Computational Anatomy (CA) is an emerging discipline aiming to understand anatomy by utilizing a comprehensive set of mathematical tools. CA focuses on providing precise statistical encodings of anatomy with direct application to a broad range of biological and medical settings.
During the past two decades, there has been an ever-increasing pace in the development of neuroimaging techniques, delivering in vivo information on the anatomy and physiological signals of different human organs through a variety of imaging modalities such as MRI, x-ray, CT, and PET. These multi-modality medical images provide valuable data for accurate interpretation and estimation of various biological parameters such as anatomical labels (e.g., identifying the brain cerebellum), disease types (e.g., Alzheimer’s disease or Amyotrophic lateral sclerosis), cognitive states (e.g., normal, mild abnormal, or several abnormal), functional connectivity between distinct anatomical regions, as well as activation responses to specific stimuli.
In the era of big neuroimaging data, Bayes' theorem provides a powerful tool to deliver statistical conclusions by combining the current information (data) and prior experience (prior probability). When sufficiently good data is available, Bayes’ theorem can utilize it fully and provide statistical inferences/estimations with the least error rate. Bayes’ theorem arose roughly three hundred years ago and has seen extensive application in many fields of science and technology, including recent neuroimaging, ever since. The last fifteen years have seen a great deal of success in the application of Bayes' theorem to the field of CA and neuroimaging. That said, given that the power and success of Bayes’ rule largely depends on the validity of its probabilistic inputs, it is still a challenge to perform Bayesian estimation and inference on the typically noisy neuroimaging data of the real world.
The primary goal of this research topic is to bring together recent developments in CA and neuroimaging through Bayesian estimation and inference, in terms of both methodologies and applications. A preference will be given to the methodological developments and scientific applications most suited to studies of the human brain utilizing large datasets of cross-sectional and longitudinal MR images, while emphasis on other species, other organs, or other types of imaging modalities is encouraged as well. Interesting topics include but are not exclusive to: Bayesian estimation of biological parameters; Bayesian inference for brain diseases; Bayesian modeling in the study of CA and neuroimaging; Bayesian model in machine learning with application to brain science.
We welcome scientists and researchers from a broad range of areas to contribute to this research topic by either developing new methodologies or applying existing approaches to scientific problems. A variety of article formats are acceptable, including original research, algorithm design, hypothesis and theorem derivation, applications, as well as short and long reviews.
Computational Anatomy (CA) is an emerging discipline aiming to understand anatomy by utilizing a comprehensive set of mathematical tools. CA focuses on providing precise statistical encodings of anatomy with direct application to a broad range of biological and medical settings.
During the past two decades, there has been an ever-increasing pace in the development of neuroimaging techniques, delivering in vivo information on the anatomy and physiological signals of different human organs through a variety of imaging modalities such as MRI, x-ray, CT, and PET. These multi-modality medical images provide valuable data for accurate interpretation and estimation of various biological parameters such as anatomical labels (e.g., identifying the brain cerebellum), disease types (e.g., Alzheimer’s disease or Amyotrophic lateral sclerosis), cognitive states (e.g., normal, mild abnormal, or several abnormal), functional connectivity between distinct anatomical regions, as well as activation responses to specific stimuli.
In the era of big neuroimaging data, Bayes' theorem provides a powerful tool to deliver statistical conclusions by combining the current information (data) and prior experience (prior probability). When sufficiently good data is available, Bayes’ theorem can utilize it fully and provide statistical inferences/estimations with the least error rate. Bayes’ theorem arose roughly three hundred years ago and has seen extensive application in many fields of science and technology, including recent neuroimaging, ever since. The last fifteen years have seen a great deal of success in the application of Bayes' theorem to the field of CA and neuroimaging. That said, given that the power and success of Bayes’ rule largely depends on the validity of its probabilistic inputs, it is still a challenge to perform Bayesian estimation and inference on the typically noisy neuroimaging data of the real world.
The primary goal of this research topic is to bring together recent developments in CA and neuroimaging through Bayesian estimation and inference, in terms of both methodologies and applications. A preference will be given to the methodological developments and scientific applications most suited to studies of the human brain utilizing large datasets of cross-sectional and longitudinal MR images, while emphasis on other species, other organs, or other types of imaging modalities is encouraged as well. Interesting topics include but are not exclusive to: Bayesian estimation of biological parameters; Bayesian inference for brain diseases; Bayesian modeling in the study of CA and neuroimaging; Bayesian model in machine learning with application to brain science.
We welcome scientists and researchers from a broad range of areas to contribute to this research topic by either developing new methodologies or applying existing approaches to scientific problems. A variety of article formats are acceptable, including original research, algorithm design, hypothesis and theorem derivation, applications, as well as short and long reviews.