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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Computational Biology

Background:

  • Alzheimer's disease (AD) is characterized by brain network alterations compared to healthy controls (HC).
  • Previous neuroimaging studies on AD brain networks are predominantly cross-sectional, limiting understanding of longitudinal changes and reproducibility.
  • Reproducibility of findings in AD neuroimaging studies remains a challenge.

Purpose of the Study:

  • To develop and apply a novel longitudinal approach for computing and comparing brain networks in Alzheimer's disease (AD) and healthy controls (HC).
  • To investigate the impact of AD on brain network properties at both global and local scales over a one-year period.
  • To assess the reproducibility of brain network findings using the proposed longitudinal method versus traditional cross-sectional approaches.

Main Methods:

  • Utilized longitudinal Alzheimer's Disease Neuroimaging Initiative (ADNI) data for whole-brain network analysis at baseline and one-year follow-up.
  • Employed a state-of-the-art approach that pools data across time points for more accurate, visit-specific network estimations in AD and HC cohorts.
  • Performed multiscale network comparisons, evaluating global metrics and specific resting-state networks, including hub node analysis.

Main Results:

  • Demonstrated a significant decrease in small-worldness in the Alzheimer's disease (AD) group at both time points compared to healthy controls (HC).
  • Identified specific local network features and disrupted hub nodes associated with AD progression.
  • Achieved high reproducibility for the healthy control (HC) brain network across visits using the longitudinal method.
  • Standard cross-sectional analyses revealed fewer meaningful differences and lower reproducibility compared to the proposed longitudinal approach.

Conclusions:

  • The novel longitudinal approach provides more accurate and reproducible insights into brain network dynamics in Alzheimer's disease (AD).
  • Alzheimer's disease (AD) is associated with progressive disruptions in brain network topology, particularly affecting small-world properties and critical hub nodes.
  • Longitudinal analysis is crucial for understanding the dynamic changes in brain networks during Alzheimer's disease (AD) progression and for validating neuroimaging findings.