Computational Analysis of Movement Patterns of Dogs with ADHD-Like Behavior

New Advances in Attention-Deficit/Hyperactivity Disorder-like Dogs

Similar to humans, dogs could suffer an Attention-Deficit/Hyperactivity Disorder-like syndrome (ADHD-like). In fact, several studies highlight the use of dogs as a model for studying ADHD. This condition entails behavioral problems expressed through impulsivity, attention issues, hyperactivity, and/or aggression, compromising the quality of life for both the caregiver and the dog. The pathophysiology of ADHD-like is complex and is associated with dysregulation of various neurotransmitters such as serotonin and dopamine. The expression of ADHD-like behavior in dogs would appear to depend on a classical gene-environment interaction as is the case with many neurological disorders in humans. In addition to the described symptomatology, ADHD-like dogs can exhibit strong comorbidities with compulsive behaviors, aggressiveness, inappropriate elimination and fearfulness, in addition to epilepsy, foreign body ingestion, and pruritus. In spite of the fact that there is no veterinary consensus about the diagnosis of ADHD-like, some validated questionnaires could be helpful, but these cannot be used as a unique diagnostic tool. The use of drugs, such as fluoxetine, in addition to an adequate environmental enrichment, relaxation protocols, and behavior modification can achieve an adequate quality of life for both the dog and caregivers.

Behavioral comorbidities treatment by fecal microbiota transplantation in canine epilepsy: a pilot study of a novel therapeutic approach

Introduction

Anxiety and cognitive dysfunction are frequent, difficult to treat and burdensome comorbidities in human and canine epilepsy. Fecal microbiota transplantation (FMT) has been shown to modulate behavior in rodent models by altering the gastrointestinal microbiota (GIM). This study aims to investigate the beneficial effects of FMT on behavioral comorbidities in a canine translational model of epilepsy.

Methods

Nine dogs with drug-resistant epilepsy (DRE) and behavioral comorbidities were recruited. The fecal donor had epilepsy with unremarkable behavior, which exhibited a complete response to phenobarbital, resulting in it being seizure-free long term. FMTs were performed three times, two weeks apart, and the dogs had follow-up visits at three and six months after FMTs. Comprehensive behavioral analysis, including formerly validated questionnaires and behavioral tests for attention deficit hyperactivity disorder (ADHD)- and fear- and anxiety-like behavior, as well as cognitive dysfunction, were conducted, followed by objective computational analysis. Blood samples were taken for the analysis of antiseizure drug (ASD) concentrations, hematology, and biochemistry. Urine neurotransmitter concentrations were measured. Fecal samples were subjected to analysis using shallow DNA shotgun sequencing, real-time polymerase chain reaction (qPCR)-based Dysbiosis Index (DI) assessment, and short-chain fatty acid (SCFA) quantification.

Results

Following FMT, the patients showed improvement in ADHD-like behavior, fear- and anxiety-like behavior, and quality of life. The excitatory neurotransmitters aspartate and glutamate were decreased, while the inhibitory neurotransmitter gamma-aminobutyric acid (GABA) and GABA/glutamate ratio were increased compared to baseline. Only minor taxonomic changes were observed, with a decrease in Firmicutes and a Blautia_A species, while a Ruminococcus species increased. Functional gene analysis, SCFA concentration, blood parameters, and ASD concentrations remained unchanged.

Discussion

Behavioral comorbidities in canine IE could be alleviated by FMT. This study highlights FMT's potential as a novel approach to improving behavioral comorbidities and enhancing the quality of life in canine patients with epilepsy.

Towards an Objective Measurement Tool for ADHD-like Traits in Family Dogs: A Comprehensive Test Battery

Family dogs exhibit neuropsychological deficits similar to attention-deficit/hyperactivity disorder (ADHD) symptoms in humans. Questionnaire methods have mostly been used to assess ADHD-like behaviours in dogs. In addition to our validated questionnaire (Dog ADHD and Functionality Rating Scale-DAFRS; 2024), we developed a simple behavioural test battery covering the ADHD symptom domains (i.e., inattention, hyperactivity, and impulsivity) in dogs. Our main aim was (i) to provide a final external validation step to the DAFRS by examining its associations with the test variables (N = 59); and (ii) to compare owner- and trainer-rated factor scores' associations with the test variables (n = 38). We developed four tests covering the ADHD symptom domains: the attention test (inattention), the plush dog test (impulsivity), the leash test, and the sit test (hyperactivity). All four behavioural variables correlated with their respective questionnaire scores, i.e., the strongest for hyperactivity, and the least strong for inattention. Both owner- and trainer-rated scores (n = 38) correlated with the relevant test variables in an expected direction. Dogs' training status was linked only to the sit test results. Test-retest analyses (n = 34) indicated moderate-to-excellent agreement across all behavioural variables. Our findings support the validity of our novel human-analogue questionnaire for dogs as the behavioural tests strongly correlate with the relevant questionnaire scores, indicating that the two constructs together can effectively assess inattention, hyperactivity, and impulsivity in dogs.

