Resting state fMRI has become a widely used tool for probing the brain. While early work assumed the resting state fMRI data to be stationary and derived static brain networks, work in the past decade paid increasing attention to the time-varying nature or the dynamics in the resting state fMRI data. The temporal variability in the rest state fMRI has become a feature rather than a nuisance and has been ascertained and exploited in the understanding of the brain dynamics. Analysis of fMRI data, assuming the existence of and transitions between a finite set of brain states, has become mainstay of resting data analysis. Such features might also play an important role in different brain disorders. The topics to be covered in this Research Topic calls for papers describing the methods for analyzing the data, the features that can be extracted, as well as applications of these methods in ascertaining the normal workings of the brain and signatures of brain disease.
Three aspects are to be covered in papers to be submitted. The first is on the methodological advances in the analysis of fMRI data that allows the extraction and characterization of brain states and their temporal features. The second covers the study of these features in normal subjects and associations of these features with other relevant biological measures, such as behavioral or genetic measures. The third aspect is the study of disease related temporal features in subjects with various brain disorders. The papers submitted can be focused on one of these aspects or cover more than one aspects.
The overall goal of this Research Topic is to provide a comprehensive coverage of the topic on the latest advances in methods, validation of the methods, understanding of brain and disease. This collection of articles will provide other researchers in the field with an appropriate state-of-the-art coverage of this hot topic.
Resting state fMRI has become a widely used tool for probing the brain. While early work assumed the resting state fMRI data to be stationary and derived static brain networks, work in the past decade paid increasing attention to the time-varying nature or the dynamics in the resting state fMRI data. The temporal variability in the rest state fMRI has become a feature rather than a nuisance and has been ascertained and exploited in the understanding of the brain dynamics. Analysis of fMRI data, assuming the existence of and transitions between a finite set of brain states, has become mainstay of resting data analysis. Such features might also play an important role in different brain disorders. The topics to be covered in this Research Topic calls for papers describing the methods for analyzing the data, the features that can be extracted, as well as applications of these methods in ascertaining the normal workings of the brain and signatures of brain disease.
Three aspects are to be covered in papers to be submitted. The first is on the methodological advances in the analysis of fMRI data that allows the extraction and characterization of brain states and their temporal features. The second covers the study of these features in normal subjects and associations of these features with other relevant biological measures, such as behavioral or genetic measures. The third aspect is the study of disease related temporal features in subjects with various brain disorders. The papers submitted can be focused on one of these aspects or cover more than one aspects.
The overall goal of this Research Topic is to provide a comprehensive coverage of the topic on the latest advances in methods, validation of the methods, understanding of brain and disease. This collection of articles will provide other researchers in the field with an appropriate state-of-the-art coverage of this hot topic.
