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Automated quantification of behavior is increasingly prevalent in neuroscience research. Human judgments can influence machine-learning-based behavior classification at multiple steps in the process, for both supervised and unsupervised approaches. Such steps include the design of the algorithm for machine learning, the methods used for animal tracking, the choice of training images, and the benchmarking of classification outcomes. However, how these design choices contribute to the interpretation of automated behavioral classifications has not been extensively characterized. Here, we quantify the effects of experimenter choices on the outputs of automated classifiers of Drosophila social behaviors. Drosophila behaviors contain a considerable degree of variability, which was reflected in the confidence levels associated with both human and computer classifications. We found that a diversity of sex combinations and tracking features was important for robust performance of the automated classifiers. In particular, features concerning the relative position of flies contained useful information for training a machine-learning algorithm. These observations shed light on the importance of human influence on tracking algorithms, the selection of training images, and the quality of annotated sample images used to benchmark the performance of a classifier (the ‘ground truth’). Evaluation of these factors is necessary for researchers to accurately interpret behavioral data quantified by a machine-learning algorithm and to further improve automated classifications.

Mobile and wearable technology presents exciting opportunities for monitoring behavior using widely available sensor data. This could support clinical research and practice aimed at improving quality of life among the growing number of people with dementia. However, it requires suitable tools for measuring behavior in a natural real-life setting that can be easily implemented by others. The objectives of this study were to develop and test a set of algorithms for measuring mobility and activity and to describe a technical setup for collecting the sensor data that these algorithms require using off-the-shelf devices. A mobility measurement module was developed to extract travel trajectories and home location from raw GPS (global positioning system) data and to use this information to calculate a set of spatial, temporal, and count-based mobility metrics. Activity measurement comprises activity bout extraction from recognized activity data and daily step counts. Location, activity, and step count data were collected using smartwatches and mobile phones, relying on open-source resources as far as possible for accessing data from device sensors. The behavioral monitoring solution was evaluated among 5 healthy subjects who simultaneously logged their movements for 1 week. The evaluation showed that the behavioral monitoring solution successfully measures travel trajectories and mobility metrics from location data and extracts multimodal activity bouts during travel between locations. While step count could be used to indicate overall daily activity level, a concern was raised regarding device validity for step count measurement, which was substantially higher from the smartwatches than the mobile phones. This study contributes to clinical research and practice by providing a comprehensive behavioral monitoring solution for use in a real-life setting that can be replicated for a range of applications where knowledge about individual mobility and activity is relevant.

The dorsal root ganglia (DRG) contain the somas of first-order sensory neurons critical for somatosensation. Due to technical difficulties, DRG neuronal activity in awake behaving animals remains unknown. Here, we develop a method for imaging DRG at cellular and subcellular resolution over weeks in awake mice. The method involves the installation of an intervertebral fusion mount to reduce spinal movement, and the implantation of a vertebral glass window without interfering animals’ motor and sensory functions. In vivo two-photon calcium imaging shows that DRG neuronal activity is higher in awake than anesthetized animals. Immediately after plantar formalin injection, DRG neuronal activity increases substantially and this activity upsurge correlates with animals’ phasic pain behavior. Repeated imaging of DRG over 5 weeks after formalin injection reveals persistent neuronal hyperactivity associated with ongoing pain. The method described here provides an important means for in vivo studies of DRG functions in sensory perception and disorders. Imaging sensory neurons in the dorsal root ganglion (DRG) in awake animals is challenging due to motion artefacts and other technical issues. Here the authors develop an intervertebral fusion procedure which minimizes spinal movement thereby enabling chronic imaging of DRG neurons in awake, behaving mice.

Fast object tracking in real time allows convenient tracking of very large numbers of animals and closed-loop experiments that control stimuli for many animals in parallel. We developed MARGO, a MATLAB-based, real-time animal tracking suite for custom behavioral experiments. We demonstrated that MARGO can rapidly and accurately track large numbers of animals in parallel over very long timescales, typically when spatially separated such as in multiwell plates. We incorporated control of peripheral hardware, and implemented a flexible software architecture for defining new experimental routines. These features enable closed-loop delivery of stimuli to many individuals simultaneously. We highlight MARGO's ability to coordinate tracking and hardware control with two custom behavioral assays (measuring phototaxis and optomotor response) and one optogenetic operant conditioning assay. There are currently several open source animal trackers. MARGO's strengths are 1) fast and accurate tracking, 2) high throughput, 3) an accessible interface and data output and 4) real-time closed-loop hardware control for for sensory and optogenetic stimuli, all of which are optimized for large-scale experiments.

