation enhances our understanding of rodent interactions and facilitates the development of more sophisticated recognition methods. Combining them with adaptation techniques may enable us to apply automated recognition methods to a variety of animals and experiment settings.

Automated video tracking of thrips behavior to assess host-plant resistance in multiple parallel two-choice setups.

BACKGROUND: Piercing-sucking insects cause severe damage in crops. Breeding for host-plant resistance can significantly reduce the yield losses caused by these insects, but host-plant resistance is a complex trait that is difficult to phenotype quickly and reliably. Current phenotyping methods mainly focus on labor-intensive and time-consuming end-point measurements of plant fitness. Characterizing insect behavior as a proxy for host-plant resistance could be a promising time-saving alternative to end-point measurements. RESULTS: We present a phenotyping platform that allows screening for host-plant resistance against Western flower thrips (WFT, Frankliniella occidentalis (Pergande)) in a parallel two-choice setup using automated video tracking of thrips behavior. The platform was used to establish host-plant preference of WFT with a large plant population of 345 wild Arabidopsis accessions and the method was optimized with two extreme accessions from this population that differed in resistance towards WFT. To this end, the behavior of 88 WFT individuals was simultaneously tracked in 88 parallel two-choice arenas during 8 h. Host-plant preference of WFT was established both by the time thrips spent on either accession and various behavioral parameters related to movement (searching) and non-movement (feeding) events. CONCLUSION: In comparison to 6-day end-point choice assays with whole plants or detached leaves, the automated video-tracking choice assay developed here delivered similar results, but with higher time- and resource efficiency. This method can therefore be a reliable and effective high throughput phenotyping tool to assess host-plant resistance to thrips in large plant populations.

A novel 3D method of locomotor analysis in adult zebrafish: Implications for automated detection of CNS drug-evoked phenotypes.

BACKGROUND: Expanding the spectrum of organisms to model human brain phenotypes is critical for our improved understanding of the pathobiology of neuropsychiatric disorders. Given the clear limitations of existing mammalian models, there is an urgent need for low-cost, high-throughput in-vivo technologies for drug and gene discovery. NEW METHOD: Here, we introduce a new automated method for generating 3D (X,Y,Z) swim trajectories in adult zebrafish (Danio rerio), to improve their neurophenotyping. RESULTS: Based on the Track3D module of EthoVision XT video tracking software (Noldus Information Technology), this tool enhances the efficient, high-throughput 3D analyses of zebrafish behavioral responses. Applied to adult zebrafish behavior, this 3D method is highly sensitive to various classes of psychotropic drugs, including selected psychostimulant and hallucinogenic agents. COMPARISON WITH EXISTING METHODS: Our present method offers a marked advance in the existing 2D and 3D methods of zebrafish behavioral phenotyping, minimizing research time and recording high-resolution, automatically synchronized videos with calculated, high-precision object positioning. CONCLUSIONS: Our novel approach brings practical simplicity and 'integrative' capacity to the often complex and error-prone quantification of zebrafish behavioral phenotypes. Illustrating the value of 3D swim path reconstructions for identifying experimentally-evoked phenotypic profiles, this method fosters innovative, ethologically relevant, and fully automated small molecule screens using adult zebrafish.

Automated image-based tracking and its application in ecology.

The behavior of individuals determines the strength and outcome of ecological interactions, which drive population, community, and ecosystem organization. Bio-logging, such as telemetry and animal-borne imaging, provides essential individual viewpoints, tracks, and life histories, but requires capture of individuals and is often impractical to scale. Recent developments in automated image-based tracking offers opportunities to remotely quantify and understand individual behavior at scales and resolutions not previously possible, providing an essential supplement to other tracking methodologies in ecology. Automated image-based tracking should continue to advance the field of ecology by enabling better understanding of the linkages between individual and higher-level ecological processes, via high-throughput quantitative analysis of complex ecological patterns and processes across scales, including analysis of environmental drivers.

Assessing behavioural and cognitive domains of autism spectrum disorders in rodents: current status and future perspectives.

