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Local structure-function relationships in human brain networks across the lifespan

Author

Listed:
  • Farnaz Zamani Esfahlani

    (Indiana University)

  • Joshua Faskowitz

    (Indiana University
    Indiana University)

  • Jonah Slack

    (Indiana University)

  • Bratislav Mišić

    (Montréal Neurological Institute, McGill University)

  • Richard F. Betzel

    (Indiana University
    Indiana University
    Indiana University
    Indiana University)

Abstract

A growing number of studies have used stylized network models of communication to predict brain function from structure. Most have focused on a small set of models applied globally. Here, we compare a large number of models at both global and regional levels. We find that globally most predictors perform poorly. At the regional level, performance improves but heterogeneously, both in terms of variance explained and the optimal model. Next, we expose synergies among predictors by using pairs to jointly predict FC. Finally, we assess age-related differences in global and regional coupling across the human lifespan. We find global decreases in the magnitude of structure-function coupling with age. We find that these decreases are driven by reduced coupling in sensorimotor regions, while higher-order cognitive systems preserve local coupling with age. Our results describe patterns of structure-function coupling across the cortex and how this may change with age.

Suggested Citation

  • Farnaz Zamani Esfahlani & Joshua Faskowitz & Jonah Slack & Bratislav Mišić & Richard F. Betzel, 2022. "Local structure-function relationships in human brain networks across the lifespan," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29770-y
    DOI: 10.1038/s41467-022-29770-y
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    References listed on IDEAS

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    1. Yaqian Yang & Zhiming Zheng & Longzhao Liu & Hongwei Zheng & Yi Zhen & Yi Zheng & Xin Wang & Shaoting Tang, 2023. "Enhanced brain structure-function tethering in transmodal cortex revealed by high-frequency eigenmodes," Nature Communications, Nature, vol. 14(1), pages 1-14, December.

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