Physiological signals may be used as objective markers to identify emotions, which play relevant roles in social and daily life. To measure these signals, the use of contact-free techniques, such as Infrared Thermal Imaging (IRTI), is indispensable to individuals who have sensory sensitivity. The goal of this study is to propose an experimental design to analyze five emotions (disgust, fear, happiness, sadness and surprise) from facial thermal images of typically developing (TD) children aged 7-11 years using emissivity variation, as recorded by IRTI. For the emotion analysis, a dataset considered emotional dimensions (valence and arousal), facial bilateral sides and emotion classification accuracy. The results evidence the efficiency of the experimental design with interesting findings, such as the correlation between the valence and the thermal decrement in nose; disgust and happiness as potent triggers of facial emissivity variations; and significant emissivity variations in nose, cheeks and periorbital regions associated with different emotions. Moreover, facial thermal asymmetry was revealed with a distinct thermal tendency in the cheeks, and classification accuracy reached a mean value greater than 85%. From the results, the emissivity variations were an efficient marker to analyze emotions in facial thermal images, and IRTI was confirmed to be an outstanding technique to study emotions. This study contributes a robust dataset to analyze the emotions of 7-11-year-old TD children, an age range for which there is a gap in the literature.

There is a critical need for real-time tracking of behavioral indicators of mental disorders. Mobile sensing platforms that objectively and noninvasively collect, store, and analyze behavioral indicators have not yet been clinically validated or scalable. The aim of our study was to report on models of clinical symptoms for post-traumatic stress disorder (PTSD) and depression derived from a scalable mobile sensing platform. A total of 73 participants (67% [49/73] male, 48% [35/73] non-Hispanic white, 33% [24/73] veteran status) who reported at least one symptom of PTSD or depression completed a 12-week field trial. Behavioral indicators were collected through the noninvasive mobile sensing platform on participants' mobile phones. Clinical symptoms were measured through validated clinical interviews with a licensed clinical social worker. A combination hypothesis and data-driven approach was used to derive key features for modeling symptoms, including the sum of outgoing calls, count of unique numbers texted, absolute distance traveled, dynamic variation of the voice, speaking rate, and voice quality. Participants also reported ease of use and data sharing concerns. Behavioral indicators predicted clinically assessed symptoms of depression and PTSD (cross-validated area under the curve [AUC] for depressed mood=.74, fatigue=.56, interest in activities=.75, and social connectedness=.83). Participants reported comfort sharing individual data with physicians (Mean 3.08, SD 1.22), mental health providers (Mean 3.25, SD 1.39), and medical researchers (Mean 3.03, SD 1.36). Behavioral indicators passively collected through a mobile sensing platform predicted symptoms of depression and PTSD. The use of mobile sensing platforms can provide clinically validated behavioral indicators in real time; however, further validation of these models and this platform in large clinical samples is needed.

Hallucinogens induce the head-twitch response (HTR), a rapid reciprocal head movement, in mice. Although head twitches are usually identified by direct observation, they can also be assessed using a head-mounted magnet and a magnetometer. Procedures have been developed to automate the analysis of magnetometer recordings by detecting events that match the frequency, duration, and amplitude of the HTR. However, there is considerable variability in the features of head twitches, and behaviors such as jumping have similar characteristics, reducing the reliability of these methods. We have developed an automated method that can detect head twitches unambiguously, without relying on features in the amplitude-time domain. To detect the behavior, events are transformed into a visual representation in the time-frequency domain (a scalogram), deep features are extracted using the pretrained convolutional neural network (CNN) ResNet-50, and then the images are classified using a Support Vector Machine (SVM) algorithm. These procedures were used to analyze recordings from 237 mice containing 11,312 HTR. After transformation to scalograms, the multistage CNN-SVM approach detected 11,244 (99.4%) of the HTR. The procedures were insensitive to other behaviors, including jumping and seizures. Deep learning based on scalograms can be used to automate HTR detection with robust sensitivity and reliability.

