Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography.

By Gaolang Gong, Yong He, Luis Concha, Catherine Lebel, D. Gross, Alan C. Evans, Christian Beaulieu. Cerebral Cortex, 2009

1,129 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/cfe2ae8886119c7ddeb4e1917838b60ae15a0ee8

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

Convergence and divergence of thickness correlations with diffusion connections across the human cerebral cortex (2012, methodology): “Such integrated metrics mathematically correspond to the areas under the metric curve and have been applied in recent brain network studies (Bassett et al., 2008; Gong et al., 2009a; He et al., 2007, 2008).”
Frequency-dependent functional connectivity within resting-state networks: An atlas-based MEG beamformer solution (2012, neutral): “…for a direction anatomical interpretation of MEG data, as well as a direct comparison with (functional) connectivity and network studies based on anatomical MRI (Diffusion Tensor Imaging, Voxel-based Morphometry) and functional MRI (e.g. Gong et al., 2009; van den Heuvel and Hulshoff Pol, 2010).”
Graph-Based Network Analysis of Resting-State Functional MRI (2010, neutral): “…into the topological reconfiguration of the brain in response to external task modulation (Eguiluz et al., 2005; Pachou et al., 2008; Bassett et al., 2009; Micheloyannis et al., 2009; Wang et al., 2010) or pathological attacks (for reviews, see Bassett and Bullmore, 2009 and He et al., 2009a).”
Hemisphere- and gender-related differences in small-world brain networks: A resting-state functional MRI study (2011, methodology): “However, in a study by Gong et al. (2009b), the cortical networks of females were more efficient both locally and globally.”
Modeling the Impact of Lesions in the Human Brain (2009, background): “The availability of whole-brain structural connectivity data sets [3–7], for the first time, allows for the computational study of the effects of localized structural lesions on neural dynamics.”
Conserved and variable architecture of human white matter connectivity (2011, positive): “Masks for each of the N regions of a cortical atlas were transformed into the subject's native space in a multi-step process (Gong et al., 2009; Shu et al., 2009).”
The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture (2016, positive): “In addition, we also computed the structural connectome between all identified subregions, again binarized them using a threshold for stray connectivity of ≥2 and then employed a nonparametric 1-tailed sign test to determine the connections that are consistent across subjects (Gong et al. 2009).”
The minimum spanning tree: An unbiased method for brain network analysis (2015, positive): “The rationale for this is that many empirical studies use parcellation schemes based on the Automated Anatomical Labeling (AAL) atlas, which (without sub-cortical regions) contains 78 nodes (Gong et al., 2009; Tzourio-Mazoyer et al., 2002).”
Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks (2017, neutral): “The anatomical connectivity is the physical connections or interactions between two anatomical areas of the brain and can be obtained with the aid of structural imaging in collaboration with diffusion tensor tractography methods.(15) Functional connectivity (FC) attempts to establish the connection ”
Network-Level Structure-Function Relationships in Human Neocortex (2016, background): “Similarly, the structural and functional connectivity profiles of brain areas are often correlated, such that central, well-connected structural hubs (Hagmann et al. 2008; Gong et al. 2009) are also likely to play central roles in shaping functional connectivity (Buckner et al.”
Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks (2010, methodology): “Moreover, the degree distribution of these structural networks was found as following an exponentially truncated power-law distribution [18,42,43,47].”
BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics (2013, background): “edges are often estimated by gray matter morphological correlation [6,7] or white matter fiber connections [8,9] in structural data”
Structure and function of complex brain networks (2013, background): “Several studies of human structural brain networks have attempted to identify hubs, and most studies have converged on a set of regions including portions of the medial and superior parietal cortex as well as selected regions in orbitofrontal, superior frontal, and lateral prefrontal cortex.(56,58) ”
Comparison of Characteristics between Region- and Voxel-Based Network Analyses in Resting-State fMRI Data (2009, neutral): “…et al. 2007; Eguiluz, et al. 2005; van den Heuvel, et al. 2008) with the degree distribution following a power law distribution, but this has not been observed in region-based networks (Achard, et al. 2006; Bassett, et al. 2006; Gong, et al. 2009; He, et al. 2007; Iturria-Medina, et al. 2008).”
The resting brain: unconstrained yet reliable. (2009, neutral): “2008), diffusion tensor imaging (Gong et al. 2008), and cortical”