The brain is the most complex organ in the human body. It consists of billions of neurons interconnected in an incredibly complex way. As such, to understand how the brain works it is necessary to use analysis methods that are well-suited to its complex structure.
In recent years, complexity and connectivity algorithms have been introduced to solve problems in different fields of science, from weather forecasting to the analysis of social networks. Given the highly complex and interconnected nature of the brain, the use of complexity and connectivity methods for the analysis of brain activity can provide further insight into the intricate brain processes. These methods can potentially highlight subtle changes in the brain structure and activity that result from different neurodegenerative disorders, such as dementia due to Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, and amyotrophic lateral sclerosis, among others. Although complexity and connectivity methods have been used extensively to analyze brain dynamics, the interplay between both concepts is still an open issue. Can novel complexity and connectivity methods provide further knowledge about the specific brain changes associated with different neurodegenerative diseases? How can novel methodological approaches disentangle the complex interplay between altered non-linear brain dynamics and disrupted functional brain networks in neurodegenerative diseases? How is the multi-scale nature of brain dynamics reflected on the functional brain network? How can brain microstates be inferred from current connectivity methods? Indeed, further research is required to answer these and other related cutting-edge research topics.
This Research Topic aims at filling the gap between complexity and connectivity analyses, encouraging novel studies focused on characterizing the complex dynamics of brain networks for the identification of the functional signature of neurodegenerative disorders. We are aiming at bringing together researchers with an interest in non-linear analysis for the characterization of complexity, those working on complex network theory, and computational neuroscience researchers, and at stimulating collaboration between researchers in these fields. We welcome submissions of original research papers that can help to gain further insight on fundamental aspects and novel approaches on complexity and/or connectivity in the brain in neurodegenerative disorders, using different brain imaging methods (e.g. electroencephalography, magnetoencephalography, functional magnetic resonance imaging, positron emission tomography and/or near infrared spectroscopy).
We are looking for contributions that cover, but are not limited to, the following topics:
• Novel computational methods to estimate the complexity and/or connectivity of brain networks.
• Machine learning and complexity or connectivity analyses.
• Characterization of complex brain dynamics to identify the functional signature of neurodegenerative disorders.
• Usefulness of the combination of neuroimaging techniques and complexity/connectivity metrics to help in the diagnosis of neurodegenerative disorders.
• Modeling of the neurodegenerative mechanisms involved in dementia and other brain disorders.
• Multimodal approaches to understand the underlying neurodegenerative processes involved in different brain pathologies.
The brain is the most complex organ in the human body. It consists of billions of neurons interconnected in an incredibly complex way. As such, to understand how the brain works it is necessary to use analysis methods that are well-suited to its complex structure.
In recent years, complexity and connectivity algorithms have been introduced to solve problems in different fields of science, from weather forecasting to the analysis of social networks. Given the highly complex and interconnected nature of the brain, the use of complexity and connectivity methods for the analysis of brain activity can provide further insight into the intricate brain processes. These methods can potentially highlight subtle changes in the brain structure and activity that result from different neurodegenerative disorders, such as dementia due to Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, and amyotrophic lateral sclerosis, among others. Although complexity and connectivity methods have been used extensively to analyze brain dynamics, the interplay between both concepts is still an open issue. Can novel complexity and connectivity methods provide further knowledge about the specific brain changes associated with different neurodegenerative diseases? How can novel methodological approaches disentangle the complex interplay between altered non-linear brain dynamics and disrupted functional brain networks in neurodegenerative diseases? How is the multi-scale nature of brain dynamics reflected on the functional brain network? How can brain microstates be inferred from current connectivity methods? Indeed, further research is required to answer these and other related cutting-edge research topics.
This Research Topic aims at filling the gap between complexity and connectivity analyses, encouraging novel studies focused on characterizing the complex dynamics of brain networks for the identification of the functional signature of neurodegenerative disorders. We are aiming at bringing together researchers with an interest in non-linear analysis for the characterization of complexity, those working on complex network theory, and computational neuroscience researchers, and at stimulating collaboration between researchers in these fields. We welcome submissions of original research papers that can help to gain further insight on fundamental aspects and novel approaches on complexity and/or connectivity in the brain in neurodegenerative disorders, using different brain imaging methods (e.g. electroencephalography, magnetoencephalography, functional magnetic resonance imaging, positron emission tomography and/or near infrared spectroscopy).
We are looking for contributions that cover, but are not limited to, the following topics:
• Novel computational methods to estimate the complexity and/or connectivity of brain networks.
• Machine learning and complexity or connectivity analyses.
• Characterization of complex brain dynamics to identify the functional signature of neurodegenerative disorders.
• Usefulness of the combination of neuroimaging techniques and complexity/connectivity metrics to help in the diagnosis of neurodegenerative disorders.
• Modeling of the neurodegenerative mechanisms involved in dementia and other brain disorders.
• Multimodal approaches to understand the underlying neurodegenerative processes involved in different brain pathologies.