Examination of rodent vocalizations in experimental conditions can yield valuable insights into how disease manifests and progresses over time. It can also be used as an index of social interest, motivation, emotional development or motor function depending on the animal model under investigation. Most mouse communication is produced in ultrasonic frequencies beyond human hearing. These ultrasonic vocalizations (USV) are typically described and evaluated using expert defined classification of the spectrographic appearance or simplistic acoustic metrics resulting in nine call types. In this study, we aimed to replicate the standard expert-defined call types of communicative vocal behavior in mice by using acoustic analysis to characterize USVs and a principled supervised learning setup. We used four feature selection algorithms to select parsimonious subsets with maximum predictive accuracy, which are then presented into support vector machines (SVM) and random forests (RF). We assessed the resulting models using 10-fold cross-validation with 100 repetitions for statistical confidence and found that a parsimonious subset of 8 acoustic measures presented to RF led to 85% correct out-of-sample classification, replicating the experts’ labels. Acoustic measures can be used by labs to describe USVs and compare data between groups, and provide insight into vocal-behavioral patterns of mice by automating the process on matching the experts’ call types.

In Drosophila , the clock that controls rest-activity rhythms synchronizes with light-dark cycles through either the blue-light sensitive cryptochrome (Cry) located in most clock neurons, or rhodopsin-expressing histaminergic photoreceptors. Here we show that, in the absence of Cry, each of the two histamine receptors Ort and HisCl1 contribute to entrain the clock whereas no entrainment occurs in the absence of the two receptors. In contrast to Ort, HisCl1 does not restore entrainment when expressed in the optic lobe interneurons. Indeed, HisCl1 is expressed in wild-type photoreceptors and entrainment is strongly impaired in flies with photoreceptors mutant for HisCl1. Rescuing HisCl1 expression in the Rh6-expressing photoreceptors restores entrainment but it does not in other photoreceptors, which send histaminergic inputs to Rh6-expressing photoreceptors. Our results thus show that Rh6-expressing neurons contribute to circadian entrainment as both photoreceptors and interneurons, recalling the dual function of melanopsin-expressing ganglion cells in the mammalian retina. The role of the HisCl1 histamine receptor in the Drosophila visual system remains unclear. This study shows that HisCl1 is expressed in Rh6-photoreceptors where its function is sufficient for circadian entrainment by incorporating synaptic inputs from other photoreceptors.

Background Compensatory movements frequently emerge in the process of motor recovery after a stroke. Given their potential for unfavorable long-term effects, it is crucial to assess and document compensatory movements throughout rehabilitation. However, clinically applicable assessment tools are currently limited. Deep learning methods have shown promising potential for assessing movement quality and addressing this gap. A crucial prerequisite for developing an accurate measurement tool is ensuring reliability in assessing compensatory movements, which is essential for establishing a valid ground truth. Objective The study aimed to assess inter- and intra-rater reliability of occupational and physical therapists’ visual assessment of compensatory movements based on video analysis. Methods Experienced therapists evaluated video-recorded performances of a standardized drinking task through an online labeling system. The standardized drinking task was performed by seven individuals with mild to moderate upper limb motor impairments after a stroke. The therapists rated compensatory movements in predetermined body segments and movement phases using a slider with a continuous scale ranging from 0 (no compensation) to 100 (maximum compensation). The collected data were analyzed using a generalized-linear mixed effects model with zero-inflated beta regression to estimate variance components. Intraclass correlation coefficients (ICC) were calculated to assess inter- and intra-rater reliability. Results Twenty-two therapists participated in this study. Inter-rater reliability was good for the phases of reaching, drinking, and returning (ICC ≥ .0.75), and moderate for both phases of transporting. Intra-rater reliability was excellent for the drinking phase (ICC > 0.9) and moderate to good for the phases of reaching, transporting, and returning of our cohort. ICCs for smoothness and interjoint coordination were poor for both inter- and intra-rater reliability. The data analysis unveiled a wide range of credible intervals for the ICCs across all domains examined in this study. Conclusions While this study shows promising inter- and intra-rater reliability for the drinking phases within our sample, the wide credible intervals raise the possibility that these results may have occurred by chance. Consequently, we cannot recommend the establishment of a ground truth for the automatic assessment of compensatory movements during a drinking task based on therapists’ ratings alone.