Graph analysis of functional brain networks: practical issues in translational neuroscience
By F. de Vico Fallani, Jonas Richiardi, Mario Chavez, Sophie Achard. Philosophical Transactions of the Royal Society B: Biological Sciences, 2014
449 citations recorded by the source corpus.
The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective, communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires the know-how of all the methodological steps of the pipeline that manipulate the input brain signals and extract the functional network properties. On the other hand, knowledge of the neural phenomenon under study is required to perform physiologically relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes.
Subfields: Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/b79209890a19820d734e7616bc16bf06bf32a758
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In Cognitive Neuroscience
Citations in the corpus
- Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, background): “However, graph analysis in human neuroscience faces a number of issues that remain unaddressed, restricting its interpretation and application (De Vico Fallani et al., 2014).”
- Real-time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG (2015, unknown): “We also note that the framework allows for graph-theoretic measures [50], such as degree, flow, and asymmetry ratio to be easily applied to connectivity matrices, although we do not study these”
- On Generalizing Neural Node Embedding Methods to Multi-Network Problems (2017, background): “In other cases, it is desirable to perform analysis across a collection of multiple graphs, such as the brain graphs of several MRI patients [4] or snapshots of a time-evolving graph [11].”