Whole-brain anatomical networks: Does the choice of nodes matter?

By Andrew Zalesky, Alex Fornito, Ian H. Harding, Luca Cocchi, Murat Yücel, Christos Pantelis, E. Bullmore. NeuroImage, 2010

1,177 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/2c12b48de1eae2e7b497a7c5d7320af614bc9c08

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Functional network organization of the human brain (2011, background): “A recognized limitation within graph theoretic investigations of structural and functional brain networks is the current lack of validated parcellation strategies (see Fornito et al., 2010; Wig et al., 2011; Zalesky et al., 2010) for comprehensive discussions).”
Cognitive Network Neuroscience (2015, background): “It is important to note that all parcellation schemes can be defined across a range of spatial resolutions, enabling the exploration of multiresolution structure in the human brain (Bassett, Brown, et al., 2011; Bassett et al., 2010; Zalesky et al., 2010; Meunier et al., 2009).”
Cognitive relevance of the community structure of the human brain functional coactivation network (2013, methodology): “We subdivided or parcellated the gray matter of the brain (excluding the cerebellum) into 638 similarly sized regions that respected anatomical landmarks (44, 45).”
Network attributes for segregation and integration in the human brain. (2013, background): “Using one or several of these metrics, several recent studies of networks derived from diffusion imaging and tractography [43,44,45 ] have identified specific cortical regions as putative hubs, with some of them aggregated into a highly resilient structural core [46].”
The human connectome: Origins and challenges (2013, background): “Received 24 January 2013 Revised 11 March 2013 Accepted 12 March 2013 Available online 23 March 2013”
Eigenvector Centrality Mapping for Analyzing Connectivity Patterns in fMRI Data of the Human Brain (2010, methodology): “Here, we propose to apply this measure to all voxels in a region of interest covering the entire cerebrum thereby avoiding any selection bias [10].”
Network-based statistic: Identifying differences in brain networks (2010, background): “…which they are interconnected (Gong et al., 2009; Hagmann et al., 2007, 2008; Itturia-Medina et al., 2008; Li et al., 2009; Skudlarski et al., 2008; Zalesky et al., 2010), or in some cases, a pseudo-measure of the tract-averaged anisotropy (Robinson et al., 2010) or an estimate of fiber bundle…”
Brain graphs: graphical models of the human brain connectome. (2011, methodology): “An alternative approach may be to apply a preliminary statistical threshold to each edge and then test the null hypothesis at the level of suprathreshold clusters of interconnected edges (Zalesky et al. 2010a).”
A whole brain fMRI atlas generated via spatially constrained spectral clustering (2011, methodology): “Similar results were shown in an evaluation of random parcellation for calculating small world network statistics from DTI data [Zalesky et al. 2010].”
Abnormal rich club organization and functional brain dynamics in schizophrenia. (2013, neutral): “0) [33-86] Antipsychotic medication, No.”
Mathematical framework for large-scale brain network modeling in The Virtual Brain (2015, neutral): “If U v = 2 is coarsely divided, not only the connection topology changes (Zalesky et al., 2010a) but the shortest fiber length represented in K v = 2 .”
Graph analysis of functional brain networks: practical issues in translational neuroscience (2014, background): “Unsurprisingly, the number of regions in the atlas, and therefore the number of brain nodes, has a large impact on the extracted graph metrics [26, 27, 28].”