Surface area and cortical thickness descriptors reveal different attributes of the structural human brain networks

Sanabria‐Diaz, Gretel; Melie‐García, Lester; Iturria-Medina, Yasser; Alemán‐Gómez, Yasser; Hernández-González, Gertrudis; Valdés-Urrutia, Lourdes; Galán, Lı́dice; Valdés‐Sosa, Pedro A.

doi:10.1016/j.neuroimage.2010.01.028

Cited by 172 publications

(170 citation statements)

References 69 publications

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“…However, the computational burden in the vertex level (see Section 2.6) prevented performing the analysis of the other network properties as done for regional networks in Ref. [8]. Also, it is unclear whether the jackknife technique, known to fail for the estimation of S.E.…”

Section: Standard Errors Of the Correlation Coefficients And Average mentioning

confidence: 99%

“…Anatomical connectivity networks have been derived based on magnetic resonance imaging (MRI)-based cortical thickness measurements [4][5][6], local gray matter volumes [7], regional cortical surface area [8] or diffusion MRI [9][10][11][12]. This work is related to Ref.…”

Section: Introductionmentioning

confidence: 99%

“…The influence of the atlas choice on the topological parameters of networks derived based on cortical thickness and regional cortical surface area was addressed in Ref. [8], where the authors found differences in various parameters due to the selected parcellation scheme. In Ref.…”

Section: Introductionmentioning

confidence: 99%

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The impact of sampling density upon cortical network analysis: regions or points

Tohka

Evans

2012

Magnetic Resonance Imaging

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Section: Standard Errors Of the Correlation Coefficients And Average mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The impact of sampling density upon cortical network analysis: regions or points

Tohka

Evans

2012

Magnetic Resonance Imaging

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“…In addition, a weight can be attributed to each edge to better quantify connectivity. In the literature, the question of the effects of different weights or thresholds on the networks has been addressed (Cheng et al, 2012;Iturria-Medina et al, 2007;Rubinov and Bassett, 2011;Sanabria-Diaz et al, 2010;van den Heuvel and Sporns, 2011). Several scalar metrics can be computed from the connectivity matrix of the network and enable a better understanding of the complex organization and topology of the connectome.…”

Section: Introductionmentioning

confidence: 99%

Repeatability Analysis of Global and Local Metrics of Brain Structural Networks

Andreotti

Jann²,

Melie‐García³

et al. 2014

Brain Connectivity

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Computational network analysis provides new methods to analyze the human connectome. Brain structural networks can be characterized by global and local metrics that recently gave promising insights for diagnosis and further understanding of neurological, psychiatric, and neurodegenerative disorders. In order to ensure the validity of results in clinical settings, the precision and repeatability of the networks and the associated metrics must be evaluated. In the present study, 19 healthy subjects underwent two consecutive measurements enabling us to test reproducibility of the brain network and its global and local metrics. As it is known that the network topology depends on the network density, the effects of setting a common density threshold for all networks were also assessed. Results showed good to excellent repeatability for global metrics, while for local metrics it was more variable and some metrics were found to have locally poor repeatability. Moreover, between-subjects differences were slightly inflated when the density was not fixed. At the global level, these findings confirm previous results on the validity of global network metrics as clinical biomarkers. However, the new results in our work indicate that the remaining variability at the local level as well as the effect of methodological characteristics on the network topology should be considered in the analysis of brain structural networks and especially in network comparisons.

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“…For three days we had the opportunity to participate in the anniversary of the Cuban Neuroscience Center (CNEURO) and to join in the inauguration of their new research facility. The relatively small core of CNEURO scientists has had a disproportionate impact on basic methods in neuroscience, particularly in electrophysiology (20), neuroimaging (21), and neuroinformatics (22). For example, few research centers have been as influential in the quantitative analysis of brain electrical activity as have scientists at the CNEURO (23).…”

mentioning

confidence: 99%