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Brain networks show remarkable consistency between structure and function across many disorders. This suggests a common mechanism of nodal stress driving network degeneration in brain diseases.

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Area of Science:

  • Neuroscience
  • Systems Neuroscience
  • Computational Neuroscience

Background:

  • Network architecture is a fundamental brain organizational principle across scales.
  • The Network Degeneration Hypothesis (NDH) posits selective network pathology in neurodegenerative disorders.
  • Emerging evidence suggests this pathology may extend to neuropsychiatric disorders.

Purpose of the Study:

  • To investigate the generalizability of the Network Degeneration Hypothesis across neuropsychiatric disorders.
  • To compare functional and structural network organization in the brain.
  • To identify common mechanisms underlying network pathology in diverse brain disorders.

Main Methods:

  • Conducted large-scale functional and structural neuroimaging meta-analyses using the BrainMap database.
  • Included over 7,000 functional neuroimaging experiments across >100 task paradigms.
  • Included over 2,000 structural neuroimaging studies from patients with >40 brain disorders.

Main Results:

  • High concordance (68%) was found between functional and structural brain networks (pFWE < 0.01), supporting a broader scope for NDH.
  • This structure-function correspondence remained robust across different network model orders.
  • A significant positive linear association between disease and behavioral entropy (p=0.0006, R²=0.53) indicated nodal stress as a potential common mechanism.
  • Metabolic cost varied significantly along the identified transdiagnostic/multimodal gradient.

Conclusions:

  • The study confirms a broad applicability of the Network Degeneration Hypothesis, extending beyond neurodegeneration to encompass neuropsychiatric disorders.
  • Nodal stress, reflected by behavioral entropy and metabolic cost, appears to be a unifying mechanism driving network pathology across diverse brain conditions.
  • Findings highlight the utility of network science approaches for understanding transdiagnostic mechanisms in brain disorders.