About this Research Topic
This Research Topic is the second volume of Research Topic "Home Cage-based Phenotyping in Rodents: Innovation, Standardization, Reproducibility and Translational Improvement". Please see the first volume here.
A large spectrum of ‘classical’ behavioral tests exists to assess the emotional state and cognitive function of mice, including anxiety and fear, both innate and learned. These tests are however prone to bias/not ideal, as they rely on: (1) general short test duration, (2) require experimenter-based interference (coercion: transfer, handling, novelty) as unspecific stressors, and (3) do not always take in consideration the nocturnal activity that murine species naturally display, it’s why tests might be frequently performed consecutively in diurnal period (inactivity phase) and only sometimes in the nocturnal period (activity phase) period.
Significant outputs, linked to different performances in tests, have been reported, depending on the environmental factors and the moment of testing during the circadian phase. These factors combined with an often-poor referral to experimental details in manuscripts negatively impact the results’ reproducibility. In the light of the following observations, we are particularly interested in combining different perspectives on how the elimination of stress factors deriving from the classic experimental setups, influence animal behavior. There is the need to define behavioral patterns relying on objective and not biased grounds, that could potentially be better markers of specific stress susceptibility (e.g. avoidance, freezing, stretch-attend posture, thigmotaxis or hypoactivity, elevated glucocorticoid levels, tachycardia), pharmacological indicators or highly selective spatio-temporal brain interventions (e.g. chemo- or optogenetics). In order to do so, the integration of behavioral readouts with a wide spectrum of physiological measures to improve specificity of conclusions rather than reductionist approaches is warranted. This requires a shift away from subjective data assessment to automated data acquisition followed by advanced data mining (big data) ranging from new algorithms to machine learning increasing findings’ replicability. We need well-defined and standardized criteria to improve data comparability, instead of unavailable (e.g. unknown since proprietary) parameters.
The most important challenge is that the experimental setups need to be rethink in order to eliminate any potential bias source. The behavioral test should be freely available in the home cage of the animal that would perform the experiment without being forced to do so by the experimenter who often fails to ensure a unstress testing experience (due to time constraints, new environment or not proper animal-care training and maintenance). In this way, animals would participate to behavioral test removing any possible environmental stressor that may bias their behavior and its interpretation.
The proposed Research Topic aims to collect objective translational measures to describe symptoms and provide models with a better face validity. This will refine animal-centred behavior tests assessing emotional states and cognitive function and improve our understanding of behavioral modification that are correlated to physiological adjustments during disease progression in animal models. We are particularly interested in suggesting novel objective recommendations/guidelines of how home cage-based behavioral experiments should be performed to reduce the intra- and interlaboratory variability, improving replicability by avoiding interpretational ambiguity in experimental conclusions. This will increase the translational validity from bench to bedside in the behavioral neuroscience field.
We will welcome Original Research, Reviews, Mini-Reviews, Opinions, and Perspectives focusing on the following areas:
1. Home cage-based behavioral phenotyping by using tests monitoring emotional behavior and cognitive function in healthy animals or during the disease progression in stroke and neurodegenerative animal models:
a. Data analysis of home cage patterns/activity, Chronobiology;
b. Integration of physiological analysis (EEG, ECG, BP, glucose, temperature, neurochemical measurements, etc.)
2. Benefits from animal welfare aspect like the reduction of ‘stress’ (coercion) as refinement, extended time scale, animals decide when to participate in tests (motivation), forage voluntarily to obtain their required amount of food and food restriction regimes are mild or not necessary at all;
3. Scientific benefits like high-content over longer time scales and faster learning curves for complex cognitive tasks lead to a better discrimination of
a. Species-specific social behaviors,
b. Cognitive deficits related truly to cognitive function or emotional statues (‘stress’),
c. Disease progression description (e.g. neurodegenerative disease);
d. Genotypes and treatment effects.
4. Technical developments including new sensors and refined automated analyses including machine learning approaches and Limitations of the approaches (demands, costs, complexity, space, etc.);
5. Is there really a need for comparison with ‘classical’ tests? What is the gold standard in Behavioural Neuroscience?
Topic Editor Dr. Stefano Gaburro is working as “Scientific Director” for the company Tecniplast S.p.A.. The other Topic Editors declare no competing interests with regards to the Research Topic.
A large spectrum of ‘classical’ behavioral tests exists to assess the emotional state and cognitive function of mice, including anxiety and fear, both innate and learned. These tests are however prone to bias/not ideal, as they rely on: (1) general short test duration, (2) require experimenter-based interference (coercion: transfer, handling, novelty) as unspecific stressors, and (3) do not always take in consideration the nocturnal activity that murine species naturally display, it’s why tests might be frequently performed consecutively in diurnal period (inactivity phase) and only sometimes in the nocturnal period (activity phase) period.
Significant outputs, linked to different performances in tests, have been reported, depending on the environmental factors and the moment of testing during the circadian phase. These factors combined with an often-poor referral to experimental details in manuscripts negatively impact the results’ reproducibility. In the light of the following observations, we are particularly interested in combining different perspectives on how the elimination of stress factors deriving from the classic experimental setups, influence animal behavior. There is the need to define behavioral patterns relying on objective and not biased grounds, that could potentially be better markers of specific stress susceptibility (e.g. avoidance, freezing, stretch-attend posture, thigmotaxis or hypoactivity, elevated glucocorticoid levels, tachycardia), pharmacological indicators or highly selective spatio-temporal brain interventions (e.g. chemo- or optogenetics). In order to do so, the integration of behavioral readouts with a wide spectrum of physiological measures to improve specificity of conclusions rather than reductionist approaches is warranted. This requires a shift away from subjective data assessment to automated data acquisition followed by advanced data mining (big data) ranging from new algorithms to machine learning increasing findings’ replicability. We need well-defined and standardized criteria to improve data comparability, instead of unavailable (e.g. unknown since proprietary) parameters.
The most important challenge is that the experimental setups need to be rethink in order to eliminate any potential bias source. The behavioral test should be freely available in the home cage of the animal that would perform the experiment without being forced to do so by the experimenter who often fails to ensure a unstress testing experience (due to time constraints, new environment or not proper animal-care training and maintenance). In this way, animals would participate to behavioral test removing any possible environmental stressor that may bias their behavior and its interpretation.
The proposed Research Topic aims to collect objective translational measures to describe symptoms and provide models with a better face validity. This will refine animal-centred behavior tests assessing emotional states and cognitive function and improve our understanding of behavioral modification that are correlated to physiological adjustments during disease progression in animal models. We are particularly interested in suggesting novel objective recommendations/guidelines of how home cage-based behavioral experiments should be performed to reduce the intra- and interlaboratory variability, improving replicability by avoiding interpretational ambiguity in experimental conclusions. This will increase the translational validity from bench to bedside in the behavioral neuroscience field.
We will welcome Original Research, Reviews, Mini-Reviews, Opinions, and Perspectives focusing on the following areas:
1. Home cage-based behavioral phenotyping by using tests monitoring emotional behavior and cognitive function in healthy animals or during the disease progression in stroke and neurodegenerative animal models:
a. Data analysis of home cage patterns/activity, Chronobiology;
b. Integration of physiological analysis (EEG, ECG, BP, glucose, temperature, neurochemical measurements, etc.)
2. Benefits from animal welfare aspect like the reduction of ‘stress’ (coercion) as refinement, extended time scale, animals decide when to participate in tests (motivation), forage voluntarily to obtain their required amount of food and food restriction regimes are mild or not necessary at all;
3. Scientific benefits like high-content over longer time scales and faster learning curves for complex cognitive tasks lead to a better discrimination of
a. Species-specific social behaviors,
b. Cognitive deficits related truly to cognitive function or emotional statues (‘stress’),
c. Disease progression description (e.g. neurodegenerative disease);
d. Genotypes and treatment effects.
4. Technical developments including new sensors and refined automated analyses including machine learning approaches and Limitations of the approaches (demands, costs, complexity, space, etc.);
5. Is there really a need for comparison with ‘classical’ tests? What is the gold standard in Behavioural Neuroscience?
Topic Editor Dr. Stefano Gaburro is working as “Scientific Director” for the company Tecniplast S.p.A.. The other Topic Editors declare no competing interests with regards to the Research Topic.
Keywords: Emotional function and pathology, Home cage behavior, Learning and memory, Long-term monitoring, Neurodegenerative disease
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
