Graph analysis of the human connectome: Promise, progress, and pitfalls

By Alex Fornito, Andrew Zalesky, Michael Breakspear. NeuroImage, 2013

751 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/0f38481a31af65a0d43daeebc8fafc0a8d7c3943

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Graph analysis of functional brain networks: practical issues in translational neuroscience (2014, background): “The emerging area of complex networks has led to a paradigm shift in the neuroscience community, though many issues remain unaddressed [10].”
EEG-Based Emotion Recognition Using Regularized Graph Neural Networks (2019, background): “Graph analysis for human brain has been studied extensively in the neuroscience literature [17], [18].”
Mathematical framework for large-scale brain network modeling in The Virtual Brain (2015, neutral): “For a review on macroconnectomics see Craddock et al. (2013) and Fornito et al. (2013); and for parcellation schemes see Caspers et al. (2013).”
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, methodology): “For instance, in a white matter anatomical network taken by diffusion MRI, we can obtain a weighted network using various information, such as fiber number, fiber length, and fractional anisotropy (Fornito et al., 2013; Zhong et al., 2015).”
Contributions and challenges for network models in cognitive neuroscience (2014, background): “Data on brain connectivity can be rendered in the form of network models 8–10 : essentially, simplified representations of brain systems as sets of neural elements and their interconnections.”
Towards quantitative connectivity analysis: reducing tractography biases (2014, positive): “A growing number of connectomics studies exploit structural properties of 3 these pathways or streamlines to make ’connectivity’ comparisons between groups or individuals (Fornito et al., 2013; 4 Hagmann et al., 2008; Ng et al., 2013; Sporns, 2010).”
The minimum spanning tree: An unbiased method for brain network analysis (2015, neutral): “For thorough studies on this topic we refer to Fornito et al. (2013) and van Wijk et al. (2010).”