Thousands of genetically modified rodent models have been developed, allowing testing for pathological mechanisms of human diseases and functions of genes. From neurodevelopmental diseases such as autistic spectrum disorder or schizophrenia, to a variety of neurodegenerative diseases such as Alzheimer’s disease, the ultimate functional outcomes are behavioral and cognitive outcomes.
Despite success in generating different models, repeated failures in clinical trials have highlighted significant shortcomings regarding how the models are tested. For example, multiple anti-amyloid drugs that proved highly effective at improving cognitive phenotypes in mouse models did not work in clinical trials. The situation in modeling other CNS diseases is strikingly similar. These limitations call for a revision of methods and approaches adopted in preclinical behavioral testing.
The translational relevance of mouse models of human CNS diseases depends on our ability to decode disease- or symptom-relevant concepts (i.e., episodic memory, social motivation, anxiety, depression, etc.) into measures of animal behavior and vice versa. In contrast to human studies, most animal behavioral studies rely on a limited number of tests or single variables to interpret and translate the findings into a human-relevant symptom or disease. Multiple tests/variables bring, however, its own risks related to inflation in the number of comparisons, which, if not appropriately addressed statistically, can reduce reproducibility.
In addition, the use of multi-transgenic/knockout models that more adequately reflect the complex nature of human disease, requires a more complicated experimental design, multiple control groups, and ultimately, a larger number of cases. These requirements call for the development of new high-throughput tools that allow testing many subjects in a reproducible manner, with transparent techniques for data handling and analyses.
The goal of this Research Topic is to shed light on existing limitations in preclinical behavioral/cognitive testing and to cover promising and novel research that explicitly addresses reproducibility, statistical methods, and model validity in experimental design and data interpretation.
Areas to be covered include, but are not limited to:
? Behavioral/cognitive studies in rodent models based on multiple tasks and/or variables. Multi-dimensionality of the data sets and task/variable dependencies should be statistically addressed facilitating determination of behavioral/cognitive fingerprints translationally-relevant concepts such as anxiety, depression, social motivation, etc.
? Novel and/or well-known assays testing behavior/cognition with attention to reproducibility. Reproducibility can be broadly addressed, for example, by explicit exploratory and confirmatory stages in a study design, comparisons of explored phenomena in different strain backgrounds, a repetition of previously published findings, etc.
? Reports of behavioral/cognitive studies with open access to raw data, explicit statistical power, and/or implementation of alternatives to p-level as a basis of biological significance. Such reports will serve as index papers for the scientific community at large to estimate task-specific variabilities and calculate statistical power and sample sizes needed for future research.
? Automated phenotyping of cognitive/behavioral traits in undisturbed colonies or ecologically relevant design using high-throughput tools and highly controlled experimental environment (i.e. long observation time and control for stress effects). Implementation of complex statistical algorithms, accessibility of raw data, and analysis pipelines are particularly encouraged.
? Studies taking into account the social and dominance status of the animals as a main biological variable to explain individual and group variability.
? Bayesian approach to behavioral tests, which may prove a more sensitive measure of subtle trends, necessary to account for complex behavioral phenomena. Advantages and disadvantages of Bayesian-inspired ideas of statistical understanding of behavioral tests.
Thousands of genetically modified rodent models have been developed, allowing testing for pathological mechanisms of human diseases and functions of genes. From neurodevelopmental diseases such as autistic spectrum disorder or schizophrenia, to a variety of neurodegenerative diseases such as Alzheimer’s disease, the ultimate functional outcomes are behavioral and cognitive outcomes.
Despite success in generating different models, repeated failures in clinical trials have highlighted significant shortcomings regarding how the models are tested. For example, multiple anti-amyloid drugs that proved highly effective at improving cognitive phenotypes in mouse models did not work in clinical trials. The situation in modeling other CNS diseases is strikingly similar. These limitations call for a revision of methods and approaches adopted in preclinical behavioral testing.
The translational relevance of mouse models of human CNS diseases depends on our ability to decode disease- or symptom-relevant concepts (i.e., episodic memory, social motivation, anxiety, depression, etc.) into measures of animal behavior and vice versa. In contrast to human studies, most animal behavioral studies rely on a limited number of tests or single variables to interpret and translate the findings into a human-relevant symptom or disease. Multiple tests/variables bring, however, its own risks related to inflation in the number of comparisons, which, if not appropriately addressed statistically, can reduce reproducibility.
In addition, the use of multi-transgenic/knockout models that more adequately reflect the complex nature of human disease, requires a more complicated experimental design, multiple control groups, and ultimately, a larger number of cases. These requirements call for the development of new high-throughput tools that allow testing many subjects in a reproducible manner, with transparent techniques for data handling and analyses.
The goal of this Research Topic is to shed light on existing limitations in preclinical behavioral/cognitive testing and to cover promising and novel research that explicitly addresses reproducibility, statistical methods, and model validity in experimental design and data interpretation.
Areas to be covered include, but are not limited to:
? Behavioral/cognitive studies in rodent models based on multiple tasks and/or variables. Multi-dimensionality of the data sets and task/variable dependencies should be statistically addressed facilitating determination of behavioral/cognitive fingerprints translationally-relevant concepts such as anxiety, depression, social motivation, etc.
? Novel and/or well-known assays testing behavior/cognition with attention to reproducibility. Reproducibility can be broadly addressed, for example, by explicit exploratory and confirmatory stages in a study design, comparisons of explored phenomena in different strain backgrounds, a repetition of previously published findings, etc.
? Reports of behavioral/cognitive studies with open access to raw data, explicit statistical power, and/or implementation of alternatives to p-level as a basis of biological significance. Such reports will serve as index papers for the scientific community at large to estimate task-specific variabilities and calculate statistical power and sample sizes needed for future research.
? Automated phenotyping of cognitive/behavioral traits in undisturbed colonies or ecologically relevant design using high-throughput tools and highly controlled experimental environment (i.e. long observation time and control for stress effects). Implementation of complex statistical algorithms, accessibility of raw data, and analysis pipelines are particularly encouraged.
? Studies taking into account the social and dominance status of the animals as a main biological variable to explain individual and group variability.
? Bayesian approach to behavioral tests, which may prove a more sensitive measure of subtle trends, necessary to account for complex behavioral phenomena. Advantages and disadvantages of Bayesian-inspired ideas of statistical understanding of behavioral tests.
