Studying the human brain anatomical network via diffusion-weighted MRI and Graph Theory

By Yasser Iturria-Medina, Roberto C. Sotero, Erick Jorge Canales-Rodríguez, Yasser Alemán-Gómez, Lester Melie-García. NeuroImage, 2008

538 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computational Theory and Mathematics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b239dd3589c3454fd4a416b3aa876a5810d17e37

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In Cognitive Neuroscience

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Citations in the corpus

A Network Diffusion Model of Disease Progression in Dementia (2012, methodology): “Each streamline is assigned a probability score according to established criteria (Iturria-Medina et al., 2008).”
The hubs of the human connectome are generally implicated in the anatomy of brain disorders (2014, background): “Computational analysis confirmed and extended previous in silico brain studies (Achard et al., 2006; Kaiser et al., 2007; Iturria-Medina et al., 2008; Alstott et al., 2009), demonstrating that targeted attacks on hubs, or the longer distance edges, of the normal DTI connectome were especially likely”
Comparison of Characteristics between Region- and Voxel-Based Network Analyses in Resting-State fMRI Data (2009, unknown): “…in the human brain (Bullmore and Sporns 2009) using various in-vivo imaging methods, such as diffusion tensor imaging (DTI) (Gong, et al. 2009; Iturria-Medina, et al. 2008), diffusion spectrum imaging (Hagmann, et al. 2008; Hagmann, et al. 2007), and cortical thickness analysis (He, et al.…”
Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease. (2009, unknown): “There is now accumulating evidence that different types of structural brain networks display a ‘small-world’ type network organization characterized by a combination of high local clustering as well as short path lengths (Watts and Strogatz, 1998; Hilgetag et al., 2000; He et al., 2007; Iturria-Medi”
Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks (2010, methodology): “[42] have proposed several measures that could indirectly quantify the strength and density of connections.”
Towards quantitative connectivity analysis: reducing tractography biases (2014, unknown): “However, white matter bundles have various position, shape, 5 size and length making their reconstruction a challenge for tractography algorithms (Jbabdi and Johansen-Berg, 2011; 6 ⇤ E-mail address: gabriel.p.girard@usherbrooke.ca (G. Girard) Preprint submitted to NeuroImage February 4, 2014 Jones,…”
Network-based statistic: Identifying differences in brain networks (2010, background): “, 1999), hundreds of thousands of streamlines are generated to etch out the trajectories of these fiber 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.,”
Structural connectomics in brain diseases (2013, unknown): “By reporting exponential (Hagmann et al., 2008) or exponentially truncated power laws (Gong et al., 2008; Iturria-Medina et al., 2008) several authors confirmed that most nodes have degrees in a relatively narrow low range, while few nodes have higher degree.”
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).”
Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, unknown): “In most of these cases, classification accuracy exceeded 75%, with most showing accuracy > 80%.”
Parcellation‐dependent small‐world brain functional networks: A resting‐state fMRI study (2009, background): “Further work could be conducted by using continuous weighted correlation values in the construction of brain networks (Achard et al., 2006; Jiang et al. 2004; Latora and Marchiori, 2003).”
Network hubs in the human brain. (2013, background): “For example, a structural vulnerability analysis suggested an important role for the precuneus, insular, superior parietal, and superior frontal regions in global communication processes [62].”
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.,…”
Power Law in Sparsified Deep Neural Networks (2018, unknown): “Such networks are sometimes called broad-scale or truncated scale-free networks [6], and have been observed for example in the human brain anatomical network [7].”
Experimental and theoretical approaches to conscious processing. (2011, background): “The precuneus, in particular, may also operate as a cortical ''hub'' with a massive degree of interconnectivity (Hagmann et al., 2008; Iturria-Medina et al., 2008).”