The establishment of robust and replicable behavioural testing paradigms with translational value for psychiatric diseases is a major step forward in developing and testing etiology-directed treatment for these complex disorders. Based on the existing literature, we have generated an inventory of applied rodent behavioural testing paradigms relevant to autism spectrum disorders (ASD). This inventory focused on previously used paradigms that assess behavioural domains that are affected in ASD, such as social interaction, social communication, repetitive behaviours and behavioural inflexibility, cognition as well as anxiety behaviour. A wide range of behavioural testing paradigms for rodents were identified. However, the level of face and construct validity is highly variable. The predictive validity of these paradigms is unknown, as etiology-directed treatments for ASD are currently not on the market. To optimise these studies, future efforts should address aspects of reproducibility and take into account data about the neurodevelopmental underpinnings and trajectory of ASD. In addition, with the increasing knowledge of processes underlying ASD, such as sensory information processes and synaptic plasticity, phenotyping efforts should include multi-level automated analysis of, for example, representative task-related behavioural and electrophysiological read-outs.

Muscle hyperalgesia correlates with motor function in complex regional pain syndrome type 1.

UNLABELLED: At present it is unclear if disturbed sensory processing plays a role in the development of the commonly observed motor impairments in patients with complex regional pain syndrome (CRPS). This study aims to investigate the relation between sensory and motor functioning in CRPS patients with and without dystonia. Patients with CRPS of the arm and controls underwent comprehensive quantitative sensory testing and kinematic analysis of repetitive finger movements. Both CRPS groups showed thermal hypoesthesia to cold and warm stimuli and hyperalgesia to cold stimuli. A decreased pressure pain threshold reflecting muscle hyperalgesia emerged as the most prominent sensory abnormality in both patient groups and was most pronounced in CRPS patients with dystonia. Moreover, the decreased pressure pain threshold was the only nociceptive parameter that related to measures of motor function in both patients and controls. CRPS patients with dystonia had an increased 2-point discrimination as compared to controls and CRPS patients without dystonia. This finding was also reported in other types of dystonia and has been associated to cortical reorganization in response to impaired motor function. We hypothesize that increased sensitivity of the circuitry mediating muscle nociception may play a crucial role in impaired motor control in CRPS. PERSPECTIVE: This is the first study linking a sensory dysfunction, ie, muscle hyperalgesia, to motor impairment in CRPS. Circuitries mediating muscle nociception may therefore play an important role in impaired motor control in CRPS.

A 3D analysis of flight behavior of Anopheles gambiae sensu stricto malaria mosquitoes in response to human odor and heat.

Female mosquitoes use odor and heat as cues to navigate to a suitable landing site on their blood host. The way these cues affect flight behavior and modulate anemotactic responses, however, is poorly understood. We studied in-flight behavioral responses of females of the nocturnal malaria mosquito Anopheles gambiae sensu stricto to human odor and heat. Flight-path characteristics in a wind tunnel (flow 20 cm/s) were quantified in three dimensions. With wind as the only stimulus (control), short and close to straight upwind flights were recorded. With heat alone, flights were similarly short and direct. The presence of human odor, in contrast, caused prolonged and highly convoluted flight patterns. The combination of odor+heat resulted in longer flights with more landings on the source than to either cue alone. Flight speed was greatest (mean groundspeed 27.2 cm/s) for odor+heat. Odor alone resulted in decreased flight speed when mosquitoes arrived within 30 cm of the source whereas mosquitoes exposed to odor+heat maintained a high flight speed while flying in the odor plume, until they arrived within 15 cm of the source. Human odor evoked an increase in crosswind flights with an additive effect of heat at close range (<15 cm) to the source. This was found for both horizontal and vertical flight components. However, mosquitoes nevertheless made upwind progress when flying in the odor+heat generated plume, suggesting that mosquitoes scan their environment intensively while they progress upwind towards their host. These observations may help to improve the efficacy of trapping systems for malaria mosquitoes by (1) optimizing the site of odor release relative to trap entry and (2) adding a heat source which enhances a landing response.

Pain relief is associated with improvement in motor function in complex regional pain syndrome type 1: secondary analysis of a placebo-controlled study on the effects of ketamine.

