Functional Magnetic Resonance Imaging (fMRI) has become a widely used tool for probing both normal function and disease-related changes of the brain. To date, various methods have been successfully applied to characterize different aspects of brain activities, such as General Linear Model (GLM) in task-based fMRI analysis, and Independent Component Analysis (ICA) in resting-state fMRI data modeling. However, there are still many challenges in extracting hidden features from these high-dimensional data and in properly modeling the spatial-temporal characteristics of the fMRI signal. On the other hand, through recent fMRI studies it has become clear that brain functional changes occur not only in neurological and psychiatric disorders, but also in other conditions that do not primarily affect the brain such as diabetes, obesity, and heart failure. For example, fMRI studies found aberrant insular and cerebellar neural response to Valsalva maneuver in heart failure patients, which is associated with abnormal autonomic nervous functions. Other fMRI studies found altered brain-gut axis in patients with irritable bowel syndrome. Moreover, significant changes in the activity of the reward system were reported in patients with diabetes or obesity. Altogether, the need for processing vast amounts of fMRI data collected from patients affected by various diseases or disorders places higher demands on the development of new methods for fMRI data modeling. These innovative methods may help us to characterize brain activities and extract extra-brain information such as brain-heart or brain-gut interactions.
Nowadays, data mining and modeling methods have developed in different fields, such as nonlinear dynamics, complex networks and control system, artificial intelligence and machine learning, as well as economic and social science. These methods have gained increasing attention among neuroscientists. Introducing these innovative methods to fMRI studies will provide us with more tools to explore the neuropathological mechanisms underlying different brain-related diseases/disorders, including those diseases that do not originate from the brain but affect brain function, such as diabetes, heart failure, and obesity.
The aim of this Research Topic is to provide an interdisciplinary platform for researchers to exchange information and ideas about state-of-the-art methods for fMRI data analysis and their application in the study of various diseases and disorders that are related to the brain.
We welcome original research articles and review articles that focus on one of the two following aspects, or both:
- Methodological advances in the analysis of fMRI (or of other neuroimaging data, such as EEG, MEG, etc., if the innovative method can be applied to fMRI) that allow the extraction and characterization of brain states and their spatial-temporal-spectral features;
- The study of these brain fMRI features in patients affected by various diseases/disorders and their associations with other relevant biological measures, such as behavioral, physiological, or genetic assessments.
Functional Magnetic Resonance Imaging (fMRI) has become a widely used tool for probing both normal function and disease-related changes of the brain. To date, various methods have been successfully applied to characterize different aspects of brain activities, such as General Linear Model (GLM) in task-based fMRI analysis, and Independent Component Analysis (ICA) in resting-state fMRI data modeling. However, there are still many challenges in extracting hidden features from these high-dimensional data and in properly modeling the spatial-temporal characteristics of the fMRI signal. On the other hand, through recent fMRI studies it has become clear that brain functional changes occur not only in neurological and psychiatric disorders, but also in other conditions that do not primarily affect the brain such as diabetes, obesity, and heart failure. For example, fMRI studies found aberrant insular and cerebellar neural response to Valsalva maneuver in heart failure patients, which is associated with abnormal autonomic nervous functions. Other fMRI studies found altered brain-gut axis in patients with irritable bowel syndrome. Moreover, significant changes in the activity of the reward system were reported in patients with diabetes or obesity. Altogether, the need for processing vast amounts of fMRI data collected from patients affected by various diseases or disorders places higher demands on the development of new methods for fMRI data modeling. These innovative methods may help us to characterize brain activities and extract extra-brain information such as brain-heart or brain-gut interactions.
Nowadays, data mining and modeling methods have developed in different fields, such as nonlinear dynamics, complex networks and control system, artificial intelligence and machine learning, as well as economic and social science. These methods have gained increasing attention among neuroscientists. Introducing these innovative methods to fMRI studies will provide us with more tools to explore the neuropathological mechanisms underlying different brain-related diseases/disorders, including those diseases that do not originate from the brain but affect brain function, such as diabetes, heart failure, and obesity.
The aim of this Research Topic is to provide an interdisciplinary platform for researchers to exchange information and ideas about state-of-the-art methods for fMRI data analysis and their application in the study of various diseases and disorders that are related to the brain.
We welcome original research articles and review articles that focus on one of the two following aspects, or both:
- Methodological advances in the analysis of fMRI (or of other neuroimaging data, such as EEG, MEG, etc., if the innovative method can be applied to fMRI) that allow the extraction and characterization of brain states and their spatial-temporal-spectral features;
- The study of these brain fMRI features in patients affected by various diseases/disorders and their associations with other relevant biological measures, such as behavioral, physiological, or genetic assessments.
