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Related Experiment Video

Updated: Dec 14, 2025

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Predicting dysfunctional age-related task activations from resting-state network alterations.

Ravi D Mill1, Brian A Gordon2, David A Balota3

  • 1Center for Molecular and Behavioral Neuroscience, Rutgers University, 197 University Avenue, Newark, NJ 07102, USA.

Neuroimage
|July 19, 2020
PubMed
Summary
This summary is machine-generated.

Alzheimer's disease (AD) alters brain connectivity, disrupting neural activity flow. This model predicts early AD-related brain changes and cognitive issues in at-risk individuals, even before symptoms appear.

Keywords:
AgingAlzheimer'sFunctional connectivityTask activationfMRI

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Computational Biology

Background:

  • Alzheimer's disease (AD) is associated with altered functional magnetic resonance imaging (fMRI) task activations and resting-state functional connectivity (restFC).
  • These fMRI markers of unhealthy aging are typically studied independently.
  • A unifying mechanism linking restFC changes to aberrant task activations in early AD is needed.

Purpose of the Study:

  • To propose and validate an activity flow model explaining how altered restFC in Alzheimer's disease (AD) leads to aberrant task activations.
  • To investigate this mechanism in clinically normal older adults stratified by AD risk.
  • To assess the predictive potential of this model for task-related brain activity and behavior.

Main Methods:

  • Utilized a large sample of clinically normal older adults, categorized into healthy and at-risk subgroups based on amyloid PET imaging and apolipoprotein genetics.
  • Applied an activity flow model inspired by neural network simulations to predict task activations based on restFC alterations.
  • Correlated predicted task activations with individual differences in task behavior.

Main Results:

  • The activity flow model successfully transformed healthy brain activations into at-risk AD-like activations when applied to at-risk connectivity data.
  • Reliably predicted task activations in at-risk individuals.
  • Predicted activations showed significant relationships with individual differences in task behavior.

Conclusions:

  • Altered intrinsic functional connections and subsequent disruption of neural activity flow represent a core mechanism underlying early Alzheimer's disease (AD) dysfunction.
  • This activity flow model offers clinical potential for predicting task-related brain dysfunction and cognitive deficits in at-risk individuals without requiring cognitive tasks during scanning.