UNLABELLED: There are indications of motor circuit changes in patients with complex regional pain syndrome (CRPS). Nevertheless, although several studies have analyzed motor behavior in CRPS, a relation with pain could not be detected. This might be explained by the use of cross-sectional designs in these studies, in which pain is considered as a trait- rather than a state-dependent variable. We therefore studied the time-dependent relation between pain and motor function in affected arms of 29 CRPS patients during their participation in a placebo-controlled ketamine study. Movement parameters (velocity, frequency, amplitude, and number of arrests) were assessed during a finger tapping task. Linear mixed model analysis of the effects of pain (numerical rating scale score), treatment (ketamine/placebo), and week (1, 3, 6, and 12 weeks after treatment) on the movement parameters revealed that pain intensity was significantly (inversely) related to motor function, irrespective of whether patients had received ketamine or placebo. Movement parameters changed 3-12% per point numerical rating scale change. Because patients were unaware of possible effects of ketamine on motor function, these findings suggest that motor function changes were mediated by, or occurred simultaneously with, changes in pain intensity. By improving motor function, pain relief may offer a window of opportunity for rehabilitation programs in CRPS. PERSPECTIVE: This article provides evidence for a direct relation between pain and motor function in CRPS, which indicates that pain relief may be an important factor in the treatment of motor disturbances in this condition. These findings may help to advance our understanding of the pathways underlying motor disturbances in CRPS.

An automated system for the recognition of various specific rat behaviours.

The automated measurement of rodent behaviour is crucial to advance research in neuroscience and pharmacology. Rats and mice are used as models for human diseases; their behaviour is studied to discover and develop new drugs for psychiatric and neurological disorders and to establish the effect of genetic variation on behavioural changes. Such behaviour is primarily labelled by humans. Manual annotation is labour intensive, error-prone and subject to individual interpretation. We present a system for automated behaviour recognition (ABR) that recognises the rat behaviours 'drink', 'eat', 'sniff', 'groom', 'jump', 'rear unsupported', 'rear wall', 'rest', 'twitch' and 'walk'. The ABR system needs no on-site training; the only inputs needed are the sizes of the cage and the animal. This is a major advantage over other systems that need to be trained with hand-labelled data before they can be used in a new experimental setup. Furthermore, ABR uses an overhead camera view, which is more practical in lab situations and facilitates high-throughput testing more easily than a side-view setup. ABR has been validated by comparison with manual behavioural scoring by an expert. For this, animals were treated with two types of psychopharmaca: a stimulant drug (Amphetamine) and a sedative drug (Diazepam). The effects of drug treatment on certain behavioural categories were measured and compared for both analysis methods. Statistical analysis showed that ABR found similar behavioural effects as the human observer. We conclude that our ABR system represents a significant step forward in the automated observation of rodent behaviour.

Automated high-throughput neurophenotyping of zebrafish social behavior.

Zebrafish (Danio rerio) are rapidly becoming an important model organism in neuroscience research, representing an excellent species to study complex social phenotypes. Zebrafish actively form shoals, which can be used to quantify their shoaling behaviors, highly sensitive to various experimental manipulations. Recent advances in video-tracking techniques have enabled simultaneous tracking of multiple subjects, previously assessed by manual scoring of animal behavior. Here we examined the effect of group-size in the shoaling paradigm (ranging from 2 to 8 fish), and evaluated the ability of novel video-tracking tools to accurately track an entire shoal, compared to traditional manual analysis of shoaling phenotypes. To further validate our approach, the effects of the psychotropic drugs lysergic acid diethylamide (LSD) and 3,4-methlenedioxymethamphetamine (MDMA), as well as exposure to alarm pheromone, previously shown to affect zebrafish shoaling, were examined. Overall, a significant difference in group size was shown in the 2-fish vs. the 3-, 4-, 5-, 6-, 7- and 8-fish groups. Moreover, both LSD and MDMA treatments reduced shoaling (assessed by increased inter-fish distance) as well as proximity (time spent together) among fish. In contrast, exposure to alarm pheromone yielded an increase in shoaling and in proximity in a time-dependent manner. Importantly, a highly significant correlation for manual vs. automated analyses was revealed across all experiments. Collectively, this study further supports the utility of zebrafish to study social behavior, also demonstrating the capacity of video-tracking technology to assess zebrafish shoaling in a high-throughput and reliable manner.

The Observer XT: a tool for the integration and synchronization of multimodal signals.

The Observer was originally developed as a manual event recorder for the collection, management, analysis, and presentation of observational data in animals. Because of the flexibility of later versions, it became clear that The Observer was suitable for almost any study involving collection of observational data in both animals and humans. Furthermore, the most recent version of The Observer (The Observer XT) allows the integration and synchronization of multimodal signals from various sources, such as observational, video, tracking, and physiological data. This article describes how The Observer XT was used to integrate and synchronize video, observational, tracking, and physiological data from an experiment carried out in 2001 at the Wageningen Institute of Animal Sciences of Wageningen University and Research Centre. The integration and synchronization of these multimodal signals in The Observer XT allows the user to draw a more complete picture of the phenomena under study.