Automation in canine science: enhancing human capabilities and overcoming adoption barriers

The emerging field of canine science has been slow in adopting automated approaches for data analysis. However, with the dramatic increase in the volume and complexity of the collected behavioral data, this is now beginning to change. This paper aims to systematize the field of automation in canine science. We provide an examination of current automation processes and pipelines by providing a literature review of state-of-the-art studies applying automation in this field. In addition, via an empirical study with researchers in animal behavior, we explore their perceptions and attitudes toward automated approaches for better understanding barriers for a wider adoption of automation. The insights derived from this research could facilitate more effective and widespread utilization of automation within canine science, addressing current challenges and enhancing the analysis of increasingly complex and voluminous behavioral data. This could potentially revolutionize the field, allowing for more objective and quantifiable assessments of dog behavior, which would ultimately contribute to our understanding of dog-human interactions and canine welfare.

Epilepsy is more than a simple seizure disorder: Parallels between human and canine cognitive and behavioural comorbidities

Psychiatric and cognitive comorbidities have been known to play a major role in human epilepsy for a long time. People with epilepsy (PWE) frequently express signs of varying psychiatric and cognitive disorders affecting their quality and quantity of life (QoL/QaoL). Over the last few years, research on behavioural comorbidities and their effect on the underlying disease have been performed in canine epilepsy. The following article reviews manifestations of comorbidities in canine epilepsy with an emphasis on patterns of clinical signs and their effects on QoL and QaoL. Cognitive and behavioural alterations in epileptic dogs are mainly represented by fear-/anxiety related behaviour and cognitive impairment (CI). Reduced trainability and altered reactions to daily situations are common results of comorbid changes posing obstacles in everyday life of owners and their dog. In addition, clinical signs similar to attention deficit hyperactivity disorder (ADHD) in humans have been reported. Canine attention-deficit-hyperactivity-disorder-like (c-ADHD-like) behaviour should, however, be evaluated critically, as there are no official criteria for diagnosis of ADHD or ADHD-like behaviour in dogs, and some of the reported signs of c-ADHD-like behaviour could be confused with anxiety-associated behaviour. Many intrinsic and extrinsic factors could potentially influence the development of behavioural and cognitive comorbidities in canine epilepsy. In particular, seizure frequency/severity, signalment and factors concerning disease management, such as pharmacotherapy and nutrition, are closely linked with the presence of the aforementioned comorbid disorders. Further studies of behavioural alterations in epileptic dogs are needed to comprehend the complexity of clinical signs and their multifactorial origin.

Development of a human-analogue, 3-symptom domain Dog ADHD and Functionality Rating Scale (DAFRS)

The family dog, in its natural environment, exhibits neuropsychological deficits redolent of human psychiatric disorders, including behaviours that are similar to human attention-deficit/hyperactivity disorder (ADHD) symptoms. Based on standard questionnaire methods in humans, we aimed to develop and validate a detailed, psychometrically improved tool to assess owner views on relevant dog behaviours. We modified available questionnaires by adding items that allow for separate analysis of impulsivity, and items on functional impairment. We collected data from 1168 owners for different validation steps of the new questionnaire and, similarly to assessment of humans where teachers also evaluate as an expert control, we collected data from dog trainers. Exploratory and confirmatory factor analysis revealed 3 factors: inattention (IA), hyperactivity (H) and impulsivity (I), corresponding to all three human symptom dimensions in dogs. Test-retest analyses showed excellent agreement between measurements for all factors. Similarly to findings with humans, trainer-owner rating comparisons showed fair (IA) to moderate (H, I) agreement. As in humans, greater ADHD scores were associated with greater functional impairment scores. We suggest that in dogs, similarly to humans, parallel examination of (extreme) ADHD and functional impairment scores could help distinguish diagnosable individuals, after further validation of the questionnaire using a relevant behaviour test.

