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Virtual lesions in MEG reveal increasing vulnerability of the language network from early childhood through adolescence

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  • Brady J. Williamson

    (University of Cincinnati)

  • Hansel M. Greiner

    (Cincinnati Children’s Hospital Medical Center
    University of Cincinnati)

  • Darren S. Kadis

    (Hospital for Sick Children
    University of Toronto)

Abstract

In childhood, language outcomes following brain injury are inversely related to age. Neuroimaging findings suggest that extensive representation and/or topological redundancy may confer the pediatric advantage. Here, we assess whole brain and language network resilience using in silico attacks, for 85 children participating in a magnetoencephalography (MEG) study. Nodes are targeted based on eigenvector centrality, betweenness centrality, or at random. The size of each connected component is assessed after iterated node removal; the percolation point, or moment of dis-integration, is defined as the first instance where the second largest component peaks in size. To overcome known effects of fixed thresholding on subsequent graph and resilience analyses, we study percolation across all possible network densities, within a Functional Data Analysis (FDA) framework. We observe age-related increases in vulnerability for random and betweenness centrality-based attacks for whole-brain and stories networks (adjusted-p

Suggested Citation

  • Brady J. Williamson & Hansel M. Greiner & Darren S. Kadis, 2023. "Virtual lesions in MEG reveal increasing vulnerability of the language network from early childhood through adolescence," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43165-7
    DOI: 10.1038/s41467-023-43165-7
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    References listed on IDEAS

    as
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    2. Philip T. Reiss & R. Todd Ogden, 2010. "Functional Generalized Linear Models with Images as Predictors," Biometrics, The International Biometric Society, vol. 66(1), pages 61-69, March.
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