Characterizing brain anatomical connections using diffusion weighted MRI and graph theory

By Yasser Iturria-Medina, Erick Jorge Canales-Rodríguez, Lester Melie-García, Pedro A. Valdés-Hernández, Eduardo Martínez-Montes, Yasser Alemán-Gómez, Jose Miguel Sanchez Bornot. NeuroImage, 2007

364 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/f253627bfa7485c0e6e5e8f53a682d7149f0f790

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

1. Olaf Sporns
2. E. Bullmore
3. Yong He
4. D. Bassett
5. Andrew Zalesky
6. Alan C. Evans
7. Alex Fornito
8. Jinhui Wang

In Radiology, Nuclear Medicine and Imaging

1. Olaf Sporns
2. E. Bullmore
3. Yong He
4. D. Bassett
5. Alan C. Evans
6. Andrew Zalesky
7. Alex Fornito
8. Jinhui Wang

Citations in the corpus

• Anatomically-constrained tractography: Improved diffusion MRI streamlines tractography through effective use of anatomical information (2012, background): “, 2007), or restricting propagation to the volume of the white matter (and optionally also grey matter) based on segmentation of an image with high anatomical tissue contrast and spatial resolution (e.g. Guevara et al., 2011; Iturria-Medina et al., 2007; Sotiropoulos et al., 2010).”
• Sex differences in the structural connectome of the human brain (2013, background): “Although SCs of genders have displayed small-world architecture with broad-scale characteristics (34, 35), sex differences in network efficiency have been reported (36), with women having greater overall cortical connectivity (37).”
• Networks of anatomical covariance (2013, neutral): “Notwithstanding these concerns, WM tractography has found widespread application in connectivity analysis (Hagmann et al., 2007, 2008; Iturria-Medina et al., 2007, 2008, 2011; Rubinov and Sporns, 2010).”
• Conserved and variable architecture of human white matter connectivity (2011, neutral): “However, additional work could also include an assessment of weighted network measures in addition to more detailed quantification of regional reproducibility (Hagmann et al., 2008; Iturria-Medina et al., 2007).”
• Mapping the Structural Core of Human Cerebral Cortex (2008, background): “This step is equivalent to computing the principal eigenvector field in DTI.”
• Complex brain networks: graph theoretical analysis of structural and functional systems (2009, methodology): “Human brain structural networks have also been mapped using diffusion imaging and tractography. a map of 70–90 cortical and basal brain grey matter areas was constructed using diffusion tensor imaging (DTI) and analysed using methods from graph theory 54,55 .”
• 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).”
• Studying the human brain anatomical network via diffusion-weighted MRI and Graph Theory (2008, methodology): “…(DW-MRI) techniques in the last decade makes possible the noninvasive study of the anatomical circuitry of the living human brain (Mori et al., 1999; Conturo et al., 1999; Tuch, 2002; Parker et al., 2002; Koch et al., 2002; Behrens et al., 2003; Hagmann et al., 2006; Iturria-Medina et al., 2007).”
• SIFT: Spherical-deconvolution informed filtering of tractograms (2013, background): “…into account information such as the size of the nodes, the distance between nodes, and the degree of anisotropy along the connecting path (e.g. Colon-Perez et al., 2012; Hagmann et al., 2007; Iturria-Medina et al., 2007); however the biological interpretation of these weights is still ambiguous.”
• Identification and Classification of Hubs in Brain Networks (2007, methodology): “apply network analysis to human anatomical data [64,68], and”
• Mathematical framework for large-scale brain network modeling in The Virtual Brain (2015, neutral): “These modalities include, but are not limited to, intracranial Electroencephalography (iEEG), Electroencephalography (EEG) (Nunez and Srinivasan, 1981; Nunez, 1995; Niedermeyer and Lopes Da Silva, 2005), Magnetoencephalography (MEG) (Hämäläinen and Sarvas, 1987; Hämäläinen, 1992; Hämäläinen et al., ”
• Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks (2010, result): “Similar results has been obtained with DTI and DSI [18, 41–44].”
• Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory (2010, background): “Recent advances in tractography allowed for more direct studies of anatomical network structure (white matter) based on diffusion tensor imaging and diffusion spectrum imaging [8–10].”
• Whole-brain anatomical networks: Does the choice of nodes matter? (2010, methodology): “While several alternative tractographic methods have been customized to network mapping (e.g. Hagmann et al., 2007; Jbabdi et al., 2007; Itturia-Medina et al., 2008a; Zalesky, 2008; Zalesky and Fornito, 2009), this study utilized the DTI and HARDI versions of FACT streamline tracking (Mori et al.,…”
• Diffusion Tensor Imaging and Beyond (2011, neutral): “Other probabilistic methods have also been proposed, including: the use of fast marching algorithms (179), front evolution methods (176), random-walk models (180), and graphtheoretic approaches (181).”