Digitally-enhanced dog behavioral testing

Behavioral traits in dogs are assessed for a wide range of purposes such as determining selection for breeding, chance of being adopted or prediction of working aptitude. Most methods for assessing behavioral traits are questionnaire or observation-based, requiring significant amounts of time, effort and expertise. In addition, these methods might be also susceptible to subjectivity and bias, negatively impacting their reliability. In this study, we proposed an automated computational approach that may provide a more objective, robust and resource-efficient alternative to current solutions. Using part of a 'Stranger Test' protocol, we tested n = 53 dogs for their response to the presence and neutral actions of a stranger. Dog coping styles were scored by three dog behavior experts. Moreover, data were collected from their owners/trainers using the Canine Behavioral Assessment and Research Questionnaire (C-BARQ). An unsupervised clustering of the dogs' trajectories revealed two main clusters showing a significant difference in the stranger-directed fear C-BARQ category, as well as a good separation between (sufficiently) relaxed dogs and dogs with excessive behaviors towards strangers based on expert scoring. Based on the clustering, we obtained a machine learning classifier for expert scoring of coping styles towards strangers, which reached an accuracy of 78%. We also obtained a regression model predicting C-BARQ scores with varying performance, the best being Owner-Directed Aggression (with a mean average error of 0.108) and Excitability (with a mean square error of 0.032). This case study demonstrates a novel paradigm of 'machine-based' dog behavioral assessment, highlighting the value and great promise of AI in this context.

BrachySound: machine learning based assessment of respiratory sounds in dogs

The early and accurate diagnosis of brachycephalic obstructive airway syndrome (BOAS) in dogs is pivotal for effective treatment and enhanced canine well-being. Owners often do underestimate the severity of BOAS in their dogs. In addition, traditional diagnostic methods, which include pharyngolaryngeal auscultation, are often compromised by subjectivity, are time-intensive and depend on the veterinary surgeon's experience. Hence, new fast, reliable assessment methods for BOAS are required. The aim of the current study was to use machine learning techniques to bridge this scientific gap. In this study, machine learning models were employed to objectively analyze 366 audio samples from 69 Pugs and 79 other brachycephalic breeds, recorded with an electronic stethoscope during a 15-min standardized exercise test. In classifying the BOAS test results as to whether the dog is affected or not, our models achieved a peak accuracy of 0.85, using subsets from the Pugs dataset. For predictions of the BOAS results from recordings at rest in Pugs and various brachycephalic breeds, accuracies of 0.68 and 0.65 were observed, respectively. Notably, the detection of laryngeal sounds achieved an F1 score of 0.80. These results highlight the potential of machine learning models to significantly streamline the examination process, offering a more objective assessment than traditional methods. This research indicates a turning point towards a data-driven, objective, and efficient approach in canine health assessment, fostering standardized and objective BOAS diagnostics.

Behavior-Based Video Summarization System for Dog Health and Welfare Monitoring

The popularity of dogs has been increasing owing to factors such as the physical and mental health benefits associated with raising them. While owners care about their dogs' health and welfare, it is difficult for them to assess these, and frequent veterinary checkups represent a growing financial burden. In this study, we propose a behavior-based video summarization and visualization system for monitoring a dog's behavioral patterns to help assess its health and welfare. The system proceeds in four modules: (1) a video data collection and preprocessing module; (2) an object detection-based module for retrieving image sequences where the dog is alone and cropping them to reduce background noise; (3) a dog behavior recognition module using two-stream EfficientNetV2 to extract appearance and motion features from the cropped images and their respective optical flow, followed by a long short-term memory (LSTM) model to recognize the dog's behaviors; and (4) a summarization and visualization module to provide effective visual summaries of the dog's location and behavior information to help assess and understand its health and welfare. The experimental results show that the system achieved an average F1 score of 0.955 for behavior recognition, with an execution time allowing real-time processing, while the summarization and visualization results demonstrate how the system can help owners assess and understand their dog's health and welfare.

Using machine learning to track dogs' exploratory behaviour in the presence and absence of their caregiver

Machine-learning-based behavioural tracking is a rapidly growing method in the behavioural sciences providing data with high spatial and temporal resolution while reducing the risk of observer bias. Nevertheless, only a few canine behaviour studies have applied this method. In the current study, we used three-dimensional (3D) tracking of the dogs' bodies to study how separation from the caregiver affected the dogs' behaviour in a novel environment. During the study, the dogs could move freely in a room equipped with trial-unique objects. We manipulated across trials whether the owner and/or a stranger was present in the room to evaluate the secure base effect, the tendency to explore and play more in the presence of the caregiver compared to another person. This secure base effect is considered a key characteristic of human attachment bonds and has also been described for the dog-caregiver relationship. The tracking data were internally consistent and highly correlated with human scorings and measurements. The results show that both the owner and stranger significantly increased the dogs' exploration; the dogs also spent more time in the proximity of the owner and stranger location when they were present. Even though the presence of both owner and stranger had a significant effect on the dogs' behaviour, the effect of the owner was more pronounced. Moreover, in the presence of the stranger the dogs spent more time close to their owner and showed a reduced tail-wagging asymmetry to the right side further supporting the distinct effect of owner and stranger on the dogs' behaviour. We conclude that machine-driven 3D tracking provides an efficient and reliable access for detailed behavioural analyses of dogs' exploration and attachment-related behaviours.

