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A Neuroimaging Signature of Cognitive Aging from Whole-Brain Functional Connectivity

  • Rongtao Jiang*
  • , Dustin Scheinost
  • , Nianming Zuo
  • , Jing Wu
  • , Shile Qi
  • , Qinghao Liang
  • , Dongmei Zhi
  • , Na Luo
  • , Young Chul Chung
  • , Sha Liu
  • , Yong Xu
  • , Jing Sui*
  • , Vince Calhoun
  • *Corresponding author for this work
  • Yale University
  • CAS - Institute of Automation
  • University of Chinese Academy of Sciences
  • Capital Medical University
  • Nanjing University of Aeronautics and Astronautics
  • Beijing Normal University
  • Shanxi Medical University

Research output: Contribution to journal › Journal article › peer-review

Abstract

Cognitive decline is amongst one of the most commonly reported complaints during normal aging. Despite evidence that age and cognition are linked with similar neural correlates, no previous studies have directly ascertained how these two constructs overlap in the brain in terms of neuroimaging-based prediction. Based on a long lifespan healthy cohort (CamCAN, aged 19–89 years, n = 567), it is shown that both cognitive function (domains spanning executive function, emotion processing, motor function, and memory) and human age can be reliably predicted from unique patterns of functional connectivity, with models generalizable in two external datasets (n = 533 and n = 453). Results show that cognitive decline and normal aging both manifest decrease within-network connections (especially default mode and ventral attention networks) and increase between-network connections (somatomotor network). Whereas dorsal attention network is an exception, which is highly predictive on cognitive ability but is weakly correlated with aging. Further, the positively weighted connections in predicting fluid intelligence significantly mediate its association with age. Together, these findings offer insights into why normal aging is often associated with cognitive decline in terms of brain network organization, indicating a process of neural dedifferentiation and compensational theory.

Original languageEnglish
Article number2201621
JournalAdvanced Science
Volume9
Issue number24
DOIs
StatePublished - 2022.08.25

Keywords

  • biomarker
  • brain age
  • cognition aging
  • individualized prediction
  • predictive neuroimaging

Quacquarelli Symonds(QS) Subject Topics

  • Materials Science
  • Medicine
  • Engineering - Chemical
  • Physics & Astronomy
  • Biological Sciences

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