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Research ArticleFunctional

Brain Network Architecture and Global Intelligence in Children with Focal Epilepsy

M.J. Paldino, F. Golriz, M.L. Chapieski, W. Zhang and Z.D. Chu
American Journal of Neuroradiology February 2017, 38 (2) 349-356; DOI: https://doi.org/10.3174/ajnr.A4975
M.J. Paldino
aFrom the Departments of Radiology (M.J.P., F.G., Z.D.C.)
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F. Golriz
aFrom the Departments of Radiology (M.J.P., F.G., Z.D.C.)
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M.L. Chapieski
bPsychology (M.L.C.)
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W. Zhang
cOutcomes and Impact Service (W.Z.), Texas Children's Hospital, Houston, Texas.
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Z.D. Chu
aFrom the Departments of Radiology (M.J.P., F.G., Z.D.C.)
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    Fig 1.

    The importance of global metrics of network architecture with respect to full-scale intelligence quotient. The independent contribution of each metric was estimated as the error of the prediction of IQ of the machine learning algorithm compared with the error that results when that metric is negated. The most negative value of importance defines the limit of noise. Hence, variables with importance greater in magnitude than the most negative variable are significant.

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    Fig 2.

    Graphic representation of the relationships of path length (A) and clustering coefficient (B) to the full-scale intelligence quotient.

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    Fig 3.

    The importance of global metrics of network architecture with respect to the full-scale intelligence quotient for patients requiring anesthesia (A) and those who were nonsedated (B).

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    Fig 4.

    Graphic representation of the relationships of path length (A) and clustering coefficient (B) to full-scale intelligence quotient for patients requiring anesthesia.

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    Table 1:

    Metrics of network architecture

    MetricDescription
    Clustering coefficientThe fraction of the nodes of a given neighbor that are also neighbors of each other; reflects segregation/subspecialization in the network
    TransitivityThe fraction of node triplets in the network that form a completely connected triangle; reflects segregation/functional subspecialization in the network
    ModularityThe degree to which nodes tend to segregate into relatively independent modules; reflects segregation/subspecialization within the network
    Characteristic path lengthMinimum number of edges required to traverse the distance between 2 nodes averaged over the network; reflects the ease of information transfer across the network
    Global efficiencyInverse of the mean characteristic path length averaged over the network; reflects integration in the network
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    Table 2:

    Anesthetic medications administered during MR imaging

    Drug CombinationNo. of PatientsAge Range (median) (yr)
    Propofol only99–17 (11)
    Propofol + sevoflurane68–16 (14)
    Propofol + Dex44–19 (15)
    Propofol + sevoflurane + Dex111–11 (11)
    None109–19 (16)
    • Note:—Dex indicates dexmedetomidine.

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American Journal of Neuroradiology: 38 (2)
American Journal of Neuroradiology
Vol. 38, Issue 2
1 Feb 2017
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M.J. Paldino, F. Golriz, M.L. Chapieski, W. Zhang, Z.D. Chu
Brain Network Architecture and Global Intelligence in Children with Focal Epilepsy
American Journal of Neuroradiology Feb 2017, 38 (2) 349-356; DOI: 10.3174/ajnr.A4975

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Brain Network Architecture and Global Intelligence in Children with Focal Epilepsy
M.J. Paldino, F. Golriz, M.L. Chapieski, W. Zhang, Z.D. Chu
American Journal of Neuroradiology Feb 2017, 38 (2) 349-356; DOI: 10.3174/ajnr.A4975
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