Dog Behavior Recognition Based on Multimodal Data from a Camera and Wearable Device

Although various studies on monitoring dog behavior have been conducted, methods that can minimize or compensate data noise are required. This paper proposes multimodal data-based dog behavior recognition that fuses video and sensor data using a camera and a wearable device. The video data represent the moving area of dogs to detect the dogs. The sensor data represent the movement of the dogs and extract features that affect dog behavior recognition. Seven types of behavior recognition were conducted, and the results of the two data types were used to recognize the dog’s behavior through a fusion model based on deep learning. Experimentation determined that, among FasterRCNN, YOLOv3, and YOLOv4, the object detection rate and behavior recognition accuracy were the highest when YOLOv4 was used. In addition, the sensor data showed the best performance when all statistical features were selected. Finally, it was confirmed that the performance of multimodal data-based fusion models was improved over that of single data-based models and that the CNN-LSTM-based model had the best performance. The method presented in this study can be applied for dog treatment or health monitoring, and it is expected to provide a simple way to estimate the amount of activity.

Objective Video-Based Assessment of ADHD-Like Canine Behavior Using Machine Learning

Simple Summary

This paper applies machine learning techniques to propose an objective video-based method for assessing the degree of canine ADHD-like behavior in veterinary consultation room. The method is evaluated using clinical data of dog patients in a veterinary clinic, as well as in a focus group of experts.

Abstract

Canine ADHD-like behavior is a behavioral problem that often compromises dogs’ well-being, as well as the quality of life of their owners; early diagnosis and clinical intervention are often critical for successful treatment, which usually involves medication and/or behavioral modification. Diagnosis mainly relies on owner reports and some assessment scales, which are subject to subjectivity. This study is the first to propose an objective method for automated assessment of ADHD-like behavior based on video taken in a consultation room. We trained a machine learning classifier to differentiate between dogs clinically treated in the context of ADHD-like behavior and health control group with 81% accuracy; we then used its output to score the degree of exhibited ADHD-like behavior. In a preliminary evaluation in clinical context, in 8 out of 11 patients receiving medical treatment to treat excessive ADHD-like behavior, H-score was reduced. We further discuss the potential applications of the provided artifacts in clinical settings, based on feedback on H-score received from a focus group of four behavior experts.

Computerized assisted evaluation system for canine cardiomegaly via key points detection with deep learning

Cardiomegaly is the main imaging finding for canine heart diseases. There are many advances in the field of medical diagnosing based on imaging with deep learning for human being. However there are also increasing realization of the potential of using deep learning in veterinary medicine. We reported a clinically applicable assisted platform for diagnosing the canine cardiomegaly with deep learning. VHS (vertebral heart score) is a measuring method used for the heart size of a dog. The concrete value of VHS is calculated with the relative position of 16 key points detected by the system, and this result is then combined with VHS reference range of all dog breeds to assist in the evaluation of the canine cardiomegaly. We adopted HRNet (high resolution network) to detect 16 key points (12 and four key points located on vertebra and heart respectively) in 2274 lateral X-ray images (training and validation datasets) of dogs, the model was then used to detect the key points in external testing dataset (396 images), the AP (average performance) for key point detection reach 86.4 %. Then we applied an additional post processing procedure to correct the output of HRNets so that the AP reaches 90.9 %. This result signifies that this system can effectively assist the evaluation of canine cardiomegaly in a real clinical scenario.

Effects of a single oral dose of gabapentin on storm phobia in dogs: A double-blind, placebo-controlled crossover trial

BACKGROUND

Storm phobia in companion dogs is a common disorder that significantly impacts dogs' welfare. Gabapentin, the action of which is only partially understood, is widely used for its antiepileptic and analgesic properties. Only recently, the veterinary community began to use gabapentin to address phobia and anxiety in dogs. This study tested gabapentin to lower fear responses of dogs during a thunderstorm event.

METHODS

Eighteen dogs suffering from storm phobia completed our double-blind, placebo-controlled crossover trial. Each dog's behaviour was evaluated twice by his owner: once under placebo, once under gabapentin. The treatment was orally administered at least 90 min before the exposure. Gabapentin was given at a dose ranging from 25 to 30 mg/kg.

RESULTS

Our results indicate a significant reduction of the fear responses of dogs under gabapentin. The adverse effects were rare, and the most frequent amongst them was ataxia.

CONCLUSION

In this trial, gabapentin appears to be an efficient and safe molecule that should be considered as part of the treatment plan of storm phobia in dogs.