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Cognitive Development During Adulthood01:30

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Cognitive development continues throughout adulthood, undergoing significant shifts across early, middle, and late stages. Individual transition occurs from adolescent idealism to pragmatic and adaptable thinking in early adulthood. During this period, individuals learn to integrate personal beliefs with the recognition that other perspectives are equally valid. Exposure to the complexities of modern society, diverse experiences, and higher education contribute to this adaptive thought process,...
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A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
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Tracking functional network connectivity dynamics in the elderly.

Kaichao Wu1,2, Beth Jelfs3, Seedahmed S Mahmoud1

  • 1Department of Biomedical Engineering, College of Engineering, Shantou University, Shantou, China.

Frontiers in Neuroscience
|April 6, 2023
PubMed
Summary
This summary is machine-generated.

Aging significantly alters dynamic brain connectivity, impacting functional integration and segregation. Machine learning models using dynamic functional connectivity (DFNC) accurately distinguish age groups, offering insights into brain aging mechanisms.

Keywords:
agingdynamic functional network connectivityfunctional integration and segregationgraph theorymnemonic discrimination ability

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Gerontology

Background:

  • Aging affects brain organization and functional connectivity, but its impact on dynamic brain function interactions remains under-investigated.
  • Dynamic functional network connectivity (DFNC) analysis offers a novel approach to study age-related changes in brain function over time.

Purpose of the Study:

  • To investigate dynamic functional connectivity (DFNC) patterns in early adulthood and elderly individuals.
  • To explore the relationship between DFNC features and brain aging.
  • To assess the efficacy of DFNC in differentiating age groups.

Main Methods:

  • Resting-state fMRI data from young adults and elderly participants were analyzed using a DFNC pipeline.
  • The pipeline included brain parcellation, dynamic FC feature extraction, and FC dynamics examination.
  • Machine learning algorithms, including decision trees, were employed to classify age groups based on DFNC features.

Main Results:

  • Significant dynamic connectivity changes were observed in the elderly, affecting transient brain states and functional interaction methods.
  • DFNC features demonstrated high performance in distinguishing age stages, with fraction time of DFNC states achieving over 88% classification accuracy using a decision tree.
  • These dynamic FC alterations in the elderly correlated with mnemonic discrimination ability.

Conclusions:

  • The study confirms dynamic functional connectivity alterations in the elderly.
  • DFNC analysis is a promising tool for understanding brain aging mechanisms and age-related cognitive changes.
  • Altered dynamic FC impacts the balance between functional integration and segregation in the aging brain.