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Alzheimer Disease l: Introduction01:29

Alzheimer Disease l: Introduction

Alzheimer disease is a chronic, progressive, and irreversible neurodegenerative disorder and the most common cause of dementia in older adults. It leads to gradual neuronal loss, causing cognitive decline, behavioral changes, and loss of functional independence.Risk Factors and EtiologyThe disease is multifactorial. Age is the strongest risk factor, with prevalence doubling every 5 years after age 65. Genetic factors include mutations in genes such as APP, PSEN1, and PSEN2, which are associated...
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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
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Abbiategrasso Brain Bank Protocol for Collecting, Processing and Characterizing Aging Brains
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Published on: June 3, 2020

Functional system-specific brain aging across the Alzheimer's disease continuum.

Yanxi Huo1,2,3, Weijie Huang4, Zhenzhao Liu1

  • 1State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.

Translational Psychiatry
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

Brain aging patterns vary across brain systems in Alzheimer's disease (AD). Identifying these specific patterns aids in early AD detection and monitoring, improving prediction of mild cognitive impairment to AD conversion.

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Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach
10:13

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Published on: February 14, 2014

Area of Science:

  • Neuroscience
  • Radiology
  • Gerontology

Background:

  • Accelerated brain aging is linked to Alzheimer's disease (AD).
  • Understanding spatial heterogeneity of brain aging across functional systems in AD is limited.
  • Brain aging patterns may differ across the AD continuum.

Purpose of the Study:

  • To explore spatial heterogeneity of brain aging across functional systems in individuals with Alzheimer's disease (AD).
  • To develop and validate system-specific brain age models to track aging trajectories.
  • To assess the association of these aging patterns with AD biomarkers, cognition, and clinical progression.

Main Methods:

  • Developed functional system-specific brain age prediction models using structural MRI data from a healthy cohort (n=22,672).
  • Applied models to 1478 participants across the AD continuum, analyzing predicted age differences (PADs) and their change rates over up to 6 years.
  • Examined associations between PAD dynamics, AD biomarkers, cognitive performance, and conversion from mild cognitive impairment (MCI) to AD.

Main Results:

  • Individuals with progressive MCI showed early PAD deviations in the default mode network and accelerated changes in attention and control networks.
  • System-wise PAD dynamics mediated the impact of AD biomarkers on cognitive decline.
  • Integrating PAD features improved the prediction accuracy of MCI-to-AD conversion (AUC=0.95).

Conclusions:

  • Functional system-specific brain age predictions (PADs) reveal heterogeneous aging trajectories in AD.
  • PADs serve as sensitive biomarkers for early AD detection and monitoring of individualized AD risk.
  • System-specific brain aging patterns offer valuable insights into AD pathogenesis and progression.