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Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
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Biomarkers.

Georgette Argiris1, Yaakov Stern2,3, Christian G Habeck4

  • 1Columbia University Irving Medical Center, New York, NY, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 26, 2025
PubMed
Summary
This summary is machine-generated.

Brain network resilience metrics predict cognitive aging. Higher resilience in characteristic path length and clustering coefficient was linked to slower cognitive decline and better outcomes in healthy older adults.

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Network Science

Background:

  • Advancements in brain network analysis offer precise measurements of network integrity and resilience.
  • Identifying neural markers of cognitive reserve (CR) is vital for understanding individual differences in age-related cognitive changes.
  • Previous work identified network resilience moderating brain integrity's effect on cognition; this study extends this to new metrics and cohorts.

Purpose of the Study:

  • To apply a network resilience approach to a new cohort (Barcelona Brain Health Initiative).
  • To test additional network metrics (characteristic path length, clustering coefficient, largest connected component) as potential mechanisms of cognitive reserve.
  • To evaluate if these network metrics predict cognitive performance changes or moderate structural brain effects on cognition.

Main Methods:

  • Resting-state fMRI and neuropsychological testing were conducted on 569 cognitively healthy participants (ages 42-67) over ~2.5 years.
  • A targeted attack approach was used to extract resilience metrics (critical point, AUC) from brain network connectivity matrices.
  • Network metrics were analyzed for their ability to predict cognitive decline or moderate cortical thickness effects, controlling for covariates.

Main Results:

  • The critical point of characteristic path length significantly predicted cognitive decline (WAIS, TMT), with higher resilience indicating slower decline.
  • A higher area under the curve (AUC) for the clustering coefficient predicted better WAIS outcomes.
  • No significant effects were observed for the largest connected component or for the moderation of cortical thickness by brain resilience metrics.

Conclusions:

  • Targeted attack network metrics serve as valid measures of cognitive reserve.
  • Enhanced brain network resilience is associated with better cognitive outcomes, independent of structural brain changes.
  • Future research should investigate network metrics as protective factors against age-related cognitive decline within established frameworks.