Comparing connectomes across subjects and populations at different scales

Meskaldji, Djalel Eddine; Fischi-Gómez, Elda; Griffa, Alessandra; Hagmann, Patric; Morgenthaler, Stephan; Thiran, Jean-Philippe

doi:10.1016/j.neuroimage.2013.04.084

Cited by 73 publications

(79 citation statements)

References 99 publications

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“…Finally, it was found that weighted metrics are less affected by the application of different thresholds (TD and Tc) as low weights have lower influence. Therefore, although the choice of an unbiased weighting scheme that represents true connectivity remains an open issue in brain structural networks analysis (IturriaMedina et al, 2007;Jbabdi and Johansen-Berg, 2011;Meskaldji et al, 2013), weighted measures may have the advantage of not requiring setting thresholds.…”

Section: Effects Of a Common Density Threshold On Repeatabilitymentioning

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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Section: Effects Of a Common Density Threshold On Repeatabilitymentioning

confidence: 99%

Repeatability Analysis of Global and Local Metrics of Brain Structural Networks

Andreotti

Jann²,

Melie‐García³

et al. 2014

Brain Connectivity

View full text Add to dashboard Cite

show abstract

“…Since the imaging measures of connectivity can be used to model the brain as a network, it is worth to locally compare populations not only cell by cell of the connectivity matrices, but also by estimating the network measures that characterize the topological properties of the brain network (Fornito et al, 2013;Meskaldji et al, 2013a;Bassett et al, 2008). The combination of the local and the global inferences gives a better understanding of the network organization (Meskaldji et al, 2013a).…”

Section: Local Network-based Measuresmentioning

confidence: 99%

“…If the multiplicity of tests is ignored, the risk of committing false discoveries increases. As a consequence, erroneous conclusions are frequently drawn (Meskaldji et al, 2013a). On the other hand, considering multiplicity could dramatically decrease the chance of detecting real between-group effects.…”

Section: Introductionmentioning

confidence: 99%

Improved statistical evaluation of group differences in connectomes by screening–filtering strategy with application to study maturation of brain connections between childhood and adolescence

et al. 2015

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“…Although the choice of these values is somehow arbitrary but it permits to highlight the influence of thresholding on the inference. For more discussion see [9].…”

Section: Description Of the Studymentioning

confidence: 99%

“…Furthermore, prior information about the dependence structure between tests could be available in some situations. In recent work, we developed two-step procedures that exploit the data structure and prior information of positive dependence between tests [7,8,9,1]. The proposed procedures work as the following.…”

Section: Introductionmentioning

confidence: 99%

Multimodal graph theoretical analysis of functional brain connectivity using adaptive two-step strategy

Meskaldji

Ville

2014

2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)

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Recently, we proposed a two-step adaptive strategy for the statistical analysis of brain connectivity that is based on a first screening at the subnetwork level and a filtering at the connection/node level. The method was shown to guarantee strong control of type-I error through rigorous statistical proofs. In addition, the gain of power obtained by this method is considerable especially with an appropriate decomposition of the global network. Here, we discuss the extension of the two-step methods to multivariate statistics and we compare its performance against both standard methods and univariate two-step methods. We present as well a practical example of detecting topological nodal differences between functional connectivity matrices of resting state and movie-watching, respectively.

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Comparing connectomes across subjects and populations at different scales

Cited by 73 publications

References 99 publications

Repeatability Analysis of Global and Local Metrics of Brain Structural Networks

Repeatability Analysis of Global and Local Metrics of Brain Structural Networks

Improved statistical evaluation of group differences in connectomes by screening–filtering strategy with application to study maturation of brain connections between childhood and adolescence

Multimodal graph theoretical analysis of functional brain connectivity using adaptive two-step strategy

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