Mapping Human Whole-Brain Structural Networks with Diffusion MRI

By Patric Hagmann, Maciej Kurant, Xavier Gigandet, Patrick Thiran, Van J. Wedeen, Reto Meuli, Jean-Philippe Thiran. PLoS ONE, 2007

852 citations recorded by the source corpus.

Understanding the large-scale structural network formed by neurons is a major challenge in system neuroscience. A detailed connectivity map covering the entire brain would therefore be of great value. Based on diffusion MRI, we propose an efficient methodology to generate large, comprehensive and individual white matter connectional datasets of the living or dead, human or animal brain. This non-invasive tool enables us to study the basic and potentially complex network properties of the entire brain. For two human subjects we find that their individual brain networks have an exponential node degree distribution and that their global organization is in the form of a small world.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/ec2b368b4e1bfb68a006e1dfc3893baa37f9faea

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Loss of ‘Small-World’ Networks in Alzheimer's Disease: Graph Analysis of fMRI Resting-State Functional Connectivity (2010, background): “Smallworld network structures have been described in healthy anatomical [24,25] and functional brain networks [26,27,28,29] and its organizational principles of local specialization and global integration [30] are recognized as the bases supporting higher cognitive functions [31].”
GRETNA: a graph theoretical network analysis toolbox for imaging connectomics (2015, background): “…interregional morphological correlations (e.g., cortical thickness) based on structural MRI (He et al., 2007; Bassett et al., 2008; Tijms et al., 2012) or by tracing interregional fiber pathways based on diffusion MRI (Hagmann et al., 2007; Iturria-Medina et al., 2007; Gong et al., 2009).”
The resting brain: unconstrained yet reliable. (2009, background): “structural connectivity profile, as identified with diffusion spectrum imaging (Hagmann et al. 2007; Hagmann et al. 2008), diffusion tensor imaging (Gong et al.”
Rich-Club Organization of the Human Connectome (2011, background): “Studies of brain networks have shown a heavy-tailed degree distribution (Eguíluzet al., 2005; Hagmann et al., 2007; van den Heuvel et al., 2008b), indicating the existence of a number of highly connected regions (Hagmann et al.”
Structural connectomics in brain diseases (2013, neutral): “Hagmann, Sporns and others showed the feasibility and utility of mapping the human connectome in vivo with diffusion MRI and tractography (Gong et al., 2008; Hagmann, 2005; Hagmann et al., 2008; Hagmann et al., 2007; Iturria-Medina et al., 2007).”
SIFT: Spherical-deconvolution informed filtering of tractograms (2013, methodology): “…the second spatial derivative of the inhomogeneity field was regularised to enforce a smoothly-varying solution; in our experience, regularisation of the first derivative of this field (as in (Holland et al., 2010)) can lead to residual image misalignment in areas of large field perturbation.”
Towards quantitative connectivity analysis: reducing tractography biases (2014, neutral): “In the majority of cases, tractography is done inside a mask defined by a white matter segmentation of the T1- 23 weighted image or fractional anisotropy (FA) thresholded mask (Côté et al., 2013; Hagmann et al., 2007; Li et al., 24 2012b; Smith et al., 2012; Tournier et al., 2012, 2011).”
Anatomically-constrained tractography: Improved diffusion MRI streamlines tractography through effective use of anatomical information (2012, methodology): “…MR research that could benefit greatly from the ACT framework is the field of network analysis, where network nodes are defined as grey matter regions throughout the brain, and streamlines tractography is used to define the connection strengths between these nodes (e.g. Hagmann et al., 2007).”
Network-based statistic: Identifying differences in brain networks (2010, background): “…tracts and the association between a pair of nodes is measured by the number of streamlines via 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…”
Structural Insights into Aberrant Topological Patterns of Large-Scale Cortical Networks in Alzheimer's Disease (2008, result): “The result was consistent with previous neuroimaging studies showing increased structural and functional asymmetry in AD patients (Grady et al., 1986; Haxby et al., 1990).”