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Network analysis reveals abnormal functional brain circuitry in anxious dogs [1]

['Yangfeng Xu', 'Ghent Experimental Psychiatry', 'Ghep', 'Lab', 'Department Of Head', 'Skin', 'Faculty Of Medicine', 'Health Sciences', 'Ghent University', 'Ghent']

Date: 2023-05 

Anxiety is a common disease within human psychiatric disorders and has also been described as a frequently neuropsychiatric problem in dogs. Human neuroimaging studies showed abnormal functional brain networks might be involved in anxiety. In this study, we expected similar changes in network topology are also present in dogs. We performed resting-state functional MRI on 25 healthy dogs and 13 patients. The generic Canine Behavioral Assessment & Research Questionnaire was used to evaluate anxiety symptoms. We constructed functional brain networks and used graph theory to compare the differences between two groups. No significant differences in global network topology were found. However, focusing on the anxiety circuit, global efficiency and local efficiency were significantly higher, and characteristic path length was significantly lower in the amygdala in patients. We detected higher connectivity between amygdala-hippocampus, amygdala-mesencephalon, amygdala-thalamus, frontal lobe-hippocampus, frontal lobe-thalamus, and hippocampus-thalamus, all part of the anxiety circuit. Moreover, correlations between network metrics and anxiety symptoms were significant. Altered network measures in the amygdala were correlated with stranger-directed fear and excitability; altered degree in the hippocampus was related to attachment/attention seeking, trainability, and touch sensitivity; abnormal frontal lobe function was related to chasing and familiar dog aggression; attachment/attention seeking was correlated with functional connectivity between amygdala-hippocampus and amygdala-thalamus; familiar dog aggression was related to global network topology change. These findings may shed light on the aberrant topological organization of functional brain networks underlying anxiety in dogs.

Introduction

Anxiety disorders include disorders that share features of excessive fear and anxiety and related behavioral disturbances [1]. Anxiety disorders are classified as social anxiety disorder (SAD), post-traumatic stress disorder (PTSD), generalized anxiety disorder (GAD), panic disorder, and specific phobias [2]. These disabling conditions cause significant burdens to the individual and society, such as causing social relationships, suicide, and increasing healthcare costs. It has been widely reported that characteristic alterations in structural and functional connectivity (FC) are associated with anxiety [3–5], although the functional integrity and topological organization in such patients remains largely unclear.

Animal models are indispensable tools to unravel neurobiological mechanisms underlying anxiety disorders and their pathological variations. Change in neuronal activities in specific brain areas correlated with anxiety have been reported in primates [6], rodents [7], and dogs with pathological anxiety [8]. The investigation of the canine species could be of particular interest. It has been well accepted that dogs can be valid translational models for a number of human behavioral disorders [9]. Dogs can also develop these mental illnesses, and they are also relatively easily accessible and manageable compared to primates. Moreover, compared to rodents, dogs have a larger amount of frontal cortex. Thus, the canine species might be an appropriate model to investigate brain networks involved in anxiety, and together with other animal research, such as rodents, can be used as a model for human anxiety (and vice versa). The prevalence of anxiety disorders in the dog is high and the most encountered behavioral disorder in daily practice [10]. Moreover, they form a serious welfare problem not only for the well-being of the individual, but they also compromise the relationship with the owner leading to abandonment, rehoming, or even euthanasia. In the case of comorbid aggression, they result in safety hazards and are of public concern. It has been demonstrated that in several canine neuropsychiatric disorders, the neurobiological base has similar characteristics as its human counterparts [11–13], also in dogs [14]. However, till now there is no report about rs-fMRI studies in anxious dogs, even though their emotional value to humans puts an increased demand on veterinarians to implement refined diagnostic tools and provide optimized treatment. Thus, we hypothesized that similar abnormal regional neural connectivity as in humans diagnosed with anxious behaviors could be found in anxious dogs.

Resting-state functional magnetic resonance imaging (rs-fMRI) could reveal correlated spontaneous low frequency blood oxygenation level-dependent (BOLD) fluctuations in anatomically distinct regions called “resting state networks” (RSNs), which are thought to reflect neural activity or relevant functions that occur in the grey matter [15]. Functionally connected regions can be identified, and brain networks can be detected based on statistical dependencies between the BOLD time series of brain regions of interest. There are several analysis methods available for investigating the brain organization, including seed-based correlation, independent component analysis and graph theory [16]. Graph theory has been widely applied to analyze the topological properties alterations in neuropsychiatric disorders and enabled understanding of how brain disorders affect the brain cognitions based on fundamental properties of the brain network, including anxiety, depression, schizophrenia, Alzheimer’s disease, and epilepsy [17]. In graph theory, the brain is considered a network or graph with brain regions as nodes and the relationship between the nodes as edges. The brain network can then be described and quantified using graph theoretical network metrics [18]. Specifically, nodal degree measures the degree of nodes tending to cluster together, global efficiency measures the efficiency of parallel information transfer through the network, clustering coefficient measures the efficiency of information exchange within a local subnetwork or among adjacent regions, characteristic path length measures the ability for information propagation within the network, the small-world network indicates a typical network that has similar path length but higher clustering than a random network [18–20].

In this study, a combination of rs-fMRI and graph theory was used to investigate the underlying neuronal mechanisms of action of anxiety in dogs. rs-fMRI data were acquired in patient dogs with anxiety and in healthy dogs. In addition, different symptoms of anxiety were assessed using the Canine Behavioral Assessment & Research Questionnaire (C-BARQ), a canine behavioral questionnaire. The aim of this study was threefold: 1) to evaluate differences in brain network topology between healthy dogs and dogs with anxiety; 2) to identify differences in FC in regions implicated in anxiety and 3) to assess whether different symptoms of anxiety, as measured with the C-BARQ, are related to specific functional network differences. The results in dogs will benefit both veterinary medicine for anxiety-disordered animals and may serve human medicine as a natural model.

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[1] Url: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0282087

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