Mapping Anatomical Connectivity Patterns of Human Cerebral Cortex Using In Vivo Diffusion Tensor Imaging Tractography

Gong, Gaolang; He, Yong; Concha, Luis; Lebel, Catherine; Gross, Donald; Evans, Alan C.; Beaulieu, Christian

doi:10.1093/cercor/bhn102

Cited by 1,014 publications

(1,046 citation statements)

References 73 publications

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“…This gives a network density of 0.19. Note that his is denser than the density of 0.11 reported by Gong et al (2009). However, for our purposes, the BFC analysis can still provide evidence against edges which were erroneously included in the structural graph as we saw in the simulation.…”

Section: Estimated Structural Connectivity Graphmentioning

confidence: 64%

Structurally-informed Bayesian functional connectivity analysis

et al. 2014

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Functional connectivity refers to covarying activity between spatially segregated brain regions and can be studied by measuring correlation between functional magnetic resonance imaging (fMRI) timeseries. These correlations can be caused either by direct communication via active axonal pathways or indirectly via the interaction with other regions. It is not possible to discriminate between these two kinds of functional interaction simply by considering the covariance matrix. However, the non-diagonal elements of its inverse, the precision matrix, can be naturally related to direct communication between brain areas and interpreted in terms of partial correlations. In this paper, we propose a Bayesian model for functional connectivity analysis which allows estimation of a posterior density over precision matrices, and, consequently, allows one to quantify the uncertainty about estimated partial correlations. In order to make model estimation feasible it is assumed that the sparseness structure of the precision matrices is given by an estimate of structural connectivity obtained using diffusion imaging data. The model was tested on simulated data as well as resting-state fMRI data and compared with a graphical lasso analysis. The presented approach provides a theoretically solid foundation for quantifying functional connectivity in the presence of uncertainty.

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Section: Estimated Structural Connectivity Graphmentioning

confidence: 64%

Structurally-informed Bayesian functional connectivity analysis

et al. 2014

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“…Small-world structures has been reported to exist in the cortex of non-human primates based on data derived by classical tract tracing (Sporns et al, 2000;Sporns and Zwi, 2004), as well as in networks derived from diffusion imaging of the human brain (Gong et al, 2009;Hagmann et al, , 2007Iturria-Medina et al, 2008), see Fig. 5.…”

Section: Brain Structural Network Analysismentioning

confidence: 99%

“…Other groups have used similar strategies to partition the cortex using different matching criteria between subjects, using volumetric registration of atlases, like MNI, resulting in smaller networks (Gong et al, 2009;Iturria-Medina et al, 2007;Li et al, 2009). In our early works we used Tailarach coordinates (Hagmann, 2005) or arbitrary subdivisions (Hagmann et al, 2007).…”

Section: From Tracts To Network 231 Past and Current State Of Thementioning

confidence: 99%

MR connectomics: Principles and challenges

Hagmann

Cammoun

Gigandet

et al. 2010

Journal of Neuroscience Methods

255

207

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a b s t r a c t MR connectomics is an emerging framework in neuro-science that combines diffusion MRI and whole brain tractography methodologies with the analytical tools of network science. In the present work we review the current methods enabling structural connectivity mapping with MRI and show how such data can be used to infer new information of both brain structure and function. We also list the technical challenges that should be addressed in the future to achieve high-resolution maps of structural connectivity. From the resulting tremendous amount of data that is going to be accumulated soon, we discuss what new challenges must be tackled in terms of methods for advanced network analysis and visualization, as well data organization and distribution. This new framework is well suited to investigate key questions on brain complexity and we try to foresee what fields will most benefit from these approaches.

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“…The connectome results from Yap et al rely on the concept of a backbone network computed from a group of subjects [23,58]. This backbone network captures connections that are consistent across the group through the use of a signal-to-noise (SNR) connection matrix.…”

Section: Groupwise Connectome Analysismentioning

confidence: 99%

Structural network analysis of brain development in young preterm neonates

et al. 2014

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Preterm infants develop differently than those born at term and are at higher risk of brain pathology. Thus, an understanding of their development is of particular importance. Diffusion tensor imaging (DTI) of preterm infants offers a window into brain development at a very early age, an age at which that development is not yet fully understood. Recent works have used DTI to analyze structural connectome of the brain scans using network analysis. These studies have shown that, even from infancy, the brain exhibits small-world properties. Here we examine a cohort of 47 normal preterm neonates (i.e., without brain injury and with normal neurodevelopment at 18 months of age) scanned between 27 and 45 weeks post-menstrual age to further the understanding of how the structural connectome develops. We use fullbrain tractography to find white matter tracts between the 90 cortical and sub-cortical regions defined in the University of North Carolina Chapel Hill neonatal atlas. We then analyze the resulting connectomes and explore the differences between weighting edges by tract count versus fractional anisotropy. We observe that the brain networks in preterm infants, much like infants born at term, show high efficiency and clustering measures across a range of network scales. Further, the development of many individual region-pair connections, particularly in the frontal and occipital lobes, is significantly correlated with age. Finally, we observe that the preterm infant connectome remains highly efficient yet becomes more clustered across this age range, leading to a significant increase in its small-world structure.

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Mapping Anatomical Connectivity Patterns of Human Cerebral Cortex Using In Vivo Diffusion Tensor Imaging Tractography

Cited by 1,014 publications

References 73 publications

Structurally-informed Bayesian functional connectivity analysis

Structurally-informed Bayesian functional connectivity analysis

MR connectomics: Principles and challenges

Structural network analysis of brain development in young preterm neonates

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