This Research Topic on 'New Concepts in Brain Networks' will compare and contrast currently emerging approaches for investigating brain connectivity. Several novel and powerful methodologies have been inspired by recent important advances in neuroimaging: MR tractography, functional connectivity mapped by correlations in spontaneous fluctuations of fMRI signal ('resting state' fMRI, or rsfMRI), and spatial mapping of temporal correlations in electrophysiological data.
Useful analysis, however, entails prior assumptions about the characteristics of the data, assumptions which can be classified as mathematical, statistical and physiological. Different validity tests are needed for each type of assumption, and the new methodologies are much in need of such scrutiny.
Currently available connectivity analysis methods fall into two main groups. Some follow a model-based top-down approach, others are data-driven and exploratory. Within these, some methods focus on understanding immediate brain response to experimental stimuli, others on the analysis of spontaneous fluctuations related to longer-lasting brain states. Each of these methods tries to uncover some aspects of brain connectivity.
There are (at least) two key questions that are at the heart of the problem:
a) What is the relation between spontaneous fluctuations and responses to external stimuli?
b) Is synchronicity the key concept for understanding how coalitions of brain areas are formed, or should we rather focus on temporal order as a marker for revealing causal relationships between brain areas ?
In this Research Topic, these questions will be addressed from various perspectives encompassing both functional and structural connectivity, as measured in several modalities--fMRI, EEG/MEG, micro-stimulation, diffusion weighted imaging, and tracer based connectivity. Significant mathematical issues, such as the profound multiple comparison problem, will also be discussed, which arise from the combinatorial and computational complexity given by the rich spatial and temporal data.
The planned contributions can thus be roughly grouped into three main areas: functional connectivity, structural connectivity and mathematical background, with some natural overlap.
This Research Topic on 'New Concepts in Brain Networks' will compare and contrast currently emerging approaches for investigating brain connectivity. Several novel and powerful methodologies have been inspired by recent important advances in neuroimaging: MR tractography, functional connectivity mapped by correlations in spontaneous fluctuations of fMRI signal ('resting state' fMRI, or rsfMRI), and spatial mapping of temporal correlations in electrophysiological data.
Useful analysis, however, entails prior assumptions about the characteristics of the data, assumptions which can be classified as mathematical, statistical and physiological. Different validity tests are needed for each type of assumption, and the new methodologies are much in need of such scrutiny.
Currently available connectivity analysis methods fall into two main groups. Some follow a model-based top-down approach, others are data-driven and exploratory. Within these, some methods focus on understanding immediate brain response to experimental stimuli, others on the analysis of spontaneous fluctuations related to longer-lasting brain states. Each of these methods tries to uncover some aspects of brain connectivity.
There are (at least) two key questions that are at the heart of the problem:
a) What is the relation between spontaneous fluctuations and responses to external stimuli?
b) Is synchronicity the key concept for understanding how coalitions of brain areas are formed, or should we rather focus on temporal order as a marker for revealing causal relationships between brain areas ?
In this Research Topic, these questions will be addressed from various perspectives encompassing both functional and structural connectivity, as measured in several modalities--fMRI, EEG/MEG, micro-stimulation, diffusion weighted imaging, and tracer based connectivity. Significant mathematical issues, such as the profound multiple comparison problem, will also be discussed, which arise from the combinatorial and computational complexity given by the rich spatial and temporal data.
The planned contributions can thus be roughly grouped into three main areas: functional connectivity, structural connectivity and mathematical background, with some natural overlap.
