A generalised framework for detailed classification of swimming paths inside the Morris Water Maze

The Morris Water Maze is commonly used in behavioural neuroscience for the study of spatial learning with rodents. Over the years, various methods of analysing rodent data collected during this task have been proposed. These methods span from classical performance measurements to more sophisticated categorisation techniques which classify the animal swimming path into behavioural classes known as exploration strategies. Classification techniques provide additional insight into the different types of animal behaviours but still only a limited number of studies utilise them. This is primarily because they depend highly on machine learning knowledge. We have previously demonstrated that the animals implement various strategies and that classifying entire trajectories can lead to the loss of important information. In this work, we have developed a generalised and robust classification methodology to boost classification performance and nullify the need for manual tuning. We have also made available an open-source software based on this methodology

A framework to identify structured behavioral patterns within rodent spatial trajectories

Animal behavior is highly structured. Yet, structured behavioral patterns—or “statistical ethograms”—are not immediately apparent from the full spatiotemporal data that behavioral scientists usually collect. Here, we introduce a framework to quantitatively characterize rodent behavior during spatial (e.g., maze) navigation, in terms of movement building blocks or motor primitives. The hypothesis that we pursue is that rodent behavior is characterized by a small number of motor primitives, which are combined over time to produce open-ended movements. We assume motor primitives to be organized in terms of two sparsity principles: each movement is controlled using a limited subset of motor primitives (sparse superposition) and each primitive is active only for time-limited, time-contiguous portions of movements (sparse activity). We formalize this hypothesis using a sparse dictionary learning method, which we use to extract motor primitives from rodent position and velocity data collected during spatial navigation, and successively to reconstruct past trajectories and predict novel ones. Three main results validate our approach. First, rodent behavioral trajectories are robustly reconstructed from incomplete data, performing better than approaches based on standard dimensionality reduction methods, such as principal component analysis, or single sparsity. Second, the motor primitives extracted during one experimental session generalize and afford the accurate reconstruction of rodent behavior across successive experimental sessions in the same or in modified mazes. Third, in our approach the number of motor primitives associated with each maze correlates with independent measures of maze complexity, hence showing that our formalism is sensitive to essential aspects of task structure. The framework introduced here can be used by behavioral scientists and neuroscientists as an aid for behavioral and neural data analysis. Indeed, the extracted motor primitives enable the quantitative characterization of the complexity and similarity between different mazes and behavioral patterns across multiple trials (i.e., habit formation). We provide example uses of this computational framework, showing how it can be used to identify behavioural effects of maze complexity, analyze stereotyped behavior, classify behavioral choices and predict place and grid cell displacement in novel environments

A framework to identify structured behavioral patterns within rodent spatial trajectories

Animal behavior is highly structured. Yet, structured behavioral patterns—or “statistical ethograms”—are not immediately apparent from the full spatiotemporal data that behavioral scientists usually collect. Here, we introduce a framework to quantitatively characterize rodent behavior during spatial (e.g., maze) navigation, in terms of movement building blocks or motor primitives. The hypothesis that we pursue is that rodent behavior is characterized by a small number of motor primitives, which are combined over time to produce open-ended movements. We assume motor primitives to be organized in terms of two sparsity principles: each movement is controlled using a limited subset of motor primitives (sparse superposition) and each primitive is active only for time-limited, time-contiguous portions of movements (sparse activity). We formalize this hypothesis using a sparse dictionary learning method, which we use to extract motor primitives from rodent position and velocity data collected during spatial navigation, and successively to reconstruct past trajectories and predict novel ones. Three main results validate our approach. First, rodent behavioral trajectories are robustly reconstructed from incomplete data, performing better than approaches based on standard dimensionality reduction methods, such as principal component analysis, or single sparsity. Second, the motor primitives extracted during one experimental session generalize and afford the accurate reconstruction of rodent behavior across successive experimental sessions in the same or in modified mazes. Third, in our approach the number of motor primitives associated with each maze correlates with independent measures of maze complexity, hence showing that our formalism is sensitive to essential aspects of task structure. The framework introduced here can be used by behavioral scientists and neuroscientists as an aid for behavioral and neural data analysis. Indeed, the extracted motor primitives enable the quantitative characterization of the complexity and similarity between different mazes and behavioral patterns across multiple trials (i.e., habit formation). We provide example uses of this computational framework, showing how it can be used to identify behavioural effects of maze complexity, analyze stereotyped behavior, classify behavioral choices and predict place and grid cell displacement in novel environments

Behavioral characteristics as potential biomarkers of the development and phenotype of epilepsy in a rat model of temporal lobe epilepsy

The present study performed a detailed analysis of behavior in a rat model of epilepsy using both established and novel methodologies to identify behavioral impairments that may differentiate between animals with a short versus long latency to spontaneous seizures and animals with a low versus high number of seizures. Temporal lobe epilepsy was induced by electrical stimulation of the amygdala. Rats were stimulated for 25 min with 100-ms trains of 1-ms biphasic square-wave pluses that were delivered every 0.5 s. Electroencephalographic recordings were performed to classify rats into groups with a short latency ( 20 days, n = 8) to the first spontaneous seizure and into groups with a low number of seizures (62 ± 64.5, n = 8) and high number of seizures (456 ± 185, n = 7). To examine behavioral impairments, we applied the following behavioral tests during early and late stages of epilepsy: behavioral hyperexcitability, open field, novel object exploration, elevated plus maze, and Morris water maze. No differences in stress levels (e.g., touch response in the behavioral hyperexcitability test), activity (e.g., number of entries into the open arms of the elevated plus maze), or learning (e.g., latency to find the platform in the Morris water maze test during training days) were observed between animals with a short versus long latency to develop spontaneous seizures or between animals with a low versus high number of seizures. However, we found a higher motor activity measured by higher number of entries into the closed arms of the elevated plus maze at week 26 post-stimulation in animals with a high number of seizures compared with animals with a low number of seizures. The analysis of the Morris water maze data categorized the strategies that the animals used to locate the platform showing that the intensity of epilepsy and duration of epileptogenesis influenced swimming strategies. These findings indicate that behavioral impairments were relatively mild in the present model, but some learning strategies may be useful biomarkers in preclinical studies

Impact of individual differences in glucocorticoid adaptation to stress on behavior, neurophysiology and metabolism

The stress system is a key modulator of homeostasis and allows organisms to adapt to environmental changes. Proper survival is dependent on the appropriate stress response, for example initiating food (energy) intake or provoking physical reaction. However, long-term activity of the stress system is related to cardiovascular diseases, metabolic syndromes as well as accelerated aging and cognitive impairments. In order to assess the impacts of stress regulation on cardiac, metabolic and aging health processes, we used lines of rats selected for their differential glucocorticoid responsiveness to stress during juvenile period, in the different experiments presented here. The three lines of rats showed low, intermediate or high response to stress and elicited differences in biobehavioral phenotypes. Cardiovascular diseases are highly exacerbated by stress exposure and autonomic imbalance. Reduced vagal modulation has been related to a lower stress flexibility and deleterious effects on cardiac health. In a first study, we investigated the autonomic nervous system modulation of heart rate in the three lines of rats (differing in their responsiveness to stress). Electrocardiographic recordings were performed at rest and following autonomic pharmacological manipulations. Rats with intermediate reactivity to stress had a higher resting parasympathetic (vagal) modulation and a reduced heart rate compared to rats with low or high stress responses. Furthermore, pharmacological treatments showed that the sympathetic regulation of the heart was not impaired in rats with low and high responsiveness to stress. Stress can affect social interactions and, in return, social interactions can be the cause of critical stress. Furthermore, since the stress system is related to key metabolic mediators, we investigated in a second study, the general metabolism of rats from the three lines. Moreover, we paired rats from the different lines together, in mixed-line dyads, and we evaluated the differences in social interactions and the long-term effects of mixed-line pairing on metabolism. We used indirect calorimetry and mitochondrial respirometry to measure energy expenditure and mitochondrial function. We observed that the selection for differences in glucocorticoid responsiveness induced constitutive differences in energy expenditure and fuel use. Moreover, we showed that the biobehavioral phenotypes affected the social interactions between the different lines. Finally, long-term mixed-line pairing affected global and central metabolism of the rats, with rats from the low and intermediate responsive lines being more susceptible to changes. In a final experiment, we studied the interaction of two risk factors for cognitive decline, the secretion of corticosterone and aging. Indeed, dysfunctions of the stress system contribute and facilitate aging and increased glucocorticoids induce cognitive alterations. We assessed anxiety, stress responsiveness, coping-style and cognitive functions in a Morris water-maze at early-aging. Results indicated that the phenotype of the lines were stable throughout life and that learning, swimming strategies and reversal ability were different between the lines. Overall, we showed that this model is suitable to study the systems related to stress regulation. Future research may use this animal model in order to investigate further the relationship between opposite stress regulation and health