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Background The Newborn Behaviour Observation system (NBO) is a flexible relationship-based intervention designed to sensitise parents to their newborn’s capacities, to increase parental confidence and foster the bond between parent and infant. The aim of this study was to investigate the effects of an NBO intervention on maternal confidence during the first month postpartum, and on the quality of mother-infant interaction at infant age 4 months in a sample of mothers who exhibit elevated signs of distress or depression during pregnancy and/or describe prior experiences of mental health issues. Method Pregnant women with current emotional distress and/or a history of anxiety and depression were recruited from a healthcare centre in Reykjavik, between August 2016 and April 2018. The study used a two-group, randomised trial design with six measuring points, in which 54 women were randomly assigned to either the intervention or control group. The intervention group ( n  = 26) received the NBO in combination with standard care during routine home visits. The control group ( n  = 28) received the same numbers of home visits with standard care without NBO. Maternal confidence was measured using a parent questionnaire (covering learning outcomes relating to the infant’s communicative signals and maternal confidence) administered after each home visit in weeks 2, 3 and 4 postpartum. At 4 months infant age, a free-play situation involving mother-infant interaction was video-recorded in the participants’ homes and coded using the Emotional Availability Scale (EAS). Mixed effects models were used to estimate group differences in learning outcomes and maternal confidence across three time points. Two sample t-tests were used to compare the two groups’ EAS scores. Results The mothers in the intervention-group reported significantly higher maternal confidence and increased knowledge about their infant compared to the control group. Adjusted analyses suggest some evidence of a higher EAS non-hostility score in the intervention group ( p  = .031), but not for the other EAS scale scores ( p  ≥ .118). Conclusion Early home visits combining NBO with standard care enhance maternal confidence and the mother’s understanding of her infant. The small sample size makes it difficult to conclude whether repeated NBO sessions during the first month increase dimensions of maternal sensitivity in mother-infant interaction at 4 months postpartum. Trial registration ClinicalTrials.gov ID NCT04739332, Registered 02/01/2021.

To determine the concordance of Zurich Observation Pain Assessment (ZOPA) with the behavioural Pain Scale (BPS) and the Critical Care Pain Observation Tool (CPOT) to detect pain in nonverbal ICU patients. Prospective observational study [BASEC-Nr. PB_2016-02324]. A total of 49 ICU patients from cardiovascular, visceral and thoracic surgery and neurology and neurosurgery were recruited. Data from 24 patients were analyzed. Three independent observers assessed pain with the BPS, the CPOT or ZOPA prior, during and after a potential painful nursing intervention. Tools were randomized concerning the pain management after each pain assessment. Frequency of nine additional pain indicating items from a previous qualitative, explorative study was calculated. ZOPA was positive in 32 of 33 measuring cycles (97.0%; 95%CI: 84.2-99.9%), followed by the CPOT (28/33 cycles, 84.8%; 95%CI: 68.1–94.9%) and the BPS (23/33 cycles, 67.0%; 95%CI: 51.3–84.4%). In 22/33 cycles all tools were concordant (66.7%; 95%CI: 48.2-82.0%). Analgesics were provided in 29 out of 33 cycles (87.9%; 95%CI: 71.8–96.6%). Additional pain indicating items were inconsistently reported. ZOPA is concordant with the BPS and the CPOT to indicate pain but detects pain earlier due to the low threshold value. Inclusion of further items does not improve pain assessment.

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.

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.

Co-occurring emotional and behavioral problems (EBPs) frequently exist in young autistic children. There is evidence based on parental report that parenting interventions reduce child EBPs. More objective measures of child EBPs should supplement parent reported outcomes in trials. We describe the development of a new measure of child and parenting behavior, the Observation Schedule for Children with Autism–Anxiety, Behaviour and Parenting (OSCA–ABP). Participants were 83 parents/carers and their 4–8-year-old autistic children. The measure demonstrated good variance and potential sensitivity to change. Child and parenting behavior were reliably coded among verbal and minimally verbal children. Associations between reports from other informants and observed behavior showed the measure had sufficient convergent validity. The measure has promise to contribute to research and clinical practice in autism mental health beyond objective measurement in trials.

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.

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.

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.