It has been proposed that the brain works in a Bayesian manner, and based on the free-energy principle, the brain's main function is to reduce environmental uncertainty; this is a proposed model as a universal principle governing adaptive brain function and structure. There are many pathophysiological, and clinical observations that can be easily explained by predictive Bayesian brain models. However, the novel applications of Bayesian models in translational neuroscience has been understudied and underreported. For example, variational Bayesian mixed-effects inference has been successfully tested for classification studies. A multi-task Bayesian compressive sensing approach to simultaneously estimate the full posterior of the CSA-ODF and diffusion-weighted volumes from multi-shell HARDI acquisitions has been recently published
There are many other potential applications of Bayesian models in methodological design and interpretation of Translational Neuroscience (such as Bayesian simulation, Bayesian, Bias eradication approaches, analytical neuroimaging, magnitude estimation, Bayesian active probabilistic classification for psychometric field estimation, naïve Bayesian models for vero cell cytotoxicity, Bayesian priors and Bayesian optimization, etc.) but these have not been investigated to their fullest. In addition to Bayesian models, other models are being used and considered. For example, deep ensembles are alternatives to Bayesian methods for dealing with model uncertainty.
This Research Topic calls for original papers, study protocols, simulated experiments and synthetic reviews and opinions as a basis for future studies exploring the potential application of Bayesian and other models and analytics in Translational Neuroscience for prediction or anticipation of brain function in humans and animal models under physiological and pathological conditions. It is expected that submitted manuscripts will provide novel technical and methodological insight.
This Research Topic welcomes articles focusing on, but not limited, to the following topics:
- Novel application of Bayesian optimizations and Bayesian simulation and other models in translational neuroscience
- Novel Applications of Bayesian inference and mixed-effects inference
- Potential of Bayesian compressive sensing approaches
- Hierarchical Bayesian and other models
- Perspective of Bayesian and other models on magnitude estimation
- Development of unbiased Bayesian and other approaches to functional connectomics
- Development of efficient Bayesian and other spatial models for neuroimaging data
- Application of Artificial Intelligence in Translational Neuroscience
It has been proposed that the brain works in a Bayesian manner, and based on the free-energy principle, the brain's main function is to reduce environmental uncertainty; this is a proposed model as a universal principle governing adaptive brain function and structure. There are many pathophysiological, and clinical observations that can be easily explained by predictive Bayesian brain models. However, the novel applications of Bayesian models in translational neuroscience has been understudied and underreported. For example, variational Bayesian mixed-effects inference has been successfully tested for classification studies. A multi-task Bayesian compressive sensing approach to simultaneously estimate the full posterior of the CSA-ODF and diffusion-weighted volumes from multi-shell HARDI acquisitions has been recently published
There are many other potential applications of Bayesian models in methodological design and interpretation of Translational Neuroscience (such as Bayesian simulation, Bayesian, Bias eradication approaches, analytical neuroimaging, magnitude estimation, Bayesian active probabilistic classification for psychometric field estimation, naïve Bayesian models for vero cell cytotoxicity, Bayesian priors and Bayesian optimization, etc.) but these have not been investigated to their fullest. In addition to Bayesian models, other models are being used and considered. For example, deep ensembles are alternatives to Bayesian methods for dealing with model uncertainty.
This Research Topic calls for original papers, study protocols, simulated experiments and synthetic reviews and opinions as a basis for future studies exploring the potential application of Bayesian and other models and analytics in Translational Neuroscience for prediction or anticipation of brain function in humans and animal models under physiological and pathological conditions. It is expected that submitted manuscripts will provide novel technical and methodological insight.
This Research Topic welcomes articles focusing on, but not limited, to the following topics:
- Novel application of Bayesian optimizations and Bayesian simulation and other models in translational neuroscience
- Novel Applications of Bayesian inference and mixed-effects inference
- Potential of Bayesian compressive sensing approaches
- Hierarchical Bayesian and other models
- Perspective of Bayesian and other models on magnitude estimation
- Development of unbiased Bayesian and other approaches to functional connectomics
- Development of efficient Bayesian and other spatial models for neuroimaging data
- Application of Artificial Intelligence in Translational